ICCCB 2019

Keynote Speakers

2019 3rd International Conference on Computational Chemistry and Biology (ICCCB 2019) will be held in Seoul National University, Seoul, South Korea during December 19-21, 2019. The conference group sincerely invite any potential experts in Bioinformtics and related fields to deliever a speech in ICCCB 2019. If you are interested, please send your brief resume to icccb-info@cbees.net for application.

Keynote Speaker

Prof. Taesung Park
Seoul National University, South Korea

Prof. Taesung Park received his B.S. and M.S. degrees in Statistics from Seoul National University (SNU), Korea in 1984 and 1986, respectively and received his Ph.D. degree in Biostatistics from the University of Michigan in 1990. From Aug. 1991 to Aug. 1992, he worked as a visiting scientist at the NIH, USA. From Sep. 2002 to Aug. 2003, he was a visiting professor at the University of Pittsburgh. From Sep. 2009 to Aug. 2010, he was a visiting professor in Department of Biostatistics at the University of Washington. From Sep. 1999 to Sep. 2001, he worked as an associate professor in Department of Statistics at SNU. Since Oct. 2001 he worked as a professor and currently the Director of the Bioinformatics and Biostatistics Lab. at SNU. He served as the chair of the bioinformatics Program from Apr. 2005 to Mar. 2008, and the chair of Department of Statistics of SNU from Sep. 2007 and Aug. 2009. He has served editorial board members and associate editors for the international journals including Genetic Epidemiology, Computational Statistics and Data Analysis, Biometrical Journal, and International journal of Data Mining and Bioinformatics. His research areas include microarray data analysis, GWAS, gene-gene interaction analysis, and statistical genetics.

Speech Title: "Hierarchical Component Analysis for Microbiome Data Using Taxonomy Information"

Abstract: The recent advent of high-throughput sequencing technology has enabled us to study the associations between human microbiome and diseases. The DNA sequences of microbiome samples are clustered as operational taxonomic units (OTUs) according to their similarity. The OTU table containing counts of OTUs present in each sample is used to measure correlations between OTUs and disease status and find key microbes for prediction of the disease status. Various statistical methods have been proposed for such microbiome data analysis. However, none of these methods have used hierarchical structure of taxonomy information that is biologically meaningful. In this paper, we propose a hierarchical structural component model for microbiome data (HisCoM-microb) using taxonomy information as well as OTU table data. The proposed HisCoM-microb consists of two layers: one for OTUs grouped at the lowest taxonomy level and the other for OTUs grouped at the higher taxonomy level. Then we calculate simultaneously coefficient estimates of OTUs of all layers inserted in the hierarchical model. Through this analysis, we can infer the association between OTUs and disease status, considering the impact of taxonomic structure on disease status. Both simulation study and real microbiome data analysis show that our method provides a new testing approach for microbiome data which clearly reveal the relations between each taxon and disease status at the same time as finding the key microbiota of the disease.

Prof. Hans-Uwe Dahms
Kaohsiung Medical University, Taiwan

Dr. Hans-Uwe Dahms is a professor at Kaohsiung Medical University. He is interested in stress responses in general and within aquatic systems in particular. He, his colleagues and students integratively study pollution and the toxicology of stressors from physical, chemical, and biological sources. He is equally interested in climate change, the spread of diseases, antibiotic-resistance, food and drink safety from water sources, and integra-tive approaches in environmental and public health monitoring, risk assessment and management. He advised more than 25 Ph.D. students in their research and published more than 275 papers in scientific journals. He served as a reviewer for more than 70 SCI journals, as editorial board member of 12 reputed scientific journals, academic editor of PLosONE, and as editor in chief of FRONTIERS in Marine Pollution.

Speech Title: "Evaluation of In silico Toxicity Predictions"

Abstract: Chemoinformatics represents a search for chemical information resources where data are typically transformed into information and this into technologies that allow to make decisions better and faster. Such in silico approaches refer to computer applications or computer simulations. In silico approaches in pollution studies can best be understood as chemoinformatics using informational techniques applied to a range of problems in the field of chemistry related to toxicology and the effects of pollutants. To provide an example for the evaluation of in silico approaches, we will introduce to food safety issues related to food preservatives, plasticizers, and artificial sweeteners. For such assessments SMILES of the above food additives will be taken from the PubChem database. By using MarvinSketch all chemicals presented here are based on structural data retrieved from PubChem. In silico predictive models generally provide fast and economic screening tools for compound properties. They allow a high throughput and a constant optimization. They are less expensive, less time consuming, have a high reproducibility, and reduce experimental efforts. Computational approaches can also prioritize chemicals for their toxicological evaluation in order to reduce the amount of costly in vitro and ethically problematic in vivo toxicological screenings, and provide early alerts for newly developed substances. Limitations include that ADME aspects (absorption, distribution, metabolism, and excretion – which are basic pharmacokinetic descriptors) are not taken into account. There can be a lack of quality and transparency of the training set of experimental data. The programs, descriptors, and applicabilities are sometimes not clear. In addition are carcinogenicity predictions not possible based on non-genotoxic compounds.

Prof. Chanchal K. Mitra
University of Hyderabad, India

Chanchal Kumar Mitra (born November 02, 1950; W Bengal, India) is currently (2015-2017) an UGC (University Grants Commission, Government of India) Emeritus Professor at the Department of Biochemistry, University of Hyderabad. He obtained his B.Sc. (Bachelor of Science) degree from the Presidency College (University of Calcutta) with Chemistry as the main subject (1969) and his M.Sc. (Master of Science) degree from the same university in 1971 in Pure Chemistry. He did his doctoral work at the Tata Institute of Fundamental Research, Bombay (now Mumbai) on computational studies on the conformations of several aza-nucleosides and received his Ph.D. degree from the University of Bombay in 1977. He did post doctoral work at the University at Albany (New York, USA) and University of Lund (Sweden). He joined the University of Hyderabad in 1985 and retired in 2015. His current research interests are (I) biosensors and (II) modeling of metabolic pathways. He has a number of publications in the relevant areas which can be found from https://www.researchgate.net/profile/Chanchal_Mitra/contributions.

Title: "Kinetic Modeling of Sodium Glucose Co-transport"

Abstract: A simulation of the kinetics of the sodium-glucose transporters has been reported using a model widely used in literature. However, the various kinetic constants of the transporter have been replaced by 1 (as they are not available in the literature). We have also studied the effect of the membrane potential on glucose transport. The used model is leaky, i.e., sodium transport can take place independently of glucose transport. Although the results can be considered only semi-quantitative, we find that glucose transport is rather energy-intensive, because around 15 sodium ions needed to be transported for each glucose molecule carried inside. However, the process is powerful, in the sense that the final glucose concentration outside can fall almost to zero.