NDLI: Overcoming the lack of kinetic information in biochemical reactions networks

Content Provider	ACM Digital Library
Author	Beccuti, Marco Totis, Niccoló Riganti, Chiara Follia, Laura Novelli, Francesco Cordero, Francesca
Abstract	A main aspect in computational modelling of biological systems is the determination of model structure and model parameters. Due to economical and technical reasons, only part of these details are well characterized, while the rest are unknown. To deal with this difficulty, many reverse engineering and parameter estimation methods have been proposed in the literature, however these methods often need an amount of experimental data not always available. In this paper we propose an alternative approach, which overcomes model indetermination solving an Optimization Problem (OP) with an objective function that, similarly to Flux Balance Analysis, is derived from an empirical biological knowledge and does not require large amounts of data. The system behaviour is described by a set of Ordinary Differential Equations (ODE). Model indetermination is resolved selecting time-varying coefficients that maximize/ minimize the objective function at each ODE integration step. Moreover, to facilitate the modelling phase we provide a graphical formalism, based on Petri Nets, which can be used to derive the corresponding ODEs and OP. Finally, the approach is illustrated on a case study focused on cancer metabolism.
Starting Page	91
Ending Page	102
Page Count	12
File Format	PDF
ISSN	01635999
DOI	10.1145/3092819.3092830
Journal	ACM SIGMETRICS Performance Evaluation Review (PERV)
Volume Number	44
Issue Number	4
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-01-10
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software

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