Coordinated Spatial Pattern Formation in Biomolecular Communication Networks

被引:18
作者
Hori, Yutaka [1 ]
Miyazako, Hiroki [2 ]
Kumagai, Soichiro [2 ]
Hara, Shinji [2 ]
机构
[1] Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, 91125, CA
[2] Department of Information Science and Technology, University of Tokyo, Tokyo
基金
日本学术振兴会;
关键词
Molecular communication networks; Stability analysis; Turing pattern;
D O I
10.1109/TMBMC.2015.2500567
中图分类号
学科分类号
摘要
This paper proposes a control theoretic framework to model and analyze the self-organized pattern formation of molecular concentrations in biomolecular communication networks, emerging applications in synthetic biology. In biomolecular communication networks, bionanomachines, or biological cells, communicate with each other using a cell-to-cell communication mechanism mediated by a diffusible signaling molecule, thereby the dynamics of molecular concentrations are approximately modeled as a reaction-diffusion system with a single diffuser. We first introduce a feedback model representation of the reaction-diffusion system and provide a systematic local stability/instability analysis tool using the root locus of the feedback system. The instability analysis then allows us to analytically derive the conditions for the self-organized spatial pattern formation, or Turing pattern formation, of the bionanomachines. We propose a novel synthetic biocircuit motif called activator-repressor-diffuser system and show that it is one of the minimum biomolecular circuits that admit self-organized patterns over cell population. © 2015 IEEE.
引用
收藏
页码:111 / 121
页数:10
相关论文
共 40 条
[1]  
Akyildiz I.F., Fekri F., Sivakumar R., Forest C.R., Hammer B.K., Monaco: Fundamentals of molecular nano-communication networks, IEEE Wireless Commun., 19, 5, pp. 12-18, (2012)
[2]  
Nakano T., Suda T., Okaie Y., Moore M.J., Vasilakos A.V., Molecular communication among biological nanomachines: A layered architecture and research issue, IEEE Trans. Nano Biosci., 13, 3, pp. 169-197, (2014)
[3]  
Danino T., Mondragon-Palomino O., Tsimring L., Hasty J., A synchronized quorum of genetic clocks, Nature, 463, 7279, pp. 326-330, (2010)
[4]  
You L., Cox R.S., Weiss R., Arnold F.H., Programmed population control by cell-cell communication and regulated killing, Nature, 428, 6985, pp. 868-871, (2004)
[5]  
Balagadde F.K., Et al., A synthetic Escherichia coli predator-prey ecosystem, Mol. Syst. Biol., 4, 1, (2008)
[6]  
Tabor J.J., Et al., A synthetic genetic edge detection program, Cell, 137, 7, pp. 1272-1281, (2009)
[7]  
Tamsir A., Tabor J.J., Voigt C.A., Robust multicellular computing using genetically encoded nor gates and chemical wires, Nature, 469, 7329, pp. 212-215, (2011)
[8]  
Regot S., Et al., Distributed biological computation with multicellular engineered networks, Nature, 469, 7329, pp. 207-211, (2011)
[9]  
Basu S., Gerchman Y., Collins C.H., Arnold F.H., Weiss R., A synthetic multicellular system for programmed pattern formation, Nature, 434, 7037, pp. 1130-1134, (2005)
[10]  
Liu C., Et al., Sequential establishment of stripe patterns in an expanding cell population, Science, 334, 6053, pp. 238-241, (2011)