title: A decompositional approach to parameter estimation in pathway modeling: a case study of the Akt and MAPK pathways and their crosstalk.
abstract: Parameter estimation is a critical problem in modeling biological pathways. It is difficult because of the large number of parameters to be estimated and the limited experimental data available. In this paper, we propose a decompositional approach to parameter estimation. It exploits the structure of a large pathway model to break it into smaller components, whose parameters can then be estimated independently. This leads to significant improvements in computational efficiency. We present our approach in the context of Hybrid Functional Petri Net modeling and evolutionary search for parameter value estimation. However, the approach can be easily extended to other modeling frameworks and is independent of the search method used. We have tested our approach on a detailed model of the Akt and MAPK pathways with two known and one hypothesized crosstalk mechanisms. The entire model contains 84 unknown parameters. Our simulation results exhibit good correlation with experimental data, and they yield positive evidence in support of the hypothesized crosstalk between the two pathways.
authors.affiliation: Graduate School for Integrative Sciences and Engineering, National University of Singapore.
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Koh
authors.forename: Huey Fern Carol
authors.initials: HF
authors.lastname: Teong
authors.forename: Marie-Véronique
authors.initials: MV
authors.lastname: Clément
authors.forename: David
authors.initials: D
authors.lastname: Hsu
authors.forename: P S
authors.initials: PS
authors.lastname: Thiagarajan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D007700:Kinetics; D020935:MAP Kinase Signaling System / Q000502:physiology; D020928:Mitogen-Activated Protein Kinases / Q000378:metabolism*; D008954:Models, Biological; D009097:Multienzyme Complexes / Q000378:metabolism*; D016571:Neural Networks, Computer; D025941:Protein Interaction Mapping / Q000379:methods*; D051057:Proto-Oncogene Proteins c-akt / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D009097:Multienzyme Complexes; D051057:Proto-Oncogene Proteins c-akt; D020928:Mitogen-Activated Protein Kinases