eprintid: 3765
rev_number: 11
eprint_status: archive
userid: 6
dir: disk0/00/00/37/65
datestamp: 2017-09-26 09:19:39
lastmod: 2017-09-26 09:19:39
status_changed: 2017-09-26 09:19:39
type: book_section
metadata_visibility: show
creators_name: Tognazzi, Stefano
creators_name: Tribastone, Mirco
creators_name: Tschaikowski, Max
creators_name: Vandin, Andrea
creators_id: 
creators_id: mirco.tribastone@imtlucca.it
creators_id: max.tschaikowski@imtlucca.it
creators_id: andrea.vandin@imtlucca.it
title: EGAC: a genetic algorithm to compare chemical reaction networks
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: public
pres_type: paper
keywords: Chemical Reaction Networks; Ordinary Differential Equations; Model
Comparison
abstract: 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.
date: 2017
date_type: published
publisher: ACM
pagerange: 833-840
event_title: GECCO '17: the Genetic and Evolutionary Computation Conference
event_location: Berlin (Germany)
event_dates: July 15th-19th 2017
event_type: conference
id_number: doi:10.1145/3071178.3071265
refereed: TRUE
isbn: 978-1-4503-4920-8
book_title: Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '17
official_url: http://doi.org/10.1145/3071178.3071265
citation:   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)  
document_url: http://eprints.imtlucca.it/3765/1/gecco2017.pdf