Beyond element-wise interactions: identifying complex interactions in biological processes
Ladroue, Christophe, Guo, Shuixia, Kendrick, Keith M. and Feng, Jianfeng. (2009) Beyond element-wise interactions: identifying complex interactions in biological processes. PLoS One, Vol.4 (No.9). e6899. ISSN 1932-6203
WRAP_Kendrick_Biological_processes.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Official URL: http://dx.doi.org/10.1371/journal.pone.0006899
Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations.
Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction.
Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem.
|Item Type:||Journal Article|
|Subjects:||Q Science > QH Natural history > QH301 Biology|
|Divisions:||Faculty of Science > Centre for Scientific Computing
Faculty of Science > Computer Science
|Library of Congress Subject Headings (LCSH):||Saccharomyces cerevisiae, Biological systems -- Research, Causality (Physics) -- Research, Systems biology -- Research|
|Journal or Publication Title:||PLoS One|
|Publisher:||Public Library of Science|
|Official Date:||23 September 2009|
|Access rights to Published version:||Open Access|
|Funder:||Engineering and Physical Sciences Research Council (EPSRC), Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC), Hunan Shi fan da xue [Hunan Normal University] (HNU)|
|Grant number:||EP/E002331/1 (EPSRC), #10901049 (NSFC)|
1. Eichler M (2006) Graphical modelling of dynamic relationships in multivariate
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