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Springer Handbook of Automation pp 1335–1360Cite as

Automatic Control in Systems Biology

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Part of the book series: Springer Handbooks ((SHB))

Abstract

The reductionist approaches of molecular and cellular biology have produced revolutionary advances in our understanding of biological function and information processing. The difficulty associated with relating molecular components to their systemic function led to the development of systems biology, a relatively new field that aims to establish a bridge between molecular level information and systems level understanding. The novelty of systems biology lies in the emphasis on analyzing complexity in networked biological systems using integrative rather than reductionist approaches. By its very nature, systems biology is a highly interdisciplinary field that requires the effective collaboration of scientists and engineers with different technical backgrounds, and the interdisciplinary training of students to meet the rapidly evolving needs of academia, industry, and government. This chapter summarizes state-of-the-art developments of automatic control in systems biology with substantial theoretical background and illustrative examples.

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Abbreviations

2-D:

two-dimensional

BST:

biochemical systems theory

CME:

chemical master equation

DISC:

death inducing signalling complex

FBA:

flux balance analysis

FIM:

Fisher information matrix

GLUT4:

activated Akt and PKCζ trigger glucose transporter

IRS1:

insulin receptor substrate-1

MPA:

metabolic pathway analysis

MPC:

model-based predictive control

ODE:

ordinary differential equation

P/T:

place/transition

PI3K:

phosphatidylinositol-3-kinase

PRC:

phase response curve

SCN:

suprachiasmatic nucleus

SIM:

single input module

SNA:

structural network analysis

SSA:

stochastic simulation algorithm

Smac:

second mitochondrial-activator caspase

TU:

transcriptional unit

WWW:

World Wide Web

XIAP:

X-linked inhibitor of apoptosis protein

References

  1. J. Hasty, D. McMillen, F. Isaacs, J.J. Collins: Computational studies of gene regulatory networks: In numero molecular biology, Nat. Rev. Genet. 2, 268–279 (2001)

    Google Scholar 

  2. T. Ideker, T. Galitski, L. Hood: A new approach to decoding life: Systems biology, Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001)

    Google Scholar 

  3. H. Kitano: Foundations of Systems Biology (MIT Press, Cambridge 2001)

    Google Scholar 

  4. F.J. Doyle III, J. Stelling: Systems interface biology, J. R. Soc. Interface 3, 603–616 (2006)

    Google Scholar 

  5. E. Klipp, R. Herwig, A. Kowald, C. Wierling, H. Lehrach: Systems Biology in Practice: Concepts, Implementation and Application (Wiley, Weinheim 2005)

    Google Scholar 

  6. B. Palsson: Systems Biology: Properties of Reconstructed Networks (Cambridge Univ. Press, Cambridge 2006)

    Google Scholar 

  7. Z. Szallasi, J. Stelling, V. Periwal (Eds.): System Modeling in Cellular Biology: From Concepts to Nuts and Bolts (MIT Press, Cambridge 2006)

    Google Scholar 

  8. A. Arkin, J. Ross, H.H. McAdams: Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells, Genetics 149, 1633–1648 (1998)

    Google Scholar 

  9. N. Barkai, S. Leibler: Robustness in simple biochemical networks, Nature 387, 913–917 (1997)

    Google Scholar 

  10. C.V. Rao, M. Frenklach, A.P. Arkin: An allosteric model for transmembrane signaling in bacterial chemotaxis, J. Mol. Biol. 343, 291–303 (2004)

    Google Scholar 

  11. T.M. Yi, Y. Huang, M.I. Simon, J. Doyle: Robust perfect adaptation in bacterial chemotaxis through integral feedback control, Proc. Natl. Acad. Sci. USA 97, 4649–4653 (2000)

    Google Scholar 

  12. A. Goldbeter: Biochemical Oscillations and Cellular Rhythms: The Molecular Bases of Periodic and Chaotic Behavior (Cambridge Univ. Press, Cambridge 1996)

    MATH  Google Scholar 

  13. J. Stelling, E.D. Gilles, F.J. Doyle III: Robustness properties of circadian clock architectures, Proc. Natl. Acad. Sci. USA 101, 13210–13215 (2004)

    Google Scholar 

  14. T.G. Müller, D. Faller, J. Timmer, I. Swameye, O. Sandra, U. Klingmüller: Tests for cycling in a signalling pathway, J. R. Statist. Soc. Ser. C Appl. Statist. 53, 557–568 (2004)

    MATH  Google Scholar 

  15. J. Stelling, U. Sauer, Z. Szallasi, F.J. Doyle III, J. Doyle: Robustness of cellular functions, Cell 118, 675–685 (2004)

    Google Scholar 

  16. E.D. Sontag: Asymptotic amplitudes and Cauchy gains: a small-gain principle and an application to inhibitory biological feedback, Syst. Control Lett. 47, 167–179 (2002)

