Mori-Grant report / Fiscal year 2009
Keio University - Graduate School of Media and Governance - Systems Biology program - Systems Immunology group
Address: Keio University, Institute for Advanced Biosciences TTCK 14-1 baba-cho, Tsuruoka, Yamagata 997-0035 Japan
Research project: Systems biology approach for the Understanding of Innate Immune mechanism
Elucidating genetic vehicle guiding cellular attractors
Masa Tsuchiya1,2#*, Vincent Piras1,2#, Masaru Tomita1,2, Alessandro Giuliani3 & Kumar Selvarajoo1,2#*
Institute for Advanced Biosciences, Keio University, Tsuruoka, 997-0035,
2 Systems Biology Program, School of Media and Governance, Keio University, Fujisawa, 252-8520, Japan.
3 Istituto Superiore di Sanita’, Environment and Health Department, Viale Regina Elena 299, 00161, Rome, Italy.
*To whom correspondence should be addressed:
Masa Tsuchiya, Email: firstname.lastname@example.org, Tel/Fax: +81-235-29-0829
Kumar Selvarajoo, Email: email@example.com, Tel/Fax: +81-235-29-0830
Cell fate decision involves reprogramming of a precursor cell into the differentiated cell state. It is intriguing to observe a specific path chosen by the cell to take among the several possibilities that can arise from the regulation and response of multitudes of molecules during differentiation. Understanding how deterministic process emerges from network of complex molecular interactions is a fundamental issue for systems biology research.
A precursor cell differentiation study has shown that their development paths converged in time from the analysis of highly expressed gene profiles . Such convergence of a cell’s fate despite epigenetic barriers from distinct stimuli establishes “attractor” states in biology [2-5]. However, it is unclear which portion of genome (whole or partial) is relevant for the convergence to attractor states.
Individual gene measurement is biased by both technical and biological noises [6,7]. To overcome the issue of noise at single-gene level, in recent innate immunity study, we investigated the ensemble property of the population of genes [6,7]. Distributions of gene expression changes in time were shown to transit from scatter to smooth Gaussian-like distributions for groups of above 80 genes by the reduction of their expression fluctuations. Using this result, asymptotic patterns emerged to show collective genome-wide response to lipopolysaccharide (LPS) stimulated macrophages, with a small number of highly expressed genes contributing to the established proinflammatory response (local), while the rest of the lowly expressed genes collectively activating diverse processes (global) with still largely unknown significance [6,7]. In this study, our purpose is to uncover the role of emergent global response in cell fate decision.
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2. S. Huang, Bioessays 31, 546 (2009).
3. C. H. Waddington, The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology (Macmillan, New York, 1957).
4. S. A. Kauffman, J. Theor. Biol. 22, 437 (1969).
5. S. A. Kauffman, The Origins of Order (Oxford University Press, New York, 1993).
6. M. Tsuchiya, K. Selvarajoo, V. Piras, M. Tomita, and A. Giuliani, Physica A 388, 1738 (2009).
M. Tsuchiya, V. Piras, S. Choi, S. Akira, M. Tomita M, A. Giuliani, and K. Selvarajoo.