Authors: B.A. Sokhansanj, G.R. Rodrigue and J.P. Fitch
Affilation: Lawrence Livermore National Laboratory, United States
Pages: 28 - 31
Keywords: scalable fuzzy models, genetic regulation, bacteria, genomic data
Recent technological advances in high-throughput data collection (reviewed in ) give biologists the ability to study increasingly complex systems as a whole. One example is the DNA chip or microarray, an efficient high-throughput method for measuring temporal changes in the production of mRNA from thousands of genes. The large number of data produced in a microarray experiment pose challenges for visualization, interpretation, model building, and integration with other experimental data. Intuitive linguistic models used by biologists in the past are inadequate for complex systems studied at a genomic scale. A new methodology is needed to systematically develop and test mathematical biological models, both to interpret experimental observations and predict the effect of perturbations (e.g. genetic engineering, pharmaceuticals, gene therapy) on the genetic networks of organsms.