    MATH  MathSciNet  Google Scholar 

  17. D. Angeli, J.E. Ferrell, E.D. Sontag: Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feed back systems, Proc. Natl. Acad. Sci. USA 101, 1822–1827 (2004)

    Google Scholar 

  18. X. Wen, S. Fuhrman, G.S. Michaels, D.B. Carr, S. Smith, J.L. Barker, R. Somogyi: Large-scale temporal gene expression mapping of central nervous system development, Proc. Natl. Acad. Sci. USA 95, 334–339 (1998)

    Google Scholar 

  19. D.A. Lauffenburger: Cell signaling pathways as control modules: complexity for simplicity?, Proc. Natl. Acad. Sci. USA 97, 5031–5033 (2000)

    Google Scholar 

  20. A.L. Barabasi: Network biology: Understanding the cellʼs functional organization, Nat. Rev. Genet. 5, 101–113 (2004)

    Google Scholar 

  21. A.M. Malcolm, L.J. Heyer: Discovering Genomics, Proteomics, and Bioinformatics (Benjamin Cummings, San Francisco 2003)

    Google Scholar 

  22. H. Kitano: Computational systems biology, Nature 420, 206–210 (2002)

    Google Scholar 

  23. I. Edery: Circadian rhythms in a nutshell, Physiol. Genomics, 3, 59–74 (2000)

    Google Scholar 

  24. S.M. Reppert, D.R. Weaver: Coordination of circadian timing in mammals, Nature 418, 935–941 (2002)

    Google Scholar 

  25. E.D. Herzog, S.J. Aton, R. Numano, Y. Sakaki, H. Tei: Temporal precision in the mammalian circadian system: a reliable clock from less reliable neurons, J. Biol. Rhythms 19, 35–46 (2004)

    Google Scholar 

  26. A.C. Liu, D.K. Welsh, C.H. Ko, H.G. Tran, E.E. Zhang, A.A. Priest, E.D. Buhr, O. Singer, K. Meeker, I.M. Verma, F.J. Doyle III, J.S. Takahashi, S.K. Kay: Intercellular coupling confers robustness against mutations in the SCN circadian clock network, Cell 129, 605–616 (2007)

    Google Scholar 

  27. Z. Boulos, M.M. Macchi, M.P. Sturchler, K.T. Stewart, G.C. Brainard, A. Suhner, G. Wallace, R. Steffen: Light visor treatment for jet lag after westward travel across six time zones, Aviat. Space Environ. Med. 73, 953–963 (2002)

    Google Scholar 

  28. S. Daan, C.S. Pittendrigh: A functional analysis of circadian pacemakers in nocturnal rodents. II. The variability of phase response curves, J. Comput. Physiol. 106, 253–266 (1976)

    Google Scholar 

  29. J.C. Dunlap, J.J. Loros, P.J. DeCoursey (Eds.): Chronobiology: Biological timekeeping (Sinauer Associates, Inc., Sunderland 2004)

    Google Scholar 

  30. D.B. Forger, C.S. Peskin: A detailed predictive model of the mammalian circadian clock, Proc. Natl. Acad. Sci. USA 100, 14806–14811 (2003)

    Google Scholar 

  31. F. Hua, S. Hautaniemi, R. Yokoo, D.A. Lauffenburger: Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways, J. R. Soc. Interface 9, 515–526 (2006)

    Google Scholar 

  32. J.W. Stucki, H.-U. Simon: Mathematical modeling of the regulation of caspase-3 activation and degradation, J. Theor. Biol. 234, 123–131 (2005)

    MathSciNet  Google Scholar 

  33. E.Z. Bagci, Y. Vodovotz, T.R. Billiar, G.B. Ermentrout, I. Bahar: Bistability in apoptosis: Roles of bax, bcl-2, and mitochondrial permeability transition pores, Biophys. J. 90, 1546–1559 (2006)

    Google Scholar 

  34. J.E. Shoemaker, F.J. Doyle III: Identifying fragilities in biochemical networks: robust performance analysis of the Fas signaling-induced apoptosis, Biophys. J. 95, 2610–2623 (2008)

    Google Scholar 

  35. A.R. Sedaghat, A. Sherman, M.J. Quon: A mathematical model of metabolic insulin signaling pathways, Am. J. Physiol. Endocrinol. Metab. 283, E1084–E1101 (2002)

    Google Scholar 

  36. P. Smolen, D.A. Baxter, J.H. Byrne: Mathematical modeling of gene networks, Neuron 26, 567–580 (2000)

    Google Scholar 

  37. Committee on Network Science for Future Army Applications: Network Science National Research Council, Washington (2006)

