Modelling and Analysing the Information Processing Capabilities of Simple Biological Systems

Abstract Biological systems that present basic logic primitives for information processing have already been realized. Models for simulating their dynamics have also been implemented. However there is a lack of metrics that would objectively evaluate the information processing capabilities of these primitives and possibilities of their interconnectivity. With the introduction of such processing and performance descriptive quantities complex biological systems capable of information processing could be built more straightforwardly. That would bring us closer to the realization of a biological computer.

[1]  Ron Weiss,et al.  Toward in vivo Digital Circuits , 2002 .

[2]  J. Collins,et al.  Construction of a genetic toggle switch in Escherichia coli , 2000, Nature.

[3]  J. Stelling,et al.  A tunable synthetic mammalian oscillator , 2009, Nature.

[4]  M. Elowitz,et al.  A synthetic oscillatory network of transcriptional regulators , 2000, Nature.

[5]  Mads Kaern,et al.  The engineering of gene regulatory networks. , 2003, Annual review of biomedical engineering.

[6]  Linda R. Petzold,et al.  Stochastic Modeling of Gene Regulatory Networks y , 2005 .

[7]  Conrad J. Burden,et al.  A Stochastic Model of Gene Regulation Using the Chemical Master Equation , 2007 .

[8]  Uri Alon,et al.  An Introduction to Systems Biology , 2006 .

[9]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[10]  M. Bennett,et al.  A fast, robust, and tunable synthetic gene oscillator , 2008, Nature.

[11]  P. Swain,et al.  Stochastic Gene Expression in a Single Cell , 2002, Science.

[12]  D. Gillespie,et al.  Stochastic Modeling of Gene Regulatory Networks † , 2005 .

[13]  K. Burrage,et al.  Stochastic models for regulatory networks of the genetic toggle switch. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[14]  J. Taylor,et al.  Switching and finite automata theory, 2nd ed. , 1980, Proceedings of the IEEE.

[15]  T. Elston,et al.  Stochasticity in gene expression: from theories to phenotypes , 2005, Nature Reviews Genetics.

[16]  Zoltan Szallasi,et al.  Genetic Network Analysis in Light of Massively Parallel Biological Data Acquisition , 1998, Pacific Symposium on Biocomputing.

[17]  A. Loinger,et al.  Stochastic simulations of the repressilator circuit. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Linda R. Petzold,et al.  Stochastic modelling of gene regulatory networks , 2005 .

[19]  John F. Wakerly Digital Design: Principles and Practices Package (4th Edition) , 2005 .

[20]  Richard F. Tinder Engineering digital design (2nd ed.) , 2000 .

[21]  L. Adleman Computing with DNA , 1998 .