Title: Long Short Term Memory

Abstract:  Long Short Term Memory or LSTMs as they are more commonly known, are the most popular type of Recurrent Neural Network used in Machine Learning. LSTMs popularity comes from their ability to capture long-term dependencies in sequential data sets. LSTMs often outperform other RNNs and many Hidden Markov Models when applied to various applications. One popular example of LSTM use is the Netflix user rating example. Users watch a movie, rate it and then watch another movie, and continue with this pattern creating a sequence of reviews. Using LSTMs we can model this sequence and make predictions about a users favorite genre of movie as well as make predictions about future movies a user may want to watch. Finally, we look at how LSTMs can be applied to a variety of problems, including those that are non-sequential.

Title: Structural and Functional Characterization of Expected and Aberrant Metal Ion Coordination in Proteins

Abstract: Metalloproteins bind and utilize metal ions for a variety of biological purposes.   Due to the ubiquity of metalloprotein involvement throughout these processes across all domains of life, how proteins coordinate metal ions for different biochemical functions is of great relevance to understanding the implementation of these biological processes. Towards these ends, we have improved our methodology for structurally and functionally characterizing metal binding sites in metalloproteins.  Our new ligand detection method is statistically much more robust, producing estimated false positive and false negative rates of ~0.11% and ~1.2%, respectively.  Additional improvements expand both the range of metal ions and their coordination number that can be effectively analyzed.  Also, the inclusion of many additional quality control filters has significantly improved structure-function Spearman correlations as demonstrated by rho values greater than 0.90 for several metal coordination analyses and even one rho value above 0.95.     Also, improvements in bond-length distributions have revealed bond-length modes specific to chemical functional groups involved in multidentation.  Using these improved methods, we analyzed all single metal ion binding sites with Zn, Mg, Ca, Fe, and Na ions in wwPDB, producing statistically rigorous results supporting the existence of both a significant number of unexpected compressed angles and subsequent aberrant metal ion coordination geometries (CGs) within structurally known metalloproteins.  By recognizing these aberrant CGs in our clustering analyses, high correlations are achieved between structural and functional descriptions of metal ion coordination.  Moreover, distinct biochemical functions are associated with aberrant CGs versus non-aberrant CGs.

TITLE: Synchrony in a Boolean network model of the L-arabinose operon

ABSTRACT: In genetics, an operon is a segment of DNA that contains several co-transcribed genes, which together form a functional regulatory unit. Operons have primarily been studied in prokaryotes, with both the lactose and tryptophan operons in E. Coli having been classically modeled with differential equations and more recently, with Boolean networks. The L-arabinose operon in E. coli encodes proteins that function in the catabolism of arabinose. This operon has several complex features, such as a protein that acts both as an activator, a DNA looping repressing mechanism, and the lack of inducer exclusion by glucose. In this talk, I will propose a Boolean network model of the ara operon, and then show how computational algebra in Sage establishes that for 11 of the 12 choices of initial conditions, the state space contains a single fixed point that correctly predicts the biology. The final initial condition describes the case where there are medium levels of arabinose and no glucose, and it successfully predicts bistability of the system. Finally, I will compare the state space under synchronous and asynchronous update, and show how the former has several artificial cycles that go away under a general asynchronous update.

Title: Hidden Markov Models with Applications to RNA Folding

Title: Insight into Molecular through Subcellular Calcium Signaling via Multi-Scale Simulation

Abstract: Calcium is critical to a wide range of physiological processes, including neurological function, immune responses, and muscle contraction. Calcium-dependent signaling pathways enlist a variety of proteins and channels that must rapidly and selectively bind calcium against thousand-fold higher cationic concentrations. Frequently these pathways further require the co-localization of these proteins within specialized subcellular structures to function properly. Our lab has developed multi-scale simulation tools to elucidate how protein structure and co-localization facilitate intracellular calcium signaling. Developments include combining molecular simulations with a statistical mechanical model of ion binding, a homogenization theory to upscale molecular interactions into micron-scale diffusion models, and reaction-diffusion simulations that leverage sub-micron microscopy data. In this seminar, I will describe these tools and their applications toward molecular mechanisms of calcium-selective recognition and cross-talk between co-localized calcium binding proteins inside the cell.