Monte Carlo methods. Vol. 1: basics
Monte Carlo methods. Vol. 1: basics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
COPASI---a COmplex PAthway SImulator
Bioinformatics
Composing Globally Consistent Pathway Parameter Estimates Through Belief Propagation
WABI '07 Proceedings of the 7th international workshop on Algorithms in Bioinformatics
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
A Model Checking Approach to the Parameter Estimation of Biochemical Pathways
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Enhancing parameter estimation of biochemical networks by exponentially scaled search steps
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Incremental signaling pathway modeling by data integration
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
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Constructing and analyzing large biological pathway models is a significant challenge. We propose a general approach that exploits the structure of a pathway to identify pathway components, constructs the component models, and finally assembles the component models into a global pathway model. Specifically, we apply this approach to pathway parameter estimation, a main step in pathway model construction. A large biological pathway often involves many unknown parameters and the resulting high-dimensional search space poses a major computational difficulty. By exploiting the structure of a pathway and the distribution of available experimental data over the pathway, we decompose a pathway into components and perform parameter estimation for each component. However, some parameters may belong to multiple components. Independent parameter estimates from different components may be in conflict for such parameters. To reconcile these conflicts, we represent each component as a factor graph, a standard probabilistic graphical model. We then combine the resulting factor graphs and use a probabilistic inference technique called belief propagation to obtain the maximally likely parameter values that are globally consistent. We validate our approach on a synthetic pathway model based on the Akt-MAPK signaling pathways. The results indicate that the approach can potentially scale up to large pathway models.