The role of computation, engineering, physics and mathematics in learning biomechanics


Meeting Abstract

S10.4  Jan. 7  The role of computation, engineering, physics and mathematics in learning biomechanics DANIELT, T.L.*; KOEHL, M.A.R.; Univ. Washington, Seattle; Univ. Californica, Berkeley danielt@u.washington.edu

Biology is undergoing a revolution — it is becoming a highly quantitative science dealing with exceedingly massive data sets and exquisitely complex systems. It is a field that must now exploit computational horsepower and advanced engineering applications to understand complex systems (from molecules to nervous systems to moving animals) involving many interacting factors. At the center of this lies comparative biomechanics, a discipline soaked in quantitative questions. Here lies significant synergism between the life sciences and engineering and mathematical sciences, spurring the development of advanced computational, analytical and technological tools. Yet mathematical and quantitative approaches, despite their great promise, remain foreign to many biologists – a situation that can hamper significant progress. Although the advent of powerful computational tools and analytic methods heralds new progress in biology, it also presents a fundamental dilemma: state-of-the-art mathematical and computational reasoning far exceeds what students in the life sciences are currently taught. To address this issue we have developed computational tools for teaching biomechanics via Matlab and Mathematica. Our goal is to improve learning and to help bridge the gap between theory and experiment. We emphasize mechanical design principles and fluid and solid mechanics to explore how organisms work (from cells to populations). This integration allows students to ask new questions and apply a series of experimental and computational designs to build and test models of form and function. Because it is not as established as other disciplinary areas, biomechanics can be enhanced by the presence of peer-reviewed activities such as these models in the SICB digital library.

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