Abstract
The massive acquisition of data in molecular and cellular biology has led to the renaissance of an old topic: simulations of biological systems. Simulations, increasingly paired with experiments, are being successfully and routinely used by computational biologists to understand and predict the quantitative behaviour of complex systems, and to drive new experiments. Nevertheless, many experimentalists still consider simulations an esoteric discipline only for initiates. Suspicion towards simulations should dissipate as the limitations and advantages of their application are better appreciated, opening the door to their permanent adoption in everyday research.
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References
Kant, I. Critique of Pure Reason, University of Virginia Library, Electronic Text Center, Topic I, Part II, 45 http://etext.lib.virginia.edu/toc/modeng/public/KanPure.html.
Locke, J. C. W. Extension of a genetic network model by iterative experimentation and mathematical analysis. Mol. Syst. Biol. doi:10.1038/msb4100018 (28 June 2005)
Albert, M. A. et al. Experimental and in silico analyses of glycolytic flux control in bloodstream form Trypanosoma brucei. J. Biol. Chem. 280, 28306–28315 (2005)
Lee, E., Salic, A., Kruger, R., Heinrich, R. & Kirschner, M. W. The roles of APC and Axin derived from experimental and theoretical analysis of the Wnt pathway. PLoS Biol. doi:10.1371/journal.pbio.0000010 (13 October 2003)
di Bernardo, D. et al. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nature Biotechnol. 23, 377–383 (2005)
Segre, D., Vitkup, D. & Church, G. M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl Acad. Sci. USA 99, 15112–15117 (2002)
Chang, P. L. Clinical bioinformatics. Chang Gung Med. J. 28, 201–211 (2005)
Kerckhoffs, R. C. et al. Electromechanics of the paced left ventricle simulated by a straightforward mathematical model: comparison with experiments. Am. J. Physiol. Heart Circ. Physiol. 5, H1889–H1897 (2005)
Dens, E. J., Bernaerts, K., Standaert, A. R. & Van Impe, J. F. Cell division theory and individual-based modeling of microbial lag: part I. The theory of cell division. Int. J. Food Microbiol. 101, 303–318 (2005)
Becskei, A. & Serrano, L. Engineering stability in gene networks by autoregulation. Nature 405, 590–593 (2000)
Guet, C. C., Elowitz, M. B., Hsing, W. & Leibler, S. Combinatorial synthesis of genetic networks. Science 296, 1466–1470 (2002)
Shmulevich, I., Kauffman, S. A. & Aldana, M. Eukaryotic cells are dynamically ordered or critical but not chaotic. Proc. Natl Acad. Sci. USA 102, 13439–13444 (2005)
Hardy, S. & Robillard, P. N. Modeling and simulation of molecular biology systems using Petri nets: modeling goals of various approaches. J. Bioinform. Comput. Biol. 2, 595–613 (2004)
Errampalli, D. D., Priami, C. & Quaglia, P. A formal language for computational systems biology. OMICS 8, 370–380 (2004)
Batt, G. et al. Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in Escherichia coli. Bioinformatics 21 (Suppl 1), i19–i28 (2005)
Kuipers, B. in Readings in Qualitative Reasoning about Physical Systems (ed. deKleer, D. S. W. J.) 257–274 (Morgan Kaufmann, San Francisco, 1989)
Csete, M. E. & Doyle, J. C. Reverse engineering of biological complexity. Science 295, 1664–1669 (2002)
Louis, M. & Becskei, A. Binary and graded responses in gene networks. Sci. STKE 143, PE33 (2002)
Clarke, B. L. Complete set of steady states for the general stoichiometric dynamical system. J. Chem. Phys. 75, 4970–4979 (1981)
Edwards, J. S., Ibarra, R. U. & Palsson, B. O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotechnol. 19, 125–130 (2001)
Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S. & Gilles, E. D. Metabolic network structure determines key aspects of functionality and regulation. Nature 420, 190–193 (2002)
Reed, J. L., Vo, T. D., Schilling, C. H. & Palsson, B. O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4, R54 (2003)
Fell, D. A. & Small, J. R. Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem. J. 238, 781–786 (1986)
Covert, M. W., Schilling, C. H. & Palsson, B. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 213, 73–88 (2001)
Burgard, A. P. & Maranas, C. D. Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol. Bioeng. 82, 670–677 (2003)
