The Center for Cell Dynamics fosters research in cell and developmental biology that fuses experimental approaches with realistic, mechanistic computational models. The computational pursuit focuses both on high-resolution imaging methods for visualizing the interactions of molecular building blocks, and on creating computer simulations that capture systems-level properties of gene networks, the cytoskeleton, and cellular interaction, on the basis of known properties of the molecular building blocks of life. The experimental pursuit aims to develop and ground mechanistic models through detailed observation of cells and embryos, and to test predictions of those models. The Center is organized around several inter-related research projects in which the fusion of experimental and computational approaches will lead toward improved 
comprehension of fundamental biological processes, ranging from how webs of interactions among subsets of cellular macromolecules animate living cells to how the evolutionary process interacts with complex biological mechanisms. The Center for Cell Dynamics is one of 9 centers funded by the National Institutes of Health / National Institute of General Medical Sciences under the Complex Biological Systems Initiative.
In order to accomplish its mission, the Center promotes collaboration among scientists from both experimental and computational/mathematical backgrounds. The Center for Cell Dynamics is located at the University of Washington's Friday Harbor Laboratories, in a modern, well-equipped laboratory in a monastic setting with few distractions and no traditional departmental boundaries. The Center provides support for undergraduate and post bach research internships, for post-doctoral fellows and for sabbatical visitors to develop and pursue research projects involving both wet-lab and computational biology. The Center provides state-of-the-art laboratory equipment (including two confocal microscopes and a DeltaVision deconvolution microscope) and vast computational resources. The Center supports full-time resident research scientists and support staff including computer experts. Finally, the Center aims to promote the fusion of experimental and theoretical approaches through a series of focused workshops and research apprenticeship courses. The Center's work is reviewed annually by it's scientific advisory board.
Until recently, mainstream cell and developmental biologists have not found mathematical approaches or computer modeling generally useful or even interesting. In fact it is not uncommon to read claims by pundits or philosophers of science to the effect that biology is intrinsically atheoretical. Yet, this ignores the vital role that mathematically-founded theory has played in the progress of several biological disciplines, e.g. evolutionary biology (the theory of population genetics), biochemistry (the theory of enzyme kinetics), and electrophysiology (the Hodgkin-Huxley theory of action potential conduction). These instances in which experimental biologists embraced theoretical tools all reflect a common theme: the need to link lower-level facts together to develop explanations of, and predictions about, higher-level observable phenomena (e.g., population genetics synthesizes the basic facts about heredity into predictions about gene flow in populations, enzyme kinetics synthesizes the premises of chemistry to interpret kinetic data concerning the function of biological macromolecules).
Thus theory finds a role in biology whenever laboratory practitioners find themselves confronted with the task of synthesizing information, acquired in reductionist studies, about the behavior of parts (whether the parts are molecules, genes, cells, or organisms) into descriptions of the function of the whole (where the whole could mean anything from individual macromolecules consisting of monomers, to entire tissues, or even natural populations). This, in essence, is the very nature of the modern biological quest: to comprehend how the self-organizing complexity of life emerges from swarms of material components that obey the laws of physics and chemistry.
Molecular, cell, and developmental biologists increasingly find themselves confronted with greater complexity than mere human intuition can make sense of: genome projects, DNA microarrays, and other high-throughput techniques yield an embarrassment of riches on the molecular level. Once upon a time, a cell biologist interested in how cells signal each other would have had to content herself merely with identifying the major components and figuring out the order of involvement in the process of interest. Today, identifying the components is becoming quicker and easier, making it feasible in principle to synthesize these components into whole mechanisms, with constraints and dynamics that emerge from the sum of the parts, but which cannot be predicted from a mere list of those parts. Because even the simplest interesting cases involve networks consisting of dozens of molecular participants, interacting according to non-linear rules and involved in diverse feedback phenomena, it is beyond human intuition to predict what behaviors, tendencies and constraints might emerge from what recipe of participants.
That's where computer models come in: using a computer simulation, biologists can ask what behaviors to expect from some network of molecules, and perhaps more importantly, test how the predicted behavior depends on the assumptions used to concoct the model. Models, when they work, demonstrate that the molecular facts you know (or assume) can actually generate the higher-level emergent behavior you seek to explain. Especially when they fail, models can provide feedback to experiments by highlighting missing information. Models can suggest what kinds of experiments might reveal details about the mechanism in question, and can predict unanticipated behaviors in response to perturbation.
We prefer agent-based models in which each of many thousands of individual parts is governed by a small system of ordinary differential equations characterizing how it interacts with any/all the other parts. In a cytoskeletal dynamics model, a typical individual agent could be a centrosome or a single kinesin motor molecule, or a segment of a microtubule or actin filament (with the entire microtubule or actin filament comprising tens or hundreds of segments). Since any agent can, potentially, go anywhere and collide with, then interact with, any other agent, a large part of the computational task is to detect collisions. As opposed to the classic continuum model approach in which analytic solutions, or approximations thereto, are possible, these agent-based models require numerical computer solution of their systems hundreds of thousands of coupled differential equations. At first glance this seems a step backward, away from the elegant, conceptually simple approach in which just the process of formulating a classic-form continuum mathematical model distills out a conceptual understanding of what causes the phenomenon under study. It seems to be a step toward a needlessly complicated and computationally expensive kind of modeling involving more arithmetic than thought. But this disadvantage is more than compensated by the principal virtue of agent-based models: the input to such models consists only of low-level facts/assumptions, usually very simple, about how small parts interact. The entire connection between the interactions among the swarm of parts and the systems-level behavior that emerges is the computer solution of the coupled system of ordinary differential equations. The virtue of agent-based modeling is that it bypasses the use of human intuition to distill into an abstract constitutive equation the high-level consequence of many agents interacting. Since human intuition can so easily go wrong, it is a virtue to replace (error-prone) thought by (a great deal of) simple arithmetic. Agent-based modeling is a more direct demonstration that the simple interaction rules by which the myriad parts interact actually can (or cannot) account for what complex phenomena emerge from them.
The Center's research seeks to bridge the conceptual gap between detailed knowledge of the "molecular parts catalog of life" and higher-level macroscopic "emergent" descriptions of cell dynamics and behavior, using computer imaging and the most realistic feasible simulations. We are especially interested in research questions in which we can learn about the molecular toolkit from well-studied model organisms such as Drosophila or C. elegans, develop ideas about super-molecular mechanisms from modeling, and then exploit Nature's experiments (in the form of the diversity of cell and embryo behavior) to learn about the variational constraints of those mechanisms. Major research efforts are focused on: