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Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.

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Název: Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.
Autoři: Leifeld T; Institute of Automatic Control, Technische Universität Kaiserslautern, Kaiserslautern, Germany., Zhang Z; Institute of Automatic Control, Technische Universität Kaiserslautern, Kaiserslautern, Germany., Zhang P; Institute of Automatic Control, Technische Universität Kaiserslautern, Kaiserslautern, Germany.
Zdroj: Frontiers in physiology [Front Physiol] 2018 Jun 08; Vol. 9, pp. 695. Date of Electronic Publication: 2018 Jun 08 (Print Publication: 2018).
Způsob vydávání: Journal Article
Jazyk: English
Informace o časopise: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101549006 Publication Model: eCollection Cited Medium: Print ISSN: 1664-042X (Print) Linking ISSN: 1664042X NLM ISO Abbreviation: Front Physiol Subsets: PubMed not MEDLINE
Imprint Name(s): Original Publication: Lausanne : Frontiers Research Foundation
Abstrakt: Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge. Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response. Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.
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Exp Cell Res. 2000 Nov 25;261(1):91-103. (PMID: 11082279)
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PLoS One. 2008 Feb 27;3(2):e1672. (PMID: 18301750)
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Contributed Indexing: Keywords: Boolean networks; identification; network inference; prior knowledge; time series data
Entry Date(s): Date Created: 20180626 Latest Revision: 20240327
Update Code: 20250114
PubMed Central ID: PMC6002699
DOI: 10.3389/fphys.2018.00695
PMID: 29937735
Databáze: MEDLINE
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  Data: Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.
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  Data: <searchLink fieldCode="AU" term="%22Leifeld+T%22">Leifeld T</searchLink>; Institute of Automatic Control, Technische Universität Kaiserslautern, Kaiserslautern, Germany.<br /><searchLink fieldCode="AU" term="%22Zhang+Z%22">Zhang Z</searchLink>; Institute of Automatic Control, Technische Universität Kaiserslautern, Kaiserslautern, Germany.<br /><searchLink fieldCode="AU" term="%22Zhang+P%22">Zhang P</searchLink>; Institute of Automatic Control, Technische Universität Kaiserslautern, Kaiserslautern, Germany.
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  Data: <searchLink fieldCode="JN" term="%22101549006%22">Frontiers in physiology</searchLink> [Front Physiol] 2018 Jun 08; Vol. 9, pp. 695. <i>Date of Electronic Publication: </i>2018 Jun 08 (<i>Print Publication: </i>2018).
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  Data: Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge. Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response. Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.
