2008 ReconstructingChemicalReactionN

• (Cho et al., 2008) ⇒ Yong Ju Cho, Naren Ramakrishnan, and Yang Cao. (2008). “Reconstructing Chemical Reaction Networks: Data Mining Meets System Identification.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401912

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Reconstructing+chemical+reaction+networks%3A+data+mining+meets+system+identification%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401912&preflayout=flat#citedby

Quotes

Abstract

We present an approach to reconstructing chemical reaction networks from time series measurements of the concentrations of the molecules involved. Our solution strategy combines techniques from numerical sensitivity analysis and probabilistic graphical models. By modeling a chemical reaction system as a Markov network (undirected graphical model), we show how systematically probing for sensitivities between molecular species can identify the topology of the network. Given the topology, our approach next uses detailed sensitivity profiles to characterize properties of reactions such as reversibility, enzyme-catalysis, and the precise stoichiometries of the reactants and products. We demonstrate applications to reconstructing key biological systems including the yeast cell cycle. In addition to network reconstruction, our algorithm finds applications in model reduction and model comprehension. We argue that our reconstruction algorithm can serve as an important primitive for data mining in systems biology applications.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ReconstructingChemicalReactionN	Naren Ramakrishnan Yong Ju Cho Yang Cao			Reconstructing Chemical Reaction Networks: Data Mining Meets System Identification				10.1145/1401890.1401912