http://www.biomedcentral.com/bmcsystbiol/ The latest research articles published by BMC Systems Biology 2013-05-17T00:00:00Z Background: The cyclic AMP-Protein Kinase A (cAMP-PKA) pathway is an evolutionarily conserved signal transductionmechanism that regulates cellular growth and differentiation in animals and fungi. We presenta mathematical model that recapitulates the short-term and long-term dynamics of this pathway in thebudding yeast, Saccharomyces cerevisiae. Our model is aimed at recapitulating the dynamics ofcAMP signaling for wild-type cells as well as single (pde1? and pde2?) and double (pde1?pde2?)phosphodiesterase mutants. Results: Our model focuses on PKA-mediated negative feedback on the activity of phosphodiesterases andthe Ras branch of the cAMP-PKA pathway. We show that both of these types of negative feedbackare required to reproduce the wild-type signaling behavior that occurs on both short and long timescales, as well as the the observed responses of phosphodiesterase mutants. A novel feature of ourmodel is that, for a wide range of parameters, it predicts that intracellular cAMP concentrations shouldexhibit decaying oscillatory dynamics in their approach to steady state following glucose stimulation.Experimental measurements of cAMP levels in two genetic backgrounds of S. cerevisiae confirmedthe presence of decaying cAMP oscillations as predicted by the model. Conclusions: Our model of the cAMP-PKA pathway provides new insights into how yeast respond to alterations intheir nutrient environment. Because the model has both predictive and explanatory power it will serveas a foundation for future mathematical and experimental studies of this important signaling network. http://www.biomedcentral.com/1752-0509/7/40 Kevin Gonzales Ömür Kayikçi David Schaeffer Paul Magwene BMC Systems Biology 2013, null:40 2013-05-17T00:00:00Z doi:10.1186/1752-0509-7-40 /content/figures/1752-0509-7-40-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 40 2013-05-17T00:00:00Z PDF Background: In Saccharomyces cerevisiae, structural bistability generates a bimodal expression of the galactose uptake genes (GAL) when exposed to low and high glucose concentrations. This indicates that yeast cells can decide between using either the limited amount of glucose or growing on galactose under changing environmental conditions. A crucial requirement for any plausible mechanistic model of this system is that it reproduces the robustness of the bistable response observed in vivo against inter-individual parametric variability and fluctuating environmental conditions. Results: We show how a control-theoretic analysis of the robustness of a model of the GAL regulatory network may be used to establish the model's plausibility in characterizing the persistent memory of different carbon sources, without the need for extensive simulations. Chemical Reaction Network Theory is used to establish that the proposed network model is compatible with structural bistability. The robustness of each of the two operative conditions against fluctuations of the species concentrations is demonstrated by studying the Domains of Attraction of the corresponding equilibrium points. Finally, we use a global robustness analysis method based on Semi-Definite Programming to evaluate the modification of the bistable steady states induced by multiple parametric variations throughout bounded regions of the parameter space. Conclusions: Our analysis provides convincing evidence for the robustness, and hence plausibility, of the GAL regulatory network model. The proposed workflow also demonstrates the power of analytical methods from control theory to provide a direct quantitative characterization of the dynamics of multistable biomolecular regulatory systems without recourse to extensive computer simulations. http://www.biomedcentral.com/1752-0509/7/39 Luca Salerno Carlo Cosentino Alessio Merola Declan Bates Francesco Amato BMC Systems Biology 2013, null:39 2013-05-17T00:00:00Z doi:10.1186/1752-0509-7-39 /content/figures/1752-0509-7-39-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 39 2013-05-17T00:00:00Z PDF Background: Hematopoietic ontogeny is characterized by overlapping waves of primitive, fetal definitive, and adult definitive erythroid lineages. Our aim is to identify differences in the transcriptional control of these distinct erythroid cell maturation pathways by inferring and analyzing gene-interaction networks from lineage-specific expression datasets. Inferred networks are strongly connected and do not fit a scale-free model, making it difficult to identify essential regulators using the hub-essentiality standard. Results: We employed a semi-supervised machine learning approach to integrate measures of network topology with expression data to score gene essentiality. The algorithm was trained and tested on the adult and fetal definitive erythroid lineages. When applied to the primitive erythroid lineage, 144 high scoring transcription factors were found to be differentially expressed between the primitive and adult definitive erythroid lineages, including all expressed STAT-family members. Differential responses of primitive and definitive erythroblasts to a Stat3 inhibitor and IFNgamma in vitro supported the results of the computational analysis. Further investigation of the original expression data revealed a striking signature of Stat1-related genes in the adult definitive erythroid network. Among the potential pathways