Hostname: page-component-8448b6f56d-cfpbc Total loading time: 0 Render date: 2024-04-18T10:06:47.571Z Has data issue: false hasContentIssue false
  • English
  • Français

Networks' characteristics are important for systems biology

Published online by Cambridge University Press:  03 September 2014

ANDREW K. RIDER ,
TIJANA MILENKOVIĆ ,
GEOFFREY H. SIWO ,
RICHARD S. PINAPATI ,
SCOTT J. EMRICH ,
MICHAEL T. FERDIG  and
NITESH V. CHAWLA
ANDREW K. RIDER
Affiliation:
Department of Computer Science and Engineering, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
TIJANA MILENKOVIĆ
Affiliation:
Department of Computer Science and Engineering, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
GEOFFREY H. SIWO
Affiliation:
Department of Biological Sciences, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
RICHARD S. PINAPATI
Affiliation:
Department of Biological Sciences, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
SCOTT J. EMRICH
Affiliation:
Department of Computer Science and Engineering, ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA
MICHAEL T. FERDIG
Affiliation:
Department of Biological Sciences, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
NITESH V. CHAWLA
Affiliation:
Department of Computer Science and Engineering, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA (e-mail: nchawla@nd.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

A fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations.

Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology.

Keywords

systems biologynetwork inference
Type
Research Article
Information
Network Science , Volume 2 , Issue 2 , August 2014 , pp. 139 - 161
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., . . . Sherlock, G. (2000). Gene Ontology: Tool for the unification of biology. Nature Genetics, 25 (1), 25–29.Google Scholar
Barabasi, A.-L., & Oltvai, Z. N. (2004). Network biology: Understanding the cell's functional organization. Nature Reviews Genetics, 5 (2), 101–113.Google Scholar
Brem, R. B., & Kruglyak, L. (2005). The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proceedings of the National Academy of Sciences of the United States of America, 102 (5), 1572–1577.Google Scholar
Butte, A. J., & Kohane, I. S. (2000). Mutual information relevance networks: Functional genomic clustering using pairwise entropy measurements. In Altman, R., Dunker, K., Hunter, L., Lauderdale, K., & Klein, T. (Eds.), Pacific symposium for biocomputing, Vol. 5 (pp. 418–429). Hawaii.Google Scholar
Carlson, M., Zhang, B., Fang, Z., Mischel, P., Horvath, S., & Nelson, S. (2006). Gene connectivity, function, and sequence conservation: Predictions from modular yeast co-expression networks. BMC Genomics, 7 (1), 40.Google Scholar
Christie, K. R., Hong, E. L., & Cherry, J. M. (2009). Functional annotations for the Saccharomyces cerevisiae genome: The knowns and the known unknowns. Trends in Microbiology, 17 (7), 286–294.CrossRefGoogle ScholarPubMed
Datta, S., & Datta, S. (2003). Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics, 19 (4), 459–466.CrossRefGoogle ScholarPubMed
Demsar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.Google Scholar
De Smet, R., & Marchal, K. (2010). Advantages and limitations of current network inference methods. Nature Reviews Microbiology, 8 (10), 717–729.Google Scholar
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America, 95 (25), 14863–14868.Google Scholar
Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., . . . Gardner, T. S. (2007). Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology, 5 (1), e8.CrossRefGoogle ScholarPubMed
Feizi, S., Marbach, D., Medard, M., & Kellis, M. (2013). Network deconvolution as a general method to distinguish direct dependencies in networks. Nature Biotechnology, 31 (8), 726–733.Google Scholar
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 75–174.Google Scholar
Freeman, L. C. (1977). A set of measures of centrality. Sociometry, 40 (1), 35–41.Google Scholar
Grigoriev, A. (2001). A relationship between gene expression and protein interactions on the proteome scale: Analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic Acids Research, 29 (17), 3513–3519.CrossRefGoogle ScholarPubMed
Hanisch, D., Zien, A., Zimmer, R., & Lengauer, T. (2002). Co-clustering of biological networks and gene expression data. Bioinformatics, 18 (Suppl. 1), S145–S154.CrossRefGoogle ScholarPubMed
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-Means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28 (1), 100–108.Google Scholar
Ho, H., Milenković, T., Memišević, V., Aruri, J., Pržulj, N., & Ganesan, A. (2010). Protein interaction network uncovers melanogenesis regulatory network components within functional genomics datasets. BMC Systems Biology, 4 (84).Google Scholar
Hughes, T. R., Marton, M. J., Jones, A. R., Roberts, C. J., Stoughton, R., Armour, C. D., . . . Friend, S. H. (2000). Functional discovery via a compendium of expression profiles. Cell, 102 (1), 109–126.CrossRefGoogle Scholar
Ivliev, A. E., AC't Hoen, P., & Sergeeva, M. G. (2010). Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma. Cancer Research, 70 (24), 10060–10070.CrossRefGoogle ScholarPubMed
Jansen, R. (2001). Genetical genomics: The added value from segregation. Trends in Genetics, 17 (7), 388–391.Google Scholar
