The following are details of lectures held as a part of the CSE seminar series during Jan-Apr 2018.

24th January 2018. Probabilistic Models and Inference Algorithms for Single-cell Genomics: Applications in Cancer Evolution. Dr. Hamim Zafar, Graduate Researcher, Department of Computer Science, Rice University.
Abstract: Rapidly emerging single-cell DNA sequencing technologies offer promising datasets to further our understanding of diverse facets of cancer biology and genetics. Specifically, it will have a profound impact in resolving the tumor heterogeneity that complicates the diagnosis and treatment of cancer patients and causes relapse and drug resistance. However, novel computational methods are required to perform this task, which is challenged by uncertainties in the underlying evolutionary processes. Moreover, technical artifacts introduced during the sequencing process further complicate this task. Novel computational methods are required for the analysis and interpretation of large-scale single-cell genomic datasets for elucidating tumor heterogeneity and evolution.

In this talk, I will introduce probabilistic models and statistical inference algorithms for elucidating tumor heterogeneity and evolution from single-cell DNA sequencing data. These algorithms probabilistically model the possible mutational histories as well as the sources of uncertainties due to technical artifacts in the data. The mutation discovery algorithm employs a probabilistic model of the technical artifacts and dynamic programming for detecting point mutations from raw single-cell sequencing data. The second method introduces a continuous-time Markov chain to model the underlying mutational events in cancer and infers a tumor phylogeny, a binary leaf-labeled tree that represents the mutational history of a tumor helping guide patient-specific treatment. The final method introduces a tree-structured non-parametric
Bayesian clustering framework to reconstruct the cell subpopulations in a tumor and their mutation content. Using these methods, I analyze several cancer datasets and uncover tumor phylogenies, driver mutations and cell subpopulations that are more biologically plausible than previously reported analyses. To close, I will give a brief outlook on a wider range of future directions towards providing novel computational and data-driven approaches aimed at improvement of patient well-being through advancements in the understanding of biological processes and phenomena.

Time: 4:30pm –5:30pm,  (Wed)
Venue: 6/202

10th January 2018. Markov Chain Monte Carlo simulations for the rational design of molecular systems. Dr. Kaustubh Rane, Assistant Professor, Chemical Engineering.

Abstract: The rational design of molecular systems  involves the systematic manipulation of molecular-scale variables to obtain the desired properties. A famous example is the design of drug molecules in the pharmaceutical industry. The field makes extensive use of computer simulations to predict the results of the above manipulations.

The behavior of a molecular system is governed by the density of molecules at different locations. Here, density refers to how well the molecules are “packed.” The local density fluctuates, resulting in a distribution of densities. In this talk, I will explain the application of Markov Chain Monte Carlo (MCMC) simulations to predict such distributions. We will also discuss the usefulness of density-distributions in rational design and the challenges involved in performing the above MCMC simulations.