Browsing by Author "Wolynes, Peter Guy"

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    Hydrodynamics and Statistical Mechanics of Motorized Biological Systems
    (2018-09-11) Bai, Xiaoyu; Wolynes, Peter Guy
    The cytoskeleton is the fundamental machinery that determines the morphology and mechanical properties of most eukaryotic cells. It is a complex network that is constituted of semiflexible polymer proteins. Its structure is regulated by various binding proteins that crosslink together several different filamentous polymers. The cytoskeletal scaffold is constantly influenced and agitated by three superfamilies of molecular motors that are called myosins, kinesins and dyneins. These motors are enzymes that convert the chemical free-energy released from the hydrolysis of ATP into mechanical work and directed locomotion. This thesis extends earlier theoretical framework based on the kinetic Master equations to capture both the passive Brownian motion of the network constituents and active chemical processes that occur in the cytoskeleton assemblies. This improved theory also enables us quantitatively to study the dynamical evolution of the probability distribution in the high dimensional configuration space of the network using a perturbation approximation around the thermal equilibrium. The mesoscale size of the nonequilibrium cytoskeletal assemblies demands the incorporation of the hydrodynamic coupling of the chemical shot noise arising from motorization into the theoretical framework to understand correctly the impact of coupled active diffusion on the dynamics of the far-from-equilibrium cytoskeleton. We find that hydrodynamic coupling is not only important for triggering the directed motion of the motors at single molecular level, but also rewrites the long-wavelength effective steady state that is characterized by an effective Fokker-Planck equation describing the enhanced anisotropic diffusion. The analytical theory also reveals mechanical heterogeneity associated with the motorized cytoskeleton at moderate level of motor agitation and succeeds in capturing the mechanically distorted phase that is stabilized by motorization. These results are confirmed by kinetic Monte Carlo simulations. The thesis also puts forth we derived two simple yet powerful one dimensional models to study the cellular contractility and motility, where the directionality biases of motor stepping are highlighted. The simulation results agree well with the theoretical predictions and they also boost our confidence on these simply building blocks to understand the cellular contractility and motility in higher dimensions.
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    Protein Aggregation in the Formation of Long Term Memory and Neurodegenerative Diseases
    (2019-07-02) Chen, Mingchen; Wolynes, Peter Guy; Levine, Herbert
    The formation of intra- and extra-cellular amyloid fibres through protein aggregation are traditionally coupled to a series of devastating and incurable neurodegenerative disorders, as well as functional implications. Computational simulations of protein aggregation has been challenging due to the relative long time scale and the involvement of larger systems. In this thesis, we summarized the efforts of using a coarse-grained model, the associative memory, water-mediated interactions, structure and energy model (AWSEM), in the study of protein aggregation and structures of amyloids. In the first part, we explored the aggregation free energy landscapes of a mechanical prion, CPEB protein. While CPEB is aggregation prone, the aggregation process is only favorable after exerting mechanical forces. We propose that an active cytoskeleton can be the origin of this mechanical force, and this mechanical catalysis makes possible a positive feedback loop that would localize the formation of CPEB fibres to active synapse areas and mark the formation of long-term memory. In the second part, we explored the aggregation landscapes of polyglutamine repeats, which are involved in over 9 neurodegenerative diseases. Free energy analyses show the length dependence of the aggregation of polyglutamine repeats, and this length-dependence property arises from the intrinsic properties of polyglutamine repeats to form β-hairpins. Following this the aggregation of full-length Huntington Exon1 encoded protein fragments, which is the culprit in Huntington’s Diseases is explored. The simulations show that the addition of the N-terminal 17-residue sequence (NT17) facilitates polyQ aggregation by encouraging the formation of prefibrillar oligomers, while adding the C-terminal polyproline sequence (P10) inhibits aggregation. The combination of both terminal additions in HTT exon 1 fragment leads to a complex aggregation mechanism, whose basic core resembles that found for the aggregation of pure polyQ repeats. At the extrapolated physiological concentration, while the grand canonical free energy profiles are uphill for HTT exon1 fragments having 20 or 30 glutamines, the aggregation landscape for fragments with 40 repeats has become downhill, thus explaining the correlation between length and Huntington disease onset age seen clinically. After elucidating the mechanisms of protein aggregation in the above cases, the structure of amyloidogenic peptides is examined under the framework of energy landscape theory. A predictive tool, the "AWSEM-Amylometer" was developed to predict the topology and aggregation propensity of peptides. The AWSEM-Amylometer notably performs better than other software in terms of the prediction of amyloidogenic sequences, and it also predicts the amyloid topology of existing peptide amyloids accurately. Nevertheless, the tertiary assembly of those amyloidogenic segments in full-length proteins is still largely unknown. So in the final part of this thesis, another tool, the AWSEM-Ribbon model, with layered constraints on each protein monomers, in the study of amyloid structures is introduced. Adopting this view of fiber architecture leads to a practical method of predicting stable protofilament structures for arbitrary peptide sequences. We apply this scheme to variants of Aβ, the amyloid forming peptide that is characteristically associated with Alzheimer’s disease. Consistent with what is known from experiment, Aβ protofibrils are found to be polymorphic. The polymorph landscape of Aβ also suggests some evolutionary aspects of amyloid protein assembly. Overall, the simulations presented here not only provide novel indications of detailed molecular mechanisms for the formation of long-term memory and the progress of neurodegenerative diseases, but also indicate the capability of energy landscape analyses to address protein misfolding/aggregation.
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    Understanding Functional Roles of Transcription Factor Decoys in Gene Regulation via Mathematical Modeling
    (2017-04-18) Wang, Zhipeng; Wolynes, Peter Guy; Onuchic, Jose
    Gene expressions are essentially regulated by transcription factor-DNA interactions. Many transcription factors bind to DNA with remarkably low specificity, so that the functional binding sites have to compete with an enormous number of non-functional "decoy" sites. The functional roles that decoy sites play in regulating gene expressions are still largely unknown. In this thesis, I utilized mathematical modeling approaches to elucidate the functional roles of transcription factor decoys in gene regulation across different scales, using the biologically-important NFkB/IkB signaling network as a real example. My study showed that with biologically-relevant binding/unbinding kinetic rates, transcription factor decoys are able to modulate both the time-scales and the amplitude of the systems-level dynamics of gene regulatory networks. Also by means of stochastic models and Monte Carlo simulations, I was able to uncover the mechanistic principles of how decoys modulate stochastic dynamics of gene regulatory networks, given that the binding affinities of decoys are widely distributed according to experiments. My study challenges the conventional bioinformatics principle of protein-DNA interactions and provide significant scientific insights in single cell analysis. The multi-scale mathematical models developed from this thesis are also capable of providing quantitative guidance for therapeutic applications of artificial decoys for NFkB-related diseases.