Stochasticity, disorder, and noise play crucial roles in the functioning of many biological systems over many different length scales. On the molecular scale, most proteins are envisioned as pristinely folded structures, but intrinsically disordered proteins (IDPs) have no such folded state and still serve distinct purposes within the cell. At the scale of gene regulation, realistic in vivo conditions produce stochastic fluctuations in gene expression that can lead to advantageous bet-hedging strategies, but can be difficult to characterize using a deterministic framework. Even at the organismal scale, germband extension (GBE) in Drosophila melanogaster embryos systematically elongates the epithelial tissue using cell intercalation, but leaves cells in highly heterogenous geometries. Throughout this work, we will demonstrate that these characteristics are not just arbitrary artifacts to be glossed over, but are actually very intentional frameworks that are harnessed by the respective systems to their own advantage.
In some cases, they can also be harnessed by researchers to better characterize or even control the system through various biophysical techniques. In the case of IDPs, we will introduce an analytical model that can predict the conformational size of these disordered proteins and identify specific \"hot spots\" in their sequences that hold significant influence over the shape (and therefore function) of each protein. To better understand stochastic gene expression, the power of stochastic inference methods will be put on display, specifically methods using modeling systems based on the principle of Maximum Caliber. These require no direct knowledge about the architecture of the underlying genetic network and make quantitative predictions using the entire content of experimentally realistic time-series data. Finally, we will break down the process of GBE using node-based Monte Carlo simulations to show that while anisotropic tension is enough to qualitatively reproduce convergent extension, competing active extension mechanisms must be introduced in each cell to achieve heterogeneous cell configurations and quantitative agreement with experiment. These studies will collectively demonstrate that randomness and fluctuation do not always imply disarray and intractibility, but instead can convey adaptability and possibility. As such, these characteristics should be embraced by the field of biophysics.
","abstract_html":"<p>Stochasticity, disorder, and noise play crucial roles in the functioning of many biological systems over many different length scales. On the molecular scale, most proteins are envisioned as pristinely folded structures, but intrinsically disordered proteins (IDPs) have no such folded state and still serve distinct purposes within the cell. At the scale of gene regulation, realistic <em>in vivo</em> conditions produce stochastic fluctuations in gene expression that can lead to advantageous bet-hedging strategies, but can be difficult to characterize using a deterministic framework. Even at the organismal scale, germband extension (GBE) in <em>Drosophila melanogaster</em> embryos systematically elongates the epithelial tissue using cell intercalation, but leaves cells in highly heterogenous geometries. Throughout this work, we will demonstrate that these characteristics are not just arbitrary artifacts to be glossed over, but are actually very intentional frameworks that are harnessed by the respective systems to their own advantage.</p> <p>In some cases, they can also be harnessed by researchers to better characterize or even control the system through various biophysical techniques. In the case of IDPs, we will introduce an analytical model that can predict the conformational size of these disordered proteins and identify specific "hot spots" in their sequences that hold significant influence over the shape (and therefore function) of each protein. To better understand stochastic gene expression, the power of stochastic inference methods will be put on display, specifically methods using modeling systems based on the principle of Maximum Caliber. These require no direct knowledge about the architecture of the underlying genetic network and make quantitative predictions using the entire content of experimentally realistic time-series data. Finally, we will break down the process of GBE using node-based Monte Carlo simulations to show that while anisotropic tension is enough to qualitatively reproduce convergent extension, competing active extension mechanisms must be introduced in each cell to achieve heterogeneous cell configurations and quantitative agreement with experiment. These studies will collectively demonstrate that randomness and fluctuation do not always imply disarray and intractibility, but instead can convey adaptability and possibility. As such, these characteristics should be embraced by the field of biophysics.</p>","abstract_has_math":false,"creators":["Firman, Taylor Emil"],"institution":null,"degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Kingshuk Ghosh, Ph.D.","Markus Schneider, Ph.D.","Todd Blankenship","Michelle Knowles","Mark Siemens"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-01-01T08:00:00Z","date_published":"2018-01-01T08:00:00Z","updated_at":"2026-07-24T02:02:19Z","subjects":["Biophysics","MaxCal","Modeling","Morphology","Protein","Stochasticity","Biological and Chemical Physics"],"languages":["en"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.du.edu/etd/1465","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kingshuk Ghosh, Ph.D.","Markus Schneider, Ph.D.","Todd Blankenship","Michelle Knowles","Mark Siemens"]},{"key":"dc:creator","label":"Author","values":["Firman, Taylor Emil"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-07-17T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biophysics","MaxCal","Modeling","Morphology","Protein","Stochasticity","Biological and Chemical Physics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright is held by the author. User is responsible for all copyright compliance.
