SBMLBioModels: Q - S

Alzheimer's disease (AD) has a characteristic hallmark of amyloid-β (Aβ) accumulation in the brain. This accumulation of Aβ has been related to its faulty cerebral clearance. Indeed, preclinical studies that used mice to investigate Aβ clearance showed that efflux across blood-brain barrier (BBB) and brain degradation mediate efficient Aβ clearance. However, the contribution of each process to Aβ clearance remains unclear. Moreover, it is still uncertain how species differences between mouse and human could affect Aβ clearance. Here, a modified form of the brain efflux index method was used to estimate the contribution of BBB and brain degradation to Aβ clearance from the brain of wild type mice. We estimated that 62% of intracerebrally injected (125)I-Aβ40 is cleared across BBB while 38% is cleared by brain degradation. Furthermore, in vitro and in silico studies were performed to compare Aβ clearance between mouse and human BBB models. Kinetic studies for Aβ40 disposition in bEnd3 and hCMEC/D3 cells, representative in vitro mouse and human BBB models, respectively, demonstrated 30-fold higher rate of (125)I-Aβ40 uptake and 15-fold higher rate of degradation by bEnd3 compared to hCMEC/D3 cells. Expression studies showed both cells to express different levels of P-glycoprotein and RAGE, while LRP1 levels were comparable. Finally, we established a mechanistic model, which could successfully predict cellular levels of (125)I-Aβ40 and the rate of each process. Established mechanistic model suggested significantly higher rates of Aβ uptake and degradation in bEnd3 cells as rationale for the observed differences in (125)I-Aβ40 disposition between mouse and human BBB models. In conclusion, current study demonstrates the important role of BBB in the clearance of Aβ from the brain. Moreover, it provides insight into the differences between mouse and human BBB with regards to Aβ clearance and offer, for the first time, a mathematical model that describes Aβ clearance across BBB. link: http://identifiers.org/pubmed/24467845

We have developed a generic mathematical model of a cell cycle signaling network in higher eukaryotes that can be used to simulate both the G1/S and G2/M transitions. In our model, the positive feedback facilitated by CDC25 and wee1 causes bistability in cyclin-dependent kinase activity, whereas the negative feedback facilitated by SKP2 or anaphase-promoting-complex turns this bistable behavior into limit cycle behavior. The cell cycle checkpoint is a Hopf bifurcation point. These behaviors are coordinated by growth and division to maintain normal cell cycle and size homeostasis. This model successfully reproduces sizer, timer, and the restriction point features of the eukaryotic cell cycle, in addition to other experimental findings. link: http://identifiers.org/pubmed/14645053

Genome-scale metabolic modeling is a systems-based approach that attempts to capture the metabolic complexity of the whole cell, for the purpose of gaining insight into metabolic function and regulation. This is achieved by organizing the metabolic components and their corresponding interactions into a single context. The reconstruction process is a challenging and laborious task, especially during the stage of manual curation. For the mouse genome-scale metabolic model, however, we were able to rapidly reconstruct a compartmentalized model from well-curated metabolic databases online. The prototype model was comprehensive. Apart from minor compound naming and compartmentalization issues, only nine additional reactions without gene associations were added during model curation before the model was able to simulate growth in silico. Further curation led to a metabolic model that consists of 1399 genes mapped to 1757 reactions, with a total of 2037 reactions compartmentalized into the cytoplasm and mitochondria, capable of reproducing metabolic functions inferred from literatures. The reconstruction is made more tractable by developing a formal system to update the model against online databases. Effectively, we can focus our curation efforts into establishing better model annotations and gene-protein-reaction associations within the core metabolism, while relying on genome and proteome databases to build new annotations for peripheral pathways, which may bear less relevance to our modeling interest. link: http://identifiers.org/pubmed/19425150

A representative stoichiometric model is essential to perform metabolic flux analysis (MFA) using experimentally measured consumption (or production) rates as constraints. For Human Embryonic Kidney (HEK) cell culture, there is the opportunity to use an extremely well-curated and annotated human genome-scale model Recon 2 for MFA. Performing MFA using Recon 2 without any modification would have implied that cells have access to all functionality encoded by the genome, which is not realistic. The majority of intracellular fluxes are poorly determined as only extracellular exchange rates are measured. This is compounded by the fact that there is no suitable metabolic objective function to suppress non-specific fluxes. We devised a heuristic to systematically reduce Recon 2 to emphasize flux through core metabolic reactions. This implies that cells would engage these dominant metabolic pathways to grow, and any significant changes in gross metabolic phenotypes would have invoked changes in these pathways. The reduced metabolic model becomes a functionalized version of Recon 2 used for identifying significant metabolic changes in cells by flux analysis. link: http://identifiers.org/pubmed/24907410

After anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the "mitotic exit network" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast. link: http://identifiers.org/pubmed/16713564

After anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the "mitotic exit network" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast. link: http://identifiers.org/pubmed/16713564

A bioterrorist attacks usually cause epidemics of panic in a targeted population. We have presented epidemiologic aspect of this phenomenon as a three-component model–host, information on an attack and social network. We have proposed a mathematical model of panic and counter-measures as the function of time in a population exposed to a bioterrorist attack. The model comprises ordinary differential equations and graphically presented combinations of the equations parameters. Clinically, we have presented a model through a sequence of psychic conditions and disorders initiated by an act of bioterrorism. This model might be helpful for an attacked community to timely and properly apply counter-measures and to minimize human mental suffering during a bioterrorist attack. link: http://identifiers.org/pubmed/19423234

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

BACKGROUND: Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction. RESULTS: We have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083). This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that are likely to be operational during infection. CONCLUSION: Our analysis suggests that there is a robust minimal set of metabolic pathways that is required for successful replication of Salmonella inside the host cell. This model also serves as platform for the integration of high-throughput data. Its computational power allows identification of networked metabolic pathways and generation of hypotheses about metabolism during infection, which might be used for the rational design of novel antibiotics or vaccine strains. link: http://identifiers.org/pubmed/19356237