known to utilize Stat1, interferon (IFN) signaling-related genes were expressed almost exclusively within the adult definitive erythroid network. Conclusions: In vitro results support the computational prediction that differential regulation and downstream effectors of STAT signaling are key factors that distinguish the transcriptional control of primitive and definitive erythroid cell maturation. http://www.biomedcentral.com/1752-0509/7/38 Emily Greenfest-Allen Jeffrey Malik James Palis Christian Stoeckert BMC Systems Biology 2013, null:38 2013-05-15T00:00:00Z doi:10.1186/1752-0509-7-38 /content/figures/1752-0509-7-38-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 38 2013-05-15T00:00:00Z PDF 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. http://www.biomedcentral.com/1752-0509/7/37 Debjit Ray Yongchun Su Ping Ye BMC Systems Biology 2013, null:37 2013-05-01T00:00:00Z doi:10.1186/1752-0509-7-37 /content/figures/1752-0509-7-37-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 37 2013-05-01T00:00:00Z XML Background: The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network. Results: Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold for integrating transcriptomics, and flux data from four different conditions in order to identify transcriptionally controlled reactions, i.e. reactions that change both in flux and transcription between the compared conditions. Conclusion: We present a new yeast model that represents a comprehensive up-to-date collection of knowledge on yeast metabolism. The model was used for simulating the yeast metabolism under four different growth conditions and experimental data from these four conditions was integrated to the model. The model together with experimental data is a useful tool to identify condition-dependent changes of metabolism between different environmental conditions. http://www.biomedcentral.com/1752-0509/7/36 Tobias Österlund Intawat Nookaew Sergio Bordel Jens Nielsen BMC Systems Biology 2013, null:36 2013-04-30T00:00:00Z doi:10.1186/1752-0509-7-36 /content/figures/1752-0509-7-36-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 36 2013-04-30T00:00:00Z XML Background: Multiple Sclerosis (MS) is considered a T-cell-mediated autoimmune disease with a prototypical oscillatory behavior, as evidenced by the presence of clinical relapses. Understanding the dynamics of immune cells governing the course of MS, therefore, has many implications for immunotherapy. Here, we used flow cytometry to analyze the time-dependent behavior of antigen-specific effector (Teff) and regulatory (Treg) T cells and microglia in mice model of MS, Experimental Autoimmune Encephalomyelitis (EAE), and compared the observations with a mathematical cross-regulation model of T-cell dynamics in autoimmune disease. Results: We found that Teff and Treg cells specific to myelin olygodendrocyte glycoprotein (MOG) developed coupled oscillatory dynamics with a 4- to 5-day period and decreasing amplitude that was always higher for the Teff populations, in agreement with the mathematical model. Microglia activation followed the oscillations of MOG-specific Teff cells in the secondary lymphoid organs, but they were activated before MOG-specific T-cell peaks in the CNS. Finally, we assessed the role of B-cell depletion induced by anti-CD20 therapy in the dynamics of T cells in an EAE model with more severe disease after therapy. We observed that B-cell depletion decreases Teff expansion, although its oscillatory behavior persists. However, the effect of B cell depletion was more significant in the Treg population within the CNS, which matched with activation of microglia and worsening of the disease. Mathematical modeling of T-cell cross-regulation after anti-CD20 therapy suggests that B-cell depletion may influence the dynamics of T cells by fine-tuning their activation. Conclusions: The oscillatory dynamics of T-cells have an intrinsic origin in the physiological regulation of the adaptive immune response, which influences both disease phenotype and response to immunotherapy. http://www.biomedcentral.com/1752-0509/7/34 Sara Martinez-Pasamar Elena Abad Beatriz Moreno Nieves Velez de Mendizabal Ivan Martinez-Forero Jordi Garcia-Ojalvo Pablo Villoslada BMC Systems Biology 2013, null:34 2013-04-26T00:00:00Z doi:10.1186/1752-0509-7-34 /content/figures/1752-0509-7-34-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 34 2013-04-26T00:00:00Z XML Contributing reviewersThe editors of BMC Systems Biology would like to thank all our reviewers who have contributed to the journal in Volume 6 (2012). http://www.biomedcentral.com/1752-0509/7/35 Tim Sands BMC Systems Biology 2013, null:35 2013-04-12T00:00:00Z doi:10.1186/1752-0509-7-35 /content/figures/1752-0509-7-35-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 35 2013-04-12T00:00:00Z XML Background: Genome-scale metabolic models are important tools in systems biology. They permit the in-silico prediction of cellular phenotypes via mathematical optimisation procedures, most importantly flux balance analysis. Current studies on metabolic models mostly consider reaction fluxes in isolation. Based on a recently proposed metabolite-centric approach, we here describe a set of methods that enable the analysis and interpretation of flux distributions in an integrated metabolite-centric view. We demonstrate how this framework can be used for the refinement of genome-scale metabolic models. Results: We applied the metabolite-centric