TY  - CHAP
PB  - ACM
SN  - 978-1-4503-4920-8
A1  - Tognazzi, Stefano
A1  - Tribastone, Mirco
A1  - Tschaikowski, Max
A1  - Vandin, Andrea
SP  - 833
N2  - 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.
Y1  - 2017///
KW  - Chemical Reaction Networks; Ordinary Differential Equations; Model
Comparison
AV  - public
TI  - EGAC: a genetic algorithm to compare chemical reaction networks
UR  - http://doi.org/10.1145/3071178.3071265
ID  - eprints3765
T2  - Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '17
EP  - 840
ER  -