Day 2 :
Keynote Forum
Michael Christman
Coriell Institute for Medical Research, USA
Keynote:
Time : 09:05-09:45
Biography:
Michael Christman, is president and chief executive officer of the Coriell Institute for Medical Research. With an entrepreneurial spirit, Mike is guiding Coriell on new ventures in emerging science that will both further the Institute's research and add to the breadth of services it provides to scientists worldwide. Mike is an expert in genetics and genomics, with a focus on the integration of genome information into the delivery of clinical care. Prior to joining Coriell, he served as professor and founding chair of the Department of Genetics and Genomics for Boston University School of Medicine. There he led an international team of scientists in one of the first genome-wide association studies using the Framingham Heart Study cohort, published in Science magazine. Mike received his bachelor's degree in chemistry with honours from the University of North Carolina, Chapel Hill, his doctorate in biochemistry from the University of California, Berkeley, and was a Jane Coffin Childs postdoctoral fellow at the Massachusetts Institute of Technology.
Abstract:
Keynote Forum
Mark A. Feitelson
Temple University, USA
Keynote: Identification of Early Drivers of HBV associated Hepatocarcinogenesis
Time : 09:45-10:25
Biography:
Mark Feitelson received his BS in biology from the UCI in 1974. He was a graduate student in the Department of Microbiology and Immunology at the UCLA School of Medicine, where he began is studies of viral oncogenesis, and received a Ph.D. degree in 1979. He was then an American Cancer Society Postdoctoral Fellow in the Department of Medicine at Stanford University from 1979-1982. Dr. Feitelson then moved to the Fox Chase Cancer Center in Philadelphia, where he studied the biology of hepatitis B virus (HBV) with Dr. Baruch S. Blumberg, who won the Nobel Prize in medicine (1976) for his discovery of HBV. In 1991, Feitelson moved to the Department of Pathology and Cell Biology at Thomas Jefferson University where he became a full professor. In 2007, Dr. Feitelson moved to Temple University, where he is a full-professor with tenure. He is also currently the Chair of the Professional Science Master’s program in Biotechnology at Temple. His lab has produced more than 130 publications, which include two books, several book chapters, and numerous invited reviews. Since the early 1980’s, Dr. Feitelson has been interested in the pathogenesis of chronic hepatitis B virus infection. His lab has uncovered critical steps whereby the HBV encoded X antigen, HBx, contributes to the pathogenesis of chronic liver disease and the development of hepatocelular carcinoma (HCC).
Abstract:
There are more than 350 million people worldwide who are chronically infected with hepatitis B virus (HBV) and are at risk for the development of chronic liver diseases (CLD). CLD consist of hepatitis, fibrosis and cirrhosis, and finally the appearance of hepatocellular carcinoma (HCC). HCC is the 5th most common cancer and 2nd most deadly form of cancer worldwide. Although surgical resection and liver transplantation may be curative, clinical symptoms do not appear in most patients until the tumor is multinodular. The mechanisms underlying the pathogenesis of HCC have not been clearly elucidated, although the virus contribution to the development of cancer involves the expression of the hepatitis B x antigen (HBx). HBx is a trans-regulatory protein that activates many signaling pathways and alters the expression of numerous host genes, although it is not known which of these many pathways and genes drive tumorigenesis. To approach this problem, the “the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were queried to identify genes and/or pathways that are differentially expressed in HCC compared to surrounding non-tumor liver. This identified over 2,700 genes that were differentially expressed. When the latter were compared to the 140 driver mutations in all cancers, 26 drivers had changes in expression levels among patients who developed HCC. Two of these drivers showed differential expression in the liver prior to the development of HCC that inversely correlated with DNA methylation activity. These were identified as the tumor suppressor, TET methylcytosine dioxygenase 2 (TET2), and the oncogene, myeloproliferative proliferative leukemia protein (MPL). These two genes are known to be upstream regulators of other driver genes altered in HBV associated HCC. They reside in biochemical pathways known to be altered by HBx in hepatocarcinogenesis. These pathways also include genes that promote survival, growth, DNA repair, and regulate both cell cycle arrest and apoptosis. These findings imply that HBx epigenetic changes in driver gene expression appears to occur prior to the appearance of driver mutations recorded in the literature, and that changes in TET2 and MPL expression may trigger subsequent changes in the expression/activity levels of many downstream molecules that are known to drive tumorigenesis.
Keynote Forum
Corrado Priami
The Microsoft Research - University of Trento COSBI and University of Trento, Italy
Keynote: A novel data-driven identification and activity scoring of functional modules – the case of a cell differentiation process - and a minimal graphical language (bStyle) to model and simulate biological processes.
