The modeling of biochemical reaction networks is a fundamental but complex task in Systems Biology, which is traditionally performed exploiting human expertise and the available experimental data. Anyway, because of a general lack of knowledge on the molecular mechanisms occurring in living cells, an intense research activity focused on the development of reverse engineering methodologies is currently underway. This task is further complicated by the fact that a proper parameterization needs to be associated to the network, in order to investigate its dynamical behavior. In this work we propose a novel computational methodology for the reverse engineering of fully parameterized kinetic reaction networks, based on the combined use of two evolutionary programming techniques: Cartesian Genetic Programming (CGP) and Particle Swarm Optimization (PSO). In particular, CGP is used to infer the network topology, while PSO performs the parameter estimation task. To the purpose of applying our methodology in routine laboratory environments, we designed it to exploit a small set of experimental time series as target. We show that our methodology is able to reconstruct kinetic networks that perfectly fit with the target data

Nobile, M., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D. (2013). Reverse Engineering of Kinetic Reaction Networks by means of Cartesian Genetic Programming and Particle Swarm Optimization. In 2013 IEEE Congress on Evolutionary Computation, CEC 2013 (pp.1594-1601) [10.1109/CEC.2013.6557752].

Reverse Engineering of Kinetic Reaction Networks by means of Cartesian Genetic Programming and Particle Swarm Optimization

NOBILE, MARCO SALVATORE
Primo
;
BESOZZI, DANIELA;MAURI, GIANCARLO;PESCINI, DARIO
2013

Abstract

The modeling of biochemical reaction networks is a fundamental but complex task in Systems Biology, which is traditionally performed exploiting human expertise and the available experimental data. Anyway, because of a general lack of knowledge on the molecular mechanisms occurring in living cells, an intense research activity focused on the development of reverse engineering methodologies is currently underway. This task is further complicated by the fact that a proper parameterization needs to be associated to the network, in order to investigate its dynamical behavior. In this work we propose a novel computational methodology for the reverse engineering of fully parameterized kinetic reaction networks, based on the combined use of two evolutionary programming techniques: Cartesian Genetic Programming (CGP) and Particle Swarm Optimization (PSO). In particular, CGP is used to infer the network topology, while PSO performs the parameter estimation task. To the purpose of applying our methodology in routine laboratory environments, we designed it to exploit a small set of experimental time series as target. We show that our methodology is able to reconstruct kinetic networks that perfectly fit with the target data
paper
Cartesian Genetic Programming, Particle Swarm Optimization, Reverse Engineering, Parameter Estimation, Systems Biology
English
IEEE Congress on Evolutionary Computation, CEC 20-23 June
2013
2013 IEEE Congress on Evolutionary Computation, CEC 2013
978-147990454-9
2013
1
1594
1601
6557752
none
Nobile, M., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D. (2013). Reverse Engineering of Kinetic Reaction Networks by means of Cartesian Genetic Programming and Particle Swarm Optimization. In 2013 IEEE Congress on Evolutionary Computation, CEC 2013 (pp.1594-1601) [10.1109/CEC.2013.6557752].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/45574
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