Inference of regulatory network dynamics in developmental and evolutionary lineages
Regulatory networks connect regulatory proteins (e.g., transcription factors and signaling proteins) to target genes and control what genes are expressed when, translating the information encoded in an organism’s genome to context-specific responses. Identification of these networks is important to advance our understanding of many biological processes such as development, disease, response to stress, and evolution. In this talk I will present computational methods to tackle a few key problems in the inference of gene regulatory networks. The first part of the talk will focus on computational problems in mammalian regulatory networks. Understanding mammalian regulatory networks is a major challenge because of the number of regulators, large amounts of non-coding regulatory DNA, and multiple levels of regulation including chromatin state (determining what DNA is accessible by a transcription factor), and the three-dimensional organization of the genome (how DNA is packaged in the nucleus). I will present computational methods to tackle three problems in mammalian gene regulatory systems: (i) Integrative inference of genome-scale regulatory network inference, (ii) Chromatin state dynamics on cell lineages, (iii) Long-range gene regulation. Using these approaches we have derived useful insights about mammalian gene regulation including the identification of key regulators in host response and chromatin state dynamics during cell state transitions. In the second part of the talk I will present computational methods to study gene regulatory programs in evolutionary lineages. Using our approaches, we are studying the evolution of gene regulatory processes in yeast, plants and vertebrate phylogenies.
Sushmita Roy’s research focuses on developing statistical computational methods to identify the networks driving cellular functions by integrating different types of genome-wide datasets, that measure different aspects of the cellular state.
Roy is interested in identifying networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and constructing predictive models from these networks.
Specifically, some research topics of interest are:
Inference of structure and function of regulatory networks
Comparative analysis of expression modules across species
Evolution of gene regulation
Relational learning to predict function
Modeling condition-specific functional behavior
Learning causal networks
Predictive models of phenotypic response
Robotics Inspired Methods for Modeling Molecular Motion: From Molecular Docking to Antibody Assembly
At first glance, robots and proteins have little in common. Robots are commonly thought of as tools that perform tasks such as vacuuming the floor, while molecules play essential roles in biochemical processes. However, the functionality of both robots and molecules is highly dependent on their motions. Despite the dramatically different structures, complexity, and scales of robots and molecules, structural representations can be used that cross both domains, thus enabling computationally efficient motion simulation. In this talk, we explore motions of molecules and demonstrate how algorithms, tools, and ideas from robotics can be applied to molecular motion. To begin we study the problem of antibody-allergen assembly, a signaling precursor of an allergic response where antibodies bind to allergens to form multi-molecular structures. For this problem, we explore the use of reduced resolution molecular models in order to capture experimental results including aggregation patterns and cell signaling responses. Our computationally efficient solutions to this problem are inspired by multi-agent cooperative robotic motion. Our work in reduced resolution models has also been applied to the analysis of flexible structures imaged by Cryo EM. In this application we will discuss how methods to fit semi-flexible molecular models to Cryo EM tomograms are inspired by robotic conformational search and graphics. Finally, we have incorporated our work in molecular interaction modeling into a molecular docking game where users can feel atomic forces while they dock molecules. Robotics-based algorithmic solutions provide ways of aggregating player data to be able to identify low energy trajectories. This enables crowd-sourced solutions to high-dimensional molecular motion problems.
Lydia Tapia is an Associate Professor in the Department of Computer Science at the University of New Mexico. She received her Ph.D. in Computer Science from Texas A&M University and her B.S. in Computer Science from Tulane University. Her research contributions are focused on the development of computationally efficient algorithms for the simulation and analysis of high-dimensional motions for robots and molecules. Specifically, she explores problems in computational structural biology, motion under stochastic uncertainty, and reinforcement learning. Based on this work, she has been awarded two patents, one on a novel unmanned aerial vehicle design and another on a method to design allergen treatments. Lydia is the recipient of the 2016 Denice Denton Emerging Leader ABIE Award from the Anita Borg Institute, a 2016 NSF CAREER Award for her work on simulating molecular assembly, and the 2017 Computing Research Association Committee on the Status of Women in Computing Research (CRA-W) Borg Early Career Award.
Inferring developmental trajectories and causal regulations with single-cell genomics
Development is commonly regarded as a hierarchical branching process. Single-cell genomics, single-cell RNA-seq (scRNA-seq) in particular, holds the promise to resolve the dynamics of this process. However, learning the structure of complex single-cell trajectories with multiple branches remains a challenging computational problem. In this seminar, I will present the toolkit, Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit “principal graph” that passes through the middle of the data as opposed to other ad hoc methods, greatly improving the robustness and accuracy of its trajectories. I will demonstrate that Monocle 2 is able to accurately reconstruct developmental trajectories for complicated systems, including haematopoiesis involving six different cell fates. When coupled with another statistical framework, BEAM (branch expression analysis modeling), Monocle 2 is able to detect genes specific to different developmental lineages. The unprecedented high resolution of the reconstructed developmental trajectories not only enables us to determine which genes are playing important roles at the critical time point of cell fate transition, but also to directly infer causal gene regulatory networks. To this end, I have been developing a new toolkit, Scribe, which applies novel information theory techniques to detect causal interactions responsible for fate transitions. In my future lab, I envision building upon my foundational work on scRNA-seq analysis to comprehensively map cellular lineages and the corresponding regulatory hierarchies in systems like sea urchin with molecular recorders based on repurposed CRISPR-cas9 system.
Xiaojie Qiu was raised in a small village of southern China. He then attended Changchun University of Technology where he completed an undergraduate degree in bioengineering. Afterwards he earned a Masters in bioinformatics from East China Normal University in Shanghai. During his Masters, he applied dynamic systems approaches to understand the irreversibility of cell fate transitions. After a brief stint with Dr. Sui Huang, working on simulating evolution of developmental regulatory networks, at the Institute for Systems Biology (Seattle), Xiaojie started his PhD in the Molecular and Cellular Biology program at the University of Washington. Excited by the promise of single-cell genomics, Xiaojie joined Dr. Cole Trapnell’s lab in the department of Genome Sciences as his first graduate student, to develop computational methods for single-cell genomics. Xiaojie’s PhD work has made a few key contributions to the field of single-cell genomics. For example, he developed the popular single-cell genomics analysis toolkit, Monocle 2, to accurately and robustly reconstruct complex developmental trajectories. He also proposed BEAM (branch expression analysis modeling), a statistical framework that identifies genes significantly diverge between different lineages and pinpoints the precise timing of lineage specification events. Recently, in close collaboration with Dr. Sreeram Kannan, he has been developing and applying information theory techniques to detect casual interactions responsible for cell fate decisions with single-cell genomics datasets.