Cyber-enabled Discovery and Innovation (CDI) Workshop (October 30, 2007)
(Sponsored by the National Science Foundation)
In FY 2008, NSF will initiate a foundation-wide, cross cutting initiative called Cyber-enabled Discovery and Innovation (CDI). The goal of CDI is to advance science and engineering along fundamentally new pathways opened up by computational thinking, through development and application of models, algorithms, and software. This includes mathematically connected areas such as knowledge extraction from large data sets, computational experimentation, and algorithms development. The NSF will fund the program at a minimum of $26 million for 2008 with substantial increases of funding over the next five years.
In order to inform the mathematical sciences community about this interdisciplinary opportunity, the NSF will hold a one-day workshop in several of the DMS Mathematical Institutes. In particular, on October 30, a one-day workshop will be held at the Mathematical Biosciences Institute (MBI) focusing on opportunities for mathematicians who are interested in doing interdisciplinary work related to biology. The workshop will include overview talks, panel discussions, and presentations from representatives of the NSF Division of Mathematical Sciences and Directorate for Biological Sciences.
Participants
- Baltazar Aguda (Mathematical Biosciences Institute, Critical Care Center, The Ohio State University Medical Center)
- Jon Bell (MBI - Long Term Visitor, The Ohio State University)
- Mark Berliner (Department of Statistics, The Ohio State University)
- Janet Best (MBI - Long Term Visitor, The Ohio State University)
- Denis Blackmore (Mathematical Sciences, New Jersey Institute of Technology)
- Caroline Breitenberger (Department of Biochemistry, The Ohio State University)
- Daniela Calvetti (Department of Mathematics, Case Western Reserve University)
- Suncica (Sunny) Canic(Department of Mathematics, University of Houston)
- Kree Cole-McLaughlin (Department of Mathematics, University of California, Los Angeles)
- Huseyin Coskun (MBI - Postdoc, The Ohio State University)
- Keith Crank (American Statistical Association)
- Ramana Davuluri (Human Cancer Genetics, The Ohio State University)
- Judy Day (MBI - Postdoc, The Ohio State University)
- Angela Dean (Department of Statistics, The Ohio State University)
- Nathanael DePano (Department of Computer Science, University of New Orleans)
- Tamal K. Dey (Computer Science and Engineering, The Ohio State University)
- Marko Djordjevic (MBI - Postdoc, The Ohio State University)
- Hana El Samad (Department of Biochemistry and Biophysics, University of California, San Francisco)
- German Enciso (MBI - Postdoc, The Ohio State University)
- Hakan Ferhatosmanoglu (Computer Science and Engineering, The Ohio State University)
- Aaron Fogelson (Department of Mathematics, University of Utah)
- Paula Grajdeanu (MBI - Postdoc, The Ohio State University)
- Edward Green (MBI - Postdoc, The Ohio State University)
- Erich Grotewold (Plant Biology and Plant Biotechnology Center, The Ohio State University)
- John Guckenheimer (Mathematics Department, Cornell University)
- Mary Ann Horn (National Science Foundation)
- Kun Huang (Department of Biomedical Informatics, The Ohio State University)
- Oleksiy Ignatyev (Statistics and Probability, Michigan State University)
- Dan Janies (Department of Biomedical Informatics, The Ohio State University)
- Jihyoun Jeon (Program in Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center)
- Chiu-Yen Kao (MBI - Long Term Visitor, The Ohio State University)
- Yangjin Kim (MBI - Postdoc, The Ohio State University)
- Chenglong Li (Department of Medicinal Chemistry, The Ohio State University)
- Yi Li (Department of Mathematics, The Ohio State University)
- Shili Lin (Department of Statistics, The Ohio State University)
- Jun Liu (Department of Biostatistics, Harvard University)
- Yuan Lou (MBI - Long Term Visitor, Department of Mathematics, The Ohio State University)
- Raghu Machiraju (Department of Computer Science & Engineering, The Ohio State University)
- Clay Marsh (Department of Internal Medicine, The Ohio State University)
- Anastasios Matzavinos (Department of Mathematics, The Ohio State University)
- Francois Meyer (Department of Electrical Engineering, University of Colorado at Boulder)
- Simon Morgan (Data Modeling and Analysis, Los Alamos National Laboratories)
- Andrew Nevai (MBI - Postdoc, The Ohio State University)
- Qing Nie (Biomedical Engineering and Mathematics, University of California, Irvine)
- Andrew Oster (MBI - Postdoc, The Ohio State University)
- Kevin Passino (Department of Electrical and Computer Engineering, The Ohio State University)
