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Structural Biology 2018
Volume: 4
Biochemistry & Molecular Biology Journal
Page 63
March 15-16 2018
Barcelona, Spain
10
th
Edition of International Conference on
Structural Biology
M
athematical models play a significant role in providing a
numerical and analytical perspective to biological models.
When formulating these mathematical models to improve our
understanding of biological processes, it is not always possible
to find all parameter values in literature. In such cases, and in the
presence of data, inverse problems are performed to estimate
these unknown parameters. Statistical error models used during
inverse problem formulations help quantify the uncertainty
and variability that arises with using experimental data. This
process of applying mathematical and statistical techniques for
modeling physical processes is an iterative one that often leads
to new insights following every new iteration. There is a relatively
recent research effort in modeling the mechanisms of solid
organ transplants, specifically kidney transplants. We present
mathematical and statistical models to illustrate the iterative
process of modeling for renal transplant recipients infected by BK
virus. Using a second order difference-based method to eliminate
statistical error model misspecification, we show how modified
residuals from the inverse problem can be used to detect
discrepancies in mathematical model formulation. Moreover, we
illustrate the iterative process of modeling biological processes
by improving the current mathematical model to be more
biologically accurate.
nmurad@ncsu.eduThe iterative process of quantitative modeling of infection
dynamics in renal transplant recipients
Neha Murad, H T Banks
and
R Everett
North Carolina State University, USA
Biochem Mol biol J, Volume 4
DOI: 10.21767/2471-8084-C1-009