The rational parameterisation theorem for multisite post-translational modification systems
- Creators
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Thomson, Matthew
- Gunawardena, Jeremy
Abstract
Post-translational modification of proteins plays a central role in cellular regulation but its study has been hampered by the exponential increase in substrate modification forms ("modforms") with increasing numbers of sites. We consider here biochemical networks arising from post-translational modification under mass-action kinetics, allowing for multiple substrates, having different types of modification (phosphorylation, methylation, acetylation, etc.) on multiple sites, acted upon by multiple forward and reverse enzymes (in total number L), using general enzymatic mechanisms. These assumptions are substantially more general than in previous studies. We show that the steady-state modform concentrations constitute an algebraic variety that can be parameterised by rational functions of the L free enzyme concentrations, with coefficients which are rational functions of the rate constants. The parameterisation allows steady states to be calculated by solving L algebraic equations, a dramatic reduction compared to simulating an exponentially large number of differential equations. This complexity collapse enables analysis in contexts that were previously intractable and leads to biological predictions that we review. Our results lay a foundation for the systems biology of post-translational modification and suggest deeper connections between biochemical networks and algebraic geometry.
Additional Information
© 2009 Elsevier Ltd. Received 30 May 2009. Accepted 3 September 2009. Available online 16 September 2009. The research undertaken here was supported in part by NIH under Grant R01-GM081578. We thank Bernd Sturmfels for pointing us to the Matrix-Tree theorem, Alicia Dickenstein for many stimulating scientific discussions and the Statistical and Applied Mathematical Sciences Institute (SAMSI) for supporting an extended visit by J.G. to the Program on Algebraic Methods in Systems Biology and Statistics, during which this paper was drafted.Attached Files
Accepted Version - nihms146463.pdf
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Additional details
- PMCID
- PMC2800989
- Eprint ID
- 74108
- DOI
- 10.1016/j.jtbi.2009.09.003
- Resolver ID
- CaltechAUTHORS:20170206-154848264
- NIH
- R01-GM081578
- Statistical and Applied Mathematical Sciences Institute (SAMSI)
- Created
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2017-02-07Created from EPrint's datestamp field
- Updated
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2021-11-11Created from EPrint's last_modified field