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Stochastic dynamics of genetic networks: modelling and parameter identification

Eugenio Cinquemani, Andreas Milias-Argeitis, Sean Summers, John Lygeros
2008 Computer applications in the biosciences : CABIOS  
Motivation: Identification of regulatory networks is typically based on deterministic models of gene expression.  ...  Based on this model and assuming that the network of interactions is known, a method for estimating unknown parameters, such as synthesis and binding rates, from the outcomes of multiple time-course experiments  ...  Porreca, H. de Jong and D. Ropers for providing realistic parameter values for the E.coli carbon starvation response model.  ... 
doi:10.1093/bioinformatics/btn527 pmid:18845579 fatcat:qpg3y7ckmffxtnvhdhkcorjhdq

Multi-stage Evolutionary Algorithms for Efficient Identification of Gene Regulatory Networks [chapter]

Kee-Young Kim, Dong-Yeon Cho, Byoung-Tak Zhang
2006 Lecture Notes in Computer Science  
We also develop hybrid evolutionary algorithms and modified fitness evaluation function to identify the structure of gene regulatory networks and to estimate the corresponding parameters at the same time  ...  However, it is not easy to identify the structure of the true network since the number of parameters to be estimated is much larger than that of the available data.  ...  Acknowledgement This work was supported by the Korea Ministry of Science and Technology through National Research Lab (NRL) project and the Ministry of Education and Human Resources Development under the  ... 
doi:10.1007/11732242_5 fatcat:7uxcxlcblzeihkrhy3xfpijuwm

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Seong Beom Cho
2021 Processes  
Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets.  ...  In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced.  ...  Considering that the network structure is determined and represented as a regression equation, the result of the SEM is easy to interpret and allows for easy identification of regulatory relationships  ... 
doi:10.3390/pr9101758 fatcat:2lxa6q6epncvjgtje7wuhnztdi

Identification of Genetic Networks

Momiao Xiong, Jun Li, Xiangzhong Fang
2004 Genetics  
regulatory strength between gene activities.  ...  To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three  ...  The largest for estimation of the parameters will result in inconsisdifference in the gene regulatory effects of the network tent estimates of the parameters in the network.  ... 
doi:10.1093/genetics/166.2.1037 fatcat:gfssl2yvvje2tnlig7wfxhlkfa

Page 1050 of Genetics Vol. 166, Issue 2 [page]

2004 Genetics  
Identification of genetic networks consists of two steps: parameter estimation and structure discovery. In the first step, we assume that the structure of the network is known.  ...  A remarkable feature of the regulatory rela- tion among genes in the network is that the expression levels of the genes are determined by the simultaneous interaction of the regulatory relations in the  ... 

Mixed Integer Multiobjective Optimization Approaches for Systems and Synthetic Biology

Irene Otero-Muras, Julio R. Banga
2018 IFAC-PapersOnLine  
Our framework combines three important capabilities: multiple optimization objectives (taking into account trade-offs between conflicting goals), simultaneous exploration of topology and parameter spaces  ...  Our framework combines three important capabilities: multiple optimization objectives (taking into account trade-offs between conflicting goals), simultaneous exploration of topology and parameter spaces  ...  Advanced identification of models in systems biology: helping to tackle structural uncertainty in the identification of gene regulatory and signaling pathways from experimental data.  ... 
doi:10.1016/j.ifacol.2018.09.042 fatcat:ssxhczcvlzfdvou6de3msc27za

Identification of Genetic Networks

M. Xiong
2004 Genetics  
regulatory strength between gene activities.  ...  To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three  ...  The largest for estimation of the parameters will result in inconsisdifference in the gene regulatory effects of the network tent estimates of the parameters in the network.  ... 
doi:10.1534/genetics.166.2.1037 pmid:15020486 pmcid:PMC1470757 fatcat:b7e2ram2uzab7iheuogelsg6ya

Structural identification of unate-like genetic network models from time-lapse protein concentration measurements

Riccardo Porreca, Eugenio Cinquemani, John Lygeros, Giancarlo Ferrari-Trecate
2010 49th IEEE Conference on Decision and Control (CDC)  
In our previous work [1], we described a method for the structural and parametric identification of ODE models that makes use of concurrent measurements of concentrations and synthesis rates of the gene  ...  In our previous work [1], we described a method for the structural and parametric identification of ODE models that makes use of concurrent measurements of concentrations and synthesis rates of the gene  ...  Identification approaches based on piecewise affine models allow for the reconstruction of parameters and logics of gene regulatory networks [11] , [12] .  ... 
doi:10.1109/cdc.2010.5717922 dblp:conf/cdc/PorrecaCLF10 fatcat:wmsfuqw4ajhdpl2pkhiehces4e

