Synchronous versus asynchronous modeling of gene regulatory networks

Abstract

Motivation: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene–gene, protein–protein and gene–protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes.Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software.Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1–Th2 cellular differentiation process.Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.Contact: abhishek.garg@epfl.ch

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes

“…Attractor computation was performed assuming a Boolean model applying a synchronous updating scheme (Garg et al, 2008) that updates all gene states simultaneously at each step until the system reaches an attractor. For this purpose we used our own implementation (Crespo et al, 2013) written in Perl of the algorithm described by Garg and co-workers (Garg et al, 2007).…”

Improving BDD-based attractor detection for synchronous Boolean networks

“…This first technique is based on Binary Decision Diagram (BDD), a compact data structure for representing Boolean functions. Algorithms proposed in [6,10,9] explore BDDs to encode the Boolean functions in BNs, use BDD operations to capture the dynamics of the networks, and to build their corresponding transition systems. The efficient operations of BDDs are used to compute the forward and backward reachable states.…”

“…Garg et al in [10] proposed a method for detecting attractors for asynchronous BNs. Later, in [9], the synchronous BNs were considered and a combined synchronous-asynchronous modelling approach was proposed to improve the performance of attractor detection algorithms in asynchronous BNs. In a recent work [26], Zheng et al developed an algorithms based on reduced-order BDD (ROBDD) data structure, which further speeds up the computation time of attractor detection.…”

“…In general, the Boolean functions can be formed by combinations of any logical operators. However, in the context of GRNs usually the set of possible Boolean functions is restricted (see e.g., [9,20]). Here the following three types of Boolean functions are considered:…”

Modelling gene and protein regulatory networks with Answer Set Programming

“…Furthermore, as will be explained in Section 2.2, their use requires the development of specific algorithms to retrieve the steady states of the network (see e.g. Garg et al (2008)), and different algorithms can cause different execution flows.…”