Coarse-grained analysis of stochastically simulated cell populations with a positive feedback genetic network architecture. 2015

I G Aviziotis, and M E Kavousanakis, and I A Bitsanis, and A G Boudouvis
National Technical University of Athens, School of Chemical Engineering, 15780 , Athens, Greece, javiziot@chemeng.ntua.gr.

Among the different computational approaches modelling the dynamics of isogenic cell populations, discrete stochastic models can describe with sufficient accuracy the evolution of small size populations. However, for a systematic and efficient study of their long-time behaviour over a wide range of parameter values, the performance of solely direct temporal simulations requires significantly high computational time. In addition, when the dynamics of the cell populations exhibit non-trivial bistable behaviour, such an analysis becomes a prohibitive task, since a large ensemble of initial states need to be tested for the quest of possibly co-existing steady state solutions. In this work, we study cell populations which carry the lac operon network exhibiting solution multiplicity over a wide range of extracellular conditions (inducer concentration). By adopting ideas from the so-called "equation-free" methodology, we perform systems-level analysis, which includes numerical tasks such as the computation of coarse steady state solutions, coarse bifurcation analysis, as well as coarse stability analysis. Dynamically stable and unstable macroscopic (population level) steady state solutions are computed by means of bifurcation analysis utilising short bursts of fine-scale simulations, and the range of bistability is determined for different sizes of cell populations. The results are compared with the deterministic cell population balance model, which is valid for large populations, and we demonstrate the increased effect of stochasticity in small size populations with asymmetric partitioning mechanisms.

UI MeSH Term Description Entries
D007763 Lac Operon The genetic unit consisting of three structural genes, an operator and a regulatory gene. The regulatory gene controls the synthesis of the three structural genes: BETA-GALACTOSIDASE and beta-galactoside permease (involved with the metabolism of lactose), and beta-thiogalactoside acetyltransferase. Lac Gene,LacZ Genes,Lactose Operon,Gene, Lac,Gene, LacZ,Genes, Lac,Genes, LacZ,Lac Genes,Lac Operons,LacZ Gene,Lactose Operons,Operon, Lac,Operon, Lactose,Operons, Lac,Operons, Lactose
D008954 Models, Biological Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment. Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D009010 Monte Carlo Method In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993) Method, Monte Carlo
D002455 Cell Division The fission of a CELL. It includes CYTOKINESIS, when the CYTOPLASM of a cell is divided, and CELL NUCLEUS DIVISION. M Phase,Cell Division Phase,Cell Divisions,Division Phase, Cell,Division, Cell,Divisions, Cell,M Phases,Phase, Cell Division,Phase, M,Phases, M
D002468 Cell Physiological Phenomena Cellular processes, properties, and characteristics. Cell Physiological Processes,Cell Physiology,Cell Physiological Phenomenon,Cell Physiological Process,Physiology, Cell,Phenomena, Cell Physiological,Phenomenon, Cell Physiological,Physiological Process, Cell,Physiological Processes, Cell,Process, Cell Physiological,Processes, Cell Physiological
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D004926 Escherichia coli A species of gram-negative, facultatively anaerobic, rod-shaped bacteria (GRAM-NEGATIVE FACULTATIVELY ANAEROBIC RODS) commonly found in the lower part of the intestine of warm-blooded animals. It is usually nonpathogenic, but some strains are known to produce DIARRHEA and pyogenic infections. Pathogenic strains (virotypes) are classified by their specific pathogenic mechanisms such as toxins (ENTEROTOXIGENIC ESCHERICHIA COLI), etc. Alkalescens-Dispar Group,Bacillus coli,Bacterium coli,Bacterium coli commune,Diffusely Adherent Escherichia coli,E coli,EAggEC,Enteroaggregative Escherichia coli,Enterococcus coli,Diffusely Adherent E. coli,Enteroaggregative E. coli,Enteroinvasive E. coli,Enteroinvasive Escherichia coli
D005075 Biological Evolution The process of cumulative change over successive generations through which organisms acquire their distinguishing morphological and physiological characteristics. Evolution, Biological
D000465 Algorithms A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. Algorithm
D013269 Stochastic Processes Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables. Process, Stochastic,Stochastic Process,Processes, Stochastic

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