- Quantitative understanding of probabilistic behavior of living cells operated by vibrant reaction networks
강진규Views: 540, 2016.03.04 16:18:44
- Physical Review X 5, 031014 (2015)
Yu Rim Lim,1,* Ji-Hyun Kim,2,*,† Seong Jun Park,1 Gil-Suk Yang,1 Sanggeun Song,1 Suk-kyu Chang,1 Nam Ki Lee,3 and Jaeyoung Sung1,‡
1Department of Chemistry and Institute of Innovative Functional Imaging, Chung-Ang University, Seoul 156-756, Korea.
2Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA02139, U. S. A.
3Department of Physics and School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Korea.
For quantitative understanding of probabilistic behaviors of living cells, it is essential to construct a correct mathematical description of intracellular networks interacting with complex cell environments, which has been a formidable task. Here, we present a novel model and stochastic kinetics for an intracellular network interacting with hidden cell environments, employing a complete description of cell state dynamics and its coupling to the system network. Our analysis reveals that various environmental effects on the product number fluctuation of intracellular reaction networks can be collectively characterized by Laplace transform of the time-correlation function of the product creation rate fluctuation with the Laplace variable being the product decay rate. On the basis of the latter result, we propose an efficient method for quantitative analysis of the chemical fluctuation produced by intracellular networks coupled to hidden cell environments. By applying the present approach to the gene expression network, we obtain simple analytic results for the gene expression variability and the environment-induced correlations between the expression levels of mutually noninteracting genes. The theoretical results compose a unified framework for quantitative understanding of various gene expression statistics observed across a number of different systems with a small number of adjustable parameters with clear physical meanings.