Biography:
Corrado Priami is professor of Computer Science at the University of Trento. The results of his PhD thesis on stochastic pi-calculus were the basis for the foundation of the Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), of which he is the President and CEO. His research covers programming languages and computational methods for the modeling, analysis, and simulation of biological processes in the fields of systems nutrition and systems pharmacology. He published over 180 scientific papers, gave more than 60 invited talks and lectures at conferences and universities around the world, participated in many program committees for international conferences (also as chair), regularly serves in advisory and scientific boards and reviewing panels for many international funding agencies and institutions.
Abstract:
High dimensional analysis of the complex processes involved in cellular processes focus on unbiased methods to identify pathways. However, many bioinformatic tools generate lists of pathways with p values, but do not score the pathways relative to each other or to other interacting parts of the system. I present a newly developed computational method Network Activity Score finder (NASFinder) to identifiy tissue-specific, omics-determined sub-networks and the connections with their main upstream regulator receptors to obtain a systems view of the differentiation of human adipocytes. After module identification we usually move to dynamic simulation of their behavior. Many graphical languages have been designed to model and simulate biological systems. Many of them are ambiguous and does not allow a smooth mapping to computational models and many of them suffer the syndrome of a new symbol for a new case. I present a minimal and not ambiguos language and a supporting tool for it.
- Track 4: Computational Systems BiologyTrack 5: Bioinformatics Technologies in MedicineTrack 6: Stitching Bioinformatics Approach to Pharmacy
Location: Independence B
Chair
Heinrich Roder
Biodesix, Inc., USA
Co-Chair
Ravi Radhakrishnan
University of Pennsylvania, USA
Session Introduction
Heinrich Roder
Biodesix, USA
Title: Bridging the gap: From hypothesis-independent tests to understanding of biological mechanisms
Time : 10:45-11:15
Biography:
Heinrich Roder is an author of more than 100 publications and talks spanning the fields of theoretical physics, computational sciences, and molecular diagnostics. A Rhodes Scholar, he earned his DPhil in Theoretical Physics from Oxford University and has held positions at the Universities of Hanover and Bayreuth and Los Alamos National Laboratory. He is a Founder of Biodesix. Serving as CTO, he leads the work of the Research and Development team on sensitive, high-throughput MALDI mass-spectrometry profiling of blood-based samples and the development of molecular diagnostic tests using a machine learning platform incorporating elements of deep learning.
Abstract:
A hypothesis-independent approach to building clinically relevant tests allows the creation of multivariate classifiers that reflect the complexity of biological interactions without any bias from expectations about their mechanisms. However, once the classifier is created, it is of interest to understand the biological underpinnings of its performance. Biodesix has developed a new data analytic platform, the Diagnostic CortexTM that utilizes mass spectral data collected from patient serum samples to create clinically relevant tests without a prior hypotheses or molecular understanding of the underlying biology. It was successfully used to discover and validate a test predicting outcomes for patients with metastatic melanoma treated with the immune checkpoint inhibitor, Nivolumab. To broaden our biological understanding, we applied ideas similar to GSEA (Gene Set Enrichment Analysis) to mass spectral data (PSEA). This approach allowed us to find correlations between classification and sets of proteins associated with known biological functions, such as acute response, wound healing, and complement system. Through the association of mass-spectral features with functional sets of proteins, we constructed a biological score, calculated for each individual sample and serving as a measure of importance of a particular biological process. These scores, as well as their changes, were found to be associated with clinical outcomes of patients, at the same time providing some insights into related biological mechanisms.
Ravi Radhakrishnan
University of Pennsylvania, USA
Title: In silico oncology: Computational horizons in cancer systems biology and multiscale cancer modeling
Time : 11:15-11:45
Biography:
Ravi Radhakrishnan is a Professor of Bioengineering, Biochemistry & Biophysics and Chemical and Biomolecular Engineering at the University of Pennsylvania. His expertise is in Chemical Physics, Statistical Mechanics and Computational Biology. His laboratory focuses its research on the biophysics of single molecules and cell membranes and signaling mechanisms in cancer. Through his work, he has pioneered novel discovery platforms in in silico Oncology and in silico Pharmacology. He has authored over 100 articles in leading peer reviewed journals and serves as a Referee for over 50 leading journals, publishers and federal funding agencies. He also serves as an Editorial Board Member and Associate Editor for 5 journals and also regularly serves as a Panelist and Study Section Member for National Science Foundation, National Institutes of Health and several Federal Science Foundations’ in the EU. He is a Fellow of the American Institute of Medical and Biological Engineering.
Abstract:
We have developed a multi scale platform for the predictions of the effects of mutations on oncogene activation through a combination of molecular, biophysical and cellular models. We have combined the specificity of molecular modeling with the power of network models to predict the molecular mechanisms that lead to the activation of pathways. We have also employ spatial and stochastic models to describe how the effects of the tumor microenvironment can lead to oncogenic signals through non-canonical pathways. We will describe the applications of these models in the clinical contexts of non small cell lung cancer, neuroblastoma and hepatocellular carcinoma.
