Reconstructing chemical reaction networks

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Reconstructing chemical reaction networks: data mining meets system identification

Full text

Pdf (574 KB)

Source

International Conference on Knowledge Discovery and Data Mining archive
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Las Vegas, Nevada, USA

SESSION: Research papers table of contents

Pages 142-150

Year of Publication: 2008

ISBN:978-1-60558-193-4

Authors

Yong Ju Cho	Virginia Tech, Blacksburg, VA, USA
Naren Ramakrishnan	Virginia Tech, Blacksburg, VA, USA
Yang Cao	Virginia Tech, Blacksburg, VA, USA

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 13, Downloads (12 Months): 158, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1401890.1401912 What is a DOI?

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

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	A. Arkin, P. Shen, and J. Ross. A Test Case of Correlation Metric Construction of a Reaction Pathway from Measurements. Science, Vol. 277(5330):1275--1279, Aug 1997.
	2	U.S. Bhalla. Understanding Complex Signaling Networks through Models and Metaphors. Progress in Biophysics and Molecular Biology, Vol. 81(1):45--65, Jan 2003.
	3	F. Bromberg, D. Margaritis, and V. Honavar. Efficient Markov Network Structure Discovery using Independence Tests. In Proceedings of the Sixth SIAM International Conference on Data Mining. SIAM Press, 2006.
	4	Peter N. Brown , Alan C. Hindmarsh , Linda R. Petzold, Using Krylov methods in the solution of large-scale differential-algebraic systems, SIAM Journal on Scientific Computing, v.15 n.6, p.1467-1488, Nov. 1994 [doi>10.1137/0915088]
	5	K.C. Chen, L. Calzone, F.R. Csikasz-Nagy, F.R. Cross, B. Novak, and J.J. Tyson. Integrative Analysis of Cell Cycle Control in Budding Yeast. Molecular Biology of the Cell, Vol. 15:3841--3862, Aug 2004.
	6	Scott D. Cohen , Alan C. Hindmarsh, CVODE, a stiff/nonstiff ODE solver in C, Computers in Physics, v.10 n.2, p.138-143, March/April 1996
	7	A. Csikasz-Nagy, D. Battogtokh, K.C. Chen, B. Novak, and J.J. Tyson. Analysis of a Generic Model of Eukaryotic Cell Cycle Regulation. Biophys. J., Vol. 90:4361--4379, 2006.
	8	A.V. Karnaukhov and E.V. Karnaukhova. System Identification in Biophysics: A New Method based on Minimizing Square Residuals. Biofizika, Vol. 49(1):88--97, 2004.
	9	A.V. Karnaukhov, E.V. Karnaukhova, and J.R. Williamson. Numerical Matrices Method for Nonlinear System Identification and Description of Dynamics of Biochemical Reaction Networks. Biophysical Journal, Vol. 92:3459--3473, 2007.
	10	Steffen Klamt , Ernst Dieter Gilles, Minimal cut sets in biochemical reaction networks, Bioinformatics, v.20 n.2, p.226-234, January 2004 [doi>10.1093/bioinformatics/btg395]
	11	E. Klipp, R. Herwig, A. Kowald, C. Wierling, and H. Lehrach. Systems Biology in Practice. Wiley-VCH, May 2005.
	12	S.L. Lauritzen. Graphical Models. Oxford University Press, 1996.
	13	W. Marwan, A. Wagler, and R. Weismantel. A Mathematical Approach to Solve the Network Reconstruction Problem. Mathematical Methods in Operations Research, Vol. 67:117--132, 2008.
	14	M.R. Maurya, S.J. Bornheimer, V. Venkatasubramanian, and S. Subramaniam. Reduced-order Modelling of Biochemical Networks: Application to the GTpase-Cycle Signalling Module. IEE Systems Biology, Vol. 152(4):229--242, Dec 2005.
	15	J. Ross, I. Schreiber, and M.O. Vlad. Determination of Complex Reaction Mechanisms: Analysis of Chemical, Biological, and Genetic Networks. Oxford University Press, Nov 2005.
	16	W. Sha, J. Moore, K. Chen, A.D. Lassaletta, C.-S. Yi, J.J. Tyson, and J. Sible. Hysteresis drives Cell-cycle Transitions in Xenopus laevis Egg Extracts. PNAS, Vol. 100(3):975--980, Feb 2003.
	17	J.J. Tyson. Modeling the Cell Division Cycle: cdc2 and Cyclin Interactions. PNAS, Vol. 88(16):7328--7332, Aug 1991.
	18	J.J. Tyson, K.C. Chen, and B. Novak. Sniffers, Buzzers, Toggles and Blinkers: Dynamics of Regulatory and Signaling Pathways in the Cell. Current Opinion in Cell Biology, Vol. 15(2):221--231, Apr 2003.
	19	C.H. Wiggins and I. Nemenman. Process Pathway Inference via Time Series Analysis. Experimental Mechanics, Vol. 43(3):361--370, Sep 2003.