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Stochastic dynamics of genetic networks: modelling and parameter identification
2008
Computer applications in the biosciences : CABIOS
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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]
2006
Lecture Notes in Computer Science
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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
2021
Processes
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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
2004
Genetics
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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
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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
2018
IFAC-PapersOnLine
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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
2004
Genetics
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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
2010
49th IEEE Conference on Decision and Control (CDC)
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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]
2011
Lecture Notes in Computer Science
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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]
2016
bioRxiv
pre-print
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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
2016
Human Heredity
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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
2015
Current Genomics
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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
2014
BMC Bioinformatics
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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
2015
Processes
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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
2010
Computer applications in the biosciences : CABIOS
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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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