DIMACS Workshop on Complexity in Biosystems: Innovative Approaches at the Interface of Experimental and Computational Modeling

April 8 - 10, 2002
DIMACS Center, CoRE Building, Rutgers University, Piscataway, NJ

Thomas Deisboeck, Harvard Medical School, deisboec@helix.mgh.harvard.edu
Lee Segel, Weizmann Institute, lee@wisdom.weizmann.ac.il
Eduardo Sontag, Rutgers University, sontag@gauss.rutgers.edu
Raimond Winslow, The Johns Hopkins University, rwinslow@bme.jhu.edu
Presented under the auspices of the Special Focus on Computational Molecular Biology.


This workshop will bring together scientists who are already working in the novel field of complex biosystems modeling and simulation from different points of view and "newcomers" who are interested in getting involved in this exciting research area. The focus is on the challenging interface between experimental modeling (e.g., assay design and engineering) and computational simulation. The goal is to introduce and discuss innovative concepts, experimental and computational biosystems models and mathematical algorithms as well as to establish new collaborations beyond institutional or departmental boundaries.


Recently, complex biosystems science has attracted substantial interest. This is especially so because conventional biological science has produced a vast amount of data over the last few decades so that questions arise as to how to find patterns and how to relate multi-quality data sets in the quest for underlying mechanism. The most visible example is the Human Genome Project and its spin-off sciences: genomics and proteomics. However, the dynamics of such complex biological systems cannot be simply explained by combining the separately measured behavioral features of its monomers. As such a living cell is more than the sum of its organelles; other examples for biosystems include the neuronal networks in the brain, the immune system, disease processes as well as population dynamics and entire ecosystems.

It is clear that conventional, reductionism-determined research approaches must fail in understanding the mechanisms behind the complex pattern generation, the self-organization and nonlinear interaction of these multi-scaled systems. Thus to develop novel research approaches with complex biosystems science will be one of the grand challenges of the next decade. Tremendously increased computational power will help in the efforts ahead to analyze the immense amount of data in the biological and biomedical sciences in order to guide promising new experimental work. Such "experimental biosystems modeling" means e.g. the design, the development and use of novel assays, i.e. in vitro models, based on and driven by the mathematical and computational modeling. We need to come up with such new experimental methods to test and refine the predictions made with novel theoretical models - especially if based on an underlying `complex systems concept'. Most conventional experimental models, however, have been developed in the reductionism-era, i.e., they focus on one endpoint and emphasize one feature - with little dynamical information and lacking the possibility of studying more than one to two features of the system (reproducibly) at the same time. The concept was for a long time that one simply has to dissect the biology, investigate it separately, and finally put it all back together. Most scientists would now admit that although this approach has led to very significant discoveries in the past it will not be able to explain the complex behavior of most biological systems.

Therefore, experimental approaches also have to change - and we hope that the ongoing theoretical, computational and mathematical modeling and simulation efforts will support and push this development. We further hope that theoretical modeling will give us `hints' as to where to investigate in even greater detail in experiments (thus guiding future conventional approaches) and how to design and engineer these experiments settings properly to take the complexity into account - not to take it out of the calculation. The workshop should therefore bridge the gap between experimental and computational modeling experts. Innovative complex biosystems research requires even more than only biology-inspired computational science. In fact it can only be successful if multiple seemingly disparate disciplines combine their techniques and expertise, including biology, physics, engineering, mathematics and medicine, even economics and sociology. In summary, the need for truly interdisciplinary teams is apparent. This workshop therefore will try to link more closely the groups already working in this area and to get scientists involved who are just starting in this emerging scientific field. Topics will include experimental modeling concepts on the intracellular, the supracellular and the tissue level and the required combination with computational approaches such as genetic net modeling, cell signaling modeling as well as continuum and discrete modeling for multi-element systems.

The Workshop Structure

From a biological point of view, the meeting will be organized by (modeling) scales - ranging from the molecular to the cell, multicellular as well as the organ and disease process levels, with cognizance of the fact that perhaps the most interesting problems span more than one scale. Contributors will structure their presentation in terms of the categories:
(1) biomedical background and experimental platform(s)
(2) computational/mathematical modeling concept and modeling platform
(3) discussion of the modeling results (with emphasis on future work on both sides, experiment & simulations and its potential impact on biomedicine).

Given that the exploration of the input-parameter sets is one of the main bottlenecks for current modeling efforts, one focus of the discussion will be to define the requirements for novel experimental biomedical model systems. As such the workshop will be structured in "case-studies", which represent the scale-related modeling efforts. Nonetheless, after each session we will discuss the multiscale "environment", i.e., interface and challenges involved in passing to lower/higher scales so that a linkage with the other sessions is achieved. We will also have a panel discussion with audience participation on non-reductive experimentation so that new modeling paths can be considered and collaborative projects can be discussed.

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Document last modified on November 5, 2002.