The Library

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

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, Volume 3 . Article number 87. ISSN 1752-0509

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

Official Date:

4 September 2009

Dates:

Date	Event
4 September 2009	Published

Volume:

Volume 3

Article Number:

Article number 87

Identification Number:

10.1186/1752-0509-3-87

Status:

Peer Reviewed

Publication Status:

Published

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.