    Google Scholar 

  38. T.I. Lee, N.J. Rinaldi, F. Robert, D.T. Odom, Z. Bar-Joseph, G.K. Gerber, N.M. Hannett, C.T. Harbison, C.M. Thompson, I. Simon, J. Zeitlinger, E.G. Jennings, H.L. Murray, D.B. Gordon, B. Ren, J.J. Wyrick, J.-B. Tagne, T.L. Volkert, E. Fraenkel, D.K. Gifford: Transcriptional regulatory networks in Saccharomyces cerevisiae, Science 298, 799–804 (2002)

    Google Scholar 

  39. S.S. Shen-Orr, R. Milo, S. Mangan, U. Alon: Network motifs in the transcriptional regulation network of Escherichia coli, Nat. Genet. 31, 64–68 (2002)

    Google Scholar 

  40. D.E. Zak, G.E. Gonye, J.S. Schwaber, F.J. Doyle III: Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network, Genome Res. 13, 2396–2405 (2003)

    Google Scholar 

  41. N. Barkai, S. Leibler: Circadian clocks limited by noise, Nature 403, 267–268 (2000)

    Google Scholar 

  42. T. Ideker, V. Thorsson, R.M. Karp: Discovery of regulatory interactions through perturbations: Inference and experimental design, Pac. Symp. Biocomput. (2000)

    Google Scholar 

  43. M. Nagasaki, A. Doi, H. Matsuno, S. Miyano: A versatile Petri net based architecture for modeling and simulation of complex biological processes, Genome Inform. 15, 180–197 (2004)

    Google Scholar 

  44. D. Peʼer, A. Regev, G. Elidan, N. Friedman: Inferring subnetworks from perturbed expression profiles, Bioinformatics 17, S215–S224 (2001)

    Google Scholar 

  45. K. Kyoda, K. Baba, S. Onami, H. Kitano: DBRF-MEGN method: An algorithm for deducing minimum equivalent gene networks from large-scale gene expression profiles of gene deletion mutants, Bioinformatics 20, 2662–2675 (2004)

    Google Scholar 

  46. S. Kimura, K. Ide, A. Kashihara, M. Kano, M. Hatakeyama, R. Masui, N. Nakagawa, S. Yokoyama, S. Kuramitsu, A. Konagaya: Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm, Bioinformatics 21, 1154–1163 (2005)

    Google Scholar 

  47. R.J. Prill, P.A. Iglesias, A. Levchenko: Dynamic properties of network motifs contribute to biological network organization, PLoS Biology 3, e343 (2005)

    Google Scholar 

  48. B.B. Aldridge, J.M. Burke, D.A. Lauffenburger, P.K. Sorger: Physicochemical modelling of cell signalling pathways, Nat. Cell Biol. 8, 1195–1203 (2006)

    Google Scholar 

  49. G. Karlebach, R. Shamir: Modelling and analysis of gene regulatory networks, Nat. Rev. Mol. Cell Biol. 9, 770–780 (2008)

    Google Scholar 

  50. J.L. Cherry, F.R. Adler: How to make a biological switch, J. Theor. Biol. 203, 117–133 (2000)

    Google Scholar 

  51. B.N. Kholodenko, O.V. Demin, G. Moehren, J.B. Hoek: Quantification of short term signaling by the epidermal growth factor receptor, J. Biol. Chem. 274, 30169–30181 (1999)

    Google Scholar 

  52. A. Gilman, A. Arkin: Genetic `codeʼ: representations and dynamical models of genetic components and networks, Annu. Rev. Genomics Hum. Genet. 3, 341–369 (2002)

    Google Scholar 

  53. M. Csete, J. Doyle: Bow ties, metabolism and disease, Trends Biotechnol. 22, 446–450 (2004)

    Google Scholar 

  54. L. Ma, P.A. Iglesias: Quantifying robustness of biochemical network models, BMC Bioinformatics 3, 38–50 (2002)

    Google Scholar 

  55. H.R. Ueda, M. Hagiwara, H. Kitano: Robust oscillations within the interlocked feedback model of Drosophila circadian rhythm, J. Theor. Biol. 210, 401–406 (2001)

    Google Scholar 

  56. Y. Cao, D.T. Gillespie, L.R. Petzold: Accelerated stochastic simulation of the stiff enzyme-substrate reaction, J. Chem. Phys. 123, 144917 (2005)

    Google Scholar 

  57. D.B. Forger, C.S. Peskin: Stochastic simulation of the mammalian circadian clock, Proc. Natl. Acad. Sci. USA 102, 321–324 (2005)

    Google Scholar 

  58. H. Li, Y. Cao, L.R. Petzold, D.T. Gillespie: Algorithms and software for stochastic simulation of biochemical reacting systems, Biotechnol. Prog. 24, 56–61 (2008)

