eBriefing

The Systems Biology of Bugs

The Systems Biology of Bugs
Reported by
Stephanie B. H. Kelly

Posted January 14, 2011

Overview

A systems biology approach is used by scientists to help them understand important biological systems using tools from a range of disciplines, including physics, mathematics and systems analysis. Systems biologists and computational biologists collaborate on projects that integrate computational analysis, experimental design and data collection. In a symposium called The Systems Biology of Bugs held at The New York Academy of Sciences on November 8, 2010, researchers actively involved in the development and application of systems biology methodologies, including modeling, algorithms and machine-learning techniques, came together to examine bacterial genomes, and to elucidate basic signal transduction mechanisms and the genetic basis of phenotypes in the large and growing population of bacteria of biological, environmental, and clinical importance.

Richard Bonneau from New York University began with an overview of how we learn about and understand regulatory networks from genomics data. Understanding these networks can provide insight into biological interactions fundamental to applications in biology and biomedicine. In particular his talk focused on learning about co-regulated groups, or clusters of genes that are regulated and expressed together under certain conditions, and on learning what particular genes do by finding the regulatory networks in which they operate. Bonneau outlined the typical steps his group and others' take to uncover co-regulated groups of genes and then their regulatory networks: 1) Integrate not just large amounts of data, but also many different types of data, from evolution experiments, expression analysis, and sequencing studies, to name a few sources; 2) try to learn coregulated modules by adapting algorithms like cMonkey; and 3) learn the regulatory network by understanding how regulatory systems change the synthesis rate of biological products.

Repeatedly illustrating the importance of the first step, Bonneau explained two specific approaches researchers in his group have used to accomplish steps two and three. Adapting the cMonkey biclustering algorithm to include comparisons between closely related species, Bonneau's group found that they could determine a more probative bicluster of related genes and thus better infer regulatory networks. Other researchers in the group also improved this network inference process by combining time series data with many other data sources in an Inferelator pipeline to analyze genes with intermediate levels of expression.

Yuhai Tu from IBM Research then discussed the physics of biological information processing, using E. coli chemotaxis, movement based on concentration of environmental nutrients, as an example of memory and computation systems. Using a comparison of physics and biology, he described how biological machines, including "computing machines," evolved to consume energy to carry out desired functions. With conceptual tools from physics he took the audience through a story about how information is processed in biological systems, and specifically how. E. coli cells receive signals, process them and move towards favorable chemical environments.

In order to process these chemical signals and make decisions about where to move, the cells need to have some way of keeping a "memory" of their previous chemical surroundings. To probe this process, Tu worked from observations of the cells' high sensitivity to particular stimuli and tested a physics-based model of E. coli's ability to amplify chemical signals so successfully. His model is based on the Ising model for spin-co-operativity in statistical physics, and it provides a good match for the proximity-induced co-operativity of clustered chemoreceptors in E. coli.

As Tu explained, however, this model cannot account for the cells' ability to maintain such high sensitivity over a large range of different concentrations by adapting to the environment over time. That adaptation, he says, can be successfully accounted for by his group's model of the time it takes the signal to "bounce back" after being over-stimulated. The take away message from a computing perspective is that organisms consume their physical resources (energy) to perform biological functions such as adaptation, computation, thinking, etc. Moreover, analyses like the ones done in Tu's group can help us understand how much energy is consumed to maintain the accuracy of the E. coli cells' memory, under what conditions organisms can maximize performance for tasks like these, and ultimately, where the limits of molecular computing lie.

To conclude the day’s sessions, Saeed Tavazoie from Princeton University discussed another lesson to be learned from E. Coli chemotaxis: the genetic basis of adaptation to extreme environments, i.e. the genetic basis of evolutionary “fitness.” Although a great deal is now known about the genes involved in chemotaxis, that knowledge was accumulated piece-meal and over a long period of time. It is, for any biological function, a difficult task to go from whole genome sequencing to determining what mutations happened in which generation and to understanding how those mutations relate to a fitness landscape.

