Asynchronous abstraction methodology for genetic regulatory networks
Abstract
Abstract. In order to efficiently analyze a large scale system in an automated and objective manner, abstraction is essential. This paper presents an automated abstraction methodology that systematically reduces the small scale complexity found in genetic regulatory network models, while broadly preserving the large scale system behavior. Our approach is to first reduce the number of reactions through a quasisteady-state approximation-based algorithm. Second, it represents the exact molecular state of the system by a set of reduced Boolean (or nary) discrete levels. This results in a chemical master equation that is approximated by a Markov chain with a much smaller state space providing significant simulation time acceleration and computability gains. 1 Background Numerous methods have been proposed to model genetic regulatory networks [1, 2]. While many traditional approaches have relied on a differential equation representation as inferred from a set of underlying biochemical reactions, there has been a growing appreciation of their limitations [3-6]. In particular, differential equation analysis of genetic networks generally assumes that the number of molecules in a cell is high and their concentrations can be viewed as continuous quantities, while their underlying reactions occur deterministically. However, in natural genetic networks these assumptions frequently do not hold as, for example, DNA molecules are typically present in single digit quantities, while some promoters can lead to substantial fluctuations in transcription/translation rates and essentially non-deterministic expression characteristics [7, 8].
Hiroyuki Kuwahara
King Abdullah University of Science and Technology, Computational Bioscience Research Center Research Scientist
Chris Myers
Department Chair / Professor
