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Register a new userModel evaluation for glycolytic oscillations in yeast biotransformations of xenobiotics
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Anaerobic glycolysis in yeast perturbed by the reduction of xenobiotic ketones is studied numerically in two models which possess the same topology but different levels of complexity. By comparing both models predictions for concentrations and fluxes as well as steady or oscillatory temporal behavior we answer the question what phenomena require what kind of minimum model abstraction. While mean concentrations and fluxes are predicted in agreement by both models we observe different domains of oscillatory behavior in parameter space. Generic properties of the glycolytic response to ketones are discussed.
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There are many mathematical models of biochemical cell signaling pathways that contain a large number of elements (species and reactions). This is sometimes a big issue for identifying critical model elements and describing the model dynamics. Thus, techniques of model reduction can be used as a mathematical tool in order to minimize the number of variables and parameters. In this thesis, we review some well-known methods of model reduction for cell signaling pathways. We have also developed some approaches that provide us a great step forward in model reduction. The techniques are quasi steady state approximation (QSSA), quasi equilibrium approximation (QEA), lumping of species and entropy production analysis. They are applied on protein translation pathways with microRNA mechanisms, chemical reaction networks, extracellular signal regulated kinase (ERK) pathways, NFkB signal transduction pathways, elongation factors EFTu and EFTs signaling pathways and Dihydrofolate reductase (DHFR) pathways. The main aim of this thesis is to reduce the complex cell signaling pathway models. This provides one a better understanding of the dynamics of such models and gives an accurate approximate solution. Results show that there is a good agreement between the original models and the simplified models.
The living cell is an open nonequilibrium biochemical system, where ATP hydrolysis serves as the energy source for a wide range of intracellular processes including the assurance for decision-making. In the fission yeast cell cycle, the transition from G2 phase to M phase is triggered by the activation of Cdc13/Cdc2 and Cdc25, and the deactivation of Wee1. Each of these three events involves a phosphorylation-dephosphorylation (PdP) cycle, and together they form a regulatory circuit with feedback loops. Almost all quantitative models for cellular networks in the past have invalid thermodynamics due to the assumption of irreversible enzyme kinetics. We constructed a thermodynamically realistic kinetic model of the G2/M circuit, and show that the phosphorylation energy ($Delta G$), which is determined by the cellular ATP/ADP ratio, critically controls the dynamics and the bistable nature of Cdc2 activation. Using fission yeast nucleoplasmic extract (YNPE), we are able to experimentally verify our model prediction that increased , being synergistic to the accumulation of Cdc13, drives the activation of Cdc2. Furthermore, Cdc2 activation exhibits bistability and hysteresis in response to changes in phosphorylation energy. These findings suggest that adequate maintenance of phosphorylation energy ensures the bistability and robustness of the activation of Cdc2 in the G2/M transition. Free energy might play a widespread role in biological decision-making processes, connecting thermodynamics with information processing in biology.
Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a transformative tool in biotechnology.
Gamma frequency oscillations (25-140 Hz), observed in the neural activities within many brain regions, have long been regarded as a physiological basis underlying many brain functions, such as memory and attention. Among numerous theoretical and computational modeling studies, gamma oscillations have been found in biologically realistic spiking network models of the primary visual cortex. However, due to its high dimensionality and strong nonlinearity, it is generally difficult to perform detailed theoretical analysis of the emergent gamma dynamics. Here we propose a suite of Markovian model reduction methods with varying levels of complexity and applied it to spiking network models exhibiting heterogeneous dynamical regimes, ranging from homogeneous firing to strong synchrony in the gamma band. The reduced models not only successfully reproduce gamma band oscillations in the full model, but also exhibit the same dynamical features as we vary parameters. Most remarkably, the invariant measure of the coarse-grained Markov process reveals a two-dimensional surface in state space upon which the gamma dynamics mainly resides. Our results suggest that the statistical features of gamma oscillations strongly depend on the subthreshold neuronal distributions. Because of the generality of the Markovian assumptions, our dimensional reduction methods offer a powerful toolbox for theoretical examinations of many other complex cortical spatio-temporal behaviors observed in both neurophysiological experiments and numerical simulations.
Cells have evolved a metabolic control of DNA replication to respond to a wide range of nutritional conditions. Accumulating data suggest that this poorly understood control depends, at least in part, on Central Carbon Metabolism (CCM). In Bacillus subtilis , the glycolytic pyruvate kinase (PykA) is intricately linked to replication. This 585 amino-acid-long enzyme comprises a catalytic (Cat) domain that binds to phosphoenolpyruvate (PEP) and ADP to produce pyruvate and ATP, and a C-terminal domain of unknown function. Interestingly, the C-terminal domain termed PEPut interacts with Cat and is homologous a domain that, in other metabolic enzymes, are phosphorylated at a conserved TSH motif at the expense of PEP and ATP to drive sugar import and catalytic or regulatory activities. To gain insights into the role of PykA in replication, DNA synthesis was analyzed in various Cat and PEPut mutants grown in a medium where the metabolic activity of PykA is dispensable for growth. Measurements of replication parameters ( ori/ter ratio, C period and fork speed) and of the pyruvate kinase activity showed that PykA mutants exhibit replication defects resulting from side chain modifications in the PykA protein rather than from a reduction of its metabolic activity. Interestingly, Cat and PEPut have distinct commitments in replication: while Cat impacts positively and negatively replication fork speed, PEPut stimulates initiation through a process depending on Cat-PEPut interaction and growth conditions. Residues binding to PEP and ADP in Cat, stabilizing the Cat-PEPut interaction and belonging to the TSH motif of PEPut were found important for the commitment of PykA in replication. In vitro , PykA affects the activities of replication enzymes (the polymerase DnaE, helicase DnaC and primase DnaG) essential for initiation and elongation and genetically linked to pykA . Our results thus connect replication initiation and elongation to CCM metabolites (PEP, ATP and ADP), critical Cat and PEPut residues and to multiple links between PykA and the replication enzymes DnaE, DnaC and DnaG. We propose that PykA is endowed with a moonlighting activity that senses the concentration of signaling metabolites and interacts with replication enzymes to convey information on the cellular metabolic state to the replication machinery and adjust replication initiation and elongation to metabolism. This defines a new type of replication regulator proposed to be part of the metabolic control that gates replication in the cell cycle.
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