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In some cases, they can also be harnessed by researchers to better characterize or even control the system through various biophysical techniques. In the case of IDPs, we will introduce an analytical model that can predict the conformational size of these disordered proteins and identify specific \"hot spots\" in their sequences that hold significant influence over the shape (and therefore function) of each protein. To better understand stochastic gene expression, the power of stochastic inference methods will be put on display, specifically methods using modeling systems based on the principle of Maximum Caliber. These require no direct knowledge about the architecture of the underlying genetic network and make quantitative predictions using the entire content of experimentally realistic time-series data. Finally, we will break down the process of GBE using node-based Monte Carlo simulations to show that while anisotropic tension is enough to qualitatively reproduce convergent extension, competing active extension mechanisms must be introduced in each cell to achieve heterogeneous cell configurations and quantitative agreement with experiment. These studies will collectively demonstrate that randomness and fluctuation do not always imply disarray and intractibility, but instead can convey adaptability and possibility. As such, these characteristics should be embraced by the field of biophysics.
"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning from Disorder and Noise in Physical Biology"]}]}],"canonical_facts":{"dc:contributor":["Kingshuk Ghosh, Ph.D.","Markus Schneider, Ph.D.","Todd Blankenship","Michelle Knowles","Mark Siemens"],"dc:creator":["Firman, Taylor Emil"],"dc:date.available":["2019-07-17T07:00:00Z"],"dc:description.abstract":["Stochasticity, disorder, and noise play crucial roles in the functioning of many biological systems over many different length scales. On the molecular scale, most proteins are envisioned as pristinely folded structures, but intrinsically disordered proteins (IDPs) have no such folded state and still serve distinct purposes within the cell. At the scale of gene regulation, realistic in vivo conditions produce stochastic fluctuations in gene expression that can lead to advantageous bet-hedging strategies, but can be difficult to characterize using a deterministic framework. Even at the organismal scale, germband extension (GBE) in Drosophila melanogaster embryos systematically elongates the epithelial tissue using cell intercalation, but leaves cells in highly heterogenous geometries. Throughout this work, we will demonstrate that these characteristics are not just arbitrary artifacts to be glossed over, but are actually very intentional frameworks that are harnessed by the respective systems to their own advantage.
In some cases, they can also be harnessed by researchers to better characterize or even control the system through various biophysical techniques. In the case of IDPs, we will introduce an analytical model that can predict the conformational size of these disordered proteins and identify specific \"hot spots\" in their sequences that hold significant influence over the shape (and therefore function) of each protein. To better understand stochastic gene expression, the power of stochastic inference methods will be put on display, specifically methods using modeling systems based on the principle of Maximum Caliber. These require no direct knowledge about the architecture of the underlying genetic network and make quantitative predictions using the entire content of experimentally realistic time-series data. Finally, we will break down the process of GBE using node-based Monte Carlo simulations to show that while anisotropic tension is enough to qualitatively reproduce convergent extension, competing active extension mechanisms must be introduced in each cell to achieve heterogeneous cell configurations and quantitative agreement with experiment. These studies will collectively demonstrate that randomness and fluctuation do not always imply disarray and intractibility, but instead can convey adaptability and possibility. As such, these characteristics should be embraced by the field of biophysics.
"],"dc:format":["application/pdf"],"dc:identifier":["https://digitalcommons.du.edu/etd/1465"],"dc:language":["en"],"dc:rights":["Copyright is held by the author. User is responsible for all copyright compliance.
"],"dc:subject":["Biophysics","MaxCal","Modeling","Morphology","Protein","Stochasticity","Biological and Chemical Physics"],"dc:title":["Learning from Disorder and Noise in Physical Biology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."]},"updated_at":"2026-07-24T02:02:19Z"}