<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hongqing Cao</style></author><author><style face="normal" font="default" size="100%">Francisco J. Romero-Campero</style></author><author><style face="normal" font="default" size="100%">Stephan Heeb</style></author><author><style face="normal" font="default" size="100%">Miguel Cámara</style></author><author><style face="normal" font="default" size="100%">Natalio Krasnogor</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolving Cell Models for Systems and Synthetic Biology</style></title><secondary-title><style face="normal" font="default" size="100%">Systems and Synthetic Biology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Systems biology. Synthetic biology. P systems. Evolutionary algorithms. Automated model design</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/66671m1186474455/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin, Germany</style></pub-location><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">55-84</style></pages><abstract><style face="normal" font="default" size="100%"> This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models. </style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>