    Google Scholar 

  59. H.H. McAdams, A. Arkin: Stochastic mechanisms in gene expression, Proc. Natl. Acad. Sci. USA 94, 814–819 (1997)

    Google Scholar 

  60. M. Samoilov, S. Plyasunov, A.P. Arkin: Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations, Proc. Natl. Acad. Sci. USA 102, 2310–2315 (2005)

    Google Scholar 

  61. D.T. Gillespie: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, J. Comput. Phys. 22, 403–434 (1976)

    MathSciNet  Google Scholar 

  62. D.T. Gillespie: Exact stochastic simulation of coupled chemical reactions, J. Phys. Chem. 81, 2340–2361 (1977)

    Google Scholar 

  63. M.B. Elowitz, A.J. Levine, E.D. Siggia, P.S. Swain: Stochastic gene expression in a single cell, Science 297, 1183–1186 (2002)

    Google Scholar 

  64. J.M. Raser, E.K. OʼShea: Control of stochasticity in eukaryotic gene expression, Science 304, 1811–1814 (2004)

    Google Scholar 

  65. P.S. Swain, M.B. Elowitz, E.D. Siggia: Intrinsic and extrinsic contributions to stochasticity in gene expression, Proc. Natl. Acad. Sci. USA 99, 12795–12800 (2002)

    Google Scholar 

  66. B.L. Clarke: Stoichiometric network analysis, Cell Biochem. Biophys. 12, 237–253 (1988)

    Google Scholar 

  67. A.D. Lander: A calculus of purpose, PLoS Biology 2, 0712 (2004)

    Google Scholar 

  68. R. Heinrich, S. Schuster: The Regulation of Cellular Systems (Chapman and Hall, New York 1996)

    MATH  Google Scholar 

  69. R.T. Rockafellar: Convex Analysis (Princeton Univ. Press, Princeton 1970)

    MATH  Google Scholar 

  70. I. Borodina, J. Nielsen: From genomes to in silico cells via metabolic networks, Curr. Opin. Biotechnol. 16, 350–355 (2005)

    Google Scholar 

  71. J.A. Papin, J. Stelling, N.D. Price, S. Klamt, S. Schuster, B.Ø. Palsson: Comparison of network-based pathway analysis methods, Trends Biotechnol. 22, 400–405 (2004)

    Google Scholar 

  72. N.D. Price, J.L. Reed, B.Ø. Palsson: Genome-scale models of microbial cells: evaluating the consequences of constraints, Nat. Rev. Microbiol. 2, 886–897 (2004)

    Google Scholar 

  73. A. Varma, B.Ø. Palsson: Metabolic flux balancing: basic concepts, scientific and practical use, Biotechnol. Bioeng. 12, 994–998 (1993)

    Google Scholar 

  74. S.S. Fong, J.Y. Marciniak, B.Ø. Palsson: Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model, J. Bacteriol. 185, 6400–6408 (2003)

    Google Scholar 

  75. S.S. Fong, B.Ø. Palsson: Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes, Nat. Genet. 36, 1056–1108 (2004)

    Google Scholar 

  76. A.P. Burgard, C.D. Maranas: Optimization-based framework for inferring and testing hypothesized metabolic objective functions, Biotechnol. Bioeng. 82, 670–677 (2003)

    Google Scholar 

  77. E. Klipp, R. Heinrich, H.-G. Holzhutter: Prediction of temporal gene expression. Metabolic optimization by re-distribution of enzyme activities, Eur. J. Biochem. 269, 5406–5413 (2002)

    Google Scholar 

  78. M. Feinberg: Chemical reaction network structure and the stability of complex isothermal reactors I. The deficiency zero and deficiency one theorems, Chem. Eng. Sci. 42, 2229–2268 (1987)

    Google Scholar 

  79. M. Feinberg: Chemical reaction network structure and the stability of complex isothermal reactors II. Multiple steady states for networks of deficiency one, Chem. Eng. Sci. 43, 1–25 (1988)

    Google Scholar 

  80. E. Sontag: Structure and stability of certain chemical networks and applications to the kinetic proofreading model of T-cell receptor signal transduction, IEEE Trans. Autom. Control 46, 1028–1047 (2001)

    MATH  MathSciNet  Google Scholar 

  81. C. Conradi, D. Flockerzi, J. Raisch, J. Stelling: Subnetwork analysis reveals dynamic features of complex (bio)chemical networks, Proc. Natl. Acad. Sci. USA 104, 19175–19180 (2007)

    Google Scholar 

  82. C. Conradi, J. Saez-Rodriguez, E.-D. Gilles, J. Raisch: Using chemical reaction network theory to discard a kinetic mechanism hypothesis, IEEE Proc. Syst. Biol. 152, 243–248 (2005)

