Artículo
Evolving cell models for systems and synthetic biology
Autor/es | Cao, Hongqing
Romero Campero, Francisco José Heeb, Stephan Cámara, Miguel Krasnogor, Natalio |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2010 |
Fecha de depósito | 2021-05-28 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Engineering and Physical Sciences Research Council (EPSRC) Biotechnology and Biological Sciences Research Council (BBSRC) |
Identificador del proyecto | EP/ E017215/1
BB/F01855X/1 |
Cita | Cao, H., Romero Campero, F.J., Heeb, S., Cámara, M. y Krasnogor, N. (2010). Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology, 4 (1), 55-84. |
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