BACKGROUND: Francisella tularensis is a prototypic example of a pathogen for which few experimental datasets exist, but for which copious high-throughout data are becoming available because of its re-emerging significance as biothreat agent. The virulence of Francisella tularensis depends on its growth capabilities within a defined environmental niche of the host cell. RESULTS: We reconstructed the metabolism of Francisella as a stoichiometric matrix. This systems biology approach demonstrated that changes in carbohydrate utilization and amino acid metabolism play a pivotal role in growth, acid resistance, and energy homeostasis during infection with Francisella. We also show how varying the expression of certain metabolic genes in different environments efficiently controls the metabolic capacity of F. tularensis. Selective gene-expression analysis showed modulation of sugar catabolism by switching from oxidative metabolism (TCA cycle) in the initial stages of infection to fatty acid oxidation and gluconeogenesis later on. Computational analysis with constraints derived from experimental data revealed a limited set of metabolic genes that are operational during infection. CONCLUSIONS: This integrated systems approach provides an important tool to understand the pathogenesis of an ill-characterized biothreat agent and to identify potential novel drug targets when rapid target identification is required should such microbes be intentionally released or become epidemic. link: http://identifiers.org/pubmed/20731870

Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets. link: http://identifiers.org/pubmed/21127196

Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets. link: http://identifiers.org/pubmed/21127196

Eukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress. Among these processes, metabolic alterations seem to play an important role.We recently discovered that yeast cells with reduced activity of the key glycolytic enzyme triosephosphate isomerase exhibit an increased resistance to the thiol-oxidizing reagent diamide. Here we show that this phenotype is conserved in Caenorhabditis elegans and that the underlying mechanism is based on a redirection of the metabolic flux from glycolysis to the pentose phosphate pathway, altering the redox equilibrium of the cytoplasmic NADP(H) pool. Remarkably, another key glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is known to be inactivated in response to various oxidant treatments, and we show that this provokes a similar redirection of the metabolic flux.The naturally occurring inactivation of GAPDH functions as a metabolic switch for rerouting the carbohydrate flux to counteract oxidative stress. As a consequence, altering the homoeostasis of cytoplasmic metabolites is a fundamental mechanism for balancing the redox state of eukaryotic cells under stress conditions. link: http://identifiers.org/pubmed/18154684

Mycobacterium tuberculosis is the focus of several investigations for design of newer drugs, as tuberculosis remains a major epidemic despite the availability of several drugs and a vaccine. Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway (MAP) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis (FBA) has been performed on the MAP model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of InhA by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis H37Rv and human proteomes indicate that, apart from the known InhA, potential targets for anti-tubercular drug design are AccD3, Fas, FabH, Pks13, DesA1/2, and DesA3. Proteins identified as essential by FBA correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of FBA for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing. link: http://identifiers.org/pubmed/16261191

Therapeutic protocols in immunotherapy are usually proposed following the intuition and experience of the therapist. In order to deduce such protocols mathematical modeling, optimal control and simulations are used instead of the therapist's experience. Clinical efficacy of dendritic cell (DC) vaccines to cancer treatment is still unclear, since dendritic cells face several obstacles in the host environment, such as immunosuppression and poor transference to the lymph nodes reducing the vaccine effect. In view of that, we have created a mathematical murine model to measure the effects of dendritic cell injections admitting such obstacles. In addition, the model considers a therapy given by bolus injections of small duration as opposed to a continual dose. Doses timing defines the therapeutic protocols, which in turn are improved to minimize the tumor mass by an optimal control algorithm. We intend to supplement therapist's experience and intuition in the protocol's implementation. Experimental results made on mice infected with melanoma with and without therapy agree with the model. It is shown that the dendritic cells' percentage that manages to reach the lymph nodes has a crucial impact on the therapy outcome. This suggests that efforts in finding better methods to deliver DC vaccines should be pursued. link: http://identifiers.org/pubmed/28912828

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

BACKGROUND: In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model. RESULTS: We apply our method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation model. When the number of state variables in the yeast model is reduced from 12 to 7, the difference between metabolite concentrations in the reduced and the full model, averaged over time and species, is only 8%. Likewise, when the number of state variables in the rat-liver beta-oxidation model is reduced from 42 to 29, the difference between the reduced model and the full model is 7.5%. CONCLUSIONS: The method has improved our understanding of the dynamics of the two networks. We found that, contrary to the general disposition, the first few metabolites which were deleted from the network during our stepwise reduction approach, are not those with the shortest convergence times. It shows that our reduction approach performs differently from other approaches that are based on time-scale separation. The method can be used to facilitate fitting of the parameters or to embed a detailed model of interest in a more coarse-grained yet realistic environment. link: http://identifiers.org/pubmed/24885656

BACKGROUND: In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model. RESULTS: We apply our method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation model. When the number of state variables in the yeast model is reduced from 12 to 7, the difference between metabolite concentrations in the reduced and the full model, averaged over time and species, is only 8%. Likewise, when the number of state variables in the rat-liver beta-oxidation model is reduced from 42 to 29, the difference between the reduced model and the full model is 7.5%. CONCLUSIONS: The method has improved our understanding of the dynamics of the two networks. We found that, contrary to the general disposition, the first few metabolites which were deleted from the network during our stepwise reduction approach, are not those with the shortest convergence times. It shows that our reduction approach performs differently from other approaches that are based on time-scale separation. The method can be used to facilitate fitting of the parameters or to embed a detailed model of interest in a more coarse-grained yet realistic environment. link: http://identifiers.org/pubmed/24885656

The present work exemplifies how parameter identifiability analysis can be used to gain insights into differences in experimental systems and how uncertainty in parameter estimates can be handled. The case study, presented here, investigates interferon-gamma (IFNγ) induced STAT1 signalling in two cell types that play a key role in pancreatic cancer development: pancreatic stellate and cancer cells. IFNγ inhibits the growth for both types of cells and may be prototypic of agents that simultaneously hit cancer and stroma cells. We combined time-course experiments with mathematical modelling to focus on the common situation in which variations between profiles of experimental time series, from different cell types, are observed. To understand how biochemical reactions are causing the observed variations, we performed a parameter identifiability analysis. We successfully identified reactions that differ in pancreatic stellate cells and cancer cells, by comparing confidence intervals of parameter value estimates and the variability of model trajectories. Our analysis shows that useful information can also be obtained from nonidentifiable parameters. For the prediction of potential therapeutic targets we studied the consequences of uncertainty in the values of identifiable and nonidentifiable parameters. Interestingly, the sensitivity of model variables is robust against parameter variations and against differences between IFNγ induced STAT1 signalling in pancreatic stellate and cancer cells. This provides the basis for a prediction of therapeutic targets that are valid for both cell types. link: http://identifiers.org/pubmed/23284277

