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Tuesday, June 10

7:30 am - 6:00 pm Registration Open 

7:30 am Breakfast Workshop (Sponsorships Available) or Morning Coffee

8:15 Chairperson’s Remarks

8:20 The Myth and Reality in Drug Discovery and Development
Ting-Chao Chou, Ph.D., Director, Preclinical Pharmacology Core, Molecular Pharmacology & Chemistry Program, Memorial Sloan-Kettering Cancer Center
I have used a very different approach for my biomedical research and drug discovery by applying the median-effect equation of the mass-action law, which is based on and derived from the biophysical, biomedical, and mathematical. This approach integrated the information into a whole body of pharmacological dynamic systems. My experience tells me that the molecular biology and cell biology, which is based on systems biology has little to do with drug discovery. Those basic research are for “knowledge” which is very remote from actual practices. Drug discovery is practical mission-oriented, emphasizing the end-results. I can use my own stories as examples to prove my points for the efficient and effective way of drug discoveries.

8:50 Using a Systems Approach to Drug R&D
Ulrik Nielsen, Ph.D., Vice President, Research, Merrimack Pharmaceuticals
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 the ErbB signaling network as an example, we will present how simulation proposed MM-121, a monoclonal anti-ErbB3 antibody, as a potentially superior approach for current therapies.

9:20 Human Metabolic Network – Reconstruction and Its Application for Drug R&D 
Igor Goryanin, Ph.D., Henrik Kacser Chair in Computational Systems Biology, Director, Edinburgh Centre for Bioinformatics, The University of Edinburgh
The presentation describes the process of systems kinetic modeling. The talk starts from the reconstruction of a high quality human metabolic network from the genome information, and highlights the existing problems in the reconstruction. The reconstructed metabolic networks provide a unified platform to integrate all the biological and medical information on genes, proteins, metabolites, disease, drugs, and drug targets for a system level study of the relationship of metabolism and disease. Furthermore, the complex network organization structure revealed by structural analysis requires us to develop a system-oriented drug design strategy. Applications of systems kinetic modeling approach are discussed, from the planning of wet lab experiments, drug research and development process to computational methods and software for systems biology.

9:50 Networking Coffee Break, Poster and Exhibit Viewing

10:45 Cancer Invasion and Metastasis: Collaborative Research Using Dynamic Signaling Pathway Models 
Fredric Gorin, Ph.D., Professor, Neurology & Neuroscience, University of California Davis

11:15 The Use of Cancer Vaccines in Combination Therapies 
James Hodge, Ph.D., Senior Scientist, Head, Recombinant Vaccine Group, Laboratory of Tumor Immunology and Biology, National Cancer Institute, NIH 
The aims of this talk are to discuss novel strategies for the use of vaccines in combination therapies and to understand the underlying mechanisms of these combinatorial therapies so that they may be translated to science-driven clinical trials. Specifically, topics covered will address the use of vaccines with (a) various modalities to direct radiation to the tumor site, (b) standard of care chemotherapeutic drugs, and (c) immune modulators such as monoclonals anti-CTLA-4 and anti-CD25, and cytokines that will either enhance dendritic cell migration/activity or enhance the activity of T cells and other effector cells.

11:45 Estimating Second Cancer Risks After Radiotherapy 
Rainer Sachs, Ph.D., Research Professor, Mathematics and Physics, University of California, Berkeley 
As patients are treated at younger ages with cancer radiotherapy and survival improves, second cancers caused by the radiation in the same or nearby organs are an increasing concern. Treatment protocols are changing and thus long latency periods prevent acquiring data on current protocols; consequently mathematical and computational models are needed to predict second cancer risks. Recent deterministic and stochastic IIP models consider cell initiation to a pre-malignant state, cell inactivation, and cell proliferation during an extended radiotherapy regimen. Some involve comparatively sophisticated quantitative methods. They can be validated by second cancer data from earlier protocols and used to estimate risks of current and prospective protocols.

12:15 Close of COLLABORATION—APPLYING SYSTEMS BIOLOGY Conference

12:30 Luncheon Technology Workshops
(Sponsorships Available) or Lunch on Your Own

12:00 pm Registration Open

2:00 Chairperson’s Remarks

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 NextBio — A New Search Engine for Large-Scale Biological Data that Embraces Open Biology Ilya Kupershmidt, Cofounder, VP Product Management, NextBio

Sponsored By

NextBio embraced the Open Biology paradigm by providing open access to its search engine for the entire life science community. NextBio’s goal is to address the explosion of information in the life sciences by enabling seamless and easy search and correlation, import and sharing of large-scale experimental data. Through NextBio researchers and clinicians can discover new findings from their own as well as publicly available data and can formulate and test new hypotheses. In this talk we will demonstrate our novel search strategy which enables individual scientists to take advantage of global collections of large-scale heterogeneous data in real time

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 BetaWorkbench: An Innovative Framework for Systems Modeling, Simulation and Analysis 
Corrado Priami, Ph.D., President & CEO, Computational and Systems Biology, Microsoft Research - University of Trento Centre for Computational and Systems Biology 
The talk will present new stochastic techniques to model and analyse genomic signalling networks as well as new software prototypes based on these techniques. The basic idea is that any biological element is represented as a program and the interaction between the elements is modeled as a message passing between the corresponding programs. This metaphor allows us to reuse concurrency theory developed in computer science in the last 30 years to study the behaviour of distributed software running on computer networks into the biology applicative domain. 

5:30 Close of Day

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