Download PDF
Pharmacopsychiatry 2012; 45(S 01): S22-S30
DOI: 10.1055/s-0032-1304653
Original Paper
© Georg Thieme Verlag KG Stuttgart · New York

Mesoscopic Models of Neurotransmission as Intermediates between Disease Simulators and Tools for Discovering Design Principles

E. O. Voit
1   Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Medical School, Atlanta, GA, USA
2   Integrative BioSystems Institute, Georgia Institute of Technology, Atlanta, GA, USA
,
Z. Qi
1   Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Medical School, Atlanta, GA, USA
2   Integrative BioSystems Institute, Georgia Institute of Technology, Atlanta, GA, USA
3   Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
,
S. Kikuchi
1   Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Medical School, Atlanta, GA, USA
2   Integrative BioSystems Institute, Georgia Institute of Technology, Atlanta, GA, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
07 May 2012 (online)

Also available at
    Permissions and Reprints

Corrected by:

Mesoscopic Models of Neurotransmission as Intermediates between Disease Simulators and Tools for Discovering Design PrinciplesPharmacopsychiatry 2012; 45(S 01): e1-e1
DOI: 10.1055/s-0032-1314850

Abstract

Two grand challenges have been declared as premier goals of computational systems biology. The first is the discovery of network motifs and design principles that help us understand and rationalize why biological systems are organized in the manner we encounter them rather than in a different fashion. The second goal is the development of computational models supporting the investigation of complex systems, in particular, as simulation platforms in personalized medicine and predictive health. Interestingly, most published systems models in biology contain between a handful and a few dozen variables. They are usually too complicated for systemic analyses of organizing principles, but they are at the same time too coarse to allow reliable simulations of diseases. While it may thus appear that the modeling efforts of the past have missed the declared targets of systems biology, we argue in this article that midsized mesoscopic models are excellent starting points for pursuing both goals in computational systems biology.

Key words

computational systems biology - design principle - dopamine - mesoscopic model - network motif - operating principle - Parkinson’s disease - schizophrenia
 

