Engineering in practice: from quantitative biology modeling to engineering education - Weber, Lisa, author; Munsky, Brian, advisor; Atadero, Rebecca, committee member; Prasad, Ashok, committee member; Reisfeld, Brad, committee member (Colorado State University. Libraries, 2024)
In quantitative analyses of biological processes, one may use many different scales of models (e.g., spatial or non-spatial, deterministic or stochastic, time-varying or at steady-state) or many different approaches to match models to experimental data (e.g., model fitting or parameter uncertainty/ sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. In Chapters 2, a variety of modeling approaches that can be utilized in analyzing biological processes are explained, with examples included of how to mathematically represent a system in order to use these various modeling approaches. Many of these mechanistic modeling approaches are demonstrated in Chapter 3 when we use a simplified gene regulation model to illustrate many of the concerns regarding modeling approach differences; these include ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we consider a time-dependent input signal (e.g., a kinase nuclear translocation) and several model hypotheses, along with simulated single cell data, to illustrate different approaches (e.g., deterministic and stochastic) in the identification of mechanisms and parameters of the same model from the same simulated data. We also explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design, and conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast [1] and human cells [2]. Different modeling approaches are used in Chapter 4 to build on the work of Scott, et al. (2018, 2019) [3, 4] to evaluate different model classes for DNA structural conformation changes, including the unwinding/rewinding dynamics of the double-stranded DNA (dsDNA) helical structure and subsequent binding interactions with complementary single-stranded oligonucleotides probes (oligos), in relation to different conditions: temperature, salt concentration, and the level of supercoiling of the DNA molecule. This is done to identify a class of models that best fit the DNA unwinding and subsequent oligo probe binding experimental data as a function of these three conditions. In this work, we demonstrate the use of additional quantitative modeling approaches, including a modified genetic algorithm along with the process of cross validation and Markov Chain Monte Carlo (MCMC) simulations with the Metropolis-Hastings (MH) algorithm [5] to explore parameter space. We also demonstrate many of the challenges that can be encountered when modeling complex biological phenomena with actual experimental data. Although much of the work described in Chapters 2 through 4 may appear to be, on the surface, just the use of various computational methods for biological processes to increase understanding of biological mechanisms, much of it also has a separate purpose. The structure of these works and an underlying aim of much of this work, namely Chapters 2 and 3, is to provide guidance with examples to make these computational approaches more accessible to scientists and engineers. Many of these approaches are included in a quantitative biology (UQ-bio) summer school that has been conducted for the last few years as well. Through the process of developing these works and seeking to make quantitative biology more accessible, a related goal manifested to improve the accessibility of engineering education as a whole, which is addressed in Chapter 5, specifically related to diversity, equity, and inclusion (DEI) in undergraduate engineering education. There have been efforts since Fall 2017 to increase the presence of DEI in the undergraduate CBE education using a bottom up approach. To date, various efforts have been incorporated into the first two years of the CBE program. In Chapter 5, these previous efforts, along with lessons learned, are detailed. A substantial, holistic approach to incorporating DEI throughout the CBE curriculum is proposed, based on a review of recent work by other engineering education researchers, to help the CBE department create a more inclusive educational experience for undergraduate students and better enable students to handle the complex challenges they may face in their careers.
Keywords: computational biology; engineering; quantitative biology; DEI education; biological modeling; engineering education