Bone, a major reservoir of body calcium, is under the hormonal control of the parathyroid hormone (PTH). Several aspects of its growth, turnover, and mechanism, occur in the absence of gonadal hormones. Sex steroids such as estrogen, nonetheless, play an important role in bone physiology, and are extremely essential to maintain bone balance in adults. In order to provide a basis for understanding the underlying mechanisms of bone remodeling as it is mediated by PTH, we propose here a mathematical model of the process. The nonlinear system model is then utilized to study the temporal effect of PTH as well as the action of estrogen replacement therapy on bone turnover. Analysis of the model is done on the assumption, supported by reported clinical evidence, that the process is characterized by highly diversified dynamics, which warrants the use of singular perturbation arguments. The model is shown to exhibit limit cycle behavior, which can develop into chaotic dynamics for certain ranges of the system's parametric values. Effects of estrogen and PTH administrations are then investigated by extending on the core model. Analysis of the model seems to indicate that the paradoxical observation that intermittent PTH administration causes net bone deposition while continuous administration causes net bone loss, and certain other reported phenomena may be attributed to the highly diversified dynamics which characterizes this nonlinear remodeling process. link: http://identifiers.org/pubmed/12753937

Bone, a major reservoir of body calcium, is under the hormonal control of the parathyroid hormone (PTH). Several aspects of its growth, turnover, and mechanism, occur in the absence of gonadal hormones. Sex steroids such as estrogen, nonetheless, play an important role in bone physiology, and are extremely essential to maintain bone balance in adults. In order to provide a basis for understanding the underlying mechanisms of bone remodeling as it is mediated by PTH, we propose here a mathematical model of the process. The nonlinear system model is then utilized to study the temporal effect of PTH as well as the action of estrogen replacement therapy on bone turnover. Analysis of the model is done on the assumption, supported by reported clinical evidence, that the process is characterized by highly diversified dynamics, which warrants the use of singular perturbation arguments. The model is shown to exhibit limit cycle behavior, which can develop into chaotic dynamics for certain ranges of the system's parametric values. Effects of estrogen and PTH administrations are then investigated by extending on the core model. Analysis of the model seems to indicate that the paradoxical observation that intermittent PTH administration causes net bone deposition while continuous administration causes net bone loss, and certain other reported phenomena may be attributed to the highly diversified dynamics which characterizes this nonlinear remodeling process. link: http://identifiers.org/pubmed/12753937

Bone, a major reservoir of body calcium, is under the hormonal control of the parathyroid hormone (PTH). Several aspects of its growth, turnover, and mechanism, occur in the absence of gonadal hormones. Sex steroids such as estrogen, nonetheless, play an important role in bone physiology, and are extremely essential to maintain bone balance in adults. In order to provide a basis for understanding the underlying mechanisms of bone remodeling as it is mediated by PTH, we propose here a mathematical model of the process. The nonlinear system model is then utilized to study the temporal effect of PTH as well as the action of estrogen replacement therapy on bone turnover. Analysis of the model is done on the assumption, supported by reported clinical evidence, that the process is characterized by highly diversified dynamics, which warrants the use of singular perturbation arguments. The model is shown to exhibit limit cycle behavior, which can develop into chaotic dynamics for certain ranges of the system's parametric values. Effects of estrogen and PTH administrations are then investigated by extending on the core model. Analysis of the model seems to indicate that the paradoxical observation that intermittent PTH administration causes net bone deposition while continuous administration causes net bone loss, and certain other reported phenomena may be attributed to the highly diversified dynamics which characterizes this nonlinear remodeling process. link: http://identifiers.org/pubmed/12753937

Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXRα/RXRα), adipocyte differentiation (PPARγ/RXRα), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications. link: http://identifiers.org/pubmed/22531117

Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXRα/RXRα), adipocyte differentiation (PPARγ/RXRα), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications. link: http://identifiers.org/pubmed/22531117

Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXRα/RXRα), adipocyte differentiation (PPARγ/RXRα), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications. link: http://identifiers.org/pubmed/22531117

Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXRα/RXRα), adipocyte differentiation (PPARγ/RXRα), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications. link: http://identifiers.org/pubmed/22531117

Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXRα/RXRα), adipocyte differentiation (PPARγ/RXRα), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications. link: http://identifiers.org/pubmed/22531117

BACKGROUND: Meiosis is the sexual reproduction process common to eukaryotes. The diploid yeast Saccharomyces cerevisiae undergoes meiosis in sporulation medium to form four haploid spores. Initiation of the process is tightly controlled by intricate networks of positive and negative feedback loops. Intriguingly, expression of early meiotic proteins occurs within a narrow time window. Further, sporulation efficiency is strikingly different for yeast strains with distinct mutations or genetic backgrounds. To investigate signal transduction pathways that regulate transient protein expression and sporulation efficiency, we develop a mathematical model using ordinary differential equations. The model describes early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signaling molecules for regulating protein activities. RESULTS: The mathematical model is capable of simulating the orderly and transient dynamics of meiotic proteins including Ime1, the master regulator of meiotic initiation, and Ime2, a kinase encoded by an early gene. The model is validated by quantitative sporulation phenotypes of single-gene knockouts. Thus, we can use the model to make novel predictions on the cooperation between proteins in the signaling pathway. Virtual perturbations on feedback loops suggest that both positive and negative feedback loops are required to terminate expression of early meiotic proteins. Bifurcation analyses on feedback loops indicate that multiple feedback loops are coordinated to modulate sporulation efficiency. In particular, positive auto-regulation of Ime2 produces a bistable system with a normal meiotic state and a more efficient meiotic state. CONCLUSIONS: By systematically scanning through feedback loops in the mathematical model, we demonstrate that, in yeast, the decisions to terminate protein expression and to sporulate at different efficiencies stem from feedback signals toward the master regulator Ime1 and the early meiotic protein Ime2. We argue that the architecture of meiotic initiation pathway generates a robust mechanism that assures a rapid and complete transition into meiosis. This type of systems-level regulation is a commonly used mechanism controlling developmental programs in yeast and other organisms. Our mathematical model uncovers key regulations that can be manipulated to enhance sporulation efficiency, an important first step in the development of new strategies for producing gametes with high quality and quantity. link: http://identifiers.org/pubmed/23631506

