Structure and parameter estimation for cell systems biology models

In this work we present a new methodology for structure and parameter estimation in cell systems biology modelling. Our modelling framework is based on P systems, an unconl computational paradigm that abstracts from the structure and functioning of the living cell. The process of designing models, consisting of both the optimisation of the modular structure and of the stochastic kinetic parameters, is performed using a memetic algorithm. Specifically, we use a nested evolutionary algorithm where the first layer evolves rule structures while the inner layer, implemented also as a genetic algorithm (GA), fine tunes the parameters of the model. Our approach consists of an incremental methodology. Starting from very simple P system modules specifying basic molecular interactions, more complicated modules are produced to model more complex molecular systems. These newly found modules are in turn added to the library of available P systems modules so as to be used subsequently to develop...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Francisco José Romero-Campero, Hongqing Cao, Miguel Cámara, Natalio Krasnogor
• Keywords: Cell Systems Biology, GECCO 2008, Layer Evolves Rule, Nested Evolutionary Algorithm, Optimization