    Google Scholar 

  83. R.R. Vallabhajosyula, V. Chickarmane, H.M. Sauro: Conservation analysis of large biochemical networks, Bioinformatics 22, 346–353 (2006)

    Google Scholar 

  84. M.W. Covert, C.H. Schilling, B. Palsson: Regulation of gene expression in flux balance models of metabolism, J. Theor. Biol. 213, 73–88 (2001)

    Google Scholar 

  85. M.W. Covert, E.M. Knight, J.L. Reed, M.J. Herrgard, B.Ø. Palsson: Integrating high-throughput and computational data elucidates bacterial networks, Nature 429, 92–96 (2004)

    Google Scholar 

  86. K. Mahadevan, J. Edwards, F.J. Doyle III: Dynamic flux balance analysis of diauxic growth in Escherichia coli, Biophys. J. 83, 1331–1340 (2002)

    Google Scholar 

  87. C.L. Barrett, C.D. Herring, J.L. Reed, B.Ø. Palsson: The global transcriptional regulatory network for metabolism in Escherichia coli exhibits few dominant functional states, Proc. Natl. Acad. Sci. USA 102, 19103–19108 (2005)

    Google Scholar 

  88. S. Klamt, J. Saez-Rodriguez, J. Lindquist, L. Simeoni, E.D. Gilles: A methodology for the structural and functional analysis of signaling and regulatory networks, BMC Bioinformatics 7, 56 (2006)

    Google Scholar 

  89. D.S. Kompala, D. Ramkrishna, N.B. Jansen, G.T. Tsao: Investigation of bacterial growth on mixed substrates. Experimental evaluation of cybernetic models, Biotechnol. Bioeng. 28, 1044–1056 (1986)

    Google Scholar 

  90. J. Varner, D. Ramkrishna: Application of cybernetic models to metabolic engineering: investigation of storage pathways, Biotechnol. Bioeng. 58, 282–291 (1998)

    Google Scholar 

  91. H.P.J. Bonarius, G. Schmid, J. Tramper: Flux analysis of underdetermined metabolic: the quest for the missing constraints, Trends Biotechnol. 15, 308–314 (1997)

    Google Scholar 

  92. J.M. Savinell, B.Ø. Palsson: Network analysis of intermediary metabolism using linear optimization: Development of mathematical formalism, J. Theor. Biol. 154, 421–454 (1992)

    Google Scholar 

  93. J.M. Savinell, B.Ø. Palsson: Network analysis of intermediary metabolism using linear optimization: Interpretation of hybridoma cell metabolism, J. Theor. Biol. 154, 455–473 (1992)

    Google Scholar 

  94. A. Varma, B.Ø. Palsson: Metabolic capabilities of Escherichia coli: I. Synthesis of biosynthetic precursors and cofactors, J. Theor. Biol. 165, 477–502 (1993)

    Google Scholar 

  95. A. Varma, B.Ø. Palsson: Metabolic capabilities of Escherichia coli: II. Optimal growth patterns, J. Theor. Biol. 165, 503–522 (1993)

    Google Scholar 

  96. D. Segre, D. Vitkup, G.M. Church: Analysis of optimality in natural and perturbed metabolic networks, Proc. Natl. Acad. Sci. USA 99, 15112–15117 (2002)

    Google Scholar 

  97. J. Stelling, S. Klamt, K. Bettenbrock, S. Schuster, E.D. Gilles: Metabolic network structure determines key aspects of functionality and regulation, Nature 420, 190–193 (2002)

    Google Scholar 

  98. A. Zaslaver, A.E. Mayo, R. Rosenberg, P. Bashkin, H. Sberro, M. Tsalyuk, M.G. Surette, U. Alon: Just-in-time transcription program in metabolic pathways, Nat. Genet. 36, 486–491 (2004)

    Google Scholar 

  99. B.N. Kholodenko: Cell-signalling dynamics in time and space, Nat. Rev. Mol. Cell. Biol. 7, 165–176 (2006)

    Google Scholar 

  100. J.J. Tyson, K.C. Chen, B. Novak: Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell, Curr. Opin. Cell. Biol. 15, 221–231 (2003)

    Google Scholar 

  101. H. El-Samad, H. Kurata, J.C. Doyle, C.A. Gross, M. Khammash: Surviving heat shock: control strategies for robustness and performance, Proc. Natl. Acad. Sci. USA 102, 2736–2741 (2005)

    Google Scholar 

  102. L. Ljung: System Identification: Theory for the User, 2nd edn. (Prentice Hall, Upper Saddle River 1999)

    Google Scholar 

  103. J. Tegner, M.K.S. Yeung, J. Hasty, J.J. Collins: Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling, Proc. Natl. Acad. Sci. USA 100, 5944–5949 (2003)