The diploid yeast Saccharomyces cerevisiae undergoes mitosis in glucose-rich medium but enters meiosis in acetate sporulation medium. The transition from mitosis to meiosis involves a remarkable adaptation of the metabolic machinery to the changing environment to meet new energy and biosynthesis requirements. Biochemical studies indicate that five metabolic pathways are active at different stages of sporulation: glutamate formation, tricarboxylic acid cycle, glyoxylate cycle, gluconeogenesis, and glycogenolysis. A dynamic synthesis of macromolecules, including nucleotides, amino acids, and lipids, is also observed. However, the metabolic requirements of sporulating cells are poorly understood. In this study, we apply flux balance analyses to uncover optimal principles driving the operation of metabolic networks over the entire period of sporulation. A meiosis-specific metabolic network is constructed, and flux distribution is simulated using ten objective functions combined with time-course expression-based reaction constraints. By systematically evaluating the correlation between computational and experimental fluxes on pathways and macromolecule syntheses, the metabolic requirements of cells are determined: sporulation requires maximization of ATP production and macromolecule syntheses in the early phase followed by maximization of carbohydrate breakdown and minimization of ATP production in the middle and late stages. Our computational models are validated by in silico deletion of enzymes known to be essential for sporulation. Finally, the models are used to predict novel metabolic genes required for sporulation. This study indicates that yeast cells have distinct metabolic requirements at different phases of meiosis, which may reflect regulation that realizes the optimal outcome of sporulation. Our meiosis-specific network models provide a framework for an in-depth understanding of the roles of enzymes and reactions, and may open new avenues for engineering metabolic pathways to improve sporulation efficiency. link: http://identifiers.org/pubmed/23675502

Cooperativity in contractile behavior of myofilament systems almost assuredly arises because of interactions between neighboring sites. These interactions may be of different kinds. Tropomyosin thin-filament regulatory units may have neighbors in steric blocking positions (off) or steric permissive positions (on). The position of these neighbors influence the tendency for the regulatory unit to assume the on or off state. Likewise, the tendency of a myosin cross-bridge to achieve a force-bearing state may be influenced by whether neighboring cross-bridges are in force-bearing states. Also, a cross-bridge in the force-bearing state may influence the tendency of a regulatory unit to enter the on state. We used a mathematical model to examine the influence of each of these three kinds of neighbor interactions on the steady-state force-pCa relation and on the dynamic force redevelopment process. Each neighbor interaction was unique in its effects on maximal Ca(2+)-activated force, position, and symmetry of the force-pCa curve and on the Hill coefficient. Also, each neighbor interaction had a distinctive effect on the time course of force development as assessed by its rate coefficient, k(dev). These diverse effects suggest that variations in all three kinds of nearest-neighbor interactions may be responsible for a wide variety of currently unexplained observations of myofilament contractile behavior. link: http://identifiers.org/pubmed/10827989

This is a testing SBML model

Acetaminophen is a widespread and commonly used painkiller all over the world. However, it can cause liver damage when taken in large doses or at repeated chronic doses. Current models of acetaminophen metabolism are complex, and limited to numerical investigation though provide results that represent clinical investigation well. We derive a mathematical model based on mass action laws aimed at capturing the main dynamics of acetaminophen metabolism, in particular the contrast between normal and overdose cases, whilst remaining simple enough for detailed mathematical analysis that can identify key parameters and quantify their role in liver toxicity. We use singular perturbation analysis to separate the different timescales describing the sequence of events in acetaminophen metabolism, systematically identifying which parameters dominate during each of the successive stages. Using this approach we determined, in terms of the model parameters, the critical dose between safe and overdose cases, timescales for exhaustion and regeneration of important cofactors for acetaminophen metabolism and total toxin accumulation as a fraction of initial dose. link: http://identifiers.org/pubmed/26348886

Diverse datasets, including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily available for specific organisms. There is currently a need to integrate these datasets within an in silico modeling framework. Constraint-based models of Escherichia coli K-12 MG1655 have been developed and used to study the bacterium's metabolism and phenotypic behavior. The most comprehensive E. coli model to date (E. coli iJE660a GSM) accounts for 660 genes and includes 627 unique biochemical reactions.An expanded genome-scale metabolic model of E. coli (iJR904 GSM/GPR) has been reconstructed which includes 904 genes and 931 unique biochemical reactions. The reactions in the expanded model are both elementally and charge balanced. Network gap analysis led to putative assignments for 55 open reading frames (ORFs). Gene to protein to reaction associations (GPR) are now directly included in the model. Comparisons between predictions made by iJR904 and iJE660a models show that they are generally similar but differ under certain circumstances. Analysis of genome-scale proton balancing shows how the flux of protons into and out of the medium is important for maximizing cellular growth.E. coli iJR904 has improved capabilities over iJE660a. iJR904 is a more complete and chemically accurate description of E. coli metabolism than iJE660a. Perhaps most importantly, iJR904 can be used for analyzing and integrating the diverse datasets. iJR904 will help to outline the genotype-phenotype relationship for E. coli K-12, as it can account for genomic, transcriptomic, proteomic and fluxomic data simultaneously. link: http://identifiers.org/pubmed/12952533

Building on the work of Martinov et al. (2000), a mathematical model is developed for the methionine cycle. A large amount of information is available about the enzymes that catalyse individual reaction steps in the cycle, from methionine to S-adenosylmethionine to S-adenosylhomocysteine to homocysteine, and the removal of mass from the cycle by the conversion of homocysteine to cystathionine. Nevertheless, the behavior of the cycle is very complicated since many substrates alter the activities of the enzymes in the reactions that produce them, and some can also alter the activities of other enzymes in the cycle. The model consists of four differential equations, based on known reaction kinetics, that can be solved to give the time course of the concentrations of the four main substrates in the cycle under various circumstances. We show that the behavior of the model in response to genetic abnormalities and dietary deficiencies is similar to the changes seen in a wide variety of experimental studies. We conduct computational "experiments" that give understanding of the regulatory behavior of the methionine cycle under normal conditions and the behavior in the presence of genetic variation and dietary deficiencies. link: http://identifiers.org/pubmed/14637052

Glutathione (GSH) plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism.We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data.We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism. link: http://identifiers.org/pubmed/18442411

