NetSciReg'15 - Network Models in Cellular Regulation
June 1, 2015 - Zaragoza, Spain

 NetSciReg'15 Flyer 
 Important Dates 
 Call for Contributed Talks 
 NetSci 2015 

Time: 1:10 - 1:30 PM

Type: Contributed presentation

Affiliation: Computer Laboratory, University of Cambridge


In the last two decades the emergence of high-throughput technologies has made available huge amounts of high-dimensional multi-omic data sets, revealing the multifaceted nature of most biological processes in living organisms [1]. Despite this abundance of data, our comprehension of structural patterns, functional prin- ciples, and systemic behaviours in complex systems is still approximate. A clear example of this gap between richness in data and poverty in knowledge is the difficulty in uncovering the relational structure between molecules within a living cell [2]. Genes, for instance, interact in a complex web of relations that or- chestrate various functions in response to both endogenous and exogenous stimuli. Traditional methods to reverse-engineering gene networks from measured expres- sion data include graphical Gaussian modeling (GGM) [3], Bayesian networks [4], relevance networks [5], and Pearson correlation networks [6], just to name a few. In this paper we propose a new approach to infer and study gene networks, com- bining methylation and gene expression data in a multilayer network [7, 8]. In- stead of using standard co-expression measures, such as Pearson's correlation co- efficient, mutual information, or Spearman's rank correlation coefficient, we build a multilayer network (Figure 1) computing a pairwise similarity between genes within each layer and between the layers by means of a Gaussian kernel. In par- ticular, we associate to each gene a feature vector consisting of three elements: the mean value of its expression/methylation level for healthy subjects, the mean value for patients, and a normalized value resulting from the t-test for the two populations (healthy subjects and patients). Our formulation implicitly embodies a plurality of relationships and functional roles between genes, making it possible to elucidate the dynamical mechanism of aggregation and disruption of connectivity structures across multiple omic layers. We analyze the role played by methylation in regulating gene expression for ten inflammatory diseases by considering traditional descriptive measures at both lo- cal and global scale and providing a Bayesian nonparametric generative model based to investigate the formation of mesoscopic structures.

Pan-Jun Kim - Graphical Abstract

References [1] A. R. Joyce and B. O. Palsson, The model organism as a system: integrating 'omics' data sets, Nature Reviews Molecular Cell Biology, vol. 7, pp. 198-210, Mar 2006.
[2] A. L. Barabasi and Z. N. Oltvai, Network biology: Understanding the cell's functional organization, Nature Genetics, vol. 5, pp. 101-114, 2004.
[3] A. Wille, P. Zimmermann, E. Vranova, A. Furholz, O. Laule, S. Bleuler, L. Hennig, A. Prelic, P. von Rohr, L. Thiele, E. Zitzler, W. Gruissem, and P. Buhlmann, Sparse graphical gaussian modeling of the isoprenoid gene network in arabidopsis thaliana, Genome Biology, vol. 5, no. 11, pp. R92+, 2004.
[4] N. Friedman, M. Linial, I. Nachman, and D. Pe'er, Using bayesian networks to analyze expression data, Journal of computational biology, vol. 7, pp. 601-620, Aug 2000.
[5] A. J. Butte and I. S. Kohane, Unsupervised knowledge discovery in medical databases using relevance networks., AMIA Annual Symposium Proceedings Archive, pp. 711-715, 1999.
[6] B. Zhang and S. Horvath, A General Framework for Weighted Gene Co-Expression Network Analysis, Statistical Applications in Genetics and Molecular Biology, vol. 4, no. 1, pp. Article 17+, 2005.
[7] S. Boccaletti, G. Bianconi, R. Criado, C. del Genio, J. Gomez-Gardenes, M. Romance, I. Sendina-Nadal, Z. Wang, and M. Zanin, The structure and dynamics of multilayer networks, Physics Reports, vol. 544, no. 1, pp. 1-122, 2014.
[8] M. Kivela, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno, and M. A. Porter, Multilayer networks, Journal of Complex Networks, vol. 2, pp. 203-271, Jul 2014.