Abstract: Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic, multiscale models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges, of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. We undertake the task of advancing calibration practices across models and dataset types for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. We compare the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. Due to the typically nonlinear and stochastic nature of multiscale models as well as unknown parameter values, various types of uncertainty are present; thus, effective assessment and quantification of such uncertainty through sensitivity analysis is important. We present ideas for global sensitivity analysis in the context of multiscale and multi-compartment models and highlight its value in model development and analysis. We present an overview of sensitivity analysis methods, approaches for extending such methods to a multiscale setting, and examples of how sensitivity analysis can inform model reduction. Through schematics and references to past work, we aim to emphasize the advantages and usefulness of such techniques.

Short Bio: Dr. Denise Kirschner is a full professor in the Department of Microbiology and Immunology at the University of Michigan Medical School. She received her Bachelors, Master’s and PhD in mathematical biology from Tulane University in Louisiana (USA).  She did graduate work also at Los Alamos National Laboratory and a postdoctoral fellowship at Vanderbilt University joint in the division of Infectious Diseases. For the past 30 years, her research focus has been on describing the host immune response to M. tuberculosis during infection at multiple spatial and time scales and in multiple physiological sites including lung, lymph nodes and blood. She has worked and collaborated extensively with clinicians and experimentalists generating data on TB with non-human primate and clinical studies. A key recent focus of her work is identifying new regimens to treat TB.  Dr. Kirschner has recently stepped down as Editor-in-Chief of the Journal of Theoretical Biology after serving for 20 years. She serves as the founding co-director of The Center for Systems Biology at the University of Michigan Medical School, an interdisciplinary center at the University of Michigan aimed to facilitate research and training between wet-lab and theoretical scientists.  She has been named a fellow of three societies: the American Association of Microbiologists, Society for Mathematical Biology and The Society for Industrial and Applied Mathematics.