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.
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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