    Google Scholar 

  104. T. MacCarthy, A. Pomiankowski, R. Seymour: Using large-scale perturbations in gene network reconstruction, BMC Bioinformatics 6, 11 (2005)

    Google Scholar 

  105. T.S. Gardner, D. di Bernardo., D. Lorenz, J.J. Collins: Inferring genetic networks and identifying compound mode of action via expression profiling, Science 301, 102–105 (2003)

    Google Scholar 

  106. O. Alter, P.O. Brown, B. Botstein: Singular value decomposition for genome wide expression data processing and modeling, Proc. Natl. Acad. Sci. USA 97, 10101–10116 (2000)

    Google Scholar 

  107. N.S. Holter, M. Mitra, A. Maritan, M. Cieplak, J.R. Banavar, N.V. Fedoroff: Fundamental patterns underlying gene expression profiles: simplicity from complexity, Proc. Natl. Acad. Sci. USA 97, 8409–8414 (2000)

    Google Scholar 

  108. P. Tamayo, D. Slonium, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E. Lander, T.R. Golub: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation, Proc. Natl. Acad. Sci. USA 44, 129–131 (1990)

    Google Scholar 

  109. P. Dʼhaeseleer, S. Liang, R. Somogyi: Genetic network inference: from co-expression clustering to reverse engineering, Bioinformatics 16, 707–726 (2000)

    Google Scholar 

  110. L. You, J. Yin: Patterns of regulation from mRNA and protein time series, Metab. Eng. 2, 210–217 (2000)

    Google Scholar 

  111. J.G. Thomas, J.M. Olson, S.J. Tapscott, L.P. Zhao: An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles, Genome Res. 11, 1227–1236 (2001)

    Google Scholar 

  112. L.P. Zhao, R. Prentice, L. Breeden: Statistical modeling of large microarray data sets to identify stimulus–response profiles, Proc. Natl. Acad. Sci. USA 98, 5631–5636 (2001)

    MATH  Google Scholar 

  113. D.B. Allison, X. Cui, G.P. Page, M. Sabripour: Microarray data analysis: from disarray to consolidation and consensus, Nat. Rev. Genet. 7, 55–65 (2006)

    Google Scholar 

  114. S. Datta, S. Datta: Comparisons and validation of statistical clustering techniques for microarray gene expression data, Bioinformatics 19, 459–466 (2003)

    Google Scholar 

  115. J. Handl, J. Knowles, D.B. Kell: Computational cluster validation in post-genomic data analysis, Bioinformatics 21, 3201–3212 (2005)

    Google Scholar 

  116. P. Dʼhaeseleer, X. Wen, S. Fuhrman, R. Somogyi: Linear modeling of mRNA expression levels during CNS development and injury, Proc. Pac. Symp. Biocomput. 4, 41–52 (1999)

    Google Scholar 

  117. A.J. Hartemink, D.K. Gifford, T.S. Jaakola, R.A. Young: Combining location and expression data for principled discovery of genetic regulatory network models, Proc. Pac. Symp. Biocomput. 7, 437–449 (2002)

    Google Scholar 

  118. D.C. Weaver, C.T. Workman, G.D. Stormo: Modeling regulatory networks with weight matrices, Proc. Pac. Symp. Biocomput. 4, 102–111 (1999)

    Google Scholar 

  119. L.F.A. Wessels, E.P. Van Someren, M.J.T. Reinders: A comparison of genetic network models, Proc. Pac. Symp. Biocomput. 6, 508–519 (2001)

    Google Scholar 

  120. N. Friedman: Inferring cellular networks using probabilistic graphical models, Science 303, 799–805 (2004)

    Google Scholar 

  121. I. Lee, S.V. Date, A.T. Adai, E.M. Marcotte: A probabilistic functional network of yeast genes, Science 306, 1555–1558 (2004)

    Google Scholar 

  122. R.K. Pearson: Discrete-Time Dynamic Models (Oxford Univ. Press, Oxford 1999)

    MATH  Google Scholar 

  123. C.C. Guet, M.B. Elowitz, W. Hsing, S. Leibler: Combinatorial synthesis of genetic networks, Science 296, 1466–1470 (2002)

    Google Scholar 

  124. M. Bansal, G. Della Gatta, D. di Bernardo: Inference of gene regulatory networks and compound mode of action from time course gene expression profiles, Bioinformatics 22, 815–822 (2006)

    Google Scholar 

  125. B.N. Kholodenko, A. Kiyatkin, F.J. Bruggeman, E. Sontag, H.V. Westerhoff, J.B. Hoek: Untangling the wires: a strategy to trace functional interactions in signaling and gene networks, Proc. Natl. Acad. Sci. USA 99, 12841–12846 (2002)