- Michael Paulaitis (Chemical & Biomolecular Engineering, The Ohio State University)
- Bobby Philip (Theoretical Division, Los Alamos National Laboratory)
- Feng Qin (Computer Science and Engineering, The Ohio State University)
- Steve Qin (Department of Biostatistics, University of Michigan)
- Michael Rempe (MBI - Postdoc, The Ohio State University)
- Joel Saltz (Department of Biomedical Informatics, College of Medicine, The Ohio State University)
- Tom Santner (Department of Statistics, The Ohio State University)
- Richard Schugart (MBI - Postdoc, The Ohio State University)
- Chih-Wen Shih (MBI - Long Term Visitor, The Ohio State University)
- Greg Smith (MBI - Long Term Visitor, The Ohio State University)
- Partha Srinivasan (MBI - Postdoc, The Ohio State University)
- Brandilyn Stigler (MBI - Postdoc, The Ohio State University)
- Yvonne Stokes (Department of Applied Mathematics, University of Adelaide)
- Shuying Sun (MBI - Postdoc, The Ohio State University)
- Barbara Szomolay (MBI - Postdoc, The Ohio State University)
- Peter Thomas (Department of Mathematics, Case Western Reserve University)
- Joanne Trgovcich (Department of Pathology, OSUMC)
- Joanne Turner (Department of Internal Medicine, The Ohio State University)
- Joe Verducci (Department of Statistics, The Ohio State University)
- Yusu Wang (Department of Computer Science, The Ohio State University)
- Zidian Xie (Plant Cellular and Molecular Biology, The Ohio State University)
- Todd Young(Department of Mathematics, Ohio University)
Abstracts and Lecture Materials
The human cardiovascular system is so complex that it remains unfeasible to numerically simulate its entire function using three-dimensional models. Studying wave propagation in pulsating arteries and local hemodynamics, however, is important in understanding the mechanisms leading to various cardiovascular complications. Many clinical treatments can only be studied in detail if a reliable model describing the response of arterial walls to the pulsatile blood flow is considered. Although fascinating progress has been made in some areas of modeling and simulation of the human cardiovascular system many of the basic difficulties remain open and will continue to present major challenges in the years to come.
In an interdisciplinary effort involving mathematicians, cardiologists, and engineers, our group has begun a comprehensive study of fluid-structure interaction between pulsatile blood flow and human arterial walls in healthy and diseased states. The speaker will give an overview of the main problems in this area and show examples of how mathematics, combined with computation, bioengineering, and cardiovascular measurements can shed light on the fundamental difficulties associated with this multi-physics and multi-scale problem. Examples of how this research aided the design of vascular devices called stents and stent-grafts used in nonsurgical treatment of aortic abdominal aneurysm and coronary artery disease will be presented. Experimental measurements performed at our Mock Circulatory Flow Loop assembled at the Texas Heart Institute, will be shown.
This is a joint work with Dr. Z. Krajcer and Dr. D. Rosenstrauch (Texas Heart Institute), Dr. C. Hartley (Baylor College of Medicine), Prof. R. Glowinski, Prof. T.W. Pan, Prof. G. Guidoboni (University of Houston), Prof. A. Mikelic (University of Lyon 1, France), and Prof. J. Tambaca (University of Zagreb, Croatia).
A synergetic partnership between experimental biology and computational techniques holds the promise of unraveling the intricacies of biological organization at an unprecedented pace. But, what exactly do mathematical approaches, modeling in particular, add to experimental studies? Most importantly, how can models be rigorously interfaced with biological data and systematically used to design optimal experiments?
In this talk, we argue that answering such questions convincingly is a fundamental challenge for Systems Biology, and report on our recent approaches for addressing these questions. Specifically, we present results that demonstrate how mathematical models can be used to optimally and algorithmically discriminate between alternative, but equally plausible, models of biological networks. We then illustrate, through biological examples, the applicability of these methods and discuss how, in combination with careful systemic analysis, they can help decipher the organizational principles of biological networks. We finally comment on our current and future experimental/mathematical adventures, which are motivated (and necessitated) by the investigation of similar realistic biological problems.