To speed this process up somewhat, Tavazoie developed a method that involves exposing mutant and wild-type E.coli of different to “extreme” environments, and ultimately determining the pathways of gene expression that contribute to the organisms’ success (or failure) in these environments. Results of this study and others clearly demonstrate the utility of putting organisms in extreme, completely foreign environments in order to understand how both complex regulation mechanisms and the environment of an organism’s evolution affect expression of that organism’s “fundamental phenotypic capacities.” Furthermore, Tavazoie explained that his work can be used to examine behaviors (unlike chemotaxis) where very little is known about their genetic basis and to find genotypes and phenotypes associated with overall “fitness.”

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Presented by:

  • Agilent Technologies

Resources

Richard Bonneau

Avila-Campillo I, Drew K, Lin J, Reiss DJ, Bonneau R. BioNetBuilder: automatic integration of biological networks. Bioinformatics 2007; 23(3):392-393.

Bonneau R, Reiss DJ, Shannon P, et al. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 2006; 7(5):R36.

Bonneau R, Facciotti MT, Reiss DJ, et al. A predictive model for transcriptional control of physiology in a free living cell. Cell 2007; 131(7):1354-1365.

Greenfield A, Madar A, Ostrer H, Bonneau R. DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models. PLoS ONE 2010; 5(10):e13397.

Ihmels J, Bergmann S, Berman J, Barkai N. Comparative gene expression analysis by differential clustering approach: application to the Candida albicans transcription program. PLoS Genet. 2005; 1(3):e39.

Lu Y, Huggins P, Bar-Joseph Z. Cross species analysis of microarray expression data. Bioinformatics 2009; 25(12):1476-1483.

Madar A, Bonneau R. Learning global models of transcriptional regulatory networks from data. Methods Mol. Biol. 2009; 541:181.

Madar A, Greenfield A, Ostrer H, Vanden-Eijnden E, Bonneau R. The Inferelator 2.0: a scalable framework for reconstruction of dynamic regulatory network models. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009; 2009:5448-5451.

Madar A, Greenfield A, Vanden-Eijnden E, Bonneau R. DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator. PLoS ONE 2010; 5(3):e9803.

McCarroll SA, Murphy CT, Zou S, et al. Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nat. Genet. 2004; 36(2):197-204.

Reiss DJ, Baliga NS, Bonneau R. Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks. BMC Bioinformatics 2006; 7:280.

Shah NH, Butterfoss GL, Nguyen K, et al. Oligo(N-aryl glycines): a new twist on structured peptoids. J. Am. Chem. Soc. 2008; 130(49):16622-16632.

Shannon PT, Reiss DJ, Bonneau R, Baliga NS. The Gaggle: an open-source software system for integrating bioinformatics software and data sources. BMC Bioinformatics 2006; 7:176.

Tirosh I, Barkai N. Comparative analysis indicates regulatory neofunctionalization of yeast duplicates. Genome Biol. 2007; 8(4):R50.

Waltman P, Kacmarczyk T, Bate AR, et al. Multi-species integrative biclustering. Genome Biol. 2010; 11(9):R96.

Yuhai Tu

Maddock JR, Shapiro L. Polar location of the chemoreceptor complex in the Escherichia coli cell. Science 1993; 259(5102):1717-1723.

Mello BA, Tu Y. Quantitative modeling of sensitivity in bacterial chemotaxis: the role of coupling among different chemoreceptor species. Proc. Natl. Acad. Sci. USA 2003; 100(14):8223-8228.

Mello BA, Tu Y. An allosteric model for heterogeneous receptor complexes: understanding bacterial chemotaxis responses to multiple stimuli. Proc. Natl. Acad. Sci. USA 2005; 102(48):17354-17359.

Mello BA, Tu Y. Effects of adaptation in maintaining high sensitivity over a wide range of backgrounds for Escherichia coli chemotaxis. Biophys. J. 2007; 92(7):2329-2337.

Shimizu TS, Tu Y, Berg HC. A modular gradient-sensing network for chemotaxis in Escherichia coli revealed by responses to time-varying stimuli. Mol. Syst. Biol. 2010; 6:382.

Sourjik V, Berg HC. Receptor sensitivity in bacterial chemotaxis. Proc. Natl. Acad. Sci. USA 2002; 99(1):123-127.

