Track 3 : 預測醫學和電腦模擬科學

Predictive and in silico Science

在創藥中系統生技的將來

The future needs the global molecular characterization of pathological pathways through a combination of computational biology and interaction discovery techniques. Next generation of discoveries start from the computational modeling of the pathological pathways using a wide variety of available information, from protein interactions to small metabolites, about the many patho-physiological routes in human. Then this seed model will be extended by including information in other organisms and determining the data that is still missing to complete it. The next step will be then to design and perform the necessary experiments to fill the gaps and, finally, perturb the system to confirm that it behaves as predicted by the model. We believe that this will revolutionize the drug discovery process, along with other applications such as prediction of adverse events, or prediction of new indications of existing drugs. A hands-on approach will be used, with the most recent examples and solutions regarding target discovery, prediction of adverse events, or prediction of new indications of existing drugs.

Patrick Aloy, Ph.D., Chief Scientifical Officer, Anaxomics Biotech; Institute for Research in Biomedicine - Barcelona Science Park

以模型模擬過之療法

Computational biology is improving our understanding of complex biological systems. Using very large biological datasets of cell signaling we have constructed detailed, mechanistic models. These may be used to predict network responses to targeted therapeutics such as monoclonal antibodies and small molecule inhibitors. Using growth factor signaling as an example, we will present how computational modeling can be used to simulate the best therapy with single agents or combinations of targeted inhibitors.

Ulrik Nielsen, Ph.D., Vice President, Research, Merrimack Pharmaceuticals

蛋白質療法的免疫原性評價

With over 50 therapeutic proteins on the market, and several 100's in clinical trials, the biotherapeutics are currently the largest growing drug segment. Over the past years, the challenges related to protein drugs become more obvious, and predictive methods to assess immunogenicity have become of age. This presentation will discuss strategies for pre-clinical prediction of immunogenicity with in silico tools, and evaluate how in silico tools can be used to optimize the protein engineering and lead selection process.

Philippe Stas, CEO, AlgoNomics

癌生物標靶的早期特定中蛋白質微陣列和量子點探針

This talk will describe a novel approach for detection of cancer markers using Quantum Dot Protein Microarrays. Quantum dots and protein microarrays are relatively new technologies that offer very unique features that together allow detection of cancer markers in biological specimens at pg/ml concentration.

Tatyana Zhukov, Ph.D., Assistant Professor, Cancer Prevention and Control Division, H. Lee Moffitt Cancer Center & Research Institute

Dana Farber Cancer Institute中多發性骨髓腫研究的統合架構

The Computational Biology and Functional Genomics Laboratory at the Dana Farber Cancer Institute is developing a novel web-based application to support Multiple Myeloma research at the Institute. The application provides research scientists an integrated view of clinical and laboratory data that allows them to better understand the relationships that exist. With this application, scientists can identify the mechanisms that can be linked to poor response to chemotherapy and use that information to develop new therapeutics. This application supports a wide range of research activities performed by a varied group of users, including study designers, biologists, statisticians and software developers. Its diverse functionality allows each group of researchers to utilize features that support their specific needs. Study designers can check participant metrics and sample availability, biologists can access statistician data and link to public-domain data sources, statisticians can export formatted data sets, and developers can rapidly develop and deploy new applications to accommodate evolving research needs. In this talk, we will cover the specifics of the application, as well as the ROI that has been achieved in terms of both cost and science.

John Quackenbush, Ph.D., Professor of Biostatistics and Computational Biology, Biostatistics and Computational Biology, Dana-Farber Cancer Institute

副作用的預測:為了從初期開始致力於藥物安全性的問題

This talk will describe a novel method to predict adverse side effects from the chemical structure only. By employing clinical databases along with chemical information, the chemical features can be identified that are most likely the reason for a certain side effect. This information can then be employed in early drug discovery.

Josef Scheiber, Ph.D., Postdoctoral Fellow, Lead Discovery Informatics/Safety Profiling, Novartis Institutes for Biomedical Research

針對一般疾患的基因預測模型的構築

Genetic prediction is at the heart of personalized medicine. The promise of a new medicine specifically tailored to the individual rests on our ability to forecast disease risk and treatment response of a patient on the basis of his genetic makeup. However, common diseases are unlikely to be caused by a single genetic variation but they are rather the result of the interaction of multiple genetic factors. While current genome-wide SNP microarrays give us the opportunity to query virtually all of the variations in the human genome, current analytical methods tend to focus on the analysis of one SNP at a time. This talk will show how to identify the complex multigenic profiles underpinning complex traits and to develop prognostic models able to predict the risk of an individual based on his genetic variations. In particular, this talk will describe the use of Bayesian networks for the purpose of developing these models and introduce a novel set of techniques and search algorithms specifically tailored the analysis of genome-wide association studies. Applications to the identification of genomic predictive models to common diseases, such as stroke, asthma and nicotine dependence, will be described.

Marco Ramoni, Ph.D., Assistant Professor, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School

GenoCAD: GenoCAD: 電腦支援之人口基因系統的設計和應用

A gap exists between the few academic groups who have the capability of running small scale proof-of-concept projects in synthetic biology and the people who could identify and benefit from biomedical and industrial applications of this technology. We are creating the infrastructure making it possible for non-specialists to design large-scale genetic systems that could be used in basic biological research or product development programs. We are adapting the workflow developed by the electronics industry to automate the design and fabrication of electronic circuits, to the design and assembly of Very Large Scale Integrated genetic systems. This talk will describe the molecular tools, algorithms, and software applications in development that will make the computer assisted design and fabrication of genetic systems a reality within a 5 year time frame.

Jean Peccoud, Ph.D., Associate Professor, Virginia Bioinformatics Institute, Virginia Tech

醫藥品開發的系統化方法: 生物模擬和生物計算

Drug development needs to find new and innovative ways to increase the probability of success. Disease modeling using bio-simulation and bio-mathematics is a promising approach to achieve this objective. However, succeeding in this area requires an interdisciplinary effort involving biologists, chemists, mathematicians and engineers. This talk will describe the use of mathematical and/or statistical models for diseases, and for variations amongst individual patients, that would greatly facilitate the task of predicting how a particular drug would interact with a patient population.

Dinesh DeAlwis, Ph.D., Lilly & Co.
Prof. Zvia Agur, Founder, Chairperson and CSO, Optimata
David de Graaf, Ph.D., Head, Systems Biology, Pfizer
M. Vidyasagar, Ph.D., Executive Vice President, Tata Consultant Services