Fall, C. P., Marland, E. S., Wagner, J. M. & Tyson, J. J. (eds) Computational Cell Biology. 1st edn. (Springer, 2002)
Savageau, M. A. Biochemical systems theory: operational differences among variant representations and their significance. J. Theor. Biol. 151, 509–530 (1991)
Liu, Q. & Jia, Y. Fluctuations-induced switch in the gene transcriptional regulatory system. Phys. Rev. E 70, 041907 (2004)
Thattai, M. & van Oudenaarden, A. Stochastic gene expression in fluctuating environments. Genetics 167, 523–530 (2004)
Hanggi, P. Stochastic resonance in biology. How noise can enhance detection of weak signals and help improve biological information processing. ChemPhysChem 3, 285–290 (2002)
Gillespie, D. T. General method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 22, 403–434 (1976)
Gillespie, D. T. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977)
Haseltine, E. L. & Rawlings, J. B. Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics. J. Chem. Phys. 117, 6959–6969 (2002)
Rathinam, M., Petzold, L. R., Cao, Y. & Gillespie, D. T. Stiffness in stochastic chemically reacting systems: the implicit tau-leaping method. J. Chem. Phys. 119, 12784–12794 (2003)
Rao, C. V. & Arkin, A. P. Stochastic chemical kinetics and the quasi-steady-state assumption: application to the Gillespie algorithm. J. Chem. Phys. 118, 4999–5010 (2003)
Stundzia, A. B. & Lumsden, C. J. Stochastic simulation of coupled reaction-diffusion processes. J. Comput. Phys. 127, 196–207 (1996)
Ander, M. et al. SmartCell, a framework to simulate cellular processes that combines stochastic approximation with diffusion and localisation: analysis of simple networks. Systems Biol. 1, 129–138 (2004)
Bezrukov, S. M., Frauenfelder, H. & Moss, F. (eds) Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems (Proc. SPIE, Vol. 5110, 2003) http://www.icm.uu.se/molbio/references/ElfMesoSpat.pdf
Salis, H. & Kaznessis, Y. Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions. J. Chem. Phys. 122, 054103 (2005)
Alfonsi, A., Cances, E., Turinici, G., Di Ventura, B. & Huisinga, W. Adaptive simulation of hybrid stochastic and deterministic models for biochemical systems. ESAIM Proc. 14, 1–13 doi:10.1051/proc:2005001 (2005)
Gitai, Z. The new bacterial cell biology: moving parts and subcellular architecture. Cell 120, 577–586 (2005)
Gorlich, D., Seewald, M. J. & Ribbeck, K. Characterization of Ran-driven cargo transport and the RanGTPase system by kinetic measurements and computer simulation. EMBO J. 22, 1088–1100 (2003)
Nedelec, F., Surrey, T. & Karsenti, E. Self-organisation and forces in the microtubule cytoskeleton. Curr. Opin. Cell Biol. 15, 118–124 (2003)
Sawai, S., Thomason, P. A. & Cox, E. C. An autoregulatory circuit for long-range self-organization in Dictyostelium cell populations. Nature 433, 323–326 (2005)
Collier, J. R., Monk, N. A., Maini, P. K. & Lewis, J. H. Pattern formation by lateral inhibition with feedback: a mathematical model of Delta–Notch intercellular signalling. J. Theor. Biol. 183, 429–446 (1996)
Wu, D., Jia, Y., Yang, L., Liu, Q. & Zhan, X. Phase synchronization and coherence resonance of stochastic calcium oscillations in coupled hepatocytes. Biophys. Chem. 115, 37–47 (2005)
Lemerle, C., Di Ventura, B. & Serrano, L. Space as the final frontier in stochastic simulations of biological systems. FEBS Lett. 579, 1789–1794 (2005)
Ellis, R. J. Macromolecular crowding: obvious but underappreciated. Trends Biochem. Sci. 26, 597–604 (2001)
Yarmush, M. L. & Banta, S. Metabolic engineering: advances in modeling and intervention in health and disease. Annu. Rev. Biomed. Eng. 5, 349–381 (2003)
Price, N. D., Reed, J. L. & Palsson, B. O. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nature Rev. Microbiol. 2, 886–897 (2004)
Fong, S. S. & Palsson, B. O. Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nature Genet. 36, 1056–1058 (2004)
Alvarez-Vasquez, F. et al. Simulation and validation of modelled sphingolipid metabolism in Saccharomyces cerevisiae. Nature 433, 425–430 (2005)
Klipp, E., Nordlander, B., Kruger, R., Gennemark, P. & Hohmann, S. Integrative model of the response of yeast to osmotic shock. Nature Biotechnol. 23, 975–982 (2005)
Abouhamad, W. N. et al. Computer-aided resolution of an experimental paradox in bacterial chemotaxis. J. Bacteriol. 180, 3757–3764 (1998)
