Mathematics 596a

Biomathematics Seminar

Fall 2010

Titles and Abstracts

September 7 5:00 PM	Logan Ahlstrom, Brian Anderson, and Ian Borukhovich Departments of Chemistry and Physics Merging Experimental and Theoretic Approaches to Predicting Protein Structure Knowing 3-d protein structure is an essential step in understanding and developing treatments of any protein related disease. Ab initio approaches, the holy grail of protein structure prediction, are presently insufficient. We present a survey of recent progress that combines experimental and computational approaches.
September 14 5:00 PM	Matthew Cordes Department of Chemistry and Biochemistry Application of Combined Experimental NMR and Ab Initio Computational Methods to Protein Structure Determination Experimental determination of protein structures using nuclear magnetic resonance (NMR) spectroscopy is difficult for medium-to-large proteins (size 20-25 kDa and above), both because of the complexity of correctly assigning resonance peaks to a large number of specific nuclei in the protein, and because enhanced relaxation processes impede collection of high sensitivity, well-resolved data.  Ab initio computational protein structure prediction also faces major barriers to correct modelling of mid- to large-sized proteins, in part because of the complexity of the conformational search problem. Recent techniques developed in the Baker and Bax labs, however, have shown that sparse, readily obtainable NMR data, coupled with ab initio prediction using the Rosetta algorithm, can successfully model structures for many moderate-sized proteins. This advance illustrates how combination of limited experimental data with a computational approach can solve problems beyond the reach of either one alone. I  will discuss these techniques and how we are trying to apply them to modelling of the structure of a ~20 kDa insect lipocalin protein in my laboratory.
September 21 5:00 PM	Osama Miyashita Department of Chemistry and Biochemistry Molecular Dynamics Simulation: Force fields and Algorithms We will present methods used to simulate proteins dynamics. This lecture will cover potential energy functions, force fields used to describe proteins in MD simulations. Algorithms to simulate the evolution of the atoms as a function of time will also be discussed. Finally, we will talk about the advantages and limitations of these methods.
September 28 5:00 PM	Joe Watkins Department of Mathematics An Introduction to Markov Chain Monte Carlo Following on our discussions on the CS-Rosetta protein structure prediction, this week, we will have a general introduction to Markov Chain Monte Carlo focusing on the Metropolis Hastings algorithm and the Gibbs sampler. We will see how this applies to simulating posterior probabilities in Bayesian statistics and relate it to the CS-Rosetta methodology and to parameter estimation in population genetics.
October 5	No Seminar
October 12 5:00 PM	David Lyttle and Simon Stump Program in Applied Mathematics and Department of Ecology and Environmental Biology Building Predictive Quantitative Models of the Immune System We will give an overview of a variety of techniques used to model moderate sized systems of highly connected networks. We will use the human immune system as our primary example.
October 19 5:00 PM	Ryan Gutenkunst Department of Molecular and Cellular Biology Rules-Based Modeling for Signal Transduction As pointed out last week, all modeling involves tradeoffs between scope (how much of the system to include) and detail (how faithfully to represent each component). This week we discuss rules-based modeling, a set of concepts and tools designed to mitigate this tension, particularly in the modeling of cellular signal transduction networks. As motivation, we will first examine the architecture and biochemistry of typical signal transduction networks and how they lead to "combinatorial complexity." We will then introduce rules-based modeling, which tackles this complexity by focusing not on reactions, but on rules for classes of reactions. Finally, we will discuss deterministic and stochastic methods for simulating rules-based models, along with open problems in the field.
October 26 5:00 PM	Ryan Gutenkunst Department of Molecular and Cellular Biology Stochastic Simulation in Systems Biology We will discuss methods for simulating stochastic systems, with a particular emphasis on applications in molecular and cellular biology. To motivate the algorithms, we will first discuss experiments revealing the important role noise plays in particular systems. We then turn to the simulation algorithms, beginning with an in-depth study of Gillespie’s algorithm. The limitations of the Gillespie algorithm then motivate several approximate schemes. Time permitting, we will also discuss methods for directly solving the chemical master equation.
November 2 5:00 PM	David Lyttle and Simon Stump Program in Applied Mathematics and Department of Ecology and Environmental Biology Applications of the Gillespie Algorithm We will investigation two applications of the Gillespie algorithm - modeling epidemics in commercial aquacultures and stochastic simulation of the yeast cell division cycle.
November 9	No Seminar
November 16 5:00 PM
November 23 5:00 PM
November 30 5:00 PM
December 7 5:00 PM