A5105659993- Gene Regulatory Network Analysis
- Microbial Metabolic Engineering and Bioproduction
- Bioinformatics and Genomic Networks
- Formal Methods in Verification
- DNA and Biological Computing
- Logic, Reasoning, and Knowledge
- Pharmacogenetics and Drug Metabolism
- PI3K/AKT/mTOR signaling in cancer
Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers
2017
Centre National de la Recherche Scientifique
2016-2017
École Centrale de Nantes
2015-2017
Laboratoire des Sciences du Numérique de Nantes
2017
This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor minimal trap domain, that is, part state-transition graph cannot be escaped. Such structures are terminal dynamics take form steady states...
The combination of numerous simple influences between the components a Biological Regulatory Network (BRN) often leads to behaviors that cannot be grasped intuitively. They thus call for development proper mathematical methods delineate their dynamical properties. As consequence, formal and computer tools modeling simulation BRNs become essential. Our recently introduced discrete formalism called Process Hitting (PH), restriction synchronous automata networks, is notably suitable such study....
Cellular homeostasis is a continuous phenomenon that if compromised can lead to several disorders including cancer. There need understand the dynamics of cellular proliferation get deeper insights into prevalence Mechanistic Target Rapamycin (mTOR) implicated as central regulator metabolic pathway involved in growth whereas its two distinct complexes mTORC1 and mTORC2 perform particular functions propagation. To date, well defined therapeutic target inhibit uncontrolled cell division, while...
Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis biological observations. However, with development high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim to provide logical approach infer BRNs based given time series data and known influences among genes. Results: We propose new methodology models expressed through...
Models of Biological Regulatory Networks are generally based on prior knowledge, either derived from literature and/or the manual analysis biological observations. With development high-throughput data, there is a growing need for methods that automatically generate admissible models. To have better understanding dynamical phenomena at stake in influences between components, it would be necessary to include delayed model. The main purpose this work resulting network as consistent possible...