A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.
One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.
The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China ; The Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
Xiaomeng
X
Zhang
Bin
B
Shao
Yangle
Y
Wu
Ouyang
O
Qi
D000465:Algorithms; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D053263:Gene Regulatory Networks; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D015203:Reproducibility of Results; D004718:Saccharomycetales / Q000235:genetics / Q000378:metabolism; D012568:Schizosaccharomyces / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*