Browsing by Author "Onuchic, Jose"

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    Adaptive sampling of Conformational Dynamics
    (2020-05-14) Hruska, Eugen; Clementi, Cecilia; Onuchic, Jose
    At the core of our limited ability to understand many biophysical processes is the challenge of predicting the conformational dynamics of biomolecules. This challenge includes many open questions around the biophysical causes of many diseases or open questions in biophysics theory. Adaptive sampling is an approach to increase our ability to predict conformational dynamics. Adaptive sampling is a class of sampling strategies, where an ensemble of molecular dynamics trajectories is generated, where the starting points for the individual trajectories depend on the previously simulated trajectories. This approach will be investigated in this thesis. The application of adaptive sampling to biomolecules is one example of the more general problem of accurately sampling the time-dynamics of high-dimensional stochastic systems. The high-dimensionality, combined with a complex energy landscape, impede simpler approaches. Due to the broad scope of the general challenge, this Dissertation will focus only on improving the prediction of conformational dynamics for proteins. Many previous approaches to unravel this challenge have achieved significant improvements. In the case of proteins, the timescales where we can predict the conformational dynamics have increased by many orders of magnitudes to the millisecond scale. Despite the improvements, the current state-of-art can only predict the accurate behavior for small proteins. This illustrates the magnitude of the challenge. For most of the larger biomolecules, we are not able to simulate the precise behavior. This is not only caused by the several magnitudes longer timescales for these larger systems but also an order of magnitude larger sizes of these biomolecules. In this thesis, the adaptive sampling of conformational dynamics will be investigated in several steps. First, the prediction of the effectivity of different adaptive sampling strategies will be discussed. Due to significant stochasticity and protein-to-protein variation, the choice of adaptive sampling strategy is not apparent. The performance of different strategies for different goals varies as well. Second, to deepen our theoretical understanding of adaptive sampling strategies, an upper limit for the performance of any adaptive sampling strategy is developed. This theoretical upper limit allows us to understand the potential and limits of adaptive sampling. Third, adaptive sampling is heavily dependent on software due to the necessary thousands or millions of individual steps. All these steps have to be executed efficiently on a High-Performance Computer (HPC). Here we show the development of the software package ExTASY. This framework allows performing all the necessary steps in adaptive sampling while reducing the workload. The innovations of ExTASY are both the high-scalability and the modularity. The modularity allows for an easy change of the adaptive sampling strategies and better maintainability. ExTASY is reducing the entry barrier to utilizing adaptive sampling. Finally, the package ExTASY will be applied to show the results of adaptive sampling for several proteins. Future developments to extend the investigated approaches to longer timescales will be addressed. All the approaches mentioned above facilitate further advancements in predicting conformational dynamics of larger biomolecules.
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    Agent-based model for developmental aggregation in Myxococcus xanthus bacteria
    (2020-04-23) Zhang, Zhaoyang; Igoshin, Oleg; Onuchic, Jose
    Collective behavior refers to social processes and events which do not reflect existing social structure (laws, conventions, and institutions), but which emerge in a”spontaneous” way. It is a common phenomenon in microbiology: a group of cells can spontaneously form different structures under different conditions. How cells interact with each other and achieve this kind of coordinated cell movement is of active scientific interest. As a model organism for bacterial collective behavior,Myxococcus xanthus is widely studied to uncover the mechanism behind bacterial collective behavior. In this work, we applied agent-based models to study the aggregation behavior of M. xanthus cells under starvation and the important cell behaviors for csgA and pilC mutants aggregation.Experiments have shown that WT M. xanthus cells perform a biased walk to-wards aggregation center and this biased walk helps aggregation. To uncover the mechanism of the biased walk, we first developed a model where each cell is modeled as an agent, represented by a point-particle and characterized by its position and moving direction. At low agent density, the model recapitulates the dynamic pat-terns observed by experiments and a previous biophysical model. At high cell density,we extended the model based on the experimental data of the biased movement to-wards aggregates. We tested two possible mechanisms for this biased movement and demonstrate that a chemotax is model with adaptation can reproduce the observed experimental results leading to the formation of stable aggregates. Furthermore, our model reproduces the experimentally observed patterns of cell alignment around aggregates. Next, we applied a data-driven agent-based model to investigate what cell behaviors are important for the rescue of aggregation in two mutants: csgA and pilC, which cannot aggregate unless mixed with wild type (WT) cells. We discovered that when mixed with WT cells, both mutants show biased movements and reduced motility inside aggregates. These behaviors are shown to be important to aggregation in our agent-based simulations. However, some mutant behaviors remain different from WT cells demonstrating that perfect recreation of WT behavior is unnecessary.This work proposes a possible mechanism of the aggregation of M. xanthus bacteria and has shown that some cell behaviors are more important than others in aggregation. Our agent-based model provides a general framework that can be used to study self-organization behaviors in other n other surface motile bacteria
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    Associative Memory Hamiltonian Modeling on DNA, Nucleosomes, and Chromatin
    (2023-09-21) Lu, Weiqi; Onuchic, Jose
    Chromatin, the complex of DNA and proteins, plays a pivotal role in the regulation of gene expression and other cellular processes. Its dynamic organization and conformational plasticity are fundamental to the proper functioning of the genome. Understanding the three-dimensional structure and dynamics of chromatin at various scales is crucial for elucidating the molecular mechanisms underlying gene expression and other biological processes. However, the inherent complexity and multiscale nature of chromatin pose significant challenges for experimental and computational studies. In this thesis, we present the development and application of an Associative Memory Hamiltonian model, the Widely Editable Chromain Model (WEChroM) to gain insights into the structure and dynamics of chromatin fibers and nucleosomes. We begin by introducing the Associative Memory Hamiltonian approach, which leverages prior knowledge of experimentally determined structures to guide molecular dynamics simulations toward biologically relevant conformations. We detail the development of WEChroM for DNA and nucleosomes and demonstrate the model’s capability to capture conformational preferences and mechanical and thermodynamically properties. We investigate the bending and twisting persistence lengths, supercoiling behavior of DNA minicircles, and DNA-protein interactions within the context of nucleosomes. We further discuss the implementation of WEChroM on the OpenMM platform and provide a tutorial on the software. We then apply the WEChroM approach to investigate higher-order chromatin structures. We elucidate organization patterns in nucleosome arrays, explore the 30-nm fiber models, and assess the impact of uniform and non-uniform linker lengths.We discuss the functional implications of nucleosome array organization and compare our theoretical predictions with experimental data. Lastly, we discuss the significance, limitations, challenges, and future directions of the WEChroM approach. In summary, this thesis contributes to the field of computational genomics by providing insights into chromatin structure and dynamics through the development and application of the WEChroM model. Our findings have broad implications for understanding genome function, gene regulation, and the molecular mechanisms behind the phenomenon.