Sourjik V, Berg HC. Binding of the Escherichia coli response regulator CheY to its target measured in vivo by fluorescence resonance energy transfer. Proc. Natl. Acad. Sci. USA 2002; 99(20):12669-12674.

Tu Y, Shimizu TS, Berg HC. Modeling the chemotactic response of Escherichia coli to time-varying stimuli. Proc. Natl. Acad. Sci. USA 2008; 105(39):14855-14860.

Saeed Tavazoie

Covert MW, Palsson BO. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J. Theor. Biol. 2003; 221(3):309-325.

Covert MW, Schilling CH, Palsson B. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 2001; 213(1):73-88.

Edwards JS, Covert M, Palsson B. Metabolic modelling of microbes: the flux-balance approach. Environ. Microbiol. 2002; 4(3):133-140.

Elena SF, Whittam TS, Winkworth CL, Riley MA, Lenski RE. Genomic divergence of Escherichia coli strains: evidence for horizontal transfer and variation in mutation rates. Int. Microbiol. 2005; 8(4):271-278.

Girgis HS, Liu Y, Ryu WS, Tavazoie S. A comprehensive genetic characterization of bacterial motility. PLoS Genet. 2007; 3(9):1644-1660.

Girgis HS, Hottes AK, Tavazoie S. Genetic architecture of intrinsic antibiotic susceptibility. PLoS ONE 2009; 4(5):e5629.

Joyce AR, Palsson BØ. Predicting gene essentiality using genome-scale in silico models. Methods Mol. Biol. 2008; 416:433-457.

Kohanski MA, Dwyer DJ, Hayete B, Lawrence CA, Collins JJ. A common mechanism of cellular death induced by bactericidal antibiotics. Cell 2007; 130(5):797-810.

Lewis NE, Hixson KK, Conrad TM, et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 2010; 6:390.

Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat. Biotechnol. 2010; 28(3):245-248.

Raghunathan A, Reed J, Shin S, Palsson B, Daefler S. Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction. BMC Syst. Biol. 2009; 3:38.

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Organizers

Andrea Califano, PhD

Columbia University
e-mail | website | publications

Andrea Califano is professor of biomedical informatics at Columbia University, where he leads several cross-campus activities in computational and system biology. Califano is also codirector of the Center for Computational Biology and Bioinformatics, director of the Center for the Multiscale Analysis of Genetic Networks, and associate director for bioinformatics at the Irving Cancer Research Center.

Califano completed his doctoral thesis in physics at the University of Florence and studied the behavior of high-dimensional dynamical systems. From 1986 to 1990, he was on the research staff in the Exploratory Computer Vision Group at the IBM Thomas J. Watson Research Center, where he worked on several algorithms for machine learning, including the interpretation of two- and three-dimensional visual scenes. In 1997 he became the program director of the IBM Computational Biology Center, and in 2000 he cofounded First Genetic Trust, Inc., to pursue translational genomics research and infrastructure related activities in the context of large-scale patient studies with a genetic components.

Aris Economides, PhD

Regeneron Pharmaceuticals
e-mail | publications

Aris N. Economides joined Regeneron Pharmaceuticals Inc in 1992 and he currently holds the position of Sr. Director, leading two groups: Genome Engineering Technologies, and Skeletal Diseases TFA. Economides is a co-inventor of the Cytokine Trap technology that led to the development of the IL-1 trap, a currently approved biologic drug (ARCALYST™). He is also a co-inventor of the VelociGene® technology, which has led to the development of VelocImmune®, a method for the generation of all-human antibodies in mice. More recently, he has been spearheading the development of new methods for the generation of transgenic mice using BAC as transgene vectors, and has also pioneered a new method for generating conditional alleles.

Gustavo Stolovitzky, PhD

IBM Research
e-mail | website | publications

Gustavo Stolovitzky is manager of the Functional Genomics and Systems Biology Group at the IBM Computational Biology Center in IBM Research. The Functional Genomics and Systems Biology group is involved in several projects, including DNA chip analysis and gene expression data mining, the reverse engineering of metabolic and gene regulatory networks, modeling cardiac muscle, describing emergent properties of the myofilament, modeling P53 signaling pathways, and performing massively parallel signature sequencing analysis.

