CHAPTER 6GPU-POWERED EVOLUTIONARY DESIGN OF MASS-ACTION-BASED MODELS OF GENE REGULATION
Marco S. Nobile
Dipartimento di Informatica, Sistemistica e Comunicazione,Università degli Studi di Milano-Bicocca, Milano, ItalySYSBIO Centre for Systems Biology, Milano, Italy
Davide Cipolla
Dipartimento di Informatica, Sistemistica e Comunicazione,Università degli Studi di Milano-Bicocca, Milano, Italy
Paolo Cazzaniga
Dipartimento di Scienze Umane e Sociali, Università degli Studi di Bergamo,Bergamo, ItalySYSBIO Centre for Systems Biology, Milano, Italy
Daniela Besozzi
Dipartimento di Informatica, Università degli Studi di Milano, Milano, ItalySYSBIO Centre for Systems Biology, Milano, Italy
6.1 INTRODUCTION
The goal of synthetic biology (SB) is to design and construct novel biological circuits—in particular, gene regulatory networks (GRNs)—that are able to reproduce a desired behavior. This task is similar to the reverse engineering (RE) problem [10], whose purpose is to identify the network of interactions among the components of a real biological system, that fits an experimentally observed dynamics. The main difference between RE and the design of a synthetic circuit is that, in RE, the target dynamics is not a specifically chosen behavior, but it usually consists in laboratory measurements of some chemical species. In this context, mathematical models and computational analysis of GRNs are needed to facilitate the experimental research and to provide useful insights for the control ...
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