view developed here to the most recent metabolic reconstruction of Escherichia coli. By compiling the balance sheets of a small number of currency metabolites, we were able to fully characterise the energy metabolism as predicted by the model and to identify a possibility for model refinement in NADPH metabolism. Selected branch points were examined in detail in order to demonstrate how a metabolite-centric view allows identifying functional roles of metabolites. Fructose 6-phosphate aldolase and the sedoheptulose bisphosphate bypass were identified as enzymatic reactions that can carry high fluxes in the model but are unlikely to exhibit significant activity in vivo. Performing a metabolite essentiality analysis, unconstrained import and export of iron ions could be identified as potentially problematic for the quality of model predictions. Conclusions: The system-wide analysis of split ratios and branch points allows a much deeper insight into the metabolic network than reaction-centric analyses. Extending an earlier metabolite-centric approach, the methods introduced here establish an integrated metabolite-centric framework for the interpretation of flux distributions in genome-scale metabolic networks that can complement the classical reaction-centric framework. Analysing fluxes and their metabolic context simultaneously opens the door to systems biological interpretations that are not apparent from isolated reaction fluxes. Particularly powerful demonstrations of this are the analyses of the complete metabolic contexts of energy metabolism and the folate-dependent one-carbon pool presented in this work. Finally, a metabolite-centric view on flux distributions can guide the refinement of metabolic reconstructions for specific growth scenarios. http://www.biomedcentral.com/1752-0509/7/33 S Alexander Riemer René Rex Dietmar Schomburg BMC Systems Biology 2013, null:33 2013-04-12T00:00:00Z doi:10.1186/1752-0509-7-33 /content/figures/1752-0509-7-33-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 33 2013-04-12T00:00:00Z XML Background: Essential hypertension (EH) is a complex disease as a consequence of interaction between environmental factors and genetic background, but the pathogenesis of EH remains elusive. The emerging tools of network medicine offer a platform to explore a complex disease at system level. In this study, we aimed to identify the key proteins and the biological regulatory pathways involving in EH and further to explore the molecular connectivities between these pathways by the topological analysis of the Protein-protein interaction (PPI) network.ResultThe extended network including one giant network consisted of 535 nodes connected via 2572 edges and two separated small networks. 27 proteins with high BC and 28 proteins with large degree have been identified. NOS3 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for blood pressure (BP) and the crosstalk between them. Finally, the robustness of NOS3 as central protein and accuracy of backbone were validated by 287 test networks. Conclusion: Our finding suggests that blood pressure variation is orchestrated by an integrated PPI network centered on NOS3. http://www.biomedcentral.com/1752-0509/7/32 Jihua Ran Hui Li Jianfeng Fu Ling Liu Yanchao Xing Xiumei Li Hongming Shen Yan Chen Xiaofang Jiang Yan Li Huiwu Li BMC Systems Biology 2013, null:32 2013-04-12T00:00:00Z doi:10.1186/1752-0509-7-32 /content/figures/1752-0509-7-32-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 32 2013-04-12T00:00:00Z XML Background: As today, there are hundreds of targeted therapies for the treatment of cancer, many of which have companion biomarkers that are in use to inform treatment decisions. If we would consider this whole arsenal of targeted therapies as a treatment option for every patient, very soon we will reach a scenario where each patient is positive for several markers suggesting their treatment with several targeted therapies. Given the documented side effects of anticancer drugs, it is clear that such a strategy is unfeasible. Results: Here, we propose a strategy that optimizes the design of combinatorial therapies to achieve the best response rates with the minimal toxicity. In this methodology markers are assigned to drugs such that we achieve a high overall response rate while using personalized combinations of minimal size. We tested this methodology in an in silico cancer patient cohort, constructed from in vitro data for 714 cell lines and 138 drugs reported by the Sanger Institute. Our analysis indicates that, even in the context of personalized medicine, combinations of three or more drugs are required to achieve high response rates. Furthermore, patient-to-patient variations in pharmacokinetics have a significant impact in the overall response rate. A 10 fold increase in the pharmacokinetics variations resulted in a significant drop the overall response rate. Conclusions: The design of optimal combinatorial therapy for anticancer treatment requires a transition from the one-drug/one-biomarker approach to global strategies that simultaneously assign makers to a catalog of drugs. The methodology reported here provides a framework to achieve this transition. http://www.biomedcentral.com/1752-0509/7/31 Alexei Vazquez BMC Systems Biology 2013, null:31 2013-04-12T00:00:00Z doi:10.1186/1752-0509-7-31 /content/figures/1752-0509-7-31-toc.gif BMC Systems Biology 1752-0509 ${item.volume} 31 2013-04-12T00:00:00Z XML