Control of angiogenesis has been the aim of numerous studies in the context of tumor growth, biological development, and regenerative medicine, yet no models currently exist with demonstrated predictive capabilities. The Principal Investigators (PIs) view the complex biological processes leading to capillary morphogenesis as a consequence of cell-level decisions that are based on global broadcast signals, limited near-neighbor communication, and stochastic decision-making with feedback control. Integrating these factors, a cell becomes programmed to follow one of several state trajectories that could be characterized as quiescence, division, or migration and capillary formation. The PIs already have evidence that points to this type of behavior, and have been able to identify sub-populations of cells receiving angiogenic stimuli that are either migratory or mitotic. To address the needs for greater understanding and for a practical tool with predictive capabilities, the PIs propose to model angiogenesis by applying modern control theory principles. Each individual cell will be modeled as an independent unit responding to set of local and global controls.
This project is important both in terms of its immediate goals with respect to understanding in vitro angiogenesis, and in the broader context of vascular network growth in cancer, regenerative medicine and developmental biology. These studies could therefore have immediate impact on the creation of in vitro systems containing one or multiple cell types that could be used to mimic the function of a particular organ, thereby facilitating the discovery of new drugs or for use in toxicity screening. This project merges two fields that have been rapidly developing in recent years with minimal interaction: stochastic control theory and angiogenesis. A combination of these efforts should prove invaluable to both fields. Two grand challenges are addressed in this proposal: 1. To enhance knowledge of cellular and biomolecular behavior as an interactive function of a combination of coupled physical and biochemical stimuli; and 2. To develop quantitative modeling and simulation methods that faithfully replicate the complexity of cell and cellular interactions based on experimental data and deal creatively with the hierarchical cellular systems.
