Inferring gene regulatory networks from temporal expression profiles under time-delay and noise
Computational Biology and Chemistry
Inferring time-varying network topologies from gene expression data
EURASIP Journal on Bioinformatics and Systems Biology
Inferring biomolecular interaction networks based on convex optimization
Computational Biology and Chemistry
Approximating the online set multicover problems via randomized winnowing
Theoretical Computer Science
Dynamic analysis and control of biochemical reaction networks
Mathematics and Computers in Simulation
Comments on selected fundamental aspects of microarray analysis
Computational Biology and Chemistry
Inferring stable genetic networks from steady-state data
Automatica (Journal of IFAC)
Approximating the online set multicover problems via randomized winnowing
WADS'05 Proceedings of the 9th international conference on Algorithms and Data Structures
Hi-index | 3.84 |
Motivation: High-throughput technologies have facilitated the acquisition of large genomics and proteomics datasets. However, these data provide snapshots of cellular behavior, rather than help us reveal causal relations. Here, we propose how these technologies can be utilized to infer the topology and strengths of connections among genes, proteins and metabolites by monitoring time-dependent responses of cellular networks to experimental interventions. Results: We demonstrate that all connections leading to a given network node, e.g. to a particular gene, can be deduced from responses to perturbations none of which directly influences that node, e.g. using strains with knock-outs to other genes. To infer all interactions from stationary data, each node should be perturbed separately or in combination with other nodes. Monitoring time series provides richer information and does not require perturbations to all nodes. Overall, the methods we propose are capable of deducing and quantifying functional interactions within and across cellular gene, signaling and metabolic networks. Supplementary Information: Supplementary material is available at http://www.dbi.tju.edu/bioinformatics2004.pdf