Prof. Ying Xu
University of Georgia, USA
Ying Xu has been the "Regents and Georgia Research Alliance Eminent Scholar" Chair of bioinformatics and computational biology and Professor in Biochemistry and Molecular Biology Department since 2003, and was the Founding Director of the Institute of Bioinformatics, the University of Georgia (UGA). He received his Ph.D. degree in theoretical computer science from the University of Colorado at Boulder in 1991. He started his bioinformatics career in 1993 when he joined Oak Ridge National Lab. His current research interests are mainly in cancer bioinformatics and systems biology. He has over 300 publications, including five books, with total citations more than 14,000 and H-Index = 62; and has given over 250 invited/contributed talks at conferences, research organizations and universities.
Speech Title: "Metabolic Reprogramming in Cancer: the Bridge that Connects Intracellular pH Stress and Cancer Behaviors"
Abstract: Cancer has been considered as a genomic disease, which has served as the guiding principle in cancer research and the basis for cancer diagnosis and treatment. However, increasingly more researchers have challenged this viewpoint in the past decade since it could not answer too many cancer related questions! We have been developing a cancer evolutionary theory in the past few years. The key idea is: persistent inflammation of certain types will lead to increased local H2O2 and iron concentrations, which together will give rise to Fenton reaction: Fe2+ + H2O2 -> Fe3+ + ∙OH + OH-. If the environment is also rich in O2∙-, which is predominantly released from neutrophils in cancer tissues, O2∙- can reduce Fe3+ back to Fe2+, hence driving the reaction to go on as long as O2∙- is available. We have discovered that (1) all cancer tissues in TCGA have persistent Fenton reactions in their cytosol and mitochondria, and (2) the rates of cytosolic Fenton reactions will saturate the pH buffer quickly, hence driving the cytosolic pH up if not neutralized. Our next key finding is that the affected cells utilize a wide range of metabolic reprogramming (MR) to produce more protons to keep the Fenton reaction-produced OH- neutralized. We have studied some 50 MRs in 14 cancer types, which each produce more protons compared to the original metabolism. Further analyses suggest that the affected cells use cell division as way to rid of the persistently produced nucleotides. I will explain how other clinical behaviors of cancer may be driven by other reprogrammed metabolisms, mainly to remove their end- or intermediate products so the proton-producing MRs can continue and keep the affected cell alive.
Prof. Bijoy K. Ghosh
Texas Tech University, USA
Bijoy received the B. Tech and M. Tech degrees in Electrical and Electronics Engg. from BITS Pilani and the Indian Institute of Technology, Kanpur, India, and the Ph.D. degree in Engineering Sciences from the Decision and Control Group of the Division of Applied Sciences, Harvard University, Cambridge, MA, in 1977, 1979 and 1983, respectively. From 1983 to 2007 Bijoy was with the Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, USA, where he was a Professor and Director of the Center for BioCybernetics and Intelligent Systems. Currently he is the Dick and Martha Brooks Regents Professor of Mathematics and Statistics at Texas Tech University, Lubbock, TX, USA. He received the Donald P. Eckmann award in 1988 from the American Automatic Control Council, the Japan Society for the Promotion of Sciences Invitation Fellowship in 1997. He became a Fellow of the IEEE in 2000, and a Fellow of the International Federation on Automatic Control in 2014. Currently he is the IEEE Control Systems Society Representative to the IEEE-USA's Medical Technology Policy Committee. Bijoy had held visiting positions at Tokyo Institute of Technology, Osaka University and Tokyo Denki University, Japan, University of Padova in Italy, Royal Institute of Technology and Institut Mittag-Leffler, Stockholm, Sweden, Yale University, USA, Technical University of Munich, Germany, Chinese Academy of Sciences, China and Indian Institute of Technology, Kharagpur, India. Bijoy's current research interest is in BioMechanics and Control Problems in Rehabilitation.
Speech Title: "Computational Biology and Control Problems arising from Pandemic Models"
Abstract: The local spread and control of a virus is important in our understanding of a pandemic and how it spreads over a community. Using epidemiological models, we shall focus on how a disease is spread and how use of drug together with human interaction pattern would help. In this talk I focus application of control and AI.