Epithelial cells respond to their physical neighborhood with mechano-sensitive behaviors required for development and tissue maintenance. These include anchorage dependence, matrix stiffness-dependent proliferation, contact inhibition of proliferation and migration, and collective migration that balances cell crawling with the maintenance of cell junctions. While required for development and tissue repair, these coordinated responses to the microenvironment also contribute to cancer metastasis. Predictive models of the signaling networks that coordinate these behaviors are critical in controlling cell behavior to halt disease. Here we propose a Boolean regulatory network model that synthesizes mechanosensitive signaling that links anchorage to a matrix of varying stiffness and cell density sensing to contact inhibition, proliferation, migration, and apoptosis. Our model can reproduce anchorage dependence and anoikis, detachment-induced cytokinesis errors, the effect of matrix stiffness on proliferation, and contact inhibition of proliferation and migration by two mechanisms that converge on the YAP transcription factor. In addition, we offer testable predictions related to cell cycle-dependent anoikis sensitivity, the molecular requirements for abolishing contact inhibition, and substrate stiffness dependent expression of the catalytic subunit of PI3K. Moreover, our model predicts heterogeneity in migratory vs. non-migratory phenotypes in sub-confluent monolayers, and co-inhibition but semi-independent induction of proliferation vs. migration as a function of cell density and mitogenic stimulation. Our model serves as a stepping-stone towards modeling mechanosensitive routes to the epithelial to mesenchymal transition, capturing the effects of the mesenchymal state on anoikis resistance, and understanding the balance between migration versus proliferation at each stage of the epithelial to mesenchymal transition. link: http://identifiers.org/doi/10.1016/j.csbj.2020.07.016

Activation of effector caspases is a final step during apoptosis. Single-cell imaging studies have demonstrated that this process may occur as a rapid, all-or-none response, triggering a complete substrate cleavage within 15 min. Based on biochemical data from HeLa cells, we have developed a computational model of apoptosome-dependent caspase activation that was sufficient to remodel the rapid kinetics of effector caspase activation observed in vivo. Sensitivity analyses predicted a critical role for caspase-3-dependent feedback signalling and the X-linked-inhibitor-of-apoptosis-protein (XIAP), but a less prominent role for the XIAP antagonist Smac. Single-cell experiments employing a caspase fluorescence resonance energy transfer substrate verified these model predictions qualitatively and quantitatively. XIAP was predicted to control this all-or-none response, with concentrations as high as 0.15 microM enabling, but concentrations >0.30 microM significantly blocking substrate cleavage. Overexpression of XIAP within these threshold concentrations produced cells showing slow effector caspase activation and submaximal substrate cleavage. Our study supports the hypothesis that high levels of XIAP control caspase activation and substrate cleavage, and may promote apoptosis resistance and sublethal caspase activation in vivo. link: http://identifiers.org/pubmed/16932741

We present a mathematical model for calcium oscillations in the cilia of olfactory sensory neurons. The underlying mechanism is based on direct negative regulation of cyclic nucleotide-gated channels by calcium/calmodulin and does not require any autocatalysis such as calcium-induced calcium release. The model is in quantitative agreement with available experimental data, both with respect to oscillations and to fast adaptation. We give predictions for the ranges of parameters in which oscillations should be observable. Relevance of the model to calcium oscillations in other systems is discussed. link: http://identifiers.org/pubmed/16326896

Serine/threonine/tyrosine-interacting protein (STYX) is a catalytically inactive member of the dual-specificity phosphatases (DUSPs) family. Whereas the role of DUSPs in cellular signaling is well explored, the function of STYX is still unknown. Here, we identify STYX as a spatial regulator of ERK signaling. We used predictive-model simulation to test several hypotheses for possible modes of STYX action. We show that STYX localizes to the nucleus, competes with nuclear DUSP4 for binding to ERK, and acts as a nuclear anchor that regulates ERK nuclear export. Depletion of STYX increases ERK activity in both cytosol and nucleus. Importantly, depletion of STYX causes an ERK-dependent fragmentation of the Golgi apparatus and inhibits Golgi polarization and directional cell migration. Finally, we show that overexpression of STYX reduces ERK1/2 activation, thereby blocking PC12 cell differentiation. Overall, our results identify STYX as an important regulator of ERK1/2 signaling critical for cell migration and PC12 cell differentiation. link: http://identifiers.org/pubmed/23847209

Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains. link: http://identifiers.org/doi/10.1038/s41540-021-00188-4

Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains. link: http://identifiers.org/doi/10.1038/s41540-021-00188-4

Despite recent advances in the field of Oncoimmunology, the success potential of immunomodulatory therapies against cancer remains to be elucidated. One of the reasons is the lack of understanding on the complex interplay between tumor growth dynamics and the associated immune system responses. Toward this goal, we consider a mathematical model of vascularized tumor growth and the corresponding effector cell recruitment dynamics. Bifurcation analysis allows for the exploration of model's dynamic behavior and the determination of these parameter regimes that result in immune-mediated tumor control. In this work, we focus on a particular tumor evasion regime that involves tumor and effector cell concentration oscillations of slowly increasing and decreasing amplitude, respectively. Considering a temporal multiscale analysis, we derive an analytically tractable mapping of model solutions onto a weakly negatively damped harmonic oscillator. Based on our analysis, we propose a theory-driven intervention strategy involving immunostimulating and immunosuppressive phases to induce long-term tumor control. link: http://identifiers.org/pubmed/26305801

Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation. link: http://identifiers.org/pubmed/17922569

Models for the diversity and evolution of pathogens have branched into two main directions: the adaptive dynamics of quantitative life-history traits (notably virulence) and the maintenance and invasion of multiple, antigenically diverse strains that interact with the host's immune memory. In a first attempt to reconcile these two approaches, we developed a simple modelling framework where two strains of pathogens, defined by a pair of life-history traits (infectious period and infectivity), interfere through a given level of cross-immunity. We used whooping cough as a potential example, but the framework proposed here could be applied to other acute infectious diseases. Specifically, we analysed the effects of these parameters on the invasion dynamics of one strain into a population, where the second strain is endemic. Whereas the deterministic version of the model converges towards stable coexistence of the two strains in most cases, stochastic simulations showed that transient epidemic dynamics can cause the extinction of either strain. Thus ecological dynamics, modulated by the immune parameters, eventually determine the adaptive value of different pathogen genotypes. We advocate an integrative view of pathogen dynamics at the crossroads of immunology, epidemiology and evolution, as a way towards efficient control of infectious diseases. link: http://identifiers.org/pubmed/16615206

Vaccines exert strong selective pressures on pathogens, favouring the spread of antigenic variants. We propose a simple mathematical model to investigate the dynamics of a novel pathogenic strain that emerges in a population where a previous strain is maintained at low endemic level by a vaccine. We compare three methods to assess the ability of the novel strain to invade and persist: algebraic rate of invasion; deterministic dynamics; and stochastic dynamics. These three techniques provide complementary predictions on the fate of the system. In particular, we emphasize the importance of stochastic simulations, which account for the possibility of extinctions of either strain. More specifically, our model suggests that the probability of persistence of an invasive strain (i) can be minimized for intermediate levels of vaccine cross-protection (i.e. immune protection against the novel strain) and (ii) is lower if cross-immunity acts through a reduced infectious period rather than through reduced susceptibility. link: http://identifiers.org/pubmed/17210532

A mathematical model examined a potential therapy for controlling viral infections using genetically modified viruses. The control of the infection is an indirect effect of the selective elimination by an engineered virus of infected cells that are the source of the pathogens. Therefore, this engineered virus could greatly compensate for a dysfunctional immune system compromised by AIDS. In vitro studies using engineered viruses have been shown to decrease the HIV-1 load about 1000-fold. However, the efficacy of this potential treatment for reducing the viral load in AIDS patients is unknown. The present model studied the interactions among the HIV-1 virus, its main host cell (activated CD4+ T cells), and a therapeutic engineered virus in an in vivo context; and it examined the conditions for controlling the pathogen. This model predicted a significant drop in the HIV-1 load, but the treatment does not eradicate HIV. A basic estimation using a currently engineered virus indicated an HIV-1 load reduction of 92% and a recovery of host cells to 17% of their normal level. Greater success (98% HIV reduction, 44% host cells recovery) is expected as more competent engineered viruses are designed. These results suggest that therapy using viruses could be an alternative to extend the survival of AIDS patients. link: http://identifiers.org/pubmed/12941536

The Roseobacter clade is a ubiquitous group of marine α-proteobacteria. To gain insight into the versatile metabolism of this clade, we took a constraint-based approach and created a genome-scale metabolic model (iDsh827) of Dinoroseobacter shibae DFL12T. Our model is the first accounting for the energy demand of motility, the light-driven ATP generation and experimentally determined specific biomass composition. To cover a large variety of environmental conditions, as well as plasmid and single gene knock-out mutants, we simulated 391,560 different physiological states using flux balance analysis. We analyzed our results with regard to energy metabolism, validated them experimentally, and revealed a pronounced metabolic response to the availability of light. Furthermore, we introduced the energy demand of motility as an important parameter in genome-scale metabolic models. The results of our simulations also gave insight into the changing usage of the two degradation routes for dimethylsulfoniopropionate, an abundant compound in the ocean. A side product of dimethylsulfoniopropionate degradation is dimethyl sulfide, which seeds cloud formation and thus enhances the reflection of sunlight. By our exhaustive simulations, we were able to identify single-gene knock-out mutants, which show an increased production of dimethyl sulfide. In addition to the single-gene knock-out simulations we studied the effect of plasmid loss on the metabolism. Moreover, we explored the possible use of a functioning phosphofructokinase for D. shibae. link: http://identifiers.org/pubmed/24098096

Many molecular details remain to be uncovered concerning the regulation of polyamine metabolism. A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism. link: http://identifiers.org/pubmed/21814788

Many molecular details remain to be uncovered concerning the regulation of polyamine metabolism. A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism. link: http://identifiers.org/pubmed/21814788

Accumulating experimental and clinical evidence suggest that the immune response to cancer is not exclusively anti-tumor. Indeed, the pro-tumor roles of the immune system - as suppliers of growth and pro-angiogenic factors or defenses against cytotoxic immune attacks, for example - have been long appreciated, but relatively few theoretical works have considered their effects. Inspired by the recently proposed "immune-mediated" theory of metastasis, we develop a mathematical model for tumor-immune interactions at two anatomically distant sites, which includes both anti- and pro-tumor immune effects, and the experimentally observed tumor-induced phenotypic plasticity of immune cells (tumor "education" of the immune cells). Upon confrontation of our model to experimental data, we use it to evaluate the implications of the immune-mediated theory of metastasis. We find that tumor education of immune cells may explain the relatively poor performance of immunotherapies, and that many metastatic phenomena, including metastatic blow-up, dormancy, and metastasis to sites of injury, can be explained by the immune-mediated theory of metastasis. Our results suggest that further work is warranted to fully elucidate the pro-tumor effects of the immune system in metastatic cancer. link: http://identifiers.org/doi/10.1101/565531

PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy. EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy. RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data. CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules. link: http://identifiers.org/pubmed/22761472

Purpose: Optimal dosing is critical for immunocytokine-based cancer immunotherapy to maximize efficacy and minimize toxicity. Cergutuzumab amunaleukin (CEA-IL2v) is a novel CEA-targeted immunocytokine. We set out to develop a mathematical model to predict intratumoral CEA-IL2v concentrations following various systemic dosing intensities.Experimental Design: Sequential measurements of CEA-IL2v plasma concentrations in 74 patients with solid tumors were applied in a series of differential equations to devise a model that also incorporates the peripheral concentrations of IL2 receptor-positive cell populations (i.e., CD8+, CD4+, NK, and B cells), which affect tumor bioavailability of CEA-IL2v. Imaging data from a subset of 14 patients were subsequently utilized to additionally predict antibody uptake in tumor tissues.Results: We created a pharmacokinetic/pharmacodynamic mathematical model that incorporates the expansion of IL2R-positive target cells at multiple dose levels and different schedules of CEA-IL2v. Model-based prediction of drug levels correlated with the concentration of IL2R-positive cells in the peripheral blood of patients. The pharmacokinetic model was further refined and extended by adding a model of antibody uptake, which is based on drug dose and the biological properties of the tumor. In silico predictions of our model correlated with imaging data and demonstrated that a dose-dense schedule comprising escalating doses and shortened intervals of drug administration can improve intratumoral drug uptake and overcome consumption of CEA-IL2v by the expanding population of IL2R-positive cells.Conclusions: The model presented here allows simulation of individualized treatment plans for optimal dosing and scheduling of immunocytokines for anticancer immunotherapy. Clin Cancer Res; 24(14); 3325-33. ©2018 AACRSee related commentary by Ruiz-Cerdá et al., p. 3236. link: http://identifiers.org/pubmed/29463551

Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus on Methanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes of M. maripaludis strain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts. link: http://identifiers.org/pubmed/27736793

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies. link: http://identifiers.org/doi/10.1016/j.smim.2014.09.013