  • References

  • 1 Lotka A. Elements of Physical Biology. Williams and Wilkins; Baltimore: 1924. (reprinted as ‘Elements of Mathematical Biology’. Dover, New York, 1956)
    Google Scholar
  • 2 von Bertalanffy L. Der Organismus als physikalisches System betrachtet. Die Naturwissenschaften 1940; 33: 521-531
    PubMedGoogle Scholar
  • 3 Mesarović MD. Systems theory and biology – view of a theoretician. In: Systems theory and biology. Mesaroviâc MD. Editor Springer; Berlin, New York: 1968. 59-87
    CrossrefGoogle Scholar
  • 4 Savageau MA. Biochemical systems analysis: a study of function and design in molecular biology. Reading, Mass.: Addison-Wesley Pub. Co. Advanced Book Program; 1976. xvii, 379
    Google Scholar
  • 5 Tomita M, Hashimoto K, Takahashi K et al. E-CELL: software environment for whole-cell simulation. Bioinformatics 1999; 15: 72-84
    CrossrefPubMedGoogle Scholar
  • 6 Hood L. Systems biology: integrating technology, biology, and computation. Mech Ageing Dev 2003; 124: 9-16
    CrossrefPubMedGoogle Scholar
  • 7 Kitano H. ed. Foundations of Systems Biology. MIT Press; Cambridge, MA: 2001
  • 8 Kitano H. Systems biology: a brief overview. Science 2002; 295 (5560) 1662-1664
    CrossrefPubMedGoogle Scholar
  • 9 Kitano H. Computer systems biology. Nature 2002; 420: 206-210
    CrossrefPubMedGoogle Scholar
  • 10 Bhalla US, Iyengar R. Emergent properties of networks of biological signaling pathways. Science 1999; 283: 381-387
    CrossrefPubMedGoogle Scholar
  • 11 Hayer A, Bhalla US. Molecular switches at the synapse emerge from receptor and kinase traffic. PLoS Comput Biol 2005; 1: 137-154
    PubMedGoogle Scholar
  • 12 Savageau MA. The challenge of reconstruction. The New Biologist 1991; 3: 101-102
    PubMedGoogle Scholar
  • 13 Alon U. Simplicity in biology. Nature 2007; 446: 497
    CrossrefPubMedGoogle Scholar
  • 14 Alon U. An introduction to systems biology: design principles of biological circuits. In: Mathematical & Computational Biology. Boca Raton, FL: Chapman & Hall/CRC; 2006
    Google Scholar
  • 15 Alves R, Sorribas A. Special issue on biological design principles. Math Biosci 2011; 231: 1-2
    CrossrefPubMedGoogle Scholar
  • 16 Hlavacek WS, Savageau MA. Completely uncoupled and perfectly coupled gene expression in repressible systems. J Mol Biol 1997; 266: 538-558
    CrossrefPubMedGoogle Scholar
  • 17 Savageau MA. A theory of alternative designs for biochemical control systems. Biomed Biochim Acta 1985; 44: 875-880
    PubMedGoogle Scholar
  • 18 Schwacke JH, Voit EO. Improved methods for the mathematically controlled comparison of biochemical systems. Theor Biol Med Model 2004; 1: 1
    CrossrefPubMedGoogle Scholar
  • 19 Savageau MA. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics 1998; 149: 1665-1676
    PubMedGoogle Scholar
  • 20 Savageau MA. Demand theory of gene regulation. II. Quantitative application to the lactose and maltose operons of Escherichia coli. Genetics 1998; 149: 1677-1691
    PubMedGoogle Scholar
  • 21 Voit EO. Design principles and operating principles: the yin and yang of optimal functioning. Math Biosci 2003; 182: 81-92
    CrossrefPubMedGoogle Scholar
  • 22 Alvarez-Vasquez F, Sims KJ, Voit EO et al. Coordination of the dynamics of yeast sphingolipid metabolism during the diauxic shift. Theor Biol Med Model 2007; 4: 42
    CrossrefPubMedGoogle Scholar
  • 23 Lee Y, Chen P-W, Voit EO. Analysis of operating principles with S-system models. Math Biosc 2011; 231: 49-60
    CrossrefPubMedGoogle Scholar
  • 24 Ma’ayan A, Cecchi GA, Wagner J et al. Ordered cyclic motifs contribute to dynamic stability in biological and engineered networks. Proc Natl Acad Sci USA 2008; 105: 19235-19249
    CrossrefPubMedGoogle Scholar
  • 25 Vodovotz Y, Constantine G, Rubin J et al. Mechanistic simulations of inflammation: Current state and future prospects. Math Biosc 2009; 217: 1-10
    CrossrefPubMedGoogle Scholar
  • 26 Curto R, Voit EO, Cascante M. Analysis of abnormalities in purine metabolism leading to gout and to neurological dysfunctions in man. Biochem J 1998; 329 (Pt 3) 477-487
    PubMedGoogle Scholar
  • 27 Bhalla US, Iyengar R. Robustness of the bistable behavior of a biological signaling feedback loop. Chaos 2001; 11: 221-226
    CrossrefPubMedGoogle Scholar
  • 28 Kikuchi S, Fujimoto K, Kitagawa N et al. Kinetic simulation of signal transduction system in hippocampal long-term potentiation with dynamic modeling of protein phosphatase 2A. Neural Netw 2003; 16: 1389-1398
    CrossrefPubMedGoogle Scholar
  • 29 Marin-Sanguino A, Gupta SK, Voit EO et al. Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases. Meth Enzym 2010; 487: 321-372
    PubMedGoogle Scholar
  • 30 Voit EO, Brigham KL. The role of systems biology in predictive health and personalized medicine. The Open Path J 2008; 2: 68-70
    PubMedGoogle Scholar
  • 31 Voit EO. A systems-theoretical framework for health and disease: inflammation and preconditioning from an abstract modeling point of view. Math Biosci 2009; 217: 11-18