Intravascular clotting is triggered when damage to the lining of a blood vessel initiates the intertwined processes of platelet aggregation and coagulation. This leads to the formation on the damaged surface of clumps of cells intermixed with a fibrous protein gel. Platelet aggregation begins when circulating blood platelets adhere to the damaged wall. Other platelets, activated by chemicals released by these platelets, bind to the already wall-adherent platelets, thus building a platelet aggregate. Coagulation is itself comprised of two distinct subprocesses. One involves a network of tightly-regulated enzymatic reactions that begins with reactions on the damaged vessel wall and continues with reactions on the surfaces of activated platelets. The final enzyme thrombin i) activates additional platelets and ii) creates monomeric fibrin which polymerizes into the fibrous gel component of the clot. This polymerization process is the second subprocess of coagulation, and it triggers a second enzymatic network, the fibrinolytic system, that begins to degrade the newly formed fibrin gel even as the coagulation system is promoting continued gel formation. These processes all occur in the face of continued blood flow past the injury, and are strongly affected by the fluid dynamics by means that are as yet poorly understood.
Pathological clotting (thrombosis) is the immediate cause of most heart attacks and strokes. Consequently it remains the focus of intense experimental investigation, most of which focuses on small component parts of the clotting process. Little research is done into the integrated actions of these parts. The reason for this is clear: the biochemical, biophysical, and biomechanical interactions important in thrombosis are complex; dynamic; spatially distributed; involve disparate physical, mechanical, and chemical processes; and span a wide range of spatial and temporal scales. Understanding these interactions poses severe challenges to traditional laboratory experimentation. Investigating clotting as an integrated system is essential, and this requires tools well suited to looking at the dynamic behavior of complex systems, namely, mathematical modeling and computation.
In this talk, I will sketch our efforts at formulating computational models of platelet aggregation that take into account biological as well as physical aspects of the process. I will describe how our modeling of coagulation, with a simple treatment of flow and platelet events, shows how these physical features strongly impact the biochemical system. I will sketch our new explorations into fibrin polymerization and gelation. Lastly, I will outline the work we plan to build computational models of the integrated processes of platelet aggregation, coagulation, fibrin polymerization, and fibrinolysis. This will require an interdisciplinary team of mathematicians, computational scientists, and clotting experimenters working closely with one another, to build realistic models of clotting, develop computational tools that fully exploit the power of new computing systems, and carry out tandem computational and experimental explorations designed from the start to maximally inform one another.
The NSF CDI Initiative is directed at "revolutionary science" made possible by "advances and innovations in computational thinking." Dynamical systems ("chaos") theory saw revolutionary advances during the past half century in which nonlinear phenomena manifest in simple models were observed in complex systems throughout science and engineering. Few of these observations were based upon quantitative models; either due to the computational demands of highly detailed models or the lack of "physical" principles and data required to parametrize the models. I will describe my collaborative research on control of movement with both neuroscientists and biomechanicians as an example in which computational thinking plays a central role in bringing theory and experiments together.
I will describe two projects in my lab on the study of proteins. One is to use sequential Monte Carlo (SMC) and particle filtering method to estimate the side-chain comformational entropy of proteins; the second is on the simulation and optimization of hydrophobic-hydrophilic (HP) protein models. In the second study, we developed a novel fragment-regrowth Monte Carlo procedure, which uses the SMC idea in Markov chain Monte Carlo iterations. When applied to the seven well-known 3-D HP models designed by other researchers, our algorithm gave the best results to-date, some of which are significantly better than the previously known best results.
Biological organization arises via spontaneous, hierarchical self-assembly processes. A principal benefit of current genome projects has been to provide the "parts lists" of components for such processes. The central task of computations in biology today is to determine the underlying principles of biomolecular recognition that enables reconstruction of these components into higher order patterns of organization. In this presentation, I introduce the Potential Distribution Theorem (PDT), which provides a theoretical foundation and a practical tool for developing algorithms to compute conformational free energies associated with these biomolecular recognition events. I will describe two research efforts aimed at implementing the PDT to elucidate the molecular basis for selectivity in the KcsA potassium ion channel, and for sensitivity to peptide-specific signals delivered through the T-cell receptor/peptide-major histocompatibility complex that can lead to T-cell activation in adaptive immune response.
Integrative biomedical research involves the coordinated acquisition, interpretation, integration and analysis of large amounts of complementary biomedical information. The goal is to generate detailed and accurate multi-scale characterization of disease initiation, progression and treatment response. I will describe two examples of integrative biomedical research, one drawn from Cardiology and the other from Radiation treatment planning. I will then discuss what needs to be accomplished from a Biomedical Informatics perspective and describe some of the resulting issues that arise in Computer Science and Mathematics.
To better "understand Complexity in Natural, Built, and Social Systems," the Cyber-Enabled Discovery and Innovation initiative seeks proposals that integrate meathematical modeling, computational thinking, and algorithmatical advances. This talk will describe statistical tools for more effectively integrating knowledge gained from computational research with physical experiments. An example drawn from bioengineering will be used illustrate the main points.