    Google Scholar 

  126. E. Sontag, A. Kiyatkin, B.N. Kholodenko: Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data, Bioinformatics 20, 1877–1886 (2004)

    Google Scholar 

  127. M. Andrec, B.N. Kholodenko, R.M. Levy, E. Sontag: Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy, J. Theor. Biol. 232, 427–441 (2005)

    MathSciNet  Google Scholar 

  128. K.-H. Cho, S.-M. Choo, P. Wellstead, O. Wolkenhauer: A unified framework for unraveling the functional interaction structure of a biomolecular network based on stimulus-response experimental data, FEBS Letters 579, 4520–4528 (2005)

    Google Scholar 

  129. G. Maria: A review of algorithms and trends in kinetic model identification for chemical and biochemical systems, Chem. Biochem. Eng. Q. 18, 195–222 (2004)

    Google Scholar 

  130. C.G. Moles, P. Mendes, J.R. Banga: Parameter estimation in biochemical pathways: a comparison of global optimization methods, Genome Res. 13, 2467–2474 (2003)

    Google Scholar 

  131. M. Rodriguez-Fernandez, P. Mendes, J.R. Banga: A hybrid approach for efficient and robust parameter estimation in biochemical pathways, Biosystems 83, 248–265 (2006)

    Google Scholar 

  132. D.J. Lockhart, E.A. Winzler: Genomics, gene expression and DNA arrays, Nature 405, 827–836 (2000)

    Google Scholar 

  133. D.W. Selinger, M.A. Wright, G.M. Church: On the complete determination of biological systems, Trends Biotechnol. 21, 251–254 (2003)

    Google Scholar 

  134. A. Wagner: Reconstructing pathways in large genetic networks from genetic perturbations, J. Comput. Biol. 11, 53–60 (2004)

    Google Scholar 

  135. A.F. Emery, A.V. Nenarokomov: Optimal experiment design, Meas. Sci. Technol. 9, 864–876 (1998)

    Google Scholar 

  136. D. Faller, U. Klingmüller, J. Timmer: Simulation methods for optimal experimental design in systems biology, Simulation, 79, 717–725 (2003)

    Google Scholar 

  137. Z. Kutalik, K.-H. Cho, O. Wolkenhauer: Optimal sampling time selection for parameter estimation in dynamic pathway modeling, Biosystems 75, 43–55 (2004)

    Google Scholar 

  138. X.-J. Feng, S. Hooshangi, D. Chen, G. Li, R. Weiss, H. Rabitz: Optimizing genetic circuits by global sensitivity analysis, Biophys. J. 87, 2195–2202 (2004)

    Google Scholar 

  139. D. Hwang, A.G. Rust, S. Ramsey, J.J. Smith, D.M. Leslie, A.D. Weston, P.D. Atauri, J.D. Aitchison, L. Hood, A.F. Siegel, H. Bolouri: A data integration methodology for systems biology, Proc. Natl. Acad. Sci. USA 102(17), 17296–17301 (2005)

    Google Scholar 

  140. M. Ronen, R. Rosenberg, B. Shraiman, U. Alon: Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics, Proc. Natl. Acad. Sci. USA 99, 10555–10560 (2002)

    Google Scholar 

  141. P.M. Kim, B. Tidor: Limitations of quantitative gene regulation models: a case study, Genome Res. 13, 2391–2395 (2003)

    Google Scholar 

  142. H.H. McAdams, A. Arkin: Its a noisy business: genetic regulation at the nanomolar scale, Trends Genet. 15, 65–69 (1999)

    Google Scholar 

  143. F.J. Doyle III, R. Gunawan, N. Bagheri, H. Mirsky, T.L. To: Circadian rhythm: A natural robust, multi-scale control system, Comput. Chem. Eng. 30, 1700–1711 (2006)

    Google Scholar 

  144. R. Gunawan, F.J. Doyle III: Isochron-based phase response analysis of circadian rhythms, Biophys. J. 91, 2131–2141 (2006)

    Google Scholar 

  145. R. Gunawan, F.J. Doyle III: Phase sensitivity analysis of circadian rhythm entrainment, J. Biol. Rhythms 22, 180–194 (2007)

    Google Scholar 

  146. N. Bagheri, J. Stelling, F.J. Doyle III: Quantitative performance metrics for robustness in circadian rhythms, Bioinformatics 23, 358–364 (2007)

    Google Scholar 

  147. S. Taylor, L. Petzold, F.J. Doyle III: Sensitivity measures for oscillating systems: Application to mammalian circadian gene network, IEEE Trans. Autom. Control 53, 177–1888 (2008)

    MathSciNet  Google Scholar 

  148. M.N. Zeilinger, E.M. Farre, S.R. Taylor, S.A. Kay, F.J. Doyle III: A novel computational model of the circadian clock in Arabidopsis that incorporates PRR7 and PRR9, Mol. Syst. Biol. 2, 58 (2006)