Junping Jing
GlaxoSmithKline R&D, USA
Title: Explore cancer genomic databases to support immuno-oncology drug discovery
Time : 11:45-12:15
Biography:
Junping Jing has been a researching Scientist in GlaxoSmithKline (GSK) for 18 years. He joined GSK in 1998 and his research included bioinformatics analysis, biomarker discovery and translational medicine. He is currently senior scientific investigator in Department of Computational Biology, fucusing on bioinformatic analysis supporting cancer immune therapies. He received his PhD in Biochemistry from New York University in 1997 and was an early pioneer in developing array-based DNA analysis platform.
Abstract:
Cancer genomic databases such as The Cancer Genome Atlas (TCGA) have become invaluable knowledgebases for cancer drug discover. Besides providing comprehensive genomic and genetic properties of cancer cells in a tumor, they also shed light on the profiles and distribution of tumor infiltriting lymphocytes (TILs) in the tumor micro-environment. Using markers uniqulely representing different lymphocytes such as cytotoxic and regulatory T cells, we have characterized the TIL districution of ~10,000 primary tumors from 30 different cancer types. The talk will provide concrete examples how we are using this anlaysis to identify novel targets, discover biomarkers and stratify patients.
Shijun Zhong
Dalian University of Technology, China
Title: Inhibitor design for blocking the protein-carbohydrate interactions
Time : 12:15-12:45
Biography:
Shijun Zhong has completed his PhD from Xiamen University and Post-doctoral studies from Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences. He is a Professor at Dalian University of Technology. He has published more than 40 papers in reputed journals and has been serving as an Editorial Board Member of repute.
Abstract:
Carbohydrate binding on protein is involved in many important biological processes. In this presentation, we shall report the research progresses in four cases: Inhibitors designed against glycogen phosphorylase via screening million compounds have been experimentally evaluated, leading to nine actives and two crystal complexes, helpful for controlling type 2 diabetes; screening of more than six million compounds against α-glucosidase, followed by molecular dynamics simulations and binding free energy calculations, suggested 10 hits for controlling the concentration of the postprandial blood glucose related to diabetes and the complications; molecular dynamics simulations were applied to the mannose and glucose bindings on flocculation proteins for helping design novel flocculation yeasts to improve yeast brewing production and; possible binding modes of inulin on exo-inulinase were studied using Amber, via 100 ns molecular dynamics simulations, for understanding the hydrolysis mechanism and improving the efficiency in food additives and ethanol productions.
Thomas A. McMurrough
Western Univeristy, Canada
Title: High-throughput investigation of protein-DNA substrate coevolution in Meganuclease family enzymes
Time : 13:30-14:00
Biography:
Thomas A McMurrough has completed his undergraute studies with an Honors Specialization in Genetics and major in Medical Cell Biology from Western University, Canada in 2011. He is currently completing his PhD in the Department of Biochemistry at the Schulich School of Medicine & Dentistry. He was recently awarded an Alexander Graham Bell Canada Graduate Scholarship from the Natural Sciences and Engineering Research Council (NSERC) of Canada and a Doctoral Excellence Research Award from Western Univeristy. He has authored two pubications including a first author manuscipt in PNAS (2014) and is currently exploring Post-doc. and industry opportunities for 2017.
Abstract:
Precision genome editing has applications in academia, biotechnology, agriculture and the development of novel human therapetics. Genome-editing strategies begin with the introduction of a double-strand break (DSB). Meganucleases are one class of enzyme currently used to introduce DSBs, and at highly specific 22-basepair DNA target sites. Although these enzymes create desirable 3’ singlestranded overhangs, the re-engineering of meganucleases to target desired sites is limited by a poor understanding of how cleavage specificity is regulated in the central target site region. We previously used intra-molecular covariation analyses to identify a network of coevolving amino acid residues within the meganuclease active site. We demonstrated that residues at computationally predicated positions were interdependent for catalysis, and identified novel combinations of residues that controlled enzymatic activity. Recently, we have explored a role for the coevolving amino acid residues in controlling central target site specificity. 1600 meganuclease protein variants were tested for in vivo activity against 26 central DNA target site variants using selective growth experiments. Active proteinsubstrate combinations were identified by Illumina® sequencing and compositional data analysis to identify protein variants with altered DNA specificity. Novel protein-DNA combinations were further validated using X-ray crystallography and by re-engineering specificity towards human genomic sequences that were previously untargetable. Our study provides a validated strategy for using intra-molecular covariation to identify functionally important protein networks, demonstrates the power of applying compositional analyses to high-throughput sequencing data, and will expand the genome-editing applicability of meganuclease enzymes.