    CrossrefPubMedGoogle Scholar
  • 32 E-Cell http://www.e-cell.org/ecell/ 2011
    PubMed
  • 33 Glossary. Systems biology: a user’s guide. http://www.nature.com/focus/systemsbiologyuserguide/appendices/glossary.html 2011
    PubMedGoogle Scholar
  • 34 Noble D. The Music of Life; Biology Beyond Genes. Oxford, U.K.: Oxford University Press; 2006
    Google Scholar
  • 35 Full RJ, Koditschek DE. Templates and anchors: Neuromechanical hypotheses of legged locomotion on land. J Exp Biol 1999; 202: 3325-3332
    PubMedGoogle Scholar
  • 36 Hayt WHJ, Kemmerly JE. Engineering Circuit Analyses. 3rd edn. New York: McGraw-Hill; 1978
    Google Scholar
  • 37 Voit EO, Ferreira AEN. Buffering in models of integrated biochemical systems. J Theor Biol 1998; 191: 429-438
    CrossrefPubMedGoogle Scholar
  • 38 Clewley R. Inferring and quantifying the role of an intrinsic current in a mechanism for a half-center bursting oscillation: A dominant scale and hybrid dynamical systems analysis. J Biol Phys 2011; 37: 285-306
    CrossrefPubMedGoogle Scholar
  • 39 Clewley R. Encoding the fine-structured mechanism of action potential dynamics with qualitative motifs. J Comput Neurosci 2011; 30: 391-408
    CrossrefPubMedGoogle Scholar
  • 40 Tretter F. Mental illness, synapses and the brain – behavioral disorders by a system of molecules withinb a system of neurons?. Pharmacopsychiatry 2010; 43 (Suppl. 01) S9-S20
    Article in Thieme ConnectPubMedGoogle Scholar
  • 41 Qi Z, Miller GW, Voit EO. Computational systems analysis of dopamine metabolism. PLoS One 2008; 3: e2444
    CrossrefPubMedGoogle Scholar
  • 42 Qi Z, Miller GW, Voit EO. A mathematical model of presynaptic dopamine homeostasis: implications for schizophrenia. Pharmacopsychiatry 2008; 41 (Suppl. 01) S89-S98
    Article in Thieme ConnectPubMedGoogle Scholar
  • 43 Qi Z, Miller GW, Voit EO. Computational analysis of determinants of dopamine (DA) dysfunction in DA nerve terminals. Synapse 2009; 63: 1133-1142
    CrossrefPubMedGoogle Scholar
  • 44 Barbano PE, Spivak M, Flajolet M et al. A mathematical tool for exploring the dynamics of biological networks. Proc Natl Acad Sci USA 2007; 104: 19169-19174
    CrossrefPubMedGoogle Scholar
  • 45 Fernandez É, Schiappa R, Girault JA et al. DARPP-32 is a robust integrator of dopamine and glutamate signals. PLoS Comput Biol 2006; 2: e176
    CrossrefPubMedGoogle Scholar
  • 46 Lindskog M, Kim M, Wikstrom MA et al. Transient calcium and dopamine increase PKA activity and DARPP-32 phosphorylation. PLoS Comput Biol 2006; 2 (09) e119
    CrossrefPubMedGoogle Scholar
  • 47 Qi Z, Miller GW, Voit EO. The internal state of medium spiny neurons varies in response to different input signals. BMC Syst Biol 2010; 4: 26
    CrossrefPubMedGoogle Scholar
  • 48 Mahajan SD, Aalinkeel R, Reynolds JL et al. Therapeutic targeting of “DARPP-32”: A key signaling molecule in the dopaminergic pathway for the treatment of opiate addiction. Int Rev Neurobiol 2009; 88: 199-222
    PubMedGoogle Scholar
  • 49 Fienberg AA, Hiroi N, Mermelstein PG et al. DARPP-32: Regulator of the efficacy of dopaminergic neurotransmission. Science 1998; 281 (5378) 838-842
    CrossrefPubMedGoogle Scholar
  • 50 Qi Z, Kikuchi S, Tretter F et al. Effects of dopamine and glutamate on synaptic plasticity: A computational modeling approach for drug abuse as comorbidity in mood disorders. Pharmacopsychiatry 2011; 44 (Suppl. 01) S62-S75
    Article in Thieme ConnectPubMedGoogle Scholar
  • 51 Bhalla US, Ram PT, Iyengar R. MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network. Science 2002; 297: 1018-1023
    CrossrefPubMedGoogle Scholar
  • 52 Ishii K, Nokamura S, Morohashi M et al. Comparison of metabolite production capability indices generated by network analysis methods. Biosystems 2008; 91: 166-170
    CrossrefPubMedGoogle Scholar
  • 53 Matsubara Y, Kikuchi S, Sugimoto M et al. Algebraic method for the analysis of signaling crosstalk. Artif Life 2008; 14: 81-94
    CrossrefPubMedGoogle Scholar
  • 54 Yanashima R, Kitagawa N, Matsubara Y et al. Network features and pathway analyses in a signal transduction cascade. Front Neuroinform 2009; 3: 13
    PubMedGoogle Scholar
  • 55 Shiraishi T, Matsuyama S, Kitano H. Large-scale analysis of network bistability for human cancers. PLoS Comput Biol 2010; 6: e1000851
    CrossrefPubMedGoogle Scholar
  • 56 Savageau MA. Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. J Theor Biol 1969; 25: 365-369
    CrossrefPubMedGoogle Scholar
  • 57 Torres NV, Voit EO. Pathway Analysis and Optimization in Metabolic Engineering. Cambridge, U.K.: Cambridge University Press; 2002
    CrossrefGoogle Scholar
  • 58 Voit EO. Computational Analysis of Biochemical Systems. A Practical Guide for Biochemists and Molecular Biologists. Cambridge, UK: Cambridge University Press; 2000. xii+530
    Google Scholar
  • 59 Voit EO. ed. Canonical Nonlinear Modeling. S-System Approach to Understanding Complexity ed. Van Nostrand Reinhold; NY: 1991. xi+365