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- Genre: Doctoral Dissertation
- Member of: Theses and Dissertations
Description
The Bayesian paradigm provides a flexible and versatile framework for modeling complex biological systems without assuming a fixed functional form or other constraints on the underlying data. This dissertation explores the use of Bayesian nonparametric methods for analyzing fluorescence microscopy data in biophysics, with a focus on enumerating diffraction-limited particles, reconstructing potentials from trajectories corrupted by measurement noise, and inferring potential energy landscapes from fluorescence intensity experiments. This research demonstrates the power and potential of Bayesian methods for solving a variety of problems in fluorescence microscopy and biophysics more broadly.
ContributorsBryan IV, J Shepard (Author) / Presse, Steve (Thesis advisor) / Ozkan, Banu (Committee member) / Wadhwa, Navish (Committee member) / Shepherd, Doug (Committee member) / Arizona State University (Publisher)
Created2023
Description
A swarm describes a group of interacting agents exhibiting complex collective behaviors. Higher-level behavioral patterns of the group are believed to emerge from simple low-level rules of decision making at the agent-level. With the potential application of swarms of aerial drones, underwater robots, and other multi-robot systems, there has been increasing interest in approaches for specifying complex, collective behavior for artificial swarms. Traditional methods for creating artificial multi-agent behaviors inspired by known swarms analyze the underlying dynamics and hand craft low-level control logics that constitute the emerging behaviors. Deep learning methods offered an approach to approximate the behaviors through optimization without much human intervention.
This thesis proposes a graph based neural network architecture, SwarmNet, for learning the swarming behaviors of multi-agent systems. Given observation of only the trajectories of an expert multi-agent system, the SwarmNet is able to learn sensible representations of the internal low-level interactions on top of being able to approximate the high-level behaviors and make long-term prediction of the motion of the system. Challenges in scaling the SwarmNet and graph neural networks in general are discussed in detail, along with measures to alleviate the scaling issue in generalization is proposed. Using the trained network as a control policy, it is shown that the combination of imitation learning and reinforcement learning improves the policy more efficiently. To some extent, it is shown that the low-level interactions are successfully identified and separated and that the separated functionality enables fine controlled custom training.
This thesis proposes a graph based neural network architecture, SwarmNet, for learning the swarming behaviors of multi-agent systems. Given observation of only the trajectories of an expert multi-agent system, the SwarmNet is able to learn sensible representations of the internal low-level interactions on top of being able to approximate the high-level behaviors and make long-term prediction of the motion of the system. Challenges in scaling the SwarmNet and graph neural networks in general are discussed in detail, along with measures to alleviate the scaling issue in generalization is proposed. Using the trained network as a control policy, it is shown that the combination of imitation learning and reinforcement learning improves the policy more efficiently. To some extent, it is shown that the low-level interactions are successfully identified and separated and that the separated functionality enables fine controlled custom training.
ContributorsZhou, Siyu (Author) / Ben Amor, Heni (Thesis advisor) / Walker, Sara I (Thesis advisor) / Davies, Paul (Committee member) / Pavlic, Ted (Committee member) / Presse, Steve (Committee member) / Arizona State University (Publisher)
Created2020
Description
The cell is a dense environment composes of proteins, nucleic acids, as well as other small molecules, which are constantly bombarding each other and interacting. These interactions and the diffusive motions are driven by internal thermal fluctuations. Upon collision, molecules can interact and form complexes. It is of interest to learn kinetic parameters such as reaction rates of one molecule converting to different species or two molecules colliding and form a new species as well as to learn diffusion coefficients.
Several experimental measurements can probe diffusion coefficients at the single-molecule and bulk level. The target of this thesis is on single-molecule methods, which can assess diffusion coefficients at the individual molecular level. For instance, super resolution methods like stochastic optical reconstruction microscopy (STORM) and photo activated localization microscopy (PALM), have a high spatial resolution with the cost of lower temporal resolution. Also, there is a different group of methods, such as MINFLUX, multi-detector tracking, which can track a single molecule with high spatio-temporal resolution. The problem with these methods is that they are only applicable to very diluted samples since they need to ensure existence of a single molecule in the region of interest (ROI).
In this thesis, the goal is to have the best of both worlds by achieving high spatio-temporal resolutions without being limited to a few molecules. To do so, one needs to refocus on fluorescence correlation spectroscopy (FCS) as a method that applies to both in vivo and in vitro systems with a high temporal resolution and relies on multiple molecules traversing a confocal volume for an extended period of time. The difficulty here is that the interpretation of the signal leads to different estimates for the kinetic parameters such as diffusion coefficients based on a different number of molecules we consider in the model. It is for this reason that the focus of this thesis is now on using Bayesian nonparametrics (BNPs) as a way to solve this model selection problem and extract kinetic parameters such as diffusion coefficients at the single-molecule level from a few photons, and thus with the highest temporal resolution as possible.
Several experimental measurements can probe diffusion coefficients at the single-molecule and bulk level. The target of this thesis is on single-molecule methods, which can assess diffusion coefficients at the individual molecular level. For instance, super resolution methods like stochastic optical reconstruction microscopy (STORM) and photo activated localization microscopy (PALM), have a high spatial resolution with the cost of lower temporal resolution. Also, there is a different group of methods, such as MINFLUX, multi-detector tracking, which can track a single molecule with high spatio-temporal resolution. The problem with these methods is that they are only applicable to very diluted samples since they need to ensure existence of a single molecule in the region of interest (ROI).
