COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 203
Box 5.24
Modeling Challenges for Computer Science
Integration Methods
- Methods for integrating dissimilar mathematical models into complex and integrated overall models
- Tools for semantic interoperability
Models
- High-performance, scalable algorithms for network analyses and cell modeling
- Methods to propagate measures of confidence from diverse data sources to complex models
Validation
- Robust model and simulation-validation techniques (e.g., sensitivity analyses of systems with huge num-
bers of parameters, integration of model scales)
- Methods for assessing the accuracy of genome-annotation systems
SOURCE: U.S. Department of Energy, Report on the Computer Science Workshop for the Genomes to Life Program, Gaithersburg, MD,
March 6-7, 2002, available at http://DOEGenomesToLife.org/compbio/.
Box 5.25
Equation-free Multiscale Computation:
Enabling Microscopic Simulators to Perform System-level Tasks
Yannis Kevrikides of Princeton University and his colleagues have developed a framework for computer-aided
multiscale analysis. This framework enables models at a “fine” (microscopic, stochastic) level of description to
perform modeling tasks at a “coarse” (macroscopic, systems) level. These macroscopic modeling tasks, yielding
information over long time and large space scales, are accomplished through appropriately initialized calls to the
microscopic simulator for only short times and small spatial domains: “patches” in macroscopic space-time.
In general, traditional modeling approaches require the derivation of macroscopic equations that govern the
time evolution of a system. With these equations in hand (usually partial differential equations (PDEs)), a variety
of analytical and numerical techniques for their solution is available. The framework of Kevrikides and col-
leagues, known as the equation-free (EF) approach can, when successful, bypass the derivation of the macro-
scopic evolution equations when these equations conceptually exist but are not available in closed form.
The advantage of this approach is that the long-term behavior of the system bypasses the computationally
intensive calculations needed to solve the PDEs that describe the system. That is, the EF approach enables an
alternative description of the physics underlying the system at the microscopic scale (i.e., its behavior on
relatively short time and space scales) provide information about the behavior of the system over relatively
large time and space scales directly without expensive computations. In effect, the EF approach constitutes a
systems identification-based, “closure on demand” computational toolkit, bridging microscopic-stochastic
simulation with traditional continuum scientific computation and numerical analysis.
SOURCE: The EF approach was first introduced by Yannis Kevrikides and colleagues in K. Theodoropoulos et al., “Coarse Stability and
Bifurcation Analysis Using Timesteppers: A Reaction Diffusion Example,” Proceedings of the National Academy of Sciences 97:9840, 2000,
available at http://www.pnas.org/cgi/reprint/97/18/9840.pdf. The text of this box is based on excerpts from an abstract describing a presen-
tation by Kevrikides on April 16, 2003, to the Singapore-MIT Alliance program on High Performance Computation for Engineered Systems
(HPCES); abstract available at http://web.mit.edu/sma/events/seminar/kevrekidis.htm.
