Dynamic self-assembly in living systems as computation
Tóm tắt
Biochemical reactions taking place in living systems that map different inputs to specific outputs are intuitively recognized as performing information processing. Conventional wisdom distinguishes such proteins, whose primary function is to transfer and process information, from proteins that perform the vast majority of the construction, maintenance, and actuation tasks of the cell (assembling and disassembling macromolecular structures, producing movement, and synthesizing and degrading molecules). In this paper, we examine the computing capabilities of biological processes in the context of the formal model of computing known as the random access machine (RAM) [Dewdney AK (1993) The New Turing Omnibus. Computer Science Press, New York], which is equivalent to a Turing machine [Minsky ML (1967) Computation: Finite and Infinite Machines. Prentice-Hall, Englewood Cliffs, NJ]. When viewed from the RAM perspective, we observe that many of these dynamic self-assembly processes – synthesis, degradation, assembly, movement – do carry out computational operations. We also show that the same computing model is applicable at other hierarchical levels of biological systems (e.g., cellular or organism networks as well as molecular networks). We present stochastic simulations of idealized protein networks designed explicitly to carry out a numeric calculation. We explore the reliability of such computations and discuss error-correction strategies (algorithms) employed by living systems. Finally, we discuss some real examples of dynamic self-assembly processes that occur in living systems, and describe the RAM computer programs they implement. Thus, by viewing the processes of living systems from the RAM perspective, a far greater fraction of these processes can be understood as computing than has been previously recognized.
Tài liệu tham khảo
Alberts B, Bray D, Johnson A, Lewis J, Raff M, Roberts K and Walter P (1998). Essential Cell Biology: An Introduction to the Molecular Biology of the Cell. Garland Publishing, New York
Alberts B, Johnson A, Lewis J, Raff M, Roberts K and Walter P (2002). Molecular Biology of the Cell. Garland Science, New York
Alon U, Surette MG, Barkai N and Leibler S (1999). Robustness in bacterial chemotaxis. Nature 397: 168–471
Agutter PS and Wheatley DN (1997). Information processing and intracellular ‘neural’ (protein) networks: considerations regarding the diffusion-based hypothesis of Bray. Biology of the Cell 89: 13–18
Arkin AP and Ross J (1994). Computational functions in biochemical reaction networks. Biophysical Journal 67: 560–578
Ben-hur A and Siegelmann ET (2004). Computation in gene networks. Chaos 14(1): 145–151
Bouchard AM and Osbourn GC (2004) Dynamic self-assembly and computation: From biological to information systems. Biologically Inspired Approaches to Advanced Information Technology. Springer-Verlag, Berlin. 95–110
Bray D (1995). Protein molecules as computational elements in living cells. Nature 376: 307–312
Bray D, Levin MD and Morton-Firth CJ (1998). Receptor clustering as a cellular mechanism to control sensitivity. Nature 393: 85–88
Burstein Z (1995). A network model of developmental gene hierarchy. Journal of Theoretical Biology 174: 1–11
Conrad M (1995). Cross-scale interactions in biomolecular information processing. BioSystems 35: 157–160
Conrad M (1999). Molecular and evolutionary computation: the tug of war between context freedom and context sensitivity. BioSystems 52: 99–110
Conrad M and Zauner K-P (1998). Conformation-driven computing: a comparison of designs based on DNA, RNA and protein. Supramolecular Science 5: 787–790
Dewdney AK (1993). The New Turing Omnibus. Computer Science Press, New York
Endy D and Brent R (2001). Modelling cellular behaviour. Nature 409: 391–395
Gibson MA and Bruck J (2000). Efficient exact stochastic simulation of chemical systems with many species and many channels. Journal of Physical Chemistry A 104: 1876–1889
Gillespie DT (1976). A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics 22: 403–434
Ideker T, Galitski T and Hood L (2001). A new approach to decoding life: systems biology. Annual Review of Genomics and Human Genetics 2: 343–372
King DG, Soller M and Kashi Y (1997). Evolutionary tuning knobs. Endeavour 21: 36–40
Kirschner M and Gerhart J (1998). Evolvability. Proceedings of the National Academy of Sciences USA 95: 8420–8427
Magnasco MO (1997). Chemical kinetics is Turing Universal. Physical Review Letters 78(6): 1190–1193
McAdams HH and Shapiro L (1995). Circuit simulation of genetic networks. Science 269: 650–656
Minsky ML (1967). Computation: Finite and Infinite Machines. Prentice-Hall, Englewood Cliffs, NJ
Park S-H, Zarrinpar A and Lim WA (2003). Rewiring MAP kinase pathways using alternative scaffold assembly mechanisms. Science 299: 1061–1064
Schnitzer MJ and Block SM (1997). Kinesin hydrolyses one ATP per 8-nm step. Nature 388: 386–390
Shannon CE (1948). A mathematical theory of communication. The Bell System Technical Journal 27: 379–423
Sköld HN, Aspengren S and Wallin M (2002). The cytoskeleton in fish melanophore melanosome positioning. Microscopy Research and Technique 58: 464–469