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Graph-Based Modeling of Biological Regulatory Networks: Introduction of Singular States
[chapter]
2005
Lecture Notes in Computer Science
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The overall behavior is difficult to grasp and the development of formal methods is needed in order to model and simulate biological regulatory networks. To model the behavior of such systems, R. ...
In the field of biological regulation, models extracted from experimental works are usually complex networks comprising intertwined feedback circuits. ...
We gratefully acknowledge the members of the ÒÓÔÓÐ R working group "observability" and ¿ for stimulating interactions. ...
doi:10.1007/978-3-540-25974-9_6
fatcat:6r26vxofnrdx7fgnjnu5jnsnuu
Inferring regulatory networks
2008
Frontiers in Bioscience
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ODE-based models 4.2. Bayesian networks 4.3. Coexpression networks 4.4. Probabilistic boolean networks 4.5. Inference from multiple sources of data 5. Network analysis in silico 5.1. ...
This review summarizes some of the major themes in computational inference of regulatory networks based on gene expression and other data sources, including transcriptional module identification, network ...
Zhiping Gu for their comments on this review, the Intramural Research Program, National Institute on Aging, NIH and the National Institutes of Health (under Grants CA109872, EB000830) for generous support ...
doi:10.2741/2677
pmid:17981545
fatcat:tywmilxq4bc6vlnf7fr6wwfz3i
Gene Regulatory Network Inference as Relaxed Graph Matching
[article]
2020
bioRxiv
pre-print
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We find that using modern gradient descent methods with superior convergence properties solving OTTER outperforms state-of-the-art gene regulatory network inference methods in predicting binding of transcription ...
PANDA is based on iterative message passing updates that resemble the gradient descent of an optimization problem, OTTER, which can be interpreted as relaxed inexact graph matching between a gene-gene ...
Acknowledgements The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. ...
doi:10.1101/2020.06.23.167999
fatcat:q2zxq2l6szafxete7oxd3q5wai
MODELING REGULATORY NETWORKS WITH WEIGHT MATRICES
1998
Biocomputing '99
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Finally, we use a derivation of this model system to predict the regulatory network from simulated input/output data sets and find that it accurately predicts all components of the model, even with noisy ...
Test regulatory networks generated with this approach display stable and cyclically stable gene expression levels, consistent with known biological systems. ...
Inclusion of environmental variables in a model system Modeling transcriptional regulatory networks in this manner facilitates introduction of environmental variables into the modeling scheme. ...
doi:10.1142/9789814447300_0011
fatcat:vdeodkf445f7dnt5y3c6ldo7f4
Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks
2017
Frontiers in Computational Neuroscience
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These techniques have been applied to the analysis of gene regulatory networks (Meyer-Bäse, 2008), however they are not quite efficient in terms of model reduction for large-scale networks since they involve ...
This yields to a new singular perturbed model The above model (Equation 18) applies to large-scale brain networks for sufficiently small network parameters δ and d . ...
doi:10.3389/fncom.2017.00087
pmid:29051730
pmcid:PMC5633615
fatcat:ngsenrxa75bvzbuybfaxa652ke
Statistical Challenges in Biological Networks
2012
Journal of Computational And Graphical Statistics
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The simplest possible way to reconstruct a regulatory or signaling network is based on the fact that biological processes are the end result of concerted action of interacting molecules. ...
A statistical model, called NetGSA-Network-based Gene Set Analysis, based on this idea was introduced by Shojaie and Michailidis ( , 2010 . A brief outline of the model is given next. ...
doi:10.1080/10618600.2012.738614
fatcat:3vvduwsaibgolom7dyv46salsy
Modeling Multi-valued Genetic Regulatory Networks Using High-Level Petri Nets
[chapter]
2005
Lecture Notes in Computer Science
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keywords: HLPN, modeling of regulatory networks, model checking. Abstract. ...
Regulatory networks are at the core of all biological functions from bio-chemical pathways to gene regulation and cell communication processes. ...
