Summarizing cellular responses as biological process networks

BMC Systems Biology

T M Murali¹  Padmavathy Rajagopalan²  Christopher D Lasher³ 

[1] Department of Computer Science, Virginia Tech, Blacksburg, VA 24061 USA;ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, VA 24061 USA;Genetics, Bioinformatics, and Computational Biology Ph.D. Program, Virginia Tech, Blacksburg, VA 24061 USA

关键词: Markov chain Monte Carlo;    Data integration;    Networks of biological processes;    Gene expression data;    Molecular interaction networks;   

Others  :  1142471
DOI  :  10.1186/1752-0509-7-68

 received in 2012-09-05, accepted in 2013-06-26,  发布年份 2013

【 摘 要 】

Background

Microarray experiments can simultaneously identify thousands of genes that show significant perturbation in expression between two experimental conditions. Response networks, computed through the integration of gene interaction networks with expression perturbation data, may themselves contain tens of thousands of interactions. Gene set enrichment has become standard for summarizing the results of these analyses in terms functionally coherent collections of genes such as biological processes. However, even these methods can yield hundreds of enriched functions that may overlap considerably.

Results

We describe a new technique called Markov chain Monte Carlo Biological Process Networks (MCMC-BPN) capable of reporting a highly non-redundant set of links between processes that describe the molecular interactions that are perturbed under a specific biological context. Each link in the BPN represents the perturbed interactions that serve as the interfaces between the two processes connected by the link.

We apply MCMC-BPN to publicly available liver-related datasets to demonstrate that the networks formed by the most probable inter-process links reported by MCMC-BPN show high relevance to each biological condition. We show that MCMC-BPN’s ability to discern the few key links from in a very large solution space by comparing results from two other methods for detecting inter-process links.

Conclusions

MCMC-BPN is successful in using few inter-process links to explain as many of the perturbed gene-gene interactions as possible. Thereby, BPNs summarize the important biological trends within a response network by reporting a digestible number of inter-process links that can be explored in greater detail.