Prof. Y-h Taguchi
Chuo University, Japan
Prof. Taguchi is currently a Professor at Department of Physics, Chuo University. Prof. Taguchi received a master degree in Statistical Physics from Tokyo Institute of Technology, Japan in 1986, and PhD degree in Non-linear Physics from Tokyo Institute of Technology, Tokyo, Japan in 1988. He worked at Tokyo Institute of Technology and Chuo University. He is with Chuo University (Tokyo, Japan) since 1997. He currently holds the Professor position at this university. His main research interests are in the area of Bioinformatics, especially, multi-omics data analysis using linear algebra. Dr. Taguchi has published a book on bioinformatics entitled “Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach” from Springer at September 2019 and more than 100 journal papers, book chapters and papers in conference proceedings. He is also serving as Academic editors of various journals including PLoS ONE, BMC Medical Genomics and Non-coding RNA (published from MDPI) as well as guest editors of nine special issues in Cells and International Journal of Molecular Sciences published also from MDPI.
Speech Title: "Application of Tensor Decomposition based Unsupervised Feature Extraction to Single Cell RNA-seq Data Analysis"
Abstract: Recently, single cell RNA-seq (scRNA-seq) has become the most advanced and popular methods to measure transcriptome in genomic science. However, generally, scRNA-seq does not have enough amount of labeling information to annotate in contrast to the standard RNA-seq, which is usually measured in well-defined samples, e.g., specific tissues or patients with specific disease. Thus, more unsupervised oriented methods are desired. In this keynote, I will introduce some or tensor decomposition (TD) methods based methods that are applicable to scRNA-seq data analysis. Compared with traditional methods developed for the scRNA-seq, TD based methods turn out to have more capability to select genes with more biologically reliable annotations. Since more single-cell based methods will be developed, tensor based methods are promising ones that can be applicable to these forthcoming single-cell oriented measurements.
Prof. Ashoka Polpitiya
Sera Prognostics Inc, USA
Ashoka Polpitiya, DSc, is a Professor in Electrical Engineering at Sri Lanka Technological Campus since 2016. Prior to this, he was the Director of Bioinformatics and Biostatistics at Sera Prognostics Inc., in Salt Lake City, Utah where he still works as a consultant. He has also worked in the past as the Lead Bioinformatician for Proteomics at the Translational Genomics Research Institute in Phoenix, Arizona and as a Senior Scientist at the Pacific Northwest National Laboratory (PNNL). He has published articles and developed software tools to address various analytics issues in Genomics and Proteomics experiments. Dr. Polpitiya received his BS in Electrical Engineering from University of Peradeniya, Sri Lanka, an MS and a PhD both from the Washington University in St. Louis in 2000 and in 2004, respectively, in Systems Science and Mathematics. He spends his time in both Sri Lanka and US, working for SLTC and Sera Prognostics.
Speech Title: "Scale-Free Genetic Interaction Networks in Heliothis virescens Challenged with Bacillus thuringiensis"
Abstract: Large scale genomic experiments allow analysis of regulatory pathways used by biological systems to transmit signals and coordinate multiple processes. These networking mechanisms allow the systems to respond and adapt to ever changing environments. It has been observed that transcription networks exhibit an approximately scale-free distribution, signifying the potential of transcription factors to regulate a multitude of target genes. These signaling networks are poorly understood in many organisms. In this project, we focus on the tobacco budworm (TBW, Heliothis virescens), which is a model insect for studying insecticide resistance, to quantitatively describe a network of hundreds or thousands of interacting genes that will give us clues about the dynamic response of this pest to insecticidal proteins from Bacillus thuringiensis (Bt) toxins. Early fourth instar larvae of TBW were exposed to a sub-lethal dose of Cry1Ac and Illumina short reads were obtained from three pools of midguts harvested at 0, 120, and 480 min after exposure. Approximately 20 million reads from each replicate were mapped to 18,728 TBW genes and weighted co-expression networks that exhibit a scale-free topology were identified. The highly interactive modules obtained from this analysis identified some of the genes associated with Bt mode of action/resistance being co-regulated with those that may be directly or indirectly involved in the response to Bt toxins in TBW.