I will illustrate how computational algebra can be useful in studying biochemical networks. Of the networks that can be modeled with mass-action kinetics, the class of toric dynamical systems is the best understood, having unique steady states given by Birch’s Theorem. One might ask whether we can characterize the limiting behavior of such systems. An algebraic approach to answering this question will be presented.

Programmatic control of transcription is an integral tool in the construction of synthetic biological systems. Current approaches focus on the manipulation of protein factors that recognize upstream DNA sequences and induce or repress transcription. While effective in small synthetic systems, extensive use of these proteins in the design of more complex systems is problematic due to the inhomogeneous kinetics and thermodynamics of the mechanism of action of each protein. As the need for more complex synthetic biological systems grows, it is ever more important to consider transcription control mechanisms that offer components that have homogeneous physics of operation. With such components, it will be feasible to model and predict the behavior of larger and more scalable designs of synthetic systems.

In this study, we investigate the antisense-RNA-mediated transcriptional attenuation mechanism of the Staphylococcus aureus plasmid pT181. Consideration of the attenuation mechanism and preliminary experiments suggest that it may be possible to design transcriptional control components based on the pT181 control system in which 1) no translation of protein factors is necessary, and 2) many orthogonally-acting transcription control units can be designed through Watson-Crick base pairing.
In particular, we make use of computational RNA folding algorithms to design mutations to the pT181 control system which are hypothesized to give a higher attenuation rate than the wild type
system. We also computationally search over the space of mutations to find a family of mutually orthogonal transcriptional control units. We then evaluate these mutants experimentally to test the predictions of our computational methodology.

Although proteins populate ensembles of structures in solution, X-ray diffraction data are traditionally interpreted using a single dominant model. To detect ensembles of side chain motions in x-ray electron density, we developed a new computational method called Ringer. Using this approach, we have identified structural fluctuations, or “soft spots,” in protein active sites and explored their effects on the biophysical properties of ligand binding. In control calculations, Ringer can detect unmodeled alternate side chain conformations at >10% occupancy with B-factors of 50 Å^2 in crystal structures up to 2.0 Å resolution. Using experimental density, Ringer identified unmodeled alternate rotamers in 5-15% of side chains, supporting the idea that the newly detected conformations are real. Using the test system, cyclophilin A, Ringer analysis also revealed unexpectedly that the conformations populated in crystals at 100 K differ from those populated at room temperature, indicating that the temperature of data collection influences the range of motion of the side chains. With these new methods, we are exploring X-ray structures of calmodulin (CaM), a calcium signaling protein that recognizes approximately 200 different peptide sequences, to test the idea that free receptors contain structural fluctuations required for bound conformations. Preliminary results have identified soft spots in the active site of apo-CaM structure necessary for diverse binding. We have also seen correlation between the side-chain ensembles detected by Ringer and NMR experiments that detect dynamics in solution, indicating these fluctuations are biologically relevant. These studies have the potential to provide powerful new tools for insight into the mechanical underpinnings of ligand specificity in CaM and in ligand recognition in general.

5:30pm-6:30pm
Reception

6:30pm-8:00pm
Dinner

8:00pm-8:45pm
Keynote Talk – Gill Bejerano, Assistant Professor of Developmental Biology and Computer Science, Stanford University

9:00pm-10:30pm
Poster Session