Advancements in Analytical Methods and Opto-Mechanical Techniques to Study Molecular Motors and Mutations

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Advancements in Analytical Methods and Opto-Mechanical Techniques to Study Molecular Motors and Mutations

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Transport of important cargo inside the cell occurs through ‘motor-proteins': nanoscale biological machines that carry cargo over tracks formed by filaments called microtubules. Defects in intracellular transport mechanisms are linked to numerous neuro-degenerative disorders such as Alzheimer's and Huntington’s disease. Understanding motor-protein functionality and related diseases at the cellular level requires detailed investigation of motor protein behavior. My thesis enables the study through the following key contributions: 1. Innovation of new experimental and computational methods to analyze transport inside cells, with unprecedented resolution and accuracy. 2. Deployment of the new methods to study motor-protein behavior and how mutations in proteins affect intracellular transport, leading to neuro-degenerative diseases. My research makes these contributions through analytical and experimental approaches. In the first part, I have developed analytical tools that are capable of probing how the motor proteins function while carrying cargoes inside the cell, with a particular focus on the transport of cargos by teams of motors. Utilizing the properties of Markov processes, I have developed a computational engine that can provide exact probabilities of the various configurations adopted by motors in the team containing single or multiple species. This methodology helps in answering previously unanswered questions on the mechanisms adopted by motors in response to external events, such as obstructions or impediments along the path of cargo travel, changing load conditions and varying ATP concentrations. The analysis predicts that motors develop a cooperative mechanism and cluster together while carrying progressively heavier loads; but tend to adopt a non-clustered configuration in response to sudden spikes in loads (which is akin to sudden obstacles along the path of travel). A further extension of the computational tool enabled the investigation of the impact of disease causing mutations on team based transport, where the mutations are known to impair single motor behavior. I analyzed the consequence of having a minority of mutated motors in a team, with the mutation that has been implicated in Huntington's disease. Under certain conditions, the teams with even a few mutated motors are seen to gain a significant competitive advantage, potentially contributing to the dysregulation of cargo transport, impairment of neuronal function and the onset of neuro-degeneration. A separate contribution of this work is the underlying analytical technique, which facilitates exact computations, detection of rare events, ease of usage and a reduction in computational resources needed for its functionality, each of which is a notable improvement over existing approaches. The above analytical conclusions need to be supported by experimental evidence, which is not possible without commensurate improvements in the available platforms of experimental interrogation. Existing instruments, in particular those using optical forces for probing such as 'Optical Tweezers', lack of utilization of a modern controls paradigm and a systems engineering based approach leading to a constrained performance. In the second part of my thesis, I developed a platform for disturbance estimation based on multi-objective synthesis. This facilitated the analysis of multi-input and multi-output systems where the regulation of a certain variable is the objective, in the presence of an external disturbance while simultaneously providing estimates of the disturbances in real time. The platform is designed to function in situations where the disturbance is altered by the noise, whose impact needs to be mitigated. I tested the efficacy of the platform on an 'Optical Tweezer' instrument using live samples of the protein Kinesin-I and demonstrated over 50 % improvement over previous studies, while working in extremely low force ranges of femto-newtons and offering a unique ability to regulate load force on the motor-protein itself. This platform can be utilized to experimentally probe the conclusions borne out of the previous analytical studies involving multiple motors and mutations and can also be extended to study faster motors in their native environment, something which was not possible using the earlier approaches. While numerous techniques exist that can study motor proteins through the dynamics of the cargo they carry, there are far fewer experimental techniques that can effectively probe the motors themselves. This is mainly due to the restrictions imposed by the extremely small dimensions of the motors and the limited probing capacity of optical instruments such as fluorescent microscopes. To tackle this issue, I have developed a unique and low cost instrument called 'TIRF-Tweezer', that combines the capabilities of total internal reflection fluorescence (TIRF) microscopy and optical tweezers. This greatly enhances the probing capacity of optical tweezers and facilitates the investigation of, amongst other things, the previously drawn conclusions on the relative configurations adopted by motors while transporting cargo under varying conditions. To further bolster such investigations, I have also developed robust biological protocols that can recreate the required cellular environments in an in-vitro environment and help provide experimental evidence to the analytical conclusions. With these analytical and experimental tools, I intend to equip biologists and biophysists with useful tools that can assist in the task of analysis of molecular motor behavior.


University of Minnesota Ph.D. dissertation.November 2018. Major: Electrical Engineering. Advisor: Murti Salapaka. 1 computer file (PDF); xii, 129 pages.

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Bhaban, Shreyas. (2018). Advancements in Analytical Methods and Opto-Mechanical Techniques to Study Molecular Motors and Mutations. Retrieved from the University Digital Conservancy,

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