Threshold Logic Gene Regulatory Networks
ABSTRACT Gene regulation is an important modeling problem in biology. The deluge of data generated by improved techniques of gene sequencing will not be of much use until we develop accurate and efficient gene regulatory network (GRN) models. In this paper a novel threshold logic gene regulatory model is proposed. This model has been demonstrated to be powerful enough to explain gene interaction and cellular processes. A novel programmable hardware implementation to speed up the gene network simulation is presented. Some insights into the extension of this model are provided.
Article: Field Programmable Gate-ArraysIEEE Design and Test of Computers 02/1998; · 1.62 Impact Factor
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ABSTRACT: MOTIVATION: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. RESULTS: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.Bioinformatics 03/2002; 18(2):261-74. · 5.32 Impact Factor
Article: A natural class of robust networks.[Show abstract] [Hide abstract]
ABSTRACT: As biological studies shift from molecular description to system analysis we need to identify the design principles of large intracellular networks. In particular, without knowing the molecular details, we want to determine how cells reliably perform essential intracellular tasks. Recent analyses of signaling pathways and regulatory transcription networks have revealed a common network architecture, termed scale-free topology. Although the structural properties of such networks have been thoroughly studied, their dynamical properties remain largely unexplored. We present a prototype for the study of dynamical systems to predict the functional robustness of intracellular networks against variations of their internal parameters. We demonstrate that the dynamical robustness of these complex networks is a direct consequence of their scale-free topology. By contrast, networks with homogeneous random topologies require fine-tuning of their internal parameters to sustain stable dynamical activity. Considering the ubiquity of scale-free networks in nature, we hypothesize that this topology is not only the result of aggregation processes such as preferential attachment; it may also be the result of evolutionary selective processes.Proceedings of the National Academy of Sciences 08/2003; 100(15):8710-4. · 9.81 Impact Factor