| Title | Learning Petri net models of non-linear gene interactions. |
| Author(s) | Mayo M |
| Institution | Department of Computer Science, University of Waikato, Private Bag 3105, Hamilton, New Zealand. mmayo@cs.waikato.ac.nz |
| Source | Biosystems 2005 Oct; 82(1):74-82. |
| MeSH | Algorithms
Animals
Computer Simulation
Gene Expression Profiling
Gene Expression Regulation
Humans
Models, Biological
Neural Networks (Computer)
Nonlinear Dynamics
Protein Interaction Mapping
Proteome
Signal Transduction
|
| Abstract | Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or "explanation" of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene-gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene-gene interactions recently reported in the literature. |
| Language | eng |
| Pub Type(s) | Evaluation Studies
Journal Article
|
| PubMed ID | 16024165 |
