The Experts below are selected from a list of 73740 Experts worldwide ranked by ideXlab platform
Julio Vera - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi valued logic
Biochimica et Biophysica Acta, 2014Co-Authors: Faiz M Khan, Ulf Schmitz, Svetoslav Nikolov, David Engelmann, Brigitte M Putzer, Olaf Wolkenhauer, Julio VeraAbstract:A decade of successful results indicates that systems biology is the appropriate approach to investigate the regulation of complex biochemical networks involving transcriptional and post-transcriptional regulations. It becomes mandatory when dealing with highly interconnected biochemical networks, composed of hundreds of compounds, or when networks are enriched in non-linear motifs like feedback and feedforward loops. An emerging dilemma is to conciliate models of massive networks and the adequate description of non-linear dynamics in a suitable Modeling framework. Boolean networks are an ideal representation of massive networks that are humble in terms of computational complexity and data demand. However, they are inappropriate when dealing with nested feedback/feedforward loops, structural motifs common in biochemical networks. On the other hand, models of ordinary differential equations (ODEs) cope well with these loops, but they require enormous amounts of quantitative data for a full characterization of the model. Here we propose Hybrid models, composed of ODE and logical sub-modules, as a strategy to handle large scale, non-linear biochemical networks that include transcriptional and post-transcriptional regulations. We illustrate the construction of this kind of models using as example a regulatory network centered on E2F1, a transcription factor involved in cancer. The Hybrid Modeling approach proposed is a good compromise between quantitative/qualitative accuracy and scalability when considering large biochemical networks with a small highly interconnected core, and module of transcriptionally regulated genes that are not part of critical regulatory loops. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.
Olivier Roux - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Modeling of Gene Regulatory Networks: Mixing Temporal and Qualitative Biological Properties
BMC Systems Biology, 2010Co-Authors: Jonathan Fromentin, Damien Eveillard, Olivier RouxAbstract:Modeling gene regulatory network is a difficult task. Experiments often fail at giving enough information for parameterizing existing models. We propose herein a dedicated Hybrid Modeling. To overcome the lack of quantitative information, our Modeling focuses on (i) the biological compound product signs and (ii) the temporal properties associated with the biological effects of the interactions. Both these constraints are easy to extract from experimental data. They aims at reasoning on a Hybrid system adapted to large gene regulatory networks, which is suitable for emphasizing biological properties.
Habtom W Ressom - One of the best experts on this subject based on the ideXlab platform.
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Reverse engineering module networks by PSO-RNN Hybrid Modeling
BMC Genomics, 2009Co-Authors: Yuji Zhang, Jianhua Xuan, Benildo G De Los Reyes, Robert Clarke, Habtom W RessomAbstract:Background Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell; (2) complex dynamic and nonlinear relationships exist among genes; (3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules. Results This study presents a novel GRN inference method by integrating gene expression data and gene functional category information. The inference is based on module network model that consists of two parts: the module selection part and the network inference part. The former determines the optimal modules through fuzzy c-mean (FCM) clustering and by incorporating gene functional category information, while the latter uses a Hybrid of particle swarm optimization and recurrent neural network (PSO-RNN) methods to infer the underlying network between modules. Our method is tested on real data from two studies: the development of rat central nervous system (CNS) and the yeast cell cycle process. The results are evaluated by comparing them to previously published results and gene ontology annotation information. Conclusion The reverse engineering of GRNs in time course gene expression data is a major obstacle in system biology due to the limited number of time points. Our experiments demonstrate that the proposed method can address this challenge by: (1) preprocessing gene expression data (e.g. normalization and missing value imputation) to reduce the data noise; (2) clustering genes based on gene expression data and gene functional category information to identify biologically meaningful modules, thereby reducing the dimensionality of the data; (3) Modeling GRNs with the PSO-RNN method between the modules to capture their nonlinear and dynamic relationships. The method is shown to lead to biologically meaningful modules and networks among the modules.
Jonathan Fromentin - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Modeling of Gene Regulatory Networks: Mixing Temporal and Qualitative Biological Properties
BMC Systems Biology, 2010Co-Authors: Jonathan Fromentin, Damien Eveillard, Olivier RouxAbstract:Modeling gene regulatory network is a difficult task. Experiments often fail at giving enough information for parameterizing existing models. We propose herein a dedicated Hybrid Modeling. To overcome the lack of quantitative information, our Modeling focuses on (i) the biological compound product signs and (ii) the temporal properties associated with the biological effects of the interactions. Both these constraints are easy to extract from experimental data. They aims at reasoning on a Hybrid system adapted to large gene regulatory networks, which is suitable for emphasizing biological properties.
M. Annunziato - One of the best experts on this subject based on the ideXlab platform.
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Urban traffic flow forecasting through statistical and neural network bagging ensemble Hybrid Modeling
Neurocomputing, 2015Co-Authors: Francesca Moretti, Stefano Panzieri, Stefano Pizzuti, M. AnnunziatoAbstract:In this paper we show a Hybrid Modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach outperforms the best of the methods it puts together.