【 授权许可】

   
2013 Lasher et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

【 参考文献 】

• [1]Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Holko M, Ayanbule O, Yefanov A, Soboleva A: NCBI GEO: archive for functional genomics data sets–10 years on. Nucleic Acids Res 2010, 39(Database):D1005-D1010.
• [2]Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, Shojatalab M, Abeygunawardena N, Berube H, Dylag M, Emam I, Farne A, Holloway E, Lukk M, Malone J, Mani R, Pilicheva E, Rayner TF, Rezwan F, Sharma A, Williams E, Bradley XZ, Adamusiak T, Brandizi M, Burdett T, Coulson R, Krestyaninova M, Kurnosov P, Maguire E, Neogi SG, Rocca-Serra P, Sansone S, et al.: ArrayExpress update–from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res 2009, 37(Database):D868-D872.
• [3]Tarcea VG, Weymouth T, Ade A, Bookvich A, Gao J, Mahavisno V, Wright Z, Chapman A, Jayapandian M, Ozgur A, Tian Y, Cavalcoli J, Mirel B, Patel J, Radev D, Athey B, States D, Jagadish HV: Michigan molecular interactions r2: from interacting proteins to pathways. Nucleic Acids Res 2009, 37(Database):D642-D646.
• [4]Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, Doerks T, Julien P, Roth A, Simonovic M, Bork P, von Mering C: STRING 8–a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 2009, 37(suppl_1):D412-416.
• [5]Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Nat Acad Sci 2001, 98(9):5116-5121.
• [6]Smyth GK: Limma: linear models for microarray data. In Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Edited by Irizarry R, Gentlemen R, Carey V, Dudoit S, Irizarry R, Huber W. New York: Springer; 2005:397-420.
• [7]Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Nat Acad Sci USA 2005, 102(43):15545-15550.
• [8]Kim S, Volsky D: BMC Bioinformatics. 2005, 6:144. BioMed Central Full Text
• [9]Lu Y, Rosenfeld R, Simon I, Nau GJ, Bar-Joseph Z: A probabilistic generative model for GO enrichment analysis. Nucleic Acids Res 2008, 36(17):e109.
• [10]Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009, 37:1-13.
• [11]Bauer S, Gagneur J, Robinson PN: GOing Bayesian: model-based gene set analysis of genome-scale data. Nucleic Acids Res 2010, 38(11):3523-3532.
• [12]Li Y, Agarwal P, Rajagopalan D: A global pathway crosstalk network. Bioinformatics 2008, 24(12):1442-1447.
• [13]Dotan-Cohen D, Letovsky S, Melkman AA, Kasif S: Biological process linkage networks. PLoS ONE 2009, 4(4):e5313.
• [14]Wang Q, Sun J, Zhou M, Yang H, Li Y, Li X, Lv S, Li X, Li Y: A novel network-based method for measuring the functional relationship between gene sets. Bioinformatics 2011, 27(11):1521-1528.
• [15]Lasher CD, Rajagopalan P, Murali TM: PLoS ONE. 2011, 6:e15247.
• [16]Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E: Equation of state calculations by fast computing machines. J Chem Phys 1953, 21(6):1087.
• [17]Kim Y, Lasher CD, Milford LM, Murali T, Rajagopalan P: A comparative study of genome-wide transcriptional profiles of primary hepatocytes in collagen sandwich and monolayer cultures. Tissue Eng Part C: Methods 2010, 16(6):1449-1460.
• [18]Wurmbach E, Chen Y, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S, Mazzaferro V, Bruix J, Bottinger E, Friedman S, Waxman S, Llovet JM: Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology 2007, 45(4):938-947.
• [19]Marcellin P: Hepatitis B and hepatitis C in 2009. Liver Int 2009, 29:1-8.
• [20]Yang JD, Roberts LR: Hepatocellular carcinoma: a global view. Nat Rev Gastroenterol Hepatol 2010, 7(8):448-458.
• [21]Ruepp A, Waegele B, Lechner M, Brauner B, Fobo G, Frishman G, Montrone C, Mewes H, Dunger-Kaltenbach I: CORUM: the comprehensive resource of mammalian protein complexes–2009. Nucleic Acids Res 2009, 38(Database):D497-D501.
• [22]Kandasamy K, Mohan SS, Raju R, Keerthikumar S, Kumar GSS, Venugopal AK, Telikicherla D, Navarro JD, Mathivanan S, Pecquet C, Gollapudi SK, Tattikota SG, Mohan S, Padhukasahasram H, Subbannayya Y, Goel R, Jacob HKC, Zhong J, Sekhar R, Nanjappa V, Balakrishnan L, Subbaiah R, Ramachandra YL, Rahiman BA, Prasad TSK, Lin J, Houtman JCD, Desiderio S, Renauld J, et al.: NetPath: a public resource of curated signal transduction pathways. Genome Biol 2010, 11:R3. BioMed Central Full Text
• [23]Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH: PID: the Pathway Interaction Database. Nucleic Acids Res 2009, 37(Database):D674-D679.
• [24]Berriz GF, Roth FP: The Synergizer service for translating gene, protein and other biological identifiers. Bioinformatics 2008, 24(19):2272-2273.
• [25]Baron D, Bihouée A, Teusan R, Dubois E, Savagner F, Steenman M, Houlgatte R, Ramstein G: MADGene: retrieval and processing of gene identifier lists for the analysis of heterogeneous microarray datasets. Bioinformatics 2011, 27(5):725-726.
• [26]Arias IM, Boyer JL, Chisari FV, Fausto M, Schachter D, Shafritz DA: The Liver: Biology and Pathobiology. Philadelphia: Lippincott Williams and Wilkins; 2001.
• [27]Andrieu C, De Freitas N, Doucet A, Jordan MI: An introduction to MCMC for machine learning. Mach Learn 2003, 50:5-43.
• [28]Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. Ser B (Methodological) 1995, 57:289-300.