Pharmacopsychiatry 2009; 42: S110-S117
DOI: 10.1055/s-0029-1216347
Original Paper

© Georg Thieme Verlag KG Stuttgart · New York

Concept Maps and Canonical Models in Neuropsychiatry

A. Marin-Sanguino 1 , R. C. H. del Rosario 1 , 4 , E. R. Mendoza 2 , 3 , 4
  • 1Department of Membrane Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany
  • 2Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-University, Munich, Germany
  • 3Department of Computer Science, University of the Philippines Diliman, Quezon City, Philippines
  • 4Institute of Mathematics, University of the Philippines Diliman, Quezon City, Philippines
Further Information

Publication History

Publication Date:
11 May 2009 (online)

Abstract

Most bioscientists engage in informal modelling in their research and explicitly document this activity's results in diagrams or “concept maps”. While canonical modelling approaches such as Biochemical Systems Theory (BST) immediately allow the construction of a corresponding system of equations, the problem of determining appropriate parameter values remains. Goel et al. introduced Concept Map Modelling (CMM) as a framework to address this problem through an interactive dialogue between experimenters and modellers. The CMM dialogue extracts the experimenters’ implicit knowledge about dynamical behaviour of the parts of the system being modelled in form of rough sketches and verbal statements, e.g. value ranges. These are then used as inputs for parameter and initial value estimates for the symbolic canonical model based on the diagram. Canonical models have the big advantage that a great variety of parameter estimation methods have been developed for them in recent years. The paper discusses the suitability of this approach for neuropsychiatry using recent work of Qi et al. on a canonical model of presynaptic dopamine metabolism. Due to the complexity of systems encountered in neuropsychiatry, hybrid models are often used to complement the canonical models discussed here.

References

  • 1 Beersma DGM. Why and how do we model circadian rhythms.  J Biol Rhythms. 2005;  20 ((4)) 304-313
  • 2 Bhattacharjee Y. Is internal timing key to mental health?.  Science. 2007;  317 1488-1490
  • 3 Chou I-C, Voit EO. Recent developments in parameter estimation and structure identification of biochemical and genomic systems.  Mathematical Biosciences. , Forthcoming
  • 4 Cinquemani E, Porreca R, Ferrari-Trecate G. et al . Subtilin production by bacillus subtilis: stochastic hybrid models and parameter identification.  IEEE Transactions on Automatic Control and IEEE Transactions on Circuits and Systems. 2008; 
  • 5 del Rosario RCH, Echavez MT, de Paz MT. et al . MADMan a Benchmarking Framework for parameter estimation in biochemical systems theory models.  Proceedings of the International Conference on Molecular Systems Biology (ICMSB08). 2008;  10-13
  • 6 Foster RG, Kreitzman L. Rhythms of Life. 2004 Profile Books, UK
  • 7 Goel G, Chou I-C, Voit EO. Biological systems modelling and analysis; a biomolecular technique of the twenty-first century.  J Biomol Tech. 2006;  17 252-269
  • 8 de Jong H, Gouze J-L, Hernandez C. et al . Qualitative simulation of genetic regulatory networks using piecewise-linear models.  Bulletin of Mathematical Biology. 2004;  66 ((2)) 301-340
  • 9 Kalsbeek A. Lecture on SCN Output Pathways, 17th European Chronobiology Summer School. Laulasmaa, Estonia 2008: 7-14
  • 10 Klipp E, Liebermeister W, Helbig A. et al . Systems biology standards – the community speaks.  Nature Biotechnology. 2007;  25 390-391
  • 11 Lusis A J, Attie AD, Reue K. Metabolic syndrome: from epidemiology to systems biology.  Nature Reviews Genetics. 2008;  9 819-830
  • 12 Marin-Sanguino A, Mendoza ER. Hybrid modelling in computational neuropsychiatry.  Pharmacopsychiatry. 2008;  41 ((S1)) S85-S88
  • 13 Qi Z, Miller GW, Voit EO. Computational analysis of dopamine metabolism.  PLoS ONE. 2008;  3 ((6)) e2444
  • 14 Qi Z, Miller GW, Voit EO. A mathematical model of presynaptic dopamine homeostasis: implications for schizophrenia.  Pharmacopsychiatry. 2008;  41 ((S1)) S89-S98
  • 15 Roenneberg T, Chua EJ, Bernardo R. et al . Modelling biological rhythms.  Current Biology. 2008;  18 R826-R835
  • 16 Roenneberg T, Merrow M. Circadian clocks – the fall and rise of physiology.  Nature Reviews Molecular Cell Biology. 2005;  6 965-971
  • 17 Schilling CH, Edwards JS, Letscher D. et al . Pathway analysis with flux balance analysis for the comprehensive study of metabolic systems.  Biotechnology and Bioengineering. 2000;  71 ((4)) 286-306
  • 18 Snoopy A. .Tool to design and execute graph-based formalisms. [Extended Version] In: Petri Net Newsletter 74 (April 2008) ISSN 0931–1084. 8–22. See also http://www-dssz.informatik.tu-cottbus.de/
  • 19 Tretter F, Scherer J. Neurobiology and the methodology of systemic modeling.  Pharmacopsychiatry. 2006;  39 ((S1)) S26-S35
  • 20 Turek F. Tips from the tip of the iceberg.  Nature. 2008;  456 881-882
  • 21 Voit EO, Qi Z, Müller GW. Steps of modelling complex biological systems.  Pharmacopsychiatry. 2008;  41 ((S1)) S78-S84

Correspondence

Dr. E. R. Mendoza

Department of Physics

Center for NanoScience

Ludwig-Maximillians-University

Geschwister-Scholl-Platz 1

80539 Munich

Germany

Email: eduardo.mendoza@physik.lmu.de