Kalir, S. & Alon, U. Using a quantitative blueprint to reprogram the dynamics of the flagella gene network. Cell 117, 713–720 (2004)
Wiley, H. S., Shvartsman, S. Y. & Lauffenburger, D. A. Computational modeling of the EGF-receptor system: a paradigm for systems biology. Trends Cell Biol. 13, 43–50 (2003)
Sasagawa, S., Ozaki, Y., Fujita, K. & Kuroda, S. Prediction and validation of the distinct dynamics of transient and sustained ERK activation. Nature Cell Biol. 7, 365–373 (2005)
Martin, B. R., Giepmans, B. N., Adams, S. R. & Tsien, R. Y. Mammalian cell-based optimization of the biarsenical-binding tetracysteine motif for improved fluorescence and affinity. Nature Biotechnol. 23, 1308–1314 (2005)
Hasty, J., McMillen, D. & Collins, J. J. Engineered gene circuits. Nature 420, 224–230 (2002)
Levine, M. & Davidson, E. H. Gene regulatory networks for development. Proc. Natl Acad. Sci. USA 102, 4936–4942 (2005)
Ortega, F., Acerenza, L., Westerhoff, H. V., Mas, F. & Cascante, M. Product dependence and bifunctionality compromise the ultrasensitivity of signal transduction cascades. Proc. Natl Acad. Sci. USA 99, 1170–1175 (2002)
Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002)
Mangan, S., Zaslaver, A. & Alon, U. The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks. J. Mol. Biol. 334, 197–204 (2003)
Schoning, J. C. & Staiger, D. At the pulse of time: protein interactions determine the pace of circadian clocks. FEBS Lett. 579, 3246–3252 (2005)
Atkinson, M. R., Savageau, M. A., Myers, J. T. & Ninfa, A. J. Development of genetic circuitry exhibiting toggle switch or oscillatory behaviour in Escherichia coli. Cell 113, 597–607 (2003)
Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000)
Kramer, B. P. et al. An engineered epigenetic transgene switch in mammalian cells. Nature Biotechnol. 22, 867–870 (2004)
Becskei, A., Seraphin, B. & Serrano, L. Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J. 20, 2528–2535 (2001)
Isaacs, F. J., Hasty, J., Cantor, C. R. & Collins, J. J. Prediction and measurement of an autoregulatory genetic module. Proc. Natl Acad. Sci. USA 100, 7714–7719 (2003)
Kobayashi, H. et al. Programmable cells: interfacing natural and engineered gene networks. Proc. Natl Acad. Sci. USA 101, 8414–8419 (2004)
Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000)
Fung, E. et al. A synthetic gene-metabolic oscillator. Nature 435, 118–122 (2005)
Hasty, J., Dolnik, M., Rottschafer, V. & Collins, J. J. Synthetic gene network for entraining and amplifying cellular oscillations. Phys. Rev. Lett. 88, 148101 (2002)
Bulter, T. et al. Design of artificial cell–cell communication using gene and metabolic networks. Proc. Natl Acad. Sci. USA 101, 2299–2304 (2004)
You, L., Cox, R. S. III, Weiss, R. & Arnold, F. H. Programmed population control by cell–cell communication and regulated killing. Nature 428, 868–871 (2004)
Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H. & Weiss, R. A synthetic multicellular system for programmed pattern formation. Nature 434, 1130–1134 (2005)
Jaenecke, S., de Lorenzo, V., Timmis, K. N. & Diaz, E. A stringently controlled expression system for analysing lateral gene transfer between bacteria. Mol. Microbiol. 21, 293–300 (1996)
Acknowledgements
We thank J. Reich, J. J. Collins, I. Mattaj and J. Gagneur for critical reading of the manuscript, and P. Beltrao and M. Isalan for helpful discussions. This work was financed partly by the EC grant, COMBIO.
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Supplementary Notes
This file contains Supplementary Figures, (summarizing the constraint-based modelling approach) and Supplementary Discussion (gives details about the simulation (for example, reaction network, parameters) for each model discussed in the main text), and gives a detailed mathematical analysis for the model discussed in Figure 2. (DOC 500 kb)
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Di Ventura, B., Lemerle, C., Michalodimitrakis, K. et al. From in vivo to in silico biology and back. Nature 443, 527–533 (2006). https://doi.org/10.1038/nature05127
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DOI: https://doi.org/10.1038/nature05127
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