NDLI: Reverse Engineering of Biochemical Reaction Networks Using Co-evolution with Eng-Genes

Content Provider	Springer Nature Link
Author	Gormley, Padhraig Li, Kang Wolkenhauer, Olaf Irwin, George W. Du, Dajun
Copyright Year	2012
Abstract	A major challenge when attempting to model biochemical reaction networks within the cell is that the dimensionality can become huge, where a large number of molecular species can be involved even in relatively small networks. This investigation attempts to infer models of these networks using a co-evolutionary algorithm that reverse engineers differential equation models of the target system from time-series data. The algorithm not only estimates the system parameters, but also the symbolic structure of the network. To reduce the problem of dimensionality, the algorithm uses a partitioning method while integrating candidate models in order to decouple system equations. In addition, the conventional evolutionary algorithm has been modified and extended to include a technique called ‘eng-genes’, where candidate models are built up from fundamental mathematical terms derived from knowledge about the target system a priori. This technique essentially focuses the search on more biologically plausible models. The approach is demonstrated on several example reaction networks. The results show that the eng-genes method of limiting the term pool using a priori knowledge improves the convergence of the reverse engineering process compared with the conventional method, resulting in more accurate and transparent models.
Starting Page	106
Ending Page	118
Page Count	13
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	5
Issue Number	1
e-ISSN	18669964
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-07-03
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Reverse engineering System identification Evolutionary programming Symbolic identification Parameter estimation Modelling Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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