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Exploring Single-molecule Heterogeneity and the Price of Cell Signaling

Wang, Tenglong
http://orcid.org/0000-0002-3012-4659
http://rave.ohiolink.edu/etdc/view?acc_num=case1638756898133314

Abstract Details

2022, Doctor of Philosophy, Case Western Reserve University, 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-molecule or bio-molecular complex until it unfolds or ruptures. The distribution of times it takes for this unfolding/rupture to occur, collected from many repetitions of the experiment, contains signatures of heterogeneity: information about the number and properties of the different conformational states that exist in a given system. We show that both machine learning techniques can effectively tease out this information, though each has its own strengths and weaknesses. The algorithms are validated on a large set of synthetic data, generated to mimic the wide range of biological parameters and experimental settings one would encounter in real-world applications.
Michael Hinczewski (Committee Chair)
Peter Thomas (Committee Member)
Harsh Mathur (Committee Member)
Lydia Kisley (Committee Member)
124 p.
Biophysics; Physics
Energetic costs of signaling; Kinase-phosphatase enzymatic network; ATP consumption; Mutual information; Information theory; Evolutionary pressure; Atomic force microscopy; Machine learning applications in Biophysics; Non-parametric Bayesian learning; Dirichlet process mixture model; "Chinese restaurant process"

Recommended Citations

Citations

  • Wang, T. (2022). Exploring Single-molecule Heterogeneity and the Price of Cell Signaling [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1638756898133314

    APA Style (7th edition)

  • Wang, Tenglong. Exploring Single-molecule Heterogeneity and the Price of Cell Signaling. 2022. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1638756898133314.

    MLA Style (8th edition)

  • Wang, Tenglong. "Exploring Single-molecule Heterogeneity and the Price of Cell Signaling." Doctoral dissertation, Case Western Reserve University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=case1638756898133314

    Chicago Manual of Style (17th edition)

case1638756898133314
48
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This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.