BACKGROUND: Rhodoferax ferrireducens is a metabolically versatile, Fe(III)-reducing, subsurface microorganism that is likely to play an important role in the carbon and metal cycles in the subsurface. It also has the unique ability to convert sugars to electricity, oxidizing the sugars to carbon dioxide with quantitative electron transfer to graphite electrodes in microbial fuel cells. In order to expand our limited knowledge about R. ferrireducens, the complete genome sequence of this organism was further annotated and then the physiology of R. ferrireducens was investigated with a constraint-based, genome-scale in silico metabolic model and laboratory studies. RESULTS: The iterative modeling and experimental approach unveiled exciting, previously unknown physiological features, including an expanded range of substrates that support growth, such as cellobiose and citrate, and provided additional insights into important features such as the stoichiometry of the electron transport chain and the ability to grow via fumarate dismutation. Further analysis explained why R. ferrireducens is unable to grow via photosynthesis or fermentation of sugars like other members of this genus and uncovered novel genes for benzoate metabolism. The genome also revealed that R. ferrireducens is well-adapted for growth in the subsurface because it appears to be capable of dealing with a number of environmental insults, including heavy metals, aromatic compounds, nutrient limitation and oxidative stress. CONCLUSION: This study demonstrates that combining genome-scale modeling with the annotation of a new genome sequence can guide experimental studies and accelerate the understanding of the physiology of under-studied yet environmentally relevant microorganisms. link: http://identifiers.org/pubmed/19772637

BACKGROUND: Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous. RESULTS: Here we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production. CONCLUSIONS: By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum and highlight remaining gaps in the existing genome annotations. link: http://identifiers.org/pubmed/20307315

A mathematical model of the interactions between a growing tumor and the immune system is presented. The equations and parameters of the model are based on experimental and clinical results from published studies. The model includes the primary cell populations involved in effector T-cell mediated tumor killing: regulatory T cells, helper T cells, and dendritic cells. A key feature is the inclusion of multiple mechanisms of immunosuppression through the main cytokines and growth factors mediating the interactions between the cell populations. Decreased access of effector cells to the tumor interior with increasing tumor size is accounted for. The model is applied to tumors with different growth rates and antigenicities to gauge the relative importance of various immunosuppressive mechanisms. The most important factors leading to tumor escape are TGF-β-induced immunosuppression, conversion of helper T cells into regulatory T cells, and the limitation of immune cell access to the full tumor at large tumor sizes. The results suggest that for a given tumor growth rate, there is an optimal antigenicity maximizing the response of the immune system. Further increases in antigenicity result in increased immunosuppression, and therefore a decrease in tumor killing rate. This result may have implications for immunotherapies which modulate the effective antigenicity. Simulation of dendritic cell therapy with the model suggests that for some tumors, there is an optimal dose of transfused dendritic cells. link: http://identifiers.org/pubmed/22051568

The paper presents a differential equation model for the feedback mechanisms between gonadotropin-releasing hormone (GnRH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), development of follicles and corpus luteum, and the production of estradiol (E2), progesterone (P4), inhibin A (IhA), and inhibin B (IhB) during the female menstrual cycle. Compared to earlier human cycle models, there are three important differences: The model presented here (a) does not involve any delay equations, (b) is based on a deterministic modeling of the GnRH pulse pattern, and (c) contains less differential equations and less parameters. These differences allow for a faster simulation and parameter identification. The focus is on modeling GnRH-receptor binding, in particular, by inclusion of a pharmacokinetic/pharmacodynamic (PK/PD) model for a GnRH agonist, Nafarelin, and a GnRH antagonist, Cetrorelix, into the menstrual cycle model. The final mathematical model describes the hormone profiles (LH, FSH, P4, E2) throughout the menstrual cycle of 12 healthy women. It correctly predicts hormonal changes following single and multiple dose administration of Nafarelin or Cetrorelix at different stages in the cycle. link: http://identifiers.org/pubmed/23206386

Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city. link: http://identifiers.org/pubmed/32289100

All living systems function out of equilibrium and exchange energy in the form of heat with their environment. Thus, heat flow can inform on the energetic costs of cellular processes, which are largely unknown. Here, we have repurposed an isothermal calorimeter to measure heat flow between developing zebrafish embryos and the surrounding medium. Heat flow increased over time with cell number. Unexpectedly, a prominent oscillatory component of the heat flow, with periods matching the synchronous early reductive cleavage divisions, persisted even when DNA synthesis and mitosis were blocked by inhibitors. Instead, the heat flow oscillations were driven by the phosphorylation and dephosphorylation reactions catalyzed by the cell-cycle oscillator, the biochemical network controlling mitotic entry and exit. We propose that the high energetic cost of cell-cycle signaling reflects the significant thermodynamic burden of imposing accurate and robust timing on cell proliferation during development. link: http://identifiers.org/pubmed/30713074

As the development of a dengue vaccine is ongoing, we simulate an hypothetical vaccine as an extra protection to the population. In a first phase, the vaccination process is studied as a new compartment in the model, and different ways of distributing the vaccines investigated: pediatric and random mass vaccines, with distinct levels of efficacy and durability. In a second step, the vaccination is seen as a control variable in the epidemiological process. In both cases, epidemic and endemic scenarios are included in order to analyze distinct outbreak realities. link: http://identifiers.org/pubmed/24513243

Immunotherapy is currently regarded as the most promising treatment to fight against cancer. This is particularly true in the treatment of chronic lymphocytic leukemia, an indolent neoplastic disease of B-lymphocytes which eventually causes the immune system's failure. In this and other areas of cancer research, mathematical modeling is pointed out as a prominent tool to analyze theoretical and practical issues. Its lack in studies of chemoimmunotherapy of chronic lymphocytic leukemia is what motivated us to come up with a simple ordinary differential equation model. It is based on ideas of de Pillis and Radunskaya and on standard pharmacokinetics-pharmacodynamics assumptions. In order to check the positivity of the state variables, we first establish an invariant region where these time-dependent variables remain positive. Afterwards, the action of the immune system, as well as the chemoimmunotherapeutic role in promoting cancer cure are investigated by means of numerical simulations and the classical linear stability analysis. The role of adoptive cellular immunotherapy is also addressed. Our overall conclusion is that chemoimmunotherapeutic protocols can be effective in treating chronic lymphocytic leukemia provided that chemotherapy is not a limiting factor to the immunotherapy efficacy. link: http://identifiers.org/doi/10.1016/j.amc.2018.12.008

