Finding Your Inner Network
Posted March 13, 2010
In a symposium at the Academy on March 14, 2005, three speakers described approaches to "reverse engineering" of genetic expression networks. Reverse engineering suggests looking "under the hood" to determine the "true" nature of the underlying molecular machinery of expression. Each of the speakers, however, took pains not to equate their model networks with the real biological system. Indeed, they used quite different models, and different methods for matching the models with experiments.
Timothy Gardner of Boston University and Nir Friedman of the Hebrew University of Jerusalem described automated techniques for creating model networks from expression data alone. Gardner's team starts with a linear model of the interactions, while Friedman's uses more complex interactions inspired by regulation models. Saeed Tavazoie of Princeton University described a method grounded in known cis regulatory apparatus which involves looking for repeated "motifs" in the DNA sequence near genes.
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Institute for Systems Biology
A nonprofit research institute dedicated to the study and application of systems biology.
Stanford Microarray Database
A microarray research database containing data from experiments by Stanford investigators and collaborators, and tools for exploring and analyzing their results.
Experimental studies of expression
Baugh, L. R., A. A. Hill, D. K. Slonim et al. 2003. Composition and dynamics of the Caenorhabditis elegans early embryonic transcriptome. Development 130: 889-900. Full Text
Gasch, A. P., P. T. Spellman, C.M. Kao et al. 2000. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11: 4241-4257. Full Text
Hill, A. A., C. P. Hunter, B. T. Tsung et al. 2000. Genomic analysis of gene expression in C. elegans. Science 290: 809-812. Full Text
Hughes, T. R., M. J. Marton, A. R. Jones et al. 2000. Functional Discovery via a Compendium of Expression Profiles. Cell 102: 109-126.
Mnaimneh, S., A. P. Davierwala, J. Haynes et al. 2004. Exploration of Essential Gene Functions via Titratable Promoter Alleles. Cell 118: 31-44.
Oliveri, P. & E. H. Davidson. 2004. Gene regulatory network analysis in sea urchin embryos. Methods Cell Biol. 74: 775-794.
Shen-Orr, S. S., R. Milo, S. Mangan & U. Alon. 2002. Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genet. 31: 64-68.
Spellman, P., G. Sherlock, M. Zhang et al. 1998. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9: 3273–3297. Full Text
Wade, J. T., D.B. Hall & K. Struhl. 2004. The transcription factor Ifh1 is a key regulator of yeast ribosomal protein genes. Nature 432: 1054-1058.
Decoding transcription networks in microbes
di Bernardo, D., M. J. Thompson, T. S. Gardner et al. 2005. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 23: 377-383. Full Text (PDF, 567 KB)
Gardner, T. S., D. di Bernardo, D. L.orenz and J. J. Collins. 2003. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301: 102-105. Full Text (PDF, 617 KB)
Gardner, T. S. & J. Faith. 2005. Reverse-engineering transcription control networks. Physics of Life Reviews 2: 65-68. Full Text (PDF, 1.25 MB)
Probabilistic models for identifying regulatory networks
Friedman, N. 2004. Inferring cellular networks using probabilistic graphical models. Science 303: 799-805.
Friedman, N., M. Linial, I. Nachman & D. Pe'er 2000. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7: 601-620. Full Text (PDF, 2.4 MB)
Nachman, I., A. Regev & N. Friedman. 2004. Inferring quantitative models of regulatory networks from expression data. Bioinformatics 20 Suppl. 1:I248-I256. Full Text (PDF, 224 KB)
Pe'er, D., A. Regev, G. Elidan & N. Friedman. 2001. Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 Suppl 1:S215-24. Full Text (PDF, 134 KB)
Segal, E., D. Pe'er & A. Regev. 2003. Learning Module Networks. In Proc. Nineteenth Conf. on Uncertainty in Artificial Intelligence (UAI). Full Text (PDF, 185 KB)
Segal, E., M. Shapira & A. Regev. 2003. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34: 166-176.
Learning biology from network-level observations
Beer, M. A. & S. Tavazoie. 2004. Predicting gene expression from sequence. Cell 117: 185-198.
Tavazoie, S., J. D. Hughes, M. J. Campbell et al. 1999. Systematic determination of genetic network architecture. Nat. Genet. 22: 281-285.
Timothy Gardner, PhD
e-mail | web site | publications
Timothy Gardner is assistant professor and research associate in Boston University's department of biomedical engineering. Through his research, Gardner aims to better understand the systems of genes, proteins, and metabolites underlying cellular function. He and colleagues hope to identify novel treatments against bacterial resistance, and to realize the full potential of microbes for use in areas such as bioremediation and energy production. Toward this end, the laboratory currently develops computational and experimental tools to map and model system-wide properties of gene regulatory networks in microbes.
Gardner received his PhD in biomedical engineering from Boston University in 2000. That same year, he co-founded Cellicon Biotechnologies, a company developing improved antibiotics through the mapping and control of cellular gene circuitry. Gardner joined the faculty of Boston University in 2003, and in 2004 he was selected as one of the world's 100 "top young innovators" by MIT's Technology Review magazine.
Nir Friedman, PhD
The Hebrew University of Jerusalem
e-mail | web site | publications
Nir Friedman is on faculty at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, and currently is visiting associate professor of computer science at Harvard University. Friedman is interested in dealing with uncertainty through the use of principled and computationally-effective approaches. His current research, concentrating on probabilistic models, focuses on inference and learning with Bayesian networks and related representations, and on the application of probabilistic models to understand biological systems and to analyze data from biological sources, such as protein and DNA sequences. His group has examined gene expression data for use in cancer classification, novel gene discovery, pathway reconstruction, and to better understand regulatory circuits.
Friedman received his MSc in mathematics and computer science from the Weizmann institute in 1992, and his PhD in computer science from Stanford University in 1997. Before joining the faculty of the Hebrew University in 1998, he served two years as a postdoctoral scholar at the University of California, Berkeley.
Saeed Tavazoie, MD, PhD
e-mail | web site | publications
Saeed Tavazoie is assistant professor of molecular biology at Princeton University's Lewis-Sigler Institute. Tavazoie is interested in better understanding the structural and dynamic properties of biological networks. He focuses primarily on the intracellular networks which regulate the expression of genes. Current projects in his laboratory include the development of algorithms to infer the structure of networks from genomic data, the development of experimental methods to validate computational predictions of networks, and the scaling of these methods to the size and complexity of metazoan genomes. Tavazoie's long-term goal is to understand how the structural and dynamic properties of networks reflect, and depend on, the physical, chemical, multi-cellular, and ecological contexts in which they have evolved.
Tavazoie received his PhD in 1999 from Harvard University and his MD in 2000 from the Massachusetts Institute of Technology and Harvard Medical School, where he also served a postdoctoral fellowship. He joined the faculty of Princeton University in 2000. In 2002 Tavazoie received a CAREER Award and accompanying five-year research grant from the National Science Foundation.
Don Monroe is a science writer based in Murray Hill, New Jersey. After getting a Ph.D. in physics from M.I.T., he spent more than fifteen years doing research in physics and electronics technology at Bell Labs. He writes on physics, technology, and biology.