Pattern Discovery in Biosequences
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Mathematical Modeling of the Influence of RKIP on the ERK Signaling Pathway
CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
Bioinformatics
Biochemistry
Bayesian ranking of biochemical system models
Bioinformatics
Structure and parameter estimation for cell systems biology models
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Towards biopathway modeling and simulation
ICATPN'03 Proceedings of the 24th international conference on Applications and theory of Petri nets
A unifying framework for modelling and analysing biochemical pathways using Petri nets
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
Petri nets for systems and synthetic biology
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
From petri nets to differential equations – an integrative approach for biochemical network analysis
ICATPN'06 Proceedings of the 27th international conference on Applications and Theory of Petri Nets and Other Models of Concurrency
Transactions on Computational Systems Biology VII
Biomodel engineering – from structure to behavior
Transactions on Computational Systems Biology XII
A hybrid approach to piecewise modelling of biochemical systems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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In this paper we describe a method and an associated computational tool to modify and piecewise enlarge the topology of a biological network model, using a set of biochemical components, in order to generate one or more models whose behaviours simulate that of a target biological system. These components are defined as continuous Petri nets and stored in a library for ease of reuse. An optimization algorithm is proposed which exploits Simulated Annealing in order to alter an initial model by reference to the desired behaviour of the target model. Simulation results on a realistic illustrative example signalling pathway show that the proposed method performs well in terms of exploiting the characteristics of simulated annealing in order to generate interesting models with behaviours close to that of the target biochemical system without any pre-knowledge on the target topology itself. In future work we plan to use the generated topologies as population candidates when using an evolutionary approach to further tune the network structure and kinetic parameters.