Explaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological systems yield a modular organization. Most modular studies focus on network structure and static properties, ignoring that gene regulation is largely driven by stimulus-response behavior. Expression time series are key to gain insight into dynamics, but have been insufficiently explored by current methods, which often (1) apply generic algorithms unsuited for expression analysis over time, due to inability to maintain the chronology of events or incorporate time dependency; (2) ignore local patterns, abundant in most interesting cases of transcriptional activity; (3) neglect physical binding or lack automatic association of regulators, focusing mainly on expression patterns; or (4) limit the discovery to a predefined number of modules. We propose Regulatory Snapshots, an integrative mining approach to identify regulatory modules over time by combining transcriptional control with response, while overcoming the above challenges. Temporal biclustering is first used to reveal transcriptional modules composed of genes showing coherent expression profiles over time. Personalized ranking is then applied to prioritize prominent regulators targeting the modules at each time point using a network of documented regulatory associations and the expression data. Custom graphics are finally depicted to expose the regulatory activity in a module at consecutive time points (snapshots). Regulatory Snapshots successfully unraveled modules underlying yeast response to heat shock and human epithelial-to-mesenchymal transition, based on regulations documented in the YEASTRACT and JASPAR databases, respectively, and available expression data. Regulatory players involved in functionally enriched processes related to these biological events were identified. Ranking scores further suggested ability to discern the primary role of a gene (target or regulator). Prototype is available at: http://kdbio.inesc-id.pt/software/regula？torysnapshots.
References
[1]
Kundaje A, Xin X, Lan C, Lianoglou S, Zhou M, et al. (2008) A predictive model of the oxygen and heme regulatory network in yeast. PLoS Computational Biology 4: e1000224.
[2]
Hecker M, Lambeck S, Toepfer S, van Someren E, Guthke R (2009) Gene regulatory network inference: data integration in dynamic models-a review. Bio Systems 96: 86–103.
[3]
Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, et al. (2010) Revealing strengths and weaknesses of methods for gene network inference. Proceedings of the National Academy of Sciences of the United States of America 107: 6286–6291.
[4]
Bar-Joseph Z, Gerber GK, Lee TI, Rinaldi NJ, Yoo JY, et al. (2003) Computational discovery of gene modules and regulatory networks. Nature Biotechnology 21: 1337–1342.
[5]
Alon U (2007) Network motifs: theory and experimental approaches. Nature reviews Genetics 8: 450–461.
[6]
Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402: C47–C52.
[7]
Barabási AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nature Reviews Genetics 12: 56–68.
[8]
Segal E, Pe’er D, Regev A, Koller D, Friedman N (2005) Learning module networks. Journal of Machine Learning Research 6: 557–588.
[9]
Novershtern N, Regev A, Friedman N (2011) Physical Module Networks: an integrative approach for reconstructing transcription regulation. Bioinformatics 27: i177–i185.
[10]
Lemmens K, Dhollander T, De Bie T, Monsieurs P, Engelen K, et al. (2006) Inferring transcriptional modules from ChIP-chip, motif and microarray data. Genome Biology 7: R37.
[11]
Tanay A, Sharan R, Kupiec M, Shamir R (2004) Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proceedings of the National Academy of Sciences of the United States of America 101: 2981–2986.
[12]
Zhang Y, Xuan J, de Los Reyes BG, Clarke R, Ressom HW (2008) Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data. BMC Bioinformatics 9: 203.
[13]
Yeang C, Jaakkola T (2003) Time series analysis of gene expression and location data. 3rd IEEE Symposium on BioInformatics and BioEngineering (BIBE’03). IEEE, volume 14, p. 755 p.
[14]
Kasabov NK, Chan ZS, Jain V, Sidorov I, Dimitrov DS (2004) Gene regulatory network discovery from time-series gene expression data a computational intelligence approach. Neural Information Processing, Springer, volume 3316 of Lecture Notes in Computer Science. pp. 1344–1353.
[15]
Kundaje A, Middendorf M, Gao F, Wiggins C, Leslie C (2005) Combining sequence and time series expression data to learn transcriptional modules. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2: 194–202.
[16]
Wu WS, Li WH (2008) Identifying gene regulatory modules of heat shock response in yeast. BMC Genomics 9: 439.
