Doctor of Philosophy, Case Western Reserve University, 2022, Physics

In the last two decades, advances in experimental techniques have opened up new vistas for understanding bio-molecules and their complex networks of interactions in the cell. In this thesis, we use theoretical modeling and machine learning to explore two surprising aspects that have been revealed by recent experiments: (i) the discovery that many different types of cellular signaling networks, in both prokaryotes and eukaryotes, can transmit at most 1 to 3 bits of information; (ii) the observation that single bio-molecules can exhibit multiple, stable conformational states with extremely heterogeneous functional properties. The first part of the thesis investigates how the energetic costs of signaling in biological networks constrain the amount of information that can be transferred through them. The focus is specifically on the kinase-phosphatase enzymatic network, one of the basic elements of cellular signaling pathways. We find a remarkably simple analytical relationship for the minimum rate of ATP consumption necessary to achieve a certain signal fidelity across a range of frequencies. This defines a fundamental performance limit for such enzymatic systems, and we find evidence that a component of the yeast osmotic shock pathway may be close to this optimality line. By quantifying the evolutionary pressures that operate on these networks, we argue that this is not a coincidence: natural selection is capable of pushing signaling systems toward optimality, particularly in unicellular organisms. Our theoretical framework is directly verifiable using existing experimental techniques, and predicts that many more examples of such optimality should exist in nature. In the second part of the thesis, we develop two machine learning methods to analyze data from single-molecule AFM pulling experiments: a supervised (deep learning) and an unsupervised (non-parametric Bayesian) algorithm. These experiments involve applying an increasing force on a bio-molecul (open full item for complete abstract)

Committee: Michael Hinczewski (Committee Chair); Peter Thomas (Committee Member); Harsh Mathur (Committee Member); Lydia Kisley (Committee Member) Subjects: Biophysics; Physics

Doctor of Education, University of Akron, 2015, Secondary Education

The purpose of this case study was to examine student technology choices when given the freedom to choose technology devices to complete a project-based learning activity in a content area of study. The study also analyzed factors affecting technology choice as well as how technology proficiency scores aligned to technology choices. Patterns and themes were identified during data analysis. Three research questions guided this study are: 1) When given a choice, what technologies do students use to accomplish a Project-based Learning mission? 2) Why does a student choose certain technologies to accomplish a Project-based Learning mission? 3) How do students' technology choices during a Project-based Learning mission align with their Atomic Learning's © Technology Skills Student Assessment scores? Data analysis of the first question indicated that for hardware choice, students overwhelmingly chose laptops to complete a project-based mission with smart phones coming in second to complete or enhance the mission. In my results section for software choice, all students chose some sort of cloud-based technology: Google Slides, Prezi, a blog, Twitter, and Google Sites. Data analysis of the second question concluded that both internal and external factors affected student technology choices. Students chose the software choice first to accomplish their project and then chose the hardware tool to work best with the software. Hardware was seen as the needed device to make the cloud based software work as best as possible. Data analysis of my final and third question indicated that self-efficacy and previous experiences are crucial components for secondary level students when choosing and using technology. Technology proficiency scores aligned to student technology choices.

Committee: Lynne Pachnowski Ph.D. (Advisor); Gary Holliday Ph.D. (Advisor); Harold Foster Ph.D. (Committee Member); John Savery Ph.D. (Committee Member); I-Chun Tsai Ph.D. (Committee Member) Subjects: Education; Educational Technology; Secondary Education; Teaching