Stability of Inferring Gene Regulatory Structure with Dynamic Bayesian Networks [chapter]

Jagath C. Rajapakse, Iti Chaturvedi
2011 Lecture Notes in Computer Science  
Assuming scale-free topologies, sample datasets are drawn from DBN to evaluate the stability of estimating the structure of GRN.  ...  Though a plethora of techniques have been used to build gene regulatory networks (GRN) from time-series gene expression data, stabilities of such techniques have not been studied.  ...  When gene regulatory networks (GRN) are modeled by BN, genes are represented at the nodes and regulatory interactions are parameterized over the connections by conditional probabilities of gene expressions  ... 
doi:10.1007/978-3-642-24855-9_21 fatcat:cphia473vreerjaybceogjlzgm

Biophysically motivated regulatory network inference: progress and prospects [article]

Richard Bonneau, Tarmo Aijo
2016 bioRxiv   pre-print
priors and data integration to constrain individual model parameters, estimation of latent regulatory factor activity under varying cell conditions, and new methods for learning and modeling regulatory  ...  transcriptional regulatory structure and dynamics.  ...  Several challenges remain for learning dynamic models including: 1) treating the parameterization of these large networks as a proper global system by simultaneously fitting all parameters [50] 2) modeling  ... 
doi:10.1101/051847 fatcat:3ril4ploovfbrkg7bj4pixtyt4

Biophysically Motivated Regulatory Network Inference: Progress and Prospects

Tarmo Äijö, Richard Bonneau
2016 Human Heredity  
priors and data integration to constrain individual model parameters, estimation of latent regulatory factor activity under varying cell conditions, and new methods for learning and modeling regulatory  ...  Given the scale of genomes and the correspondingly large scale and complexity of robust regulatory apparatus controlling genome structure and gene expression, learning regulatory interactions one or a  ...  Several challenges remain for learning dynamic models including: 1) treating the parameterization of these large networks as a proper global system by simultaneously fitting all parameters [50] 2) modeling  ... 
doi:10.1159/000446614 pmid:28076866 fatcat:fytq7wm6dnbwjohtlg3bs5dbr4

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

Zhi-Ping Liu
2015 Current Genomics  
As illustrated in (Fig. 1B) , there are four levels of clarity for the elements of A , which answer different questions about the regulatory parameters respectively. Suppose there are two genes, 1  ...  In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data.  ...  the network structures and parameters, e.g., regulatory logic, causality and strength, from the measured gene expressions by developing models and algorithms.  ... 
doi:10.2174/1389202915666141110210634 pmid:25937810 pmcid:PMC4412962 fatcat:tjn25agedzbezek47tdgbw4chu

Reverse engineering gene regulatory networks: Coupling an optimization algorithm with a parameter identification technique

Yu-Ting Hsiao, Wei-Po Lee
2014 BMC Bioinformatics  
Results: We propose an iterative approach that couples parameter identification and parameter optimization techniques, to address the two tasks simultaneously during network inference.  ...  To infer gene regulatory networks from time series gene profiles, two important tasks that are related to biological systems must be undertaken.  ...  Acknowledgements This work has been supported by National Science Council of Taiwan  ... 
doi:10.1186/1471-2105-15-s15-s8 pmid:25474560 pmcid:PMC4271569 fatcat:dnbi3u7dufgctbzmmt2p3xyikm

Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

Joseph Wayman, Adithya Sagar, Jeffrey Varner
2015 Processes  
Lastly, we showed, at least for the network architectures considered here, that we could simultaneously estimate kinetic parameters and allosteric connectivity from synthetic data.  ...  Thus, cellfree operation holds several significant advantages for model development, identification and validation.  ...  Estimating parameters and effective allosteric regulatory structures.  ... 
doi:10.3390/pr3010138 fatcat:2sqq4afca5ah7iz7dk356houdy

Identification of genetic network dynamics with unate structure

Riccardo Porreca, Eugenio Cinquemani, John Lygeros, Giancarlo Ferrari-Trecate
2010 Computer applications in the biosciences : CABIOS  
In general, identification involves fitting appropriate network structures and parameters to the data. For a given set of genes, exploring all possible network structures is clearly prohibitive.  ...  The second step explores this family and returns a pool of best fitting models along with estimates of their parameters.  ...  ACKNOWLEDGEMENTS The work of E. Cinquemani and J. Lygeros was supported in part by the SystemsX.ch research consortium under the project YeastX. Conflict of Interest: none declared.  ... 
doi:10.1093/bioinformatics/btq120 pmid:20305266 fatcat:pfkp7za75bdt7ftqgeqr2tepoa
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