relation: http://eprints.imtlucca.it/3765/
title: EGAC: a genetic algorithm to compare chemical reaction networks
creator: Tognazzi, Stefano
creator: Tribastone, Mirco
creator: Tschaikowski, Max
creator: Vandin, Andrea
subject: QA75 Electronic computers. Computer science
description: Discovering relations between chemical reaction networks (CRNs)  is a relevant problem in computational systems biology for model  reduction, to explain if a given system can be seen as an abstraction  of another one; and for model comparison, useful to establish an evolutionary  path from simpler networks to more complex ones. This  is also related to foundational issues in computer science regarding  program equivalence, in light of the established interpretation of a  CRN as a kernel programming language for concurrency. Criteria  for deciding if two CRNs can be formally related have been recently  developed, but these require that a candidate mapping be provided.  Automatically finding candidate mappings is very hard in general  since the search space essentially consists of all possible partitions  of a set. In this paper we tackle this problem by developing a genetic  algorithm for a class of CRNs called influence networks, which can  be used to model a variety of biological systems including cell-cycle  switches and gene networks. An extensive numerical evaluation  shows that our approach can successfully establish relations between  influence networks from the literature which cannot be found  by exact algorithms due to their large computational requirements.
publisher: ACM
date: 2017
type: Book Section
type: PeerReviewed
format: application/pdf
language: en
identifier: http://eprints.imtlucca.it/3765/1/gecco2017.pdf
identifier:   Tognazzi, Stefano and Tribastone, Mirco and Tschaikowski, Max and Vandin, Andrea  EGAC: a genetic algorithm to compare chemical reaction networks.   In:  Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '17.   ACM, pp. 833-840.  ISBN 978-1-4503-4920-8      (2017)  
relation: http://doi.org/10.1145/3071178.3071265
relation: doi:10.1145/3071178.3071265