This article provides models and code for numerically simulating muscle-fluid-structure interactions (FSIs). This work was presented as part of the symposium on Leading Students and Faculty to Quantitative Biology through Active Learning at the society-wide meeting of the Society for Integrative and Comparative Biology in 2015. Muscle mechanics and simple mathematical models to describe the forces generated by muscular contractions are introduced in most biomechanics and physiology courses. Often, however, the models are derived for simplifying cases such as isometric or isotonic contractions. In this article, we present a simple model of the force generated through active contraction of muscles. The muscles' forces are then used to drive the motion of flexible structures immersed in a viscous fluid. An example of an elastic band immersed in a fluid is first presented to illustrate a fully-coupled FSI in the absence of any external driving forces. In the second example, we present a valveless tube with model muscles that drive the contraction of the tube. We provide a brief overview of the numerical method used to generate these results. We also include as Supplementary Material a MATLAB code to generate these results. The code was written for flexibility so as to be easily modified to many other biological applications for educational purposes.

Early Warning Scores are not sufficiently accurate to rule in or rule out mortality in patients with sepsis, based on the evidence available, which is generally poor quality.

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Sepsis is a debilitating condition associated with a high mortality rate that greatly strains hospital resources. Though advances have been made in improving sepsis diagnosis and treatment, our understanding of the disease is far from complete. Mathematical modeling of sepsis has the potential to explore underlying biological mechanisms and patient phenotypes that contribute to variability in septic patient outcomes. We developed a comprehensive, whole-body mathematical model of sepsis pathophysiology using the BioGears Engine, a robust open-source virtual human modeling project. We describe the development of a sepsis model and the physiologic response within the BioGears framework. We then define and simulate scenarios that compare sepsis treatment regimens. As such, we demonstrate the utility of this model as a tool to augment sepsis research and as a training platform to educate medical staff.