BackgroundA kinetic model provides insights into the dynamic response of biological systems and predicts how their complex metabolic and gene regulatory networks generate particular functions. Of many biological systems, Escherichia coli metabolic pathways have been modeled extensively at the enzymatic and genetic levels, but existing models cannot accurately reproduce experimental behaviors in a batch culture, due to the inadequate estimation of a specific cell growth rate and a large number of unmeasured parameters.ResultsIn this study, we developed a detailed kinetic model for the central carbon metabolism of E. coli in a batch culture, which includes the glycolytic pathway, tricarboxylic acid cycle, pentose phosphate pathway, Entner-Doudoroff pathway, anaplerotic pathway, glyoxylate shunt, oxidative phosphorylation, phosphotransferase system (Pts), non-Pts and metabolic gene regulations by four protein transcription factors: cAMP receptor, catabolite repressor/activator, pyruvate dehydrogenase complex repressor and isocitrate lyase regulator. The kinetic parameters were estimated by a constrained optimization method on a supercomputer. The model estimated a specific growth rate based on reaction kinetics and accurately reproduced the dynamics of wild-type E. coli and multiple genetic mutants in a batch culture.ConclusionsThis model overcame the intrinsic limitations of existing kinetic models in a batch culture, predicted the effects of multilayer regulations (allosteric effectors and gene expression) on central carbon metabolism and proposed rationally designed fast-growing cells based on understandings of molecular processes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-016-0511-x) contains supplementary material, which is available to authorized users.
Flooding is one of the serious problems for soybean plants because it inhibits growth. Proteomic and metabolomic techniques were used to determine whether proteins and metabolites are altered in the root tips of soybeans under flooding stress. Two-day-old soybean plants were flooded for 2 days, and proteins and metabolites were extracted from root tips. Flooding-responsive proteins were identified using two-dimensional- or SDS-polyacrylamide gel electrophoresis- based proteomics techniques. Using both techniques, 172 proteins increased and 105 proteins decreased in abundance in the root tips of flood-stressed soybean. The abundance of methionine synthase, heat shock cognate protein, urease, and phosphoenol pyruvate carboxylase was significantly increased by flooding stress. Furthermore, 73 flooding-responsive metabolites were identified using capillary electrophoresis-mass spectrometry. The levels of gamma-aminobutyric acid, glycine, NADH2, and phosphoenol pyruvate were increased by flooding stress. Taken together, these results suggest that synthesis of phosphoenol pyruvate by way of oxaloacetate produced in the tricarboxylic acid cycle is activated in soybean root tips in response to flooding stress, and that flooding stress also leads to modulation of the urea cycle in the root tips.
The ErbB receptor signaling pathway plays an important role in the regulation of cellular proliferation, survival and differentiation, and dysregulation of the pathway is linked to various types of human cancer. Mathematical models have been developed as a practical complementary approach to deciphering the complexity of ErbB receptor signaling and elucidating how the pathways discriminate between ligands to induce different cell fates. In this study, we developed a simulator to accurately calculate the dynamic sensitivity of extracellular-signal-regulated kinase (ERK) activity (ERK*) and Akt activity (Akt*), downstream of the ErbB receptors stimulated with epidermal growth factor (EGF) and heregulin (HRG). To demonstrate the feasibility of this simulator, we estimated how the reactions critically responsible for ERK* and Akt* change with time and in response to different doses of EGF and HRG, and predicted that only a small number of reactions determine ERK* and Akt*. ERK* increased steeply with increasing HRG dose until saturation, while showing a gently rising response to EGF. Akt* had a gradual wide-range response to HRG and a blunt response to EGF. Akt* was sensitive to perturbations of intracellular kinetics, while ERK* was more robust due to multiple, negative feedback loops. Overall, the simulator predicted reactions that were critically responsible for ERK* and Akt* in response to the dose of EGF and HRG, illustrated the response characteristics of ERK* and Akt*, and estimated mechanisms for generating robustness in the ErbB signaling network.
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