2:35 Mechanisms of Toxicity
Keith Elliston, Ph.D., President & CEO, Genstruct Inc.
3:05 Large Scale in silico Animal Model Expedites Discovery of Optimal Treatments for Type 1 Diabetes
Saroja Ramanujan, Ph.D., Associate Director, In Silico Research and Development, Entelos
Matthias von Herrath, M.D., Member, Division of Developmental Immunology, La Jolla Institute for Allergy and Immunology
To date, no single pharmacologic agent has been identified that can reverse the onset of human type 1 diabetes (T1D). Combinations of existing agents are promising treatment strategies; however the uncertainty of optimal dosing regimens can greatly reduce the efficiency of laboratory studies. The collaborative research between the ADA/Entelos Diabetes Research Center (DRC) and Dr. Matthias von Herrath’s lab at the La Jolla Institute for Allergy and Immunology (LIAI) used a combined in silico/laboratory approach to identify optimal combination therapies to reverse diabetes onset in non-obese diabetic (NOD) mice. Using the T1D PhysioLab® platform, a large-scale mathematical model of T1D pathogenesis in the NOD mouse, variations in dosing and timing were simulated to identify treatment protocols that maximize the likelihood of observing diabetes reversal in the laboratory. The simulation results predicted optimal dosing regimens for oral insulin/anti-CD3 and oral insulin/exendin-4 combination therapies. By conducting laboratory experiments directly informed by in silico simulations, LIAI has been able to optimize its research design and expedite the discovery of type 1 diabetes therapeutics.
3:35 Technology Spotlight
(Sponsorship Available)
3:50 Networking Refreshment Break, Poster and Exhibit Viewing
4:30 Dialogue on Reverse Engineering Assessment and Methods: the DREAM of High Throughput Pathway
Inference
Gustavo A. Stolovitzky, Ph.D., Adj. Associate Professor of Biomedical Informatics, Columbia University & Manager, Functional Genomics & Systems Biology, IBM Research
The biotechnological advances of the last decade have confronted us with an explosion of data that need to be organized and structured before they may provide a coherent biological picture. To accomplish this task, the availability of an accurate map of the physical interactions in the cell that are responsible for cellular behavior and function would be exceedingly helpful, as these data are ultimately the result of such molecular interactions. However, all we have at this time is partially correct representation of the interactions between genes, their byproducts, and other cellular entities. DREAM, the Dialogue on Reverse Engineering Assessment and Methods, is fostering a concerted effort by computational and experimental biologists to understand the limitations and enhance the strengths of the efforts to reverse engineering cellular networks from high throughput data. In this talk I will discuss the salient arguments of the recent DREAM2 conference, where we challenged the community to blindly infer networks known to the organizers from high throughput data. I will highlight the strategies that have achieved the better inference results and discuss the state of the art in Reverse Engineering, as well as some of the challenges and opportunities awaiting us.
5:00 DREAM Award Winner--TBA
5:30 Close of Day
