Synchronous versus asynchronous modeling of gene regulatory networks

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 modelshowing 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

Dynamic simulation of regulatory networks using SQUAD

Giovanni De Micheli, Abhishek Garg, Ioannis Xenarios

Background The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. Results We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by eliciting receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. Conclusions The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

2007

Implicit methods for modeling gene regulatory networks

Abhishek Garg

Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from single-gene single-protein experimental approach to studying the behaviour of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behaviour in terms of network structures that represent the influence of different biological entities such as genes, proteins and metabolites on each other. These different biological entities interact with each other giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges that are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model large networks comprising of thousands of nodes have renewed interest in developing new computational techniques for modeling these complex biological systems. In this thesis, I introduce a framework for modeling such regulatory networks in biology based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulations in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady state behaviour, stochasticity and gene perturbation experiments. Further, the proposed algorithms have been implemented under the modeling toolbox "genYsis" using implicit representation techniques based on reduced ordered binary decision diagrams (ROBDDs) and algebraic decision diagrams (ADDs) enabling the modeling of large regulatory networks.

EPFL2009

An Efficient Method for Dynamic Analysis of Gene Regulatory Networks and in silico Gene Perturbation Experiments

Giovanni De Micheli, Ioannis Xenarios

With the increasing availability of experimental data on gene-gene and protein-protein interactions, modeling of gene regulatory networks has gained a special attention lately. To have a better understanding of these networks it is necessary to capture their dynamical properties, by computing its steady states. Various methods have been proposed to compute steady states but almost all of them suffer from the state space explosion problem with the increasing size of the networks. Hence it becomes difficult to model even moderate sized networks using these techniques. In this paper, we present a new representation of gene regulatory networks, which facilitates the steady state computation of networks as large as 1200 nodes and 5000 edges. We benchmarked and validated our algorithm on the T helper model from [8] and performed in silico knock out experiments: showing both a reduction in computation time and correct steady state identification.