Life Science and Control #4: Topics II on Cell Control Problem Analysis of Noise Induced Stochastic Fluctuations in Gene Regulatory Networks

Abstract

Stochasticity is well recognized to be of crucial importance in the analysis of gene regulatory problems. This importance stems from the fact that extremely rare but important regulatory molecules often cause a great amount of intrinsic noise within a cell. Such systems are frequently modeled at the mesoscopic level as jump Markov processes, whose probability distributions evolve according to the chemical master equation (CME). In this paper we review a number of attempts that have been made to solve the CME. These include various kinetic Monte Carlo approaches, such as the Stochastic Simulation Algorithm (SSA) and its deviates, as well as systems theory based analytical solutions to the CME, such as the Finite State Projection (FSP) method and various moment closure techniques.