The Library

Beyond element-wise interactions: identifying complex interactions in biological processes

Tools

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

Preview

PDF
WRAP_Kendrick_Biological_processes.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (880Kb)

Official URL: http://dx.doi.org/10.1371/journal.pone.0006899

Abstract

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
ISSN:	1932-6203
Date:	23 September 2009
Volume:	Vol.4
Number:	No.9
Page Range:	e6899
Identification Number:	10.1371/journal.pone.0006899
Status:	Peer Reviewed
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)
References:	1. Eichler M (2006) Graphical modelling of dynamic relationships in multivariate time series. In: Schelter B, Winterhalder M, Timmer J, eds. Handbook of Time Series Analysis, Wiley-VCH Verlage. pp 335–372. 2. Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37: 424–438. 3. Granger C (1980) Testing for causality a personal viewpoint. Journal of Economic Dynamics and Control 2: 329–352. 4. Pearl J (2000) Causality: Models, Reasoning, and Inference. Cambridge University Press. 5. Goure´vitch B, Bouquin-Jeanne`s R, Faucon G (2006) Linear and nonlinear causality between signals: methods, examples and neurophysiological applications. Biological Cybernetics 95: 349–369. 6. Wu J, Liu X, Feng J (2008) Detecting causality between different frequencies. Journal of Neuroscience Methods 167: 367–375. 7. Wiener N (1956) The theory of prediction. In: Beckenbach EF, ed. Modern mathematics for engineers, McGraw-Hill, New York, chapter 8. 8. Chen Y, Bressler SL, Ding M (2006) Frequency decomposition of conditional granger causality and application to multivariate neural field potential data. Journal of Neuroscience Methods 150: 228–237. 9. Ding M, Chen Y, Bressler SL (2006) Granger causality: Basic theory and application to neuroscience. In: Schelter B, Winterhalder M, Timmer J, eds. Handbook of Time Series Analysis: Recent Theoretical Developments and Applications, Wiley-VCH, chapter 17. 10. Geweke J (1982) Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77: 304– 313. 11. Geweke JF (1984) Measures of conditional linear dependence and feedback between time series. Journal of the American Statistical Association 79: 907–915. 12. Guo S, Wu J, Ding M, Feng J (2008) Uncovering interactions in the frequency domain. PLoS Comput Biol 4: e1000087+. 13. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, et al. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9: 3273–3297. 14. Wang RS, Wang Y, Zhang XS, Chen L (2007) Inferring transcriptional regulatory networks from high-throughput data. Bioinformatics. 15. Mewes HW, Frishman D, Gu¨ldener U, Mannhaupt G, Mayer K, et al. (2002) Mips: a database for genomes and protein sequences. Nucleic Acids Res 30: 31–34. 16. Teixeira MC, Monteiro P, Jain P, Tenreiro S, Fernandes AR, et al. (2006) The YEASTRACT database: a tool for the analysis of transcription regulatory associations in saccharomyces cerevisiae. Nucleic Acids Res 34. 17. David O, Guillemain I, Saillet S, Reyt S, Deransart C, et al. (2008) Identifying neural drivers with functional mri: An electrophysiological validation. PLoS Biology 6: e315+. 18. Tate AJ, Fischer H, Leigh AE, Kendrick KM (2006) Behavioural and neurophysiological evidence for face identity and face emotion processing in animals. Philos Trans R Soc Lond B Biol Sci 361: 2155–2172. 19. Peirce J, Leigh AE, Kendrick KM (2000) Configurational coding, familiarity and the right hemisphere advantage for face recognition in sheep. Neuropsychologia 38: 475–483. 20. Kosslyn SM, Gazzaniga MS, Galaburda AM, Rabin C (1998) Hemispheric specialization. In: Zigmond MJ, Bloom FE, Landis SC, Roberts JL, Squire LR, eds. Fundamental Neuroscience, Academic Press Inc, chapter 58. pp 1521–1542. 21. Horn RA, Johnson CR (1990) Matrix Analysis. Cambridge University Press. 22. Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, Shojatalab M, et al. (2008) Arrayexpress update–from an archive of functional genomics experiments to the atlas of gene expression. Nucl Acids Res. gkn889+. 23. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, et al. (2007) NCBI GEO: mining tens of millions of expression profiles database and tools update. Nucleic Acids Research 35: D760–D765. 24. Fletcher M, Liang B, Smith L, Knowles A, Jackson T, et al. (2008) Neural network based pattern matching and spike detection tools and services — in the CARMEN neuroinformatics project. Neural Networks 21: 1076–1084. 25. Kahvejian A, Quackenbush J, Thompson JF (2008) What would you do if you could sequence everything? Nat Biotech 26: 1125–1133. 26. Shendure J (2008) The beginning of the end for microarrays? Nat Meth 5: 585–587. 27. Friston K (2009) Causal modelling and brain connectivity in functional magnetic resonance imaging. PLoS Biology 7: e33+. 28. Needham CJ, Bradford JR, Bulpitt AJ, Westhead DR (2007) A primer on learning in bayesian networks for computational biology. PLoS Computational Biology 3: e129+. 29. Yu J, Smith AV, Wang PP, Hartemink AJ (2004) Advances to bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20: 3594+. 30. Zou C, Feng J (2009) Granger causality vs. dynamic bayesian network inference: a comparative study. BMC Bioinformatics 10: 122+. 31. Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP (2005) Causal proteinsignaling networks derived from multiparameter single-cell data. Science 308: 523–529. 32. Mukherjee S, Speed TP (2008) Network inference using informative priors. Proceedings of the National Academy of Sciences 105: 14313–14318. 33. Aebersold R, Hood LE, Watts JD (2000) Equipping scientists for the new biology. Nat Biotech 18: 359. 34. Klamt S, Haus UU, Theis F (2009) Hypergraphs and cellular networks. PLoS Comput Biol 5: e1000385+. 35. Royer L, Reimann M, Andreopoulos B, Schroeder M (2008) Unraveling protein networks with power graph analysis. PLoS Comput Biol 4: e1000108+.
URI:	http://wrap.warwick.ac.uk/id/eprint/2168