2018/11/26 by Chieh-Ting Hsu, Hsu, Chieh-Ting, Gary J. Brouhard +3
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Mathematics · #Biological Physics (physics.bio-ph) #FOS: Biological sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Microtubule and mitosis dynamics #Photosynthetic Processes and Mechanisms #Plant nutrient uptake and metabolism #Stochastic processes and statistical mechanics #Subcellular Processes (q-bio.SC)
paper · pdf · doi:10.48550/arxiv.1811.10523
openalex publication_date 2018/11/26 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
Physical models of biological systems can become difficult to interpret when\nthey have a large number of parameters. But the models themselves actually\ndepend on (i.e. are sensitive to) only a subset of those parameters. Rigorously\nidentifying this subset of "stiff" parameters has been made possible by the\ndevelopment of parameter space compression (PSC). However, PSC has only been\napplied to analytically-solvable physical models. We have generalized this\npowerful method by developing a numerical approach to PSC that can be applied\nto any computational model. We validated our method against\nanalytically-solvable models of random walk with drift and protein production\nand degradation. We then applied our method to an active area of biophysics\nresearch, namely to a simple computational model of microtubule dynamic\ninstability. Such models have become increasingly complex, perhaps\nunnecessarily. By adding two new parameters that account for prominent\nstructural features of microtubules, we identify one that can be "compressed\naway" (the "seam" in the microtubule) and another that is essential to model\nperformance (the "tapering" of microtubule ends). Furthermore, we show that the\nmicrotubule model has an underlying, low-dimensional structure that explains\nthe vast majority of our experimental data. We argue that numerical PSC can\nidentify the low-dimensional structure of any computational model in\nbiophysics. The low-dimensional structure of a model is easier to interpret and\nidentifies the mechanisms and experiments that best characterize the system.\n