Polyamines are considered as essential compounds in living cells, since they are involved in cell proliferation, transcription, and translation processes. Furthermore, polyamine homeostasis is necessary to cell survival, and its deregulation is involved in relevant processes, such as cancer and neurodegenerative disorders. Great efforts have been made to elucidate the nature of polyamine homeostasis, giving rise to relevant information concerning the behavior of the different components of polyamine metabolism, and a great amount of information has been generated. However, a complex regulation at transcriptional, translational, and metabolic levels as well as the strong relationship between polyamines and essential cell processes make it difficult to discriminate the role of polyamine regulation itself from the whole cell response when an experimental approach is given in vivo. To overcome this limitation, a bottom-up approach to model mathematically metabolic pathways could allow us to elucidate the systemic behavior from individual kinetic and molecular properties. In this paper, we propose a mathematical model of polyamine metabolism from kinetic constants and both metabolite and enzyme levels extracted from bibliographic sources. This model captures the tendencies observed in transgenic mice for the so-called key enzymes of polyamine metabolism, ornithine decarboxylase, S-adenosylmethionine decarboxylase and spermine spermidine N-acetyl transferase. Furthermore, the model shows a relevant role of S-adenosylmethionine and acetyl-CoA availability in polyamine homeostasis, which are not usually considered in systemic experimental studies. link: http://identifiers.org/pubmed/16709566

With the emergence of multifaceted bioinformatics-derived data, it is becoming possible to merge biochemical and physiological information to develop a new level of understanding of the metabolic complexity of the cell. The biosynthetic pathway of de novo pyrimidine nucleotide metabolism is an essential capability of all free-living cells, and it occupies a pivotal position relative to metabolic processes that are involved in the macromolecular synthesis of DNA, RNA and proteins, as well as energy production and cell division. This regulatory network in all enteric bacteria involves genetic, allosteric, and physiological control systems that need to be integrated into a coordinated set of metabolic checks and balances. Allosterically regulated pathways constitute an exciting and challenging biosynthetic system to be approached from a mathematical perspective. However, to date, a mathematical model quantifying the contribution of allostery in controlling the dynamics of metabolic pathways has not been proposed. In this study, a direct, rigorous mathematical model of the de novo biosynthesis of pyrimidine nucleotides is presented. We corroborate the simulations with experimental data available in the literature and validate it with derepression experiments done in our laboratory. The model is able to faithfully represent the dynamic changes in the intracellular nucleotide pools that occur during metabolic transitions of the de novo pyrimidine biosynthetic pathway and represents a step forward in understanding the role of allosteric regulation in metabolic control. link: http://identifiers.org/pubmed/15784266

CD4<sup>+</sup> T cells recognize antigens through their T cell receptors (TCRs); however, additional signals involving costimulatory receptors, for example, CD28, are required for proper T cell activation. Alternative costimulatory receptors have been proposed, including members of the Toll-like receptor (TLR) family, such as TLR5 and TLR2. To understand the molecular mechanism underlying a potential costimulatory role for TLR5, we generated detailed molecular maps and logical models for the TCR and TLR5 signaling pathways and a merged model for cross-interactions between the two pathways. Furthermore, we validated the resulting model by analyzing how T cells responded to the activation of these pathways alone or in combination, in terms of the activation of the transcriptional regulators CREB, AP-1 (c-Jun), and NF-κB (p65). Our merged model accurately predicted the experimental results, showing that the activation of TLR5 can play a similar role to that of CD28 activation with respect to AP-1, CREB, and NF-κB activation, thereby providing insights regarding the cross-regulation of these pathways in CD4+ T cells. link: http://identifiers.org/pubmed/30992399

CD4<sup>+</sup> T cells recognize antigens through their T cell receptors (TCRs); however, additional signals involving costimulatory receptors, for example, CD28, are required for proper T cell activation. Alternative costimulatory receptors have been proposed, including members of the Toll-like receptor (TLR) family, such as TLR5 and TLR2. To understand the molecular mechanism underlying a potential costimulatory role for TLR5, we generated detailed molecular maps and logical models for the TCR and TLR5 signaling pathways and a merged model for cross-interactions between the two pathways. Furthermore, we validated the resulting model by analyzing how T cells responded to the activation of these pathways alone or in combination, in terms of the activation of the transcriptional regulators CREB, AP-1 (c-Jun), and NF-κB (p65). Our merged model accurately predicted the experimental results, showing that the activation of TLR5 can play a similar role to that of CD28 activation with respect to AP-1, CREB, and NF-κB activation, thereby providing insights regarding the cross-regulation of these pathways in CD4+ T cells. link: http://identifiers.org/pubmed/30992399

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS

Run this model online at http://jjj.biochem.sun.ac.za

To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

Sucrose accumulation in developing sugar cane (Saccharum officinarum) is accompanied by a continuous synthesis and cleavage of sucrose in the storage tissues. Despite numerous studies, the factors affecting sucrose accumulation are still poorly understood, and no consistent pattern has emerged which pinpoints certain enzyme activities as important controlling steps. Here, we develop an approach based on pathway analysis and kinetic modelling to assess the biochemical control of sucrose accumulation and futile cycling in sugar cane. By using the concept of elementary flux modes, all possible routes of futile cycling of sucrose were enumerated in the metabolic system. The available kinetic data for the pathway enzymes were then collected and assembled in a kinetic model of sucrose accumulation in sugar cane culm tissue. Although no data were fitted, the model agreed well with independent experimental results: in no case was the difference between calculated and measured fluxes and concentrations greater than 2-fold. The model thus validated was then used to assess different enhancement strategies for increasing sucrose accumulation. First, the control coefficient of each enzyme in the system on futile cycling of sucrose was calculated. Secondly, the activities of those enzymes with the numerically largest control coefficients were varied over a 5-fold range to determine the effect on the degree of futile cycling, the conversion efficiency from hexoses into sucrose, and the net sucrose accumulation rate. In view of the modelling results, overexpression of the fructose or glucose transporter or the vacuolar sucrose import protein, as well as reduction of cytosolic neutral invertase levels, appear to be the most promising targets for genetic manipulation. This offers a more directed improvement strategy than cumbersome gene-by-gene manipulation. The kinetic model can be viewed and interrogated on the World Wide Web at http://jjj.biochem.sun.ac.za. link: http://identifiers.org/pubmed/11513743