[17]
Madeira SC, Teixeira MC, S-Correia I, Oliveira AL (2010) Identification of regulatory modules in time series gene expression data using a linear time biclustering algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformatics 7: 153–165.
[18]
Androulakis IP, Yang E, Almon RR (2007) Analysis of time-series gene expression data: methods, challenges, and opportunities. Annual Review of Biomedical Engineering 9: 205–228.
[19]
Zhang Y, Xuan J, de los Reyes BG, Clarke R, Ressom HW (2009) Reverse engineering module networks by PSO-RNN hybrid modeling. BMC Genomics 10: S15.
Ramakrishnan N, Tadepalli S, Watson LT, Helm RF, Antoniotti M, et al. (2010) Reverse engineering dynamic temporal models of biological processes and their relationships. Proceedings of the National Academy of Sciences of the United States of America 107: 12511–12516.
[22]
Li S, Wu L, Zhang Z (2006) Constructing biological networks through combined literature mining and microarray analysis: a LMMA approach. Bioinformatics 22: 2143–2150.
[23]
Nam H, Lee K, Lee D (2009) Identification of temporal association rules from time-series microarray data sets. BMC Bioinformatics 10: S6.
[24]
Madeira SC, Oliveira AL (2004) Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics 1: 24–45.
[25]
Gon_calves JP, Francisco AP, Mira NP, Teixeira MC, Sá-Correia I, et al. (2011) TFRank: network-based prioritization of regulatory associations underlying transcriptional responses. Bioinformatics 27: 3149–3157.
[26]
Gasch A, Spellman P, Kao C, Carmel-Harel O, Eisen M, et al. (2000) Genomic expression program in the response of yeast cells to environmental changes. Molecular Biology of the Cell 11: 4241–4257.
[27]
Keshamouni VG, Jagtap P, Michailidis G, Strahler JR, Kuick R, et al. (2009) Temporal quantitative proteomics by iTRAQ 2D-LC-MS/MS and corresponding mRNA expression analysis identify post-transcriptional modulation of actin-cytoskeleton regulators during TGF-beta-Induced epithelial-mesenchymal transition. Journal of Proteome Research 8: 35–47.
[28]
Ji L, Tan KL (2005) Identifying time-lagged gene clusters using gene expression data. Bioinformatics 21: 509–516.
[29]
Gusfield D (1997) Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Computer Science and Computational Biology Series. Cambridge University Press.
[30]
Brin S, Page L (1998) The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30: 107–117.
[31]
Andersen R, Lang KJ (2006) Communities from seed sets. In: Proceedings of the 15th international conference on World Wide Web (WWW’06). New York, NY, USA: ACM, pp 223–232:
[32]
Chung F (2007) The heat kernel as the pagerank of a graph. Proceedings of the National Academy of Sciences of the United States of America 104: 19735–19740.
[33]
Chung F, Yau S (1999) Coverings, heat kernels and spanning trees. Electronic Journal of Combinatorics 6: R12.
[34]
Yang H, King I, Lyu MR (2007) Di_usionrank: a possible penicillin for web spamming. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ‘07). New York, NY, USA: ACM, pp 431–438:
[35]
Costa I, Schnhuth A, Schliep A (2005) The Graphical Query Language: a tool for analysis of gene expression time-courses. Bioinformatics 21: 2544–2545.
[36]
Ernst J, Bar-Joseph Z (2006) STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7: 191.
[37]
Gon_calves JP, Madeira S, Oliveira A (2009) BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data. BMC Research Notes 2: 124.
[38]
Aires RS, Carri？o JA, Francisco AP, Madeira SC (2011) Baiacu: A Tool for the Visual Analysis of the Saccharomyces Cerevisiae Regulatory Network. In: 5th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB 2011). Springer, pp 49–56:
[39]
Cherry JM, Adler C, Ball C, Chervitz SA, Dwight SS, et al. (1998) SGD: Saccharomyces Genome Database. Nucleic Acids Research 26: 73–79.
[40]
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 Research 34: D446–D451.
[41]
Coleman CB, Allen PL, Valles JM, Hammond TG (2007) Transcriptional regulation of changes in growth, cell cycle, and gene expression of Saccharomyces cerevisiae due to changes in buoyancy. Biotechnology and Bioengineering 100: 334–343.
