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Stochastic modeling of chemical reactions and gene networks
From Q-bio
- Time and place
- 1:30-2:15 pm on August 1st at Los Alamos Research Park, Room 203 A/B
- Speaker
- Abhyudai Singh, University of California, Santa Barbara
- Abstract
- Living cells are characterized by small populations of key components (for example, proteins and mRNAs), which make bio-chemical reactions inherently noisy. We outline new computational techniques for quantifying noise in such bio-chemical reactions. These techniques include novel moment closure methods to compute the time evolution of lower order moments (for example, means, standard deviations) for the number of molecules of different biochemical species involved in the reaction and a new small noise approximation that provides analytical formulas for the noise in terms of the reaction parameters. We illustrate using examples how these computational techniques can be used to investigates the interplay between nonlinear dynamics and noise in gene-protein networks. These examples include various gene network motifs that reduce stochastic fluctuations in proteins and reaction cascades, which act as noise attenuators or noise magnifiers depending upon the amount of intrinsic and extrinsic noise present in the cell.
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