Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network

Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments

Implicit Methods for Probabilistic Modeling of Gene Reglatory Networks

In silico modeling of Gene Regulatory Networks (GRN) has recently aroused a lot of interest in the biological community for modeling and understanding complex pathways. Boolean Networks (BN) are a common modeling tool for in silico dynamic analysis of such pathways. Although they are known to have effectively modeled many real and complex regulatory networks, they are deterministic in nature and have shortcomings in modeling non-determinism that is inherent in biological systems. Probabilistic Boolean Networks (PBN) have been proposed to counter these shortcomings. The capabilities of PBNs have been so far under-utilised because of the lack of an efficient PBN toolbox. This work addresses some issues associated with traditional methods of PBN representation and proposes efficient algorithms to model gene regulatory networks using PBNs

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract\ud \ud \ud \ud Background\ud \ud A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it.\ud \ud \ud \ud Results\ud \ud We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered.\ud \ud \ud \ud Conclusions\ud \ud The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.This work was supported by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico), and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior).This work was supported by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico), and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior).This article has been published as part of BMC Proceedings Volume 5 Supplement 2, 2011: Proceedings of the 6th International Symposium on Bioinformatics Research and Applications (ISBRA10). The full contents of the supplement are available online at http://www.biomedcentral.com/17536561/5?issue=S2.This article has been published as part of BMC Proceedings Volume 5 Supplement 2, 2011: Proceedings of the 6th International Symposium on Bioinformatics Research and Applications (ISBRA'10). The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/5?issue=S2

Simulation study in Probabilistic Boolean Network models for genetic regulatory networks

Probabilistic Boolean Network (PBN) is widely used to model genetic regulatory networks. Evolution of the PBN is according to the transition probability matrix. Steady-state (long-run behaviour) analysis is a key aspect in studying the dynamics of genetic regulatory networks. In this paper, an efficient method to construct the sparse transition probability matrix is proposed, and the power method based on the sparse matrix-vector multiplication is applied to compute the steady-state probability distribution. Such methods provide a tool for us to study the sensitivity of the steady-state distribution to the influence of input genes, gene connections and Boolean networks. Simulation results based on a real network are given to illustrate the method and to demonstrate the steady-state analysis.link_to_subscribed_fulltex

Qualitatively modelling genetic regulatory networks : Petri net techniques and tools

The development of post-genomic technologies has led to a paradigm shift in the way we study genetic regulatory networks (GRNs) - the underlying systems which mediate cell function. To complement this, the focus is on devising scalable, unambiguous and automated formal techniques for holistically modelling and analysing these complex systems. Quantitative approaches offer one possible solution, but do not appear to be commensurate with currently available data. This motivates qualitative approaches such as Boolean networks (BNs) , which abstractly model the system without requiring such a high level of data completeness. Qualitative approaches enable fundamental dynamical properties to be studied, and are well-suited to initial investigations. However, strengthened formal techniques and tool support are required if they are to meet the demands of the biological community. This thesis aims to investigate, develop and evaluate the application of Petri nets (PNs) for qualitatively modelling and analysing GRNs. PNs are well-established in the field of computer science, and enjoy a number of attractive benefits, such a wide range of techniques and tools, which make them ideal for studying biological systems. We take an existing qualitative PN approach for modelling GRNs based on BNs, and extend it to more general models based on multi-valued networks (MVNs). Importantly, we develop tool support to automate model construction. We illustrate our approach with two detailed case studies on Boolean models for carbon stress in Escherichia coli and sporulation in Bacillus subtilis, and then consider a multi-valued model of the former. These case studies explore the analysis power of PN s by exploiting a range of techniques and tools. A number of behavioural differences are identified between the two E. coli models which lead us to question their formal relationship. We investigate this by proposing a framework for reasoning about the behaviour of MVNs at different levels of abstraction. We develop tool support for practical models, and show a number of important results which motivate the need for multi-valued modelling. Asynchronous BN s can be seen to be more biologically realistic than their synchronous counterparts. However, they have the drawback of capturing behaviour which is unrealisable in practice. We propose a novel approach for refining such behaviour using signal transition graphs, a PN formalism from asynchronous circuit design. We automate our approach, and demonstrate it using a BN of the lysis-lysogeny switch in phage A. Our results show that a more realistic asynchronous model can be derived which preserves the stochastic switch.EThOS - Electronic Theses Online ServiceGBUnited Kingdo