The Systems Biology Seminar Series brings to NIST leading scientists working in the borderlands of physical science, information science, and biology. It ordinarily meets on those Fridays for which no NIST Colloquium is scheduled.

November 17, 2006
Slides from presentation
(3.3 MB pdf)

Discovery of principles of nature from mathematical modeling of DNA microarray data

Orly Alter

Department of Biomedical Engineering, Institute for Cellular and Molecular Biology and Institute for Computational Engineering and Sciences,
University of Texas at Austin

Abstract - DNA microarrays make it possible to record the complete genomic signals that guide the progression of cellular processes. Future predictive power, discovery and control in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment and drug development. I will describe the first data-driven models that were created from these large-scale data through generalizations of matrix and tensor computations that have proven successful in describing the physical world. In these models, the mathematical variables and operations might represent biological reality: The variables, patterns uncovered in the data, might correlate with activities of cellular elements, such as regulators or transcription factors, that drive the measured signals. The operations, such as data classification and reconstruction in subspaces of selected patterns, might simulate experimental observation of the correlations and possibly also causal coordination of these activities. I will illustrate these models in comparative and integrative analyses of mRNA expression and proteins' DNA-binding data from yeast and human cell cultures. In these analyses, the ability of the models to predict previously unknown biological and physical principles is demonstrated with a prediction of a novel mechanism of regulation that correlates DNA replication initiation with RNA transcription. The predicted mechanism is in agreement with current biological understanding, and is supported by recent experimental results. These models may become the foundation of a future in which biological systems are modeled as physical systems are today.

February 2, 2007

Community Structure in Complex Networks

Michelle Girvan

Institute for Physical Science and Technology, University of Maryland

Abstract -  Many systems take the form of networks: examples include the Internet, the World-Wide Web, distribution networks, neural networks, biochemical networks, food webs, and social networks. Drawing on techniques from statistical physics and dynamical systems, researchers have begun to take a complex systems approach to understanding these networks, as they cannot be well-described by completely structured or completely random representations. Much of this work has been focused on identifying a few statistical features that seem common to many networks: the small-world property, power- law degree distributions, and network transitivity. In this talk, I will focus on another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. I will discuss a set a novel algorithms developed to find and evaluate this kind of network structure and show that they they are highly effective at discovering community structure in computer-generated and real-world networked data. Deconstructing networks in this manner can shed light on the sometimes dauntingly complex structure of networked systems.

February 16, 2007

Ultrafast Protein Folding

William Eaton

Chief, Laboratory of Chemical Physics, NIDDK, National Institutes of Health, Bethesda, MD

Abstract -  The introduction of laser-triggering methods and advances in computational capabilities are rapidly narrowing the historical gap between experimental protein folding kinetics and the time-scale accessible to atomistic molecular dynamics trajectories. It is now possible to rigorously test the validity of the mechanisms extracted from the analysis of multiple trajectories with experimental data from ultrafast folding proteins. Advances are also being made in the development of simple analytical models, which are surprisingly successful in calculating experimental properties of specific proteins. In this seminar I will discuss the connections between the equilibrium and kinetic data on the ultrafast-folding 35-residue subdomain from the villin headpiece and both atomistic simulations and a simple statisticalmechanical model.

March 2, 2007

Sequence-Resolved Detection of Pausing by Single RNA Polymerase Molecules

Arthur LaPorta

Department of Physics, and Institute for Physical Science and Technology, University of Maryland, College Park, MD

Abstract - We apply an ultrastable optical-trapping assay to follow the motion of individual molecules of RNA polymerase (RNAP) transcribing templates engineered with repeated sequences carrying imbedded, sequence-specific pause sites of known regulatory function. Both the known and ubiquitous pauses appeared at reproducible locations, identified with base-pair accuracy. Ubiquitous pauses were associated with DNA sequences that show similarities to regulatory pause sequences. Data obtained for the lifetimes and efficiencies of pauses support a model where the transition to pausing branches off of the normal elongation pathway and is mediated by a common elemental state, which corresponds to the ubiquitous pause. This result complements single-molecule studies,which showed that bacterial RNAP pauses frequently during transcriptional elongation; our results clarify the relationship of these 'ubiquitous' pauses to the underlying DNA sequence.

March 8, 2007

What is Namomedicine?

James Baker, Jr.

Director, Nanotechnology Institute for Medicine and the Biological Sciences, University of Michigan

Note: NIST Colloquium, in Red Auditorium,
Thursday, March 8

April 20, 2007

Tissue Engineering: Tracking Large Numbers of Cells

Takeo Kande

The Robotics Institute, Carnegie Mellon University

Note: NIST Colloquium, in Red Auditorium