Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Statistics
  • Help & Advice
University of Warwick

The Library

  • Login

The genotype-phenotype relationship in multicellular pattern-generating models - the neglected role of pattern descriptors

Tools
- Tools
+ Tools

Martens, Harald, Veflingstad, Siren R., Plahte, Erik, Martens, M. (Magni), Bertrand, Dominique and Omholt, Stig W.. (2009) The genotype-phenotype relationship in multicellular pattern-generating models - the neglected role of pattern descriptors. BMC Systems Biology, Vol.3 (Articl). ISSN 1752-0509

[img]
Preview
PDF
WRAP_Verflingstad_multi-cellular.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader

Download (805Kb)
Official URL: http://dx.doi.org/10.1186/1752-0509-3-87

Abstract

Background: A deep understanding of what causes the phenotypic variation arising from biological patterning processes, cannot be claimed before we are able to recreate this variation by mathematical models capable of generating genotype-phenotype maps in a causally cohesive way. However, the concept of pattern in a multicellular context implies that what matters is not the state of every single cell, but certain emergent qualities of the total cell aggregate. Thus, in order to set up a genotype-phenotype map in such a spatiotemporal pattern setting one is actually forced to establish new pattern descriptors and derive their relations to parameters of the original model. A pattern descriptor is a variable that describes and quantifies a certain qualitative feature of the pattern, for example the degree to which certain macroscopic structures are present. There is today no general procedure for how to relate a set of patterns and their characteristic features to the functional relationships, parameter values and initial values of an original pattern-generating model. Here we present a new, generic approach for explorative analysis of complex patterning models which focuses on the essential pattern features and their relations to the model parameters. The approach is illustrated on an existing model for Delta-Notch lateral inhibition over a two-dimensional lattice. Results: By combining computer simulations according to a succession of statistical experimental designs, computer graphics, automatic image analysis, human sensory descriptive analysis and multivariate data modelling, we derive a pattern descriptor model of those macroscopic, emergent aspects of the patterns that we consider of interest. The pattern descriptor model relates the values of the new, dedicated pattern descriptors to the parameter values of the original model, for example by predicting the parameter values leading to particular patterns, and provides insights that would have been hard to obtain by traditional methods. Conclusion: The results suggest that our approach may qualify as a general procedure for how to discover and relate relevant features and characteristics of emergent patterns to the functional relationships, parameter values and initial values of an underlying pattern-generating mathematical model.

Item Type: Journal Article
Subjects: Q Science > QH Natural history > QH426 Genetics
Divisions: Faculty of Science > Centre for Systems Biology
Library of Congress Subject Headings (LCSH): Phenotype -- Research, Pattern formation (Biology) -- Research, Gene mapping -- Mathematical models, Lattice theory
Journal or Publication Title: BMC Systems Biology
Publisher: BioMed Central Ltd.
ISSN: 1752-0509
Date: 4 September 2009
Volume: Vol.3
Number: Articl
Identification Number: 10.1186/1752-0509-3-87
Status: Peer Reviewed
Access rights to Published version: Open Access
Funder: National Programme for Research in Functional Genomics in Norway (FUGE), Norges forskningsråd [Norwegian Research Council]
Grant number: 151924/S10 (FUGE)
References: 1. Rajasingh H, Gjuvsland AB, Vage DI, Omholt SW: When parameters in dynamic models become phenotypes: A case study on flesh pigmentation in the chinook salmon (Oncorhynchus tshawytscha). Genetics 2008, 179:1113-1118. 2. Ehebauer M, Hayward P, Arias AM: Notch, a Universal Arbiter of Cell Fate Decisions. Science 2006, 314:1414-1415. 3. Le Borgne R: Regulation of Notch signalling by endocytosis and endosomal sorting. Current Opinion in Cell Biology 2006, 18:213-222. 4. Louvi A, Artavanis-Tsakonas S: Notch signalling in vertebrate neural development. Nature Reviews Neuroscience 2006, 7:93-102. 5. Massague J: Transforming growth factor-alpha - a model for membrane-anchored growth-factors. J Biol Chem 1990, 265:21393-21396. 6. Meir E, von Dassow G, Munro E, Odell GM: Robustness, Flexibility, and the Role of Lateral Inhibition in the Neurogenic Network. Current Biology 2002, 12:778-786. 7. Collier JR, Monk NAM, Maini PK, Lewis JH: Pattern formation by lateral inhibition with feedback: A mathematical model of Delta-Notch intercellular signalling. Journal of Theoretical Biology 1996, 183:429-446. 8. Podgorski G, Bansal M, Flann N: Regular mosaic pattern development: A study of the interplay between lateral inhibition, apoptosis and differential adhesion. Theoretical Biology and Medical Modelling 2007, 4:43. 9. Binder PM: Frustration in Complexity. Science 2008, 320:322-323. 10. Wold S, Martens H, Wold H: The multivariate calibration problem in chemistry solved by the PLS method. In Matrix Pencils Edited by: Dold A, Eckmann B. Heidelberg: Springer Verlag; 1983:286-293. Lecture Notes in Mathematics 11. Martens H, Næs T: Multivariate calibration Chichester: Wiley; 1989. 12. Martens H, Martens M: Multivariate analysis of quality: an introduction Chichester: Wiley; 2001. 13. ISO: Sensory analysis. Methodology. General guidance for establishing a sensory profile Geneva: International Organization for Standardization; 2003. 14. Martens H, Thybo AK, Andersen HJ, Karlsson AH, Donstrup S, Stodkilde- Jorgensen H, Martens M: Sensory analysis for magnetic resonance- image analysis: Using human perception and cognition to segment and assess the interior of potatoes. Lebensmittel-Wissenschaft Und-Technologie-Food Science and Technology 2002, 35:70-79. 15. Meilgaard M, Civille GV, Carr BT: Sensory evaluation techniques Boca Raton, Fla.: CRC Press; 1999. 16. Martens M, Veflingstad SR, Plahte E, Bertrand D, Martens H: A sensory scientific approach to visual pattern recognition of complex biological systems. The 8th Pangborn Sensory Science Symposium, 26.-30. July 2009, Florence, Italy [http:// www.pangborn2009.com]. 17. Plahte E: Pattern formation in discrete cell lattices. Journal of Mathematical Biology 2001, 43:411-445.
URI: http://wrap.warwick.ac.uk/id/eprint/2169

Request changes to a record

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...
twitter

Email us: publications@warwick.ac.uk
Contact Details
About Us