In this thesis, the goal is to have the best of both worlds by achieving high spatio-temporal resolutions without being limited to a few molecules. To do so, one needs to refocus on fluorescence correlation spectroscopy (FCS) as a method that applies to both in vivo and in vitro systems with a high temporal resolution and relies on multiple molecules traversing a confocal volume for an extended period of time. The difficulty here is that the interpretation of the signal leads to different estimates for the kinetic parameters such as diffusion coefficients based on a different number of molecules we consider in the model. It is for this reason that the focus of this thesis is now on using Bayesian nonparametrics (BNPs) as a way to solve this model selection problem and extract kinetic parameters such as diffusion coefficients at the single-molecule level from a few photons, and thus with the highest temporal resolution as possible.
ContributorsJazani, Sina (Author) / Presse, Steve (Thesis advisor) / Matyushov, Dmitry (Committee member) / Levitus, Marcia (Committee member) / Fricks, John (Committee member) / Arizona State University (Publisher)
Created2020
Description
Protein interactions with the environment are crucial for proper function, butinteraction mechanisms are not always understood. In G protein-coupled receptors
(GPCRs), cholesterol modulates the function in some, but not all, GPCRs. Coarse
grained molecular dynamics was used to determine a set of contact events for each
residue and fit to a biexponential to determine the time scale of the long contacts
observed in simulation. Several residues of interest were indicated in CCK1 R near
Y140, which is known to render CCK1 R insensitive to cholesterol when mutated to
alanine. A difference in the overall residence time between CCK1 R and its cholesterol
insensitive homologue CCK2 R was also observed, indicating the ability to predict
relative cholesterol binding for homologous proteins.
Occasionally large errors and poor fits to the data were observed, so several
improvements were made, including generalizing the model to include K exponential
components. The sets of residence times in the improved method were analyzed using
Bayesian nonparametrics, which allowed for error estimations and the classification of
contact events to the individual components. Ten residues in three GPCRs bound to
cholesterol in experimental structures had large tau. Slightly longer overall interaction
time for the cholesterol sensitive CB1 R over its insensitive homologue CB2 R was also
observed.
The interactions between the cystic fibrosis transmembrane conductance regulator
(CFTR) and GlyH-101, an open-channel blocker, were analyzed using molecular
dynamics. The results showed the bromine in GlyH-101 was in constant contact with
F337, which is just inside the extracellular gate. The simulations also showed an
insertion of GlyH-101 between TM1 and TM6 deeper than the starting binding pose.
Once inserted deeper between TMs 1 and 6, the number of persistent contacts also
increased. This proposed binding pose may help in future investigations of CFTR
and help determine an open-channel structure for the protein, which in turn may help
in the development of treatments for various medical conditions. Overall, the use
of molecular dynamics and state of the art analysis tools can be useful in the study
of membrane proteins and eventuallyin the development of treatments for ailments
stemming from their atypical function.
ContributorsSexton, Ricky (Author) / Beckstein, Oliver (Thesis advisor) / Presse, Steve (Committee member) / Ozkan, Sefika B. (Committee member) / Hariadi, Rizal (Committee member) / Arizona State University (Publisher)
Created2022
Description
Computational biophysics is a powerful tool for observing and understanding the microscopic machinery that underpins the biological world. Molecular modeling and simulations can help scientists understand a cell’s behavior and the mechanisms that drive it. Empirical evidence can provide information on the structure and organization of biomolecular machines, which serve as the backbone of biomolecular modeling. Experimental data from probing the cell’s inner workings can provide modelers with an initial structure from which they can hypothesize and independently verify function, complex formation, and response. Additionally, molecular data can be used to drive simulations toward less probable but equally interesting states. With the advent of machine learning, researchers now have an unprecedented opportunity to take advantage of the wealth of data collected in a biomolecular experiment. This dissertation presents a comprehensive review of atomistic modeling with cryo-electron microscopy and the development of new simulation strategies to maximize insights gained from experiments. The review covers the integration of cryo-EM and molecular dynamics, highlighting the evolution of their relationship and the recent history of MD innovations in cryo-EM modeling. It also covers the discoveries made possible by the integration of molecular modeling with cryo-EM. Next, this work presents a method for fitting small molecules into cryo-electron microscopy maps, which uses neural network potentials to parameterize a diverse set of ligands. The method obtained fitted structures commensurate with, if not better than, the structures submitted to the Protein Data Bank. Additionally, the work describes the data-guided Multi- Map methodology for ensemble refinement of molecular movies. The method shows that cryo-electron microscopy maps can be used to bias simulations along a specially constructed reaction coordinate and capture conformational transitions between known intermediates. The simulated pathways appear reversible with minimal hysteresis and require only low-resolution density information to guide the transition. Finally, the study analyzes the SARS-CoV-2 spike protein and the conformational heterogeneity of its receptor binding domain. The simulation was guided along an experimentally determined free energy landscape. The resulting motions from following a pathway of low-energy states show a degree of openness not observed in the static models. This sheds light on the mechanism by which the spike protein is utilized for host infection and provides a rational explanation for the effectiveness of certain therapeutics. This work contributes to the understanding of biomolecular modeling and the development of new strategies to provide valuable insights into the workings of cellular machinery.
ContributorsVant, John Wyatt (Author) / Singharoy, Abhishek (Thesis advisor) / Heyden, Matthias (Committee member) / Presse, Steve (Committee member) / Arizona State University (Publisher)
Created2024