[42]
Davidson JF, Whyte B, Bissinger PH, Schiestl RH (1996) Oxidative stress is involved in heat-induced cell death in Saccharomyces cerevisiae. Proceedings of the National Academy of Sciences of the United States of America 93: 5116–5121.
[43]
Hahn J, Neef D, Thiele D (2006) A stress regulatory network for co-ordinated activation of pro-teasome expression mediated by yeast heat shock transcription factor. Molecular Microbiology 60: 240–251.
[44]
Hahn JS, Thiele DJ (2004) Activation of the Saccharomyces cerevisiae Heat Shock Transcription Factor Under Glucose Starvation Conditions by Snf1 Protein Kinase. Journal of Biological Chemistry 279: 5169–5176.
[45]
Zambelli F, Pesole G, Pavesi G (2009) Pscan: finding over-represented transcription factor binding site motifs in sequences from co-regulated or co-expressed genes. Nucleic Acids Research 37: W247–52.
[46]
Beckstette M, Homann R, Giegerich R, Kurtz S (2006) Fast index based algorithms and software for matching position specific scoring matrices. BMC Bioinformatics 7: 389.
[47]
Cannito S, Novo E, di Bonzo LV, Busletta C, Colombatto S, et al. (2010) Epithelial-mesenchymal transition: from molecular mechanisms, redox regulation to implications in human health and disease. Antioxidants & Redox Signaling 12: 1383–430.
[48]
Garber K (2008) Epithelial-to-mesenchymal transition is important to metastasis, but questions remain. Journal of the National Cancer Institute 100: 232–3, 239:
[49]
Xue Y, Li L, Zhang D, Wu K, Chen Y, et al. (2011) Twisted Epithelial-to-Mesenchymal Transition Promotes Progression of Surviving Bladder Cancer T24 Cells with hTERT-Dysfunction. PloS One 6: e27748.
[50]
Hiyama K, editor. (2009) Telomeres and Telomerase in Cancer. Humana Press.
[51]
Massagué J (2008) TGFbeta in Cancer. Cell 134: 215–30.
[52]
Van Dyck F, Declercq J, Braem CV, Van de Ven WJ (2007) PLAG1, the prototype of the PLAG gene family: versatility in tumour development (review). International Journal of Oncology 30: 765–74.
[53]
Mudduluru G, Vajkoczy P, Allgayer H (2010) Myeloid zinc finger 1 induces migration, invasion, and in vivo metastasis through Axl gene expression in solid cancer. Molecular Cancer Research 8: 159–69.
[54]
Wu Z, Zheng S, Li Z, Tan J, Yu Q (2011) E2F1 suppresses Wnt/β-catenin activity through transactivation of β-catenin interacting protein ICAT. Oncogene 1: 3979–3984.
[55]
Ksienzyk A, Neumann B, Nandakumar R, Finsterbusch K, Grashoff M, et al. (2011) IRF-1 expression is essential for natural killer cells to suppress metastasis. Cancer Research.
[56]
Safran M, Dalah I, Alexander J, Rosen N, Iny Stein T, et al. (2010) GeneCards Version 3: the human gene integrator. Database: the journal of biological databases and curation 2010: baq020. baq020 p.
[57]
Kreizenbeck GM, Berger AJ, Subtil A, Rimm DL, Gould Rothberg BE (2008) Prognostic significance of cadherin-based adhesion molecules in cutaneous malignant melanoma. Cancer Epidemiology, Biomarkers & Prevention 17: 949–58.
[58]
Bozdag S, Li A, Wuchty S, Fine Ha (2010) FastMEDUSA: a parallelized tool to infer gene regulatory networks. Bioinformatics 26: 1792–1793.
[59]
Gon？alves JP, Madeira SC, Moreau Y (2010) Network-based disease candidate gene prioritization: towards global diffusion in heterogeneous association networks. Workshop on Dynamic Networks and Knowledge Discovery (DyNaK 2010). Barcelona, Spain.
[60]
Brynildsen MP, Tran LM, Liao JC (2006) Versability and connectivity efficiency of bipartite transcriptional networks. Biophysical Journal 91: 2749–2759.