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  Data: Bioinformatics. 2015 Apr 1;31(7):1154-9. (PMID: <searchLink fieldCode="PM" term="%2225619997%22">25619997)</searchLink><br />PLoS One. 2013 Jul 26;8(7):e69008. (PMID: <searchLink fieldCode="PM" term="%2223922675%22">23922675)</searchLink><br />Pac Symp Biocomput. 1998;:18-29. (PMID: <searchLink fieldCode="PM" term="%229697168%22">9697168)</searchLink><br />BMC Bioinformatics. 2006 Mar 20;7 Suppl 1:S7. (PMID: <searchLink fieldCode="PM" term="%2216723010%22">16723010)</searchLink><br />BMC Bioinformatics. 2016 Oct 6;17(1):410. (PMID: <searchLink fieldCode="PM" term="%2227716031%22">27716031)</searchLink><br />BMC Syst Biol. 2007 Feb 02;1:11. (PMID: <searchLink fieldCode="PM" term="%2217408501%22">17408501)</searchLink><br />BMC Syst Biol. 2012 Oct 18;6:133. (PMID: <searchLink fieldCode="PM" term="%2223079107%22">23079107)</searchLink><br />PLoS One. 2015 Jul 24;10(7):e0131832. (PMID: <searchLink fieldCode="PM" term="%2226207376%22">26207376)</searchLink><br />J Theor Biol. 1974 Mar;44(1):167-90. (PMID: <searchLink fieldCode="PM" term="%224595774%22">4595774)</searchLink><br />Development. 1997 May;124(10):1851-64. (PMID: <searchLink fieldCode="PM" term="%229169833%22">9169833)</searchLink><br />Pac Symp Biocomput. 1999;:17-28. (PMID: <searchLink fieldCode="PM" term="%2210380182%22">10380182)</searchLink><br />PLoS One. 2013 Jun 21;8(6):e66031. (PMID: <searchLink fieldCode="PM" term="%2223805196%22">23805196)</searchLink><br />Nucleic Acids Res. 2006 Mar 20;34(5):1608-19. (PMID: <searchLink fieldCode="PM" term="%2216549873%22">16549873)</searchLink><br />Proteomics. 2010 Mar;10(6):1202-11. (PMID: <searchLink fieldCode="PM" term="%2220077407%22">20077407)</searchLink><br />BMC Genomics. 2012;13 Suppl 6:S4. (PMID: <searchLink fieldCode="PM" term="%2223134720%22">23134720)</searchLink><br />Nature. 1959 Jun 13;183(4676):1654-5. (PMID: <searchLink fieldCode="PM" term="%2213666847%22">13666847)</searchLink><br />Mol Cancer Ther. 2003 Jul;2(7):679-84. (PMID: <searchLink fieldCode="PM" term="%2212883041%22">12883041)</searchLink><br />J Comput Biol. 2012 Jan;19(1):30-41. (PMID: <searchLink fieldCode="PM" term="%2222216865%22">22216865)</searchLink><br />IEEE Trans Neural Netw Learn Syst. 2017 Feb;28(2):464-469. (PMID: <searchLink fieldCode="PM" term="%2226829809%22">26829809)</searchLink><br />Phys Biol. 2012 Oct;9(5):055001. (PMID: <searchLink fieldCode="PM" term="%2223011283%22">23011283)</searchLink><br />Bioinformatics. 2010 May 1;26(9):1239-45. (PMID: <searchLink fieldCode="PM" term="%2220305266%22">20305266)</searchLink><br />Phys Rev Lett. 2000 Jun 12;84(24):5660-3. (PMID: <searchLink fieldCode="PM" term="%2210991019%22">10991019)</searchLink><br />IEEE Trans Neural Netw. 2011 Apr;22(4):525-36. (PMID: <searchLink fieldCode="PM" term="%2221342840%22">21342840)</searchLink><br />Bioinformatics. 2006 Jul 15;22(14):e124-31. (PMID: <searchLink fieldCode="PM" term="%2216873462%22">16873462)</searchLink><br />Biosystems. 2016 Nov;149:139-153. (PMID: <searchLink fieldCode="PM" term="%2227484338%22">27484338)</searchLink><br />IEEE/ACM Trans Comput Biol Bioinform. 2012;9(2):487-98. (PMID: <searchLink fieldCode="PM" term="%2221464514%22">21464514)</searchLink><br />Exp Cell Res. 2000 Nov 25;261(1):91-103. (PMID: <searchLink fieldCode="PM" term="%2211082279%22">11082279)</searchLink><br />BMC Proc. 2011 May 28;5 Suppl 2:S5. (PMID: <searchLink fieldCode="PM" term="%2221554763%22">21554763)</searchLink><br />Proc Natl Acad Sci U S A. 2003 Dec 9;100(25):14796-9. (PMID: <searchLink fieldCode="PM" term="%2214657375%22">14657375)</searchLink><br />PLoS One. 2008 Feb 27;3(2):e1672. (PMID: <searchLink fieldCode="PM" term="%2218301750%22">18301750)</searchLink><br />EURASIP J Bioinform Syst Biol. 2014;2014(1):10. (PMID: <searchLink fieldCode="PM" term="%2225093019%22">25093019)</searchLink><br />J Theor Biol. 1969 Mar;22(3):437-67. (PMID: <searchLink fieldCode="PM" term="%225803332%22">5803332)</searchLink>
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