Title: Instrumenting the Health Care Enterprise for Discovery in the Course of Clinical Care
Abstract: Although patients may have a wealth of imaging, genomic, monitoring, and personal device data, it has yet to be fully integrated into clinical care. We identify three reasons for the lack of integration. The first is that “Big Data” is poorly managed by most Electronic Medical Record Systems (EMRS). The data is mostly available on “cloud-native” platforms that are outside the scope of most EMRS, and even checking if such data is available on a patient often must be done outside the EMRS. The second reason is that extracting features from the Big Data that are relevant to healthcare often requires complex machine learning algorithms, such as determining if a genomic variant is protein-altering. The third reason is that applications that present the big data need to be modified constantly to reflect the current state of knowledge, such as instructing when to order a new set of genomic tests. In some cases, the applications need to be updated nightly. A new architecture for the EMRS is evolving which could unite Big Data, machine learning, and clinical care through a microservice-based architecture which can host applications focused on quite specific aspects of clinical care, such as managing cancer immunotherapy. Informatics innovation, medical research, and clinical care go hand in hand as we look to infuse science-based practice into healthcare. Innovative methods will lead to in a new ecosystem of Apps interacting with healthcare providers to fulfill a promise that is still to be determined.

Biography: Dr. Murphy is the Corporate Director of Research Informatics and Computing at Partners HealthCare, is an Associate Professor of Neurology and Biomedical Informatics at Harvard Medical School, and serves as Associate Director for the Laboratory of Computer Science at the Massachusetts General Hospital. He received his BS in Chemistry from the University of Notre Dame, and his Ph.D. in Pharmacology and Physiology and MD from the University of Chicago. Dr. Murphy’s research interests include the creation of query methods for healthcare data that enable them to be directly used by scientists even when the data is extremely large. Dr. Murphy has created high impact and widely accepted open source projects that focus on the integration of highly diverse phenotypic, imaging, and genomic data such that new discoveries can be visualized and produced from secondary use of routinely collected healthcare data and be applied to new methods of providing clinical decision support in a learning healthcare system.

Title: Challenges to Drug Design
Abstract: There are a number of problems to which these methods are being applied, and they include: the structural basis for efficient enzyme catalysis of proton and hydride transfer; the role of the metal ions in bridged bimetallo-enzyme active sites; direct visualization of proteins in action by time-resolved protein crystallography; the structural basis for reaction selectivity in enzymes; the evolution of new enzyme activities from old ones and the development of new pathways. The most important of these problems is the study of neurodegenerative diseases, especially Parkinson’s, Alzheimer’s and Lou Gehrig’s diseases, with a view toward determining the functions of gene products associated with such diseases, finding common pathways that underlie them, and the search for and design of specific inhibitors or activators as potential drugs to treat these diseases. A number of methods are being used, from development of genetic and in vitro screens to structure-based drug design methods.

Biography: Dr. Ringe received her B.A. degree in Chemistry from Barnard College in 1963 and Ph.D. degree in Organic Chemistry from Boston University in 1968. She is currently the Harold and Bernice Davis Professor of Aging and Neurodegenerative Diseases at Brandeis University, and Adjunct Professor Neurology at Harvard Medical School. Her research is aimed at the study of the molecular mechanisms of enzymes and the functions of proteins, with a focus on the determination of function for proteins of unknown function and the development of methods for intervention on that function. The tools being used include X-ray crystallography, molecular biology, kinetics, organic synthesis, genetics, and computational approaches.

There are a number of problems to which these methods are being applied, and they include: the structural basis for efficient enzyme catalysis of proton and hydride transfer; the role of the metal ions in bridged bimetallo-enzyme active sites; direct visualization of proteins in action by time-resolved protein crystallography; the structural basis for reaction selectivity in enzymes; the evolution of new enzyme activities from old ones and the development of new pathways. The most important of these problems is the study of neurodegenerative diseases, especially Parkinson’s, Alzheimer’s and Lou Gehrig’s diseases, with a view toward determining the functions of gene products associated with such diseases, finding common pathways that underlie them, and the search for and design of specific inhibitors or activators as potential drugs to treat these diseases. A number of methods are being used, from development of genetic and in vitro screens to structure-based drug design methods.

Title: Genome-scale estimation of the Tree of Life
Abstract: Estimating the Tree of Life is one of the grand computational challenges in Science, and has applications to many areas of science and biomedical research. Despite intensive research over the last several decades, many problems remain inadequately solved. In this talk I will discuss species tree estimation from genome-scale datasets. I will describe the current state of the art for these problems, what is understood about these problems from a mathematical perspective, and identify some of the open problems in this area where mathematical research, drawing from graph theory, combinatorial optimization, and probability and statistics, is needed. This talk will be accessible to mathematicians, computer scientists, probabilists and statisticians, and does not require any knowledge of biology.

Biography: Tandy Warnow is the Founder Professor of Engineering at the University of Illinois at Urbana-Champaign, where she has a dual appointment between Computer Science and Bioengineering. She is also a member of the Carl R. Woese Institute for Genomic Biology and an affiliate in six other departments at UIUC (Statistics, Mathematics, Electrical and Computer Engineering, Plant Biology, Animal Biology, and Entomology). Tandy received her PhD in Mathematics at UC Berkeley under the direction of Gene Lawler, and did postdoctoral training with Simon Tavar´e and Michael Waterman at the University of Southern California. She received the National Science Foundation Young Investigator Award in 1994, the David and Lucile Packard Foundation Award in Science and Engineering in 1996, an Emeline Bigelow Conland Fellowship at the Radcliffe Institute for Advanced Study in 2006, and a Guggenheim Foundation Fellowship for 2011. In 2016 she was elected as an ACM Fellow, and in 2017 she was elected as a ISCB Fellow. Her research combines mathematics, computer science, and statistics to develop improved models and algorithms for reconstructing complex and large-scale evolutionary histories in both biology and historical linguistics. Her current research focuses on phylogeny and alignment estimation for very large datasets (10,000 to 1,000,000 sequences), estimating species trees from collections of gene trees, and metagenomics.

Title: Demystifying Careers in the Biopharmaceutical Industry
Abstract: Come hear an insider perspective on careers in the biopharmaceutical sector: navigating the hiring process, what roles, skills and capabilities are in demand, factors to consider before pursuing a job in industry, and potential paths for professional advancement. This presentation will focus on vignettes and examples pertaining to computational roles in the context of the wider context of drug discovery and development.

Biography: Enoch S. Huang received an AB in Molecular Biology from Princeton and a PhD in Structural Biology from Stanford, where he was a NSF Pre-doctoral Fellow with Prof. Michael Levitt. He was a Jane Coffin Childs Fellow at Washington University School of Medicine. After starting his computational biology career at Cereon Genomics, he joined Pfizer’s Cambridge laboratories in 2000. In 2001, he was appointed Head of Molecular Informatics and joined the site leadership team. In 2007 he accepted a global role as Head of Computational Sciences.

Enoch has been an Adjunct Assistant Professor of Bioinformatics at Boston University since 2001. He has served on external advisory boards for Drug Discovery Today, Brandeis University, the International Society for Computational Biology, the NIH "Illuminating the Druggable Genome" program, the Rochester Institute of Technology, and the Minnesota Supercomputing Institute. Enoch has also served on the program committees at the New York Academy of Sciences, the Massachusetts Biotechnology Council, and on NIH study sections. He has authored over 30 publications and released the Open Source software package PFAAT.