Title: A Reverse-Engineering Framework for Modeling Biological Systems. Abstract: Similar reverse-engineering techniques can be applied to any system. The basic system requirements are: 1) a hypothesis of the system dynamics expressed as differential equations, 2) a set of unknowns as system inputs (i.e. controls), 3) a set of outputs that correspond to experimental observations and 4) empirical data. Based on control theory, we have developed a reverse-engineering framework for simulating musculoskeletal systems in a way that leverages experimental observations to determine individual muscle forces, which cannot be observed or measured directly. Already, this framework has eliminated much of the need for “brute force” methods, like large-scale optimizations, to solve for unknown muscle forces, and, have reduced computing costs by three orders of magnitude. Such dramatic reductions in computing time and resources allow for many more simulations to be run in a much shorter timeframe. This, in turn, enables models to be developed iteratively– at each step assessing how well the current model reproduces experimental observations and providing the opportunity to adapt the model. This is a straightforward approach to identifying the source of discrepancies between simulated and empirical data and to attribute these differences to specific modeling simplifications and assumptions. These techniques can be applied to the musculoskeletal system as a whole and to its individual components/subsystems so that models can be validated in greater detail and at various levels of complexity. By demonstrating the advantages of this framework in the analysis of a musculoskeletal system we hope to encourage its application to other complex biological systems ranging from protein dynamics to cellular interaction to artificial organs. |

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