Abstract
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.
Highlights
SOS network) as tests to generate the trajectories of the networks according to Eqn (1)
We propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments
If there’s no significant difference between baseline value and expressions in knock out strains
Summary
We use the wild type steady state of all the genes as baseline (Table 5), and combine knock-out data (Table 6) and time-series data to test our method. We first use the following rules to discretize the data: If expression of Gene X in some knock out data set is 0.2 larger (smaller) than the baseline, we set the value of. A modification of discretization is used: level of Gene X is determined according to the former procedure, but for the other genes, its knock out value is set to 0, all expressions larger than 0.2 are set to 1.(when Gene Y is knock out, its expression level is often significantly lower than its baseline level) In this way, we get the discretized knock-out data (Table 9).
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