Systems Biology (Methods in Molecular Biology)

(Tina Sui) #1

7 Summary


It has been discussed for a long time whether living systems can be
mathematically conceptualized using simple theories as they possess
very complex dynamic and emergent behaviors, and many times
display unpredictable outcomes [47]. In this chapter, we have
looked at several complex response dynamics of living cells, and
have shown simple biochemical models, based on linear and non-
linear differential equations, which can be used to successfully
understand or interpret the data. For linear models, the reaction
topology rather than kinetics plays crucial and sensitive roles
[4, 19]. For nonlinear dynamics, the parameters need to be precise
or the response cannot be accurately determined due to the stability
issue. For single-cell response, stochastic modeling can be useful in
understanding the diversifying cell fates or heterogeneous
response. We, therefore, believe mathematical models will continue
to play significant roles in unlocking further secrets on the complex
behaviors of living systems.

References



  1. Goldman AW, Burmeister Y, Cesnulevicius K,
    Herbert M, Kane M, Lescheid D, McCaffrey T,
    Schultz M, Seilheimer B, Smit A, St Laurent G
    III, Berman B (2015) Bioregulatory systems
    medicine: an innovative approach to integrat-
    ing the science of molecular networks, inflam-
    mation, and systems biology with the patient’s
    autoregulatory capacity? Front Physiol 6:225

  2. Turing AM (1952) The chemical basis of mor-
    phogenesis. Philos Trans R Soc B 237:37–72

  3. Selvarajoo K (2011) Macroscopic law of con-
    servation revealed in the population dynamics
    of Toll-like receptor signaling. Cell Commun
    Signal 9:9

  4. Selvarajoo K (2013) Immuno systems biology:
    a macroscopic approach for immune cell signal-
    ing. Springer, New York

  5. Gutenkunst RN, Waterfall JJ, Casey FP, Brown
    KS, Myers CR, Sethna JP (2007) Universally
    sloppy parameter sensitivities in systems biol-
    ogy models. PLoS Comput Biol 3:1871–1878

  6. Bakker BM, Michels PA, Opperdoes FR, Wes-
    terhoff HV (1997) Glycolysis in bloodstream
    formTrypanosoma bruceican be understood in
    terms of the kinetics of the glycolytic enzymes.
    J Biol Chem 272:3207–3215

  7. Guldberg CM, Waage P (1864) Studies
    concerning affinity, C. M. Forhandlinger:
    Videnskabs-Selskabet i Christiana, 35
    8. Leskovac V (2003) Comprehensive enzyme
    kinetics. Kluwer Academic/Plenum Pub,
    New York
    9. Bujara M, Sch€umperli M, Pellaux R,
    Heinemann M, Panke S (2011) Optimization
    of a blueprint for in vitro glycolysis by meta-
    bolic real-time analysis. Nat Chem Biol
    7:271–277

  8. Blagoev B, Ong SE, Kratchmarova I, Mann M
    (2004) Temporal analysis of phosphotyrosine-
    dependent signaling networks by quantitative
    proteomics. Nat Biotechnol 22:1139–1145

  9. Helmy M, Gohda J, Inoue J, Tomita M,
    Tsuchiya M, Selvarajoo K (2009) Predicting
    novel features of toll-like receptor 3 signaling
    in macrophages. PLoS One 4:e4661

  10. Selvarajoo K, Takada Y, Gohda J, Helmy M,
    Akira S, Tomita M, Tsuchiya M, Inoue J, Mat-
    suo K (2008) Signaling flux redistribution at
    toll-like receptor pathway junctions. PLoS One
    3:e3430

  11. Selvarajoo K (2006) Discovering differential
    activation machinery of the Toll-like receptor
    (TLR) 4 signaling pathways in Myd88 knock-
    outs. FEBS Lett 580:1457–1464

  12. Hayashi K, Piras V, Tabata S, Tomita M, Sel-
    varajoo K (2013) A systems biology approach
    to suppress TNF-induced proinflammatory
    gene expressions. Cell Commun Signal 11:84


200 Kumar Selvarajoo

Free download pdf