Stolovitzky received his PhD in mechanical engineering from Yale University and worked at The Rockefeller University and at the NEC Research Institute before coming to IBM. He has served as Joliot Invited Professor at Laboratoire de Mecanique de Fluides in Paris and as visiting scholar at the physics department of The Chinese University of Hong Kong. Stolovitzky is a member of the steering committee at the Systems Biology Discussion Group of the New York Academy of Sciences.

Jennifer Henry, PhD

The New York Academy of Sciences
e-mail

Jennifer Henry received her PhD in plant molecular biology from the University of Melbourne, Australia, with Paul Taylor at the University of Melbourne and Phil Larkin at CSIRO Plant Industry in Canberra, specializing in the genetic engineering of transgenic crops. She was then appointed as Associate Editor, then Editor, of Functional Plant Biology at CSIRO Publishing. She moved to New York for her appointment as a Publishing Manager in the Academic Journals division at Nature Publishing Group, where she was responsible for the publication of biomedical journals in nephrology, clinical pharmacology, hypertension, dermatology, and oncology.

Jennifer joined the Academy in 2009 as Director of Life Sciences and organizes 35–40 seminars each year. She is responsible for developing scientific content in coordination with the various life sciences Discussion Group steering committees, under the auspices of the Academy's Frontiers of Science program. She also generates alliances with outside organizations interested in the programmatic content.


Speakers

Richard Bonneau, PhD

New York University
e-mail | website | publications

Richard Bonneau is an Assistant Professor at New York University's Center for Genomics and Systems Biology and is jointly appointed at the Courant Institute for Mathematical Sciences. His lab is focused on a number of computational biology problems that aim to resolve key bottlenecks in biology and systems biology. His work focuses on two main categories of computational biology: learning networks from functional genomics data and predicting and designing protein and peptoid structure. In both areas Bonneau and his group have played key roles in achieving critical field-wide milestones. In the area of structure prediction Bonneau was one of the early authors on the Rosetta code, which was one of the first codes to demonstrate accurate and comprehensive ability to predict protein structure in the absence of sequence homology. His lab has also made key contributions to the areas of genomics data analysis with focus on two main areas: 1) methods for network inference that learn dynamics and topology from data (e.g. the Inferelator), and 2) methods that learn condition dependent co-regulated groups from integrations of different genomics data-types (e.g. cMonkey integrative biclustering). Bonneau strives to develop new methods that let systems-biologists specify functional relevant to biology and parameters from data automatically.

Saeed Tavazoie, PhD

Princeton University
e-mail | website | publications

Saeed Tavazoie is a professor of molecular biology and a member of the Lewis-Sigler Institute for Integrative Genomics at Princeton University. Tavazoie completed a Postdoctoral fellowship in Computational Genomics at Harvard Medical School from 1999 to 2000. He received a PhD in biophysics from Harvard University in 1999, and his thesis was titled "Experimental and computational approaches for determining the structure of transcriptional regulatory networks." Before that, he studied medicine at Harvard Medical School from 1992–1995 and received his BS in Physics in 1992. Tavazoie uses genomic and computational methods to study the structure, function, and evolution of regulatory networks across organisms ranging from bacteria to humans.

Yuhai Tu, PhD

IBM T.J. Watson Research Center
e-mail | website | publications

Yuhai Tu graduated from the Gifted Youth Program at University of Science and Technology of China (USTC) in 1987. In the same year, he passed the CUSPEA exam and went to University of California, San Diego (UCSD) for his graduate studies in physics. In 1991, he received his PhD from UCSD and was awarded the Division Prize Fellow at California Institute of Technology (Caltech) for his postdoc research. In 1994, he joined the IBM's Thomas J. Watson Research Center as a permanent research staff member. Tu has been the manager of the theory and computational physics group at the Research Center since 2002. His main research interests are statistical physics, nonlinear dynamics, surface physics and, most recently, computational biophysics. In 2004 he became a Fellow of the American Physical Society (APS).

Sponsor

Bronze

  • Agilent Technologies