Authors:
Liam G. Fearnley
;
Mark A. Ragan
and
Lars K. Nielsen
Affiliation:
The University of Queensland, Australia
Keyword(s):
Signal Transduction, Transcription, Translation, Modelling, Large Scale Models.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Algorithms and Software Tools
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Computational Molecular Systems
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Methodologies and Technologies
;
Model Design and Evaluation
;
Operational Research
;
Simulation
;
Systems Biology
Abstract:
Recent work has generated whole-cell and whole-process models capable of predicting phenotype in simple organisms. The approaches used are hindered in higher organisms and more-complex cells by a lack of kinetic parameters for reactions and events, and the difficulty of measuring and estimating these. Here, we outline a large, two-process model capable of predicting the effects of gene expression on a signal transduction network. Our method models signal transduction and the processes involved in gene expression as two separate systems, solved iteratively. We show that this approach is sufficient to capture functionally significant behaviour resulting from common network motifs. We further demonstrate that our method is scalable and efficient to the size of the largest signal transduction databases currently available. This approach enables analysis and prediction in the absence of kinetic data, but is itself held back by the lack of detailed large-scale gene expression models. Howev
er, research consortia such as ENCODE and FANTOM are rapidly adding to the knowledge of transcriptional regulation, and we anticipate that incorporating this data into our regulatory model could allow the modelling of complex cellular phenomena such as the structured progression seen in cellular differentiation.
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