Kok, S., & Domingos, P. (2005). Learning the structure of Markov logic networks. In De Raedt, & Wrobel, S. (Eds.), Proceedings of the 22nd International Conference on Machine Learning (pp. 441–448). Bonn, Germany: ACM.Google Scholar
Landgrebe, T. C. W., Paclik, P., Duin, R. P. W., & Bradley, A. P. (2006). Precision-recall operating characteristic (P-ROC) curves in imprecise environments. In Tang, Y. Y., Wang, S. P., Lorette, G., Yeung, D. S., & Yan, H. (Eds.), Proceedings of the 18th International Conference on Pattern Recognition, Vol. 4, pp. 123–127. Hong Kong.Google Scholar
Luce, R. D., & Perry, A. D. (1949). A method of matrix analysis of group structure. Psychometrika, 14 (2), 95–116.Google Scholar
Marbach, D., Prill, R. J., Schaffter, T., Mattiussi, C., Floreano, D., & Stolovitzky, G. (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 (14), 6286–6291.Google Scholar
Margolin, A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R., & Califano, A. (2006). ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinformatics, 7 (Suppl. 1), S7.Google Scholar
Markowetz, F., & Spang, R. (2007). Inferring cellular networks – A review. BMC Bioinformatics, 8 (Suppl. 6).Google Scholar
Mason, M., Fan, G., Plath, K., Zhou, Q., & Horvath, S. (2009). Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells. BMC Genomics, 10 (1), 327.CrossRefGoogle ScholarPubMed
Meyer, P., Lafitte, F., & Bontempi, G. (2008). MINET: A R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics, 9 (1), 461.Google Scholar
Milenković, T., Lai, J., & Przulj, N. (2008). GraphCrunch: A tool for large network analyses. BMC Bioinformatics, 9 (1), 70.Google Scholar
Milenković, T., Memišević, V., Bonato, A., & Pržulj, N. (2011). Dominating biological networks. PLOS ONE, 6 (8), e23016.CrossRefGoogle ScholarPubMed
Nayak, R. R., Kearns, M., Spielman, R. S., & Cheung, V. G. (2009). Coexpression network based on natural variation in human gene expression reveals gene interactions and functions. Genome Research, 19 (11), 1953–1962.Google Scholar
Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33 (3), 1065–1076.Google Scholar
Pe'er, D., Regev, A., Elidan, G., & Friedman, N. (2001). Inferring subnetworks from perturbed expression profiles. Bioinformatics, 17 (Suppl. 1), S215–S224.Google Scholar
Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (8), 1226–1238.Google Scholar
Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. In Yolum, P., Güngör, T., Gürgen, F., & Özturan, C. (Eds.), Computer and Information Sciences – ISCIS 2005, Vol. 3733, Chap. 31 (pp. 284–293). Berlin/Heidelberg: Springer.Google Scholar
Rider, A. K., Siwo, G., Emrich, S. J., Ferdig, M. T., & Chawla, N. V. (2014). A supervised learning approach to the ensemble clustering of genes. International Journal of Data Mining and Bioinformatics, 9 (2), 199–219.Google Scholar
Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31 (4), 581–603.Google Scholar
Smith, E. N., & Kruglyak, L. (2008). Gene-environment interaction in yeast gene expression. PLoS Biology, 6 (4), e83.Google Scholar
Solava, R. W., Michaels, R. P., & Milenković, T. (2012). Graphlet-based edge clustering reveals pathogen-interacting proteins. Bioinformatics, 18 (28), i480–i486. Also, in Proceedings of the 11th European Conference on Computational Biology (ECCB), Basel, Switzerland, September 9–12, 2012 (acceptance rate: 14%).Google Scholar
Steuer, R., Kurths, J., Daub, C. O., Weise, J., & Selbig, J. (2002). The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics, 18 (Suppl. 2), S231–S240.Google Scholar
Ucar, D., Neuhaus, I., Ross-MacDonald, P., Tilford, C., Parthasarathy, S., Siemers, N., & Ji, R. R. (2007). Construction of a reference gene association network from multiple profiling data: application to data analysis. Bioinformatics, 23 (20), 2716–2724.Google Scholar
van de Vijver, M. J., He, Y. D., van't Veer, L. J., Dai, H., Hart, A. A., Voskuil, D. W., . . . Bernards, R. (2002). A gene-expression signature as a predictor of survival in breast cancer. New England Journal of Medicine, 347 (25), 1999–2009.Google Scholar
van Noort, V., Snel, B., & Huynen, M. A. (2004). The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model. EMBO Reports, 5 (3), 280–284.Google Scholar
van't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A., Mao, M., . . . Friend, S. H. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415 (6871), 530–536.CrossRefGoogle ScholarPubMed
Wittkop, T., Baumbach, J., Lobo, F. P., & Rahmann, S. (2007). Large scale clustering of protein sequences with force-a layout based heuristic for weighted cluster editing. BMC Bioinformatics, 8 (1), 396.CrossRefGoogle ScholarPubMed
Zhou, X., Kao, M.-C. C., & Hung, W. (2002). Transitive functional annotation by shortest-path analysis of gene expression data. Proceedings of the National Academy of Sciences of the United States of America, 99 (20), 12783–12788.Google Scholar
Zhu, J., Zhang, B., Smith, E. N., Drees, B., Brem, R. B., Kruglyak, L., Bumgarner, R. E., & Schadt, E. E. (2008). Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genetics, 40 (7), 854–861.CrossRefGoogle ScholarPubMed
Supplementary material: PDF

Rider Supplementary Material

Figures and Tables

Download Rider Supplementary Material(PDF)
PDF 528.4 KB