Abstract

Due to the complexity of biological systems, simulation of biological networks is necessary but sometimes complicated. The classic stochastic simulation algorithm (SSA) by Gillespie and its modified versions are widely used to simulate the stochastic dynamics of biochemical reaction systems. However, it has remained a challenge to implement accurate and efficient simulation algorithms for general reaction schemes in growing cells. Here, we present a modeling and simulation tool, called ‘GeneCircuits’, which is specifically developed to simulate gene-regulation in exponentially growing bacterial cells (such as E. coli) with overlapping cell cycles. Our tool integrates three specific features of these cells that are not generally included in SSA tools: 1) the time delay between the regulation and synthesis of proteins that is due to transcription and translation processes; 2) cell cycle-dependent periodic changes of gene dosage; and 3) variations in the propensities of chemical reactions that have time-dependent reaction rates as a consequence of volume expansion and cell division. We give three biologically relevant examples to illustrate the use of our simulation tool in quantitative studies of systems biology and synthetic biology.

Highlights

  • One of the main objectives in systems biology is to quantitatively understand the behavior of biological systems, from a dynamic aspect

  • To demonstrate the capability of GeneCircuits for exploring complicated phenomenon of gene regulation systems, we applied this tool to three biological model systems

  • We extended the classic SSA algorithm to the one that simultaneously takes into account key features during gene regulation of bacterial cells with overlapping cell cycles

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Summary

Introduction

One of the main objectives in systems biology is to quantitatively understand the behavior of biological systems, from a dynamic aspect. Based on the dynamic information of biological systems, synthetic biology allows for the rationale design of artificial gene circuits. The quantitative models could help us in understanding the general principles regarding how gene regulation systems are operated [10,11,12,13,14,15]). The classic stochastic simulation algorithm (SSA) by Gillespie and its later developments are widely used to simulate the stochastic dynamics of well-stirred biochemical systems [16,17,18]. Even the simplest gene regulatory circuits in bacteria possess common features that are not trivial to implement in a general simulation tool

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