We gratefully acknowledge the members of the genopole r working groups observability and G 3 for stimulating interactions. ...
doi:10.1007/11494744_13
fatcat:aofyzrvvc5dgble7aiqfq7oih4
Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
[article]
2014
arXiv
pre-print
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Networks are a unifying framework for modeling complex systems and network inference problems are frequently encountered in many fields. ...
Results from simulated models demonstrate that RCweb recovers exactly the model structures for sparsity as low (as non-sparse) as 50% and with ratio of unobserved to observed variables as high as 2. ...
Based on that model, I derive a network structure learning approach within explicit theoretical framework. ...
arXiv:1406.0193v1
fatcat:ri6tguvymjgfjoo4bckc3wq6eu
Network comparison using directed graphlets
[article]
2015
arXiv
pre-print
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Such data is often modeled as graphs (also called networks) and studying them can lead to new insights into molecule-level organization. ...
However, a large set of interesting biological networks such as metabolic, cell signaling or transcriptional regulatory networks are intrinsically directional, and using metrics that ignore edge direction ...
of our tool by comparing its execution time with state-of-the-art approaches on several biological networks. ...
arXiv:1511.01964v1
fatcat:o3vehfodfjb3dn4gt6y6btwohi
Hierarchical analysis of piecewise affine models of gene regulatory networks
2008
Theory in biosciences
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This specific class of dynamical systems has been extensively studied for the past few years, as it provides a good framework to model gene regulatory networks. ...
Specifically adapted to these networks, an algorithm of threshold elimination is presented, that refines in certain cases the hierarchical decomposition and therefore improves the analysis. ...
This particular class of systems was first introduced by L. Glass in the 70's [10] as a model of genetic regulatory networks. ...
doi:10.1007/s12064-008-0035-y
pmid:18437441
fatcat:srbiatkdlza3la3gixvoltxyce
A survey of models for inference of gene regulatory networks
2013
Nonlinear Analysis: Modelling and Control
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In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks ( ...
The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. ...
The present edges in the graphs are determined by model selection of the network graphs. ...
doi:10.15388/na.18.4.13972
fatcat:2jil4fn4svfjpfnbk7f3uzkmoq
Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks
2006
Bioinformatics
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independent of the remaining network; (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with ...
In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores ...
In a related study, Husmeier (2003) evaluated the accuracy of reverse engineering gene regulatory networks with Bayesian networks from data simulated from realistic molecular biological pathways, where ...
doi:10.1093/bioinformatics/btl391
pmid:16844710
fatcat:thutddpvo5hpzngfcnoalwwuhm
Deriving Behavior of Boolean Bioregulatory Networks from Subnetwork Dynamics
2009
Mathematics in Computer Science
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Thomas, the structure of a biological regulatory network is captured in an interaction graph, which, together with a set of Boolean parameters, gives rise to a state transition graph describing all possible ...
In addition, we use these ideas to link the existence of certain structural motifs, namely circuits, in the interaction graph to the character and number of attractors in the state transition graph, generalizing ...
Introduction When modeling biological systems, one first has to decide what kind of modeling framework is best suited to incorporate the available data and to yield results without too many additional ...
doi:10.1007/s11786-008-0064-4
fatcat:e4huv3xvrzbnhoft735wxusnii
Review of Biological Network Data and Its Applications
2013
Genomics & Informatics
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Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization ...
In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. ...
In addition, various mathematical properties and models of a network have been developed in graph theory. ...
doi:10.5808/gi.2013.11.4.200
pmid:24465231
pmcid:PMC3897847
fatcat:v72gan3nara6xfqwbxwwljzouq
Application of formal methods to biological regulatory networks: extending Thomas' asynchronous logical approach with temporal logic
2004
Journal of Theoretical Biology
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Based on the discrete definition of biological regulatory networks developed by Rene´Thomas, we provide a computer science formal approach to treat temporal properties of biological regulatory networks ...
This application of formal methods from computer science to biological regulatory networks should open the way to many other fruitful applications. r ...
Let us finally mention that the biological experiments are made in Rouen in cooperation with a team of the university hospital center of ...
doi:10.1016/j.jtbi.2004.04.003
pmid:15234201
fatcat:5d4b6cqi3fa57anzcfuh5zybnu
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