Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A compositional approach to performance modelling
A compositional approach to performance modelling
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A brief history of process algebra
Theoretical Computer Science - Process algebra
The crucial role of CS in systems and synthetic biology
Communications of the ACM - Web searching in a multilingual world
A flexible and scalable experimentation layer
Proceedings of the 40th Conference on Winter Simulation
Evolving stochastic processes using feature tests and genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Bio-PEPA: A framework for the modelling and analysis of biological systems
Theoretical Computer Science
Design and development of software tools for Bio-PEPA
Winter Simulation Conference
Evolving Bio-PEPA process algebra models using genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
Process algebras are an effective method for defining models of complex interacting biological processes, but defining a model requires expertise from both modeller and domain expert. In addition, even with the right model, tuning parameters to allow model outputs to match experimental data can be difficult. This is the well-known parameter fitting problem. Evolutionary algorithms provide effective methods for finding solutions to optimisation problems with large search spaces and are well suited to investigating parameter fitting problems. We present the Evolving Process Algebra (EPA) framework which combines an evolutionary computation approach with process algebra modelling to produce parameter distribution data that provides insight into the parameter space of the biological system under investigation. The EPA framework is demonstrated through application to a novel example: T helper cell activation in the immune system in the presence of co-infection.