    Google Scholar 

  149. H. Mirsky, R. Gunawan, S. Taylor, J. Stelling, F.J. Doyle III: Noise Propagation and Sensitivity in Mammalian Circadian Clocks, AIChE Annu. Meet. (San Francisco 2006)

    Google Scholar 

  150. N. Bagheri, S.R. Taylor, K. Meeker, L.R. Petzold, F.J. Doyle III: Synchrony and entrainment properties of robust circadian oscillators, J. R. Soc. Interface 5, S17–S28 (2008)

    Google Scholar 

  151. T.L. To, M.A. Henson, E.D. Herzog, F.J. Doyle III: A molecular model for intercellular synchronization in the mammalian circadian clock, Biophys. J. 92, 3792–3803 (2007)

    Google Scholar 

  152. H.K. Khalil: Nonlinear Systems (Prentice Hall, Upper Saddle River 2002)

    MATH  Google Scholar 

  153. A. Varma, M. Morbidelli, H. Wu: Parametric Sensitivity in Chemical Systems (Oxford Univ. Press, New York 1999)

    Google Scholar 

  154. R. Larter: Sensitivity analysis of autonomous oscillators: separation of secular terms and determination of structural stability, J. Phys. Chem. 87, 3114–3121 (1983)

    Google Scholar 

  155. R. Tomovic, M. Vukobratovic: General Sensitivity Theory (Elsevier, New York 1972)

    MATH  Google Scholar 

  156. D.E. Zak, J. Stelling, F.J. Doyle III: Sensitivity analysis of oscillatory (bio)chemical systems, Comput. Chem. Eng. 29, 663–673 (2005)

    Google Scholar 

  157. N. Bagheri, J. Stelling, F.J. Doyle III: Circadian phase entrainment via nonlinear model predictive control, Intl. J. Robust Nonlinear Control 17, 1555–1571 (2007)

    MATH  MathSciNet  Google Scholar 

  158. G. Strang: Linear Algebra and ist Applications (Saunders College Publishing, New York 1988)

    Google Scholar 

  159. C.H. Johnson: Forty years of PRCs – what have we learned?, Chronobiol. Int. 16, 711–743 (1999)

    Google Scholar 

  160. National Research Council: Network Science (National Academies Press, Washington 2005)

    Google Scholar 

  161. L. Lamberg: Bodyrhythms: Chronobiology and Peak Performance (William Morrow, New York 1994)

    Google Scholar 

  162. N. Bagheri, J. Stelling, F.J. Doyle III: Circadian phase resetting and multiple control targets, PLoS Comput. Biol. 4, e10000104 (2008)

    MathSciNet  Google Scholar 

  163. L. Hood, J.R. Heath, M.E. Phelps, B. Lin: Systems biology and new technologies enable predictive and preventable medicine, Science 306, 640–643 (2004)

    Google Scholar 

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Authors and Affiliations

  1. Department of Chemical Engineering, University of California, Santa Barbara, 93106, Santa Barbara, CA, USA

    Henry Mirsky BSc

  2. Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr. 26, 4058, Basel, Switzerland

    Jörg Stelling PhD

  3. Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4 Blk E5 #02-16, 117576, Singapore, Singapore

    Rudiyanto Gunawan PhD

  4. Department of Biological Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave. 16–463, 02139, Cambridge, MA, USA

    Neda Bagheri MSc

  5. Department of Computer Science, Colby College, Colby College, 5855 Mayflower Hill Dr., 04901, Waterville, ME, USA

    Stephanie R. Taylor PhD

  6. Department of Chemical Engineering, University of California, Santa Barbara, 93106, Santa Barbara, CA, USA

    Eric Kwei

  7. Department of Chemical Engineering, University of California, 93106-5080, Santa Barbara, CA, USA

    Jason E. Shoemaker

  8. Department of Chemical Engineering, University of California, 93106-5080, Santa Barbara, CA, USA

    Francis J. Doyle III Dr.

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  1. Henry Mirsky BSc
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  8. Francis J. Doyle III Dr.
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Correspondence to Henry Mirsky BSc , Jörg Stelling PhD , Rudiyanto Gunawan PhD , Neda Bagheri MSc , Stephanie R. Taylor PhD , Eric Kwei , Jason E. Shoemaker or Francis J. Doyle III Dr. .

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  1. PRISM Center, and School of Industrial Engineering, Purdue University, 315 N. Grant Street, 47907, West Lafayette, IN, USA

    Shimon Y. Nof

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Mirsky, H. et al. (2009). Automatic Control in Systems Biology. In: Nof, S. (eds) Springer Handbook of Automation. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78831-7_75

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