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    Decoding biological gene regulatory networks by quantitative modeling
    (2017-04-20) Huang, Bin; Onuchic, Jose
    Gene regulatory network is essential to regulate the biological functions of cells. With the rapid development of “omics” technologies, the network can be inferred for a certain biological function. However, it still remains a challenge to understand the complex network at a systematic level. In this thesis, we utilized quantitative modeling approaches to study the nonlinear dynamics and the design principles of these biological gene regulatory networks. We aim to explain the existing experimental observations with the model, and further propose reasonable hypothesis for future experimental designs. More importantly, the understanding of the circuit’s regulatory mechanism would benefit the design of a de novo gene circuit for a new biological function. We first studied the plasticity of cell migration phenotypes during cancer metastasis, which contains two key cellular plasticity mechanisms - epithelial-tomesenchymal transition (EMT) and mesenchymal-to-amoeboid transition (MAT). In this study, we quantitatively modeled the core Rac1/RhoA gene regulatory circuit for MAT and later connected it with the core regulatory circuit for EMT. We found four different stable states, consistent with the amoeboid (A), mesenchymal (M), the hybrid amoeboid/mesenchymal (A/M), and the hybrid epithelial/mesenchymal (E/M) phenotypes that are observed in the experiment. We also explored the effects of microRNAs and EMT-inducing signals like Hepatocyte Growth Factor (HGF), and provided a new insight for the transitions among these phenotypes. To improve the traditional modeling approaches, we developed a new computational modeling method called Random Circuit Perturbation (RACIPE) to explore the dynamic behavior of gene regulatory circuits without the requirement of detailed kinetic parameters. We applied RACIPE on several gene circuits, and found the existence of robust gene expression patterns even though the model parameters are wildly perturbed. We also showed the powerful aspect of RACIPE to decipher the operating principles of the circuits. This kind of quantitative models not only works for gene regulatory network, but also is capable to be extended to study the cell-cell interactions among cancer and immune cells. The results shown the co-occurrence of three cancer states: low risk cancer with intermediate immunity (L), intermediate risk cancer with high immunity (I) and high risk cancer with low immunity state (H). We further used the model to assess the different combinations of cancer therapies.
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    Strontium Laser Cooling and Trapping Apparatus
    (2015-04-23) Camargo, Francisco; Killian, Tom C; Dunning, Barry; Onuchic, Jose
    This work describes the assembly and use of an apparatus for laser cooling and trapping of atomic strontium in a broad-transition magneto-optical trap and magnetic trap. An all-diode 461nm laser system is used to drive the main cooling transition, 1S0 → 1P1. The lifetimes, loading rates, and temperatures of the trapped atoms are characterized.
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    The Modular Network Structure of Complex Biological Systems: Cancer, Cognition and Genes
    (2020-11-23) Ye, Fengdan; Pascual, Maria; Onuchic, Jose
    Recent years have witnessed a surge in the application of graph theory to complex biological systems. The ability of graph theory to extract essential knowledge from the plethora of information embedded in a complex system has proven rewarding in many disciplines ranging from evolutionary biology to cancer prediction. The modular structure of complex networks, a branch of graph theory, is the focus of this text. Its guiding hypothesis, derived from statistical physics, states that modularity correlates with performances of complex biological systems and that the direction of correlation is mediated by environmental stress. This text tests and expands the theory of modularity in three main contexts - gene co-expression networks, human brain networks, and genome-scale metabolic networks. It is demonstrated that modularity of cancer-associated gene co-expression network is predictive of cancer aggressiveness, that modularity of resting-state functional connectivity in healthy young adults correlates with cognitive performance and the correlation is mediated by task complexity, and that modularity of human brain metabolic network not only predicts risk for Alzheimer’s disease but also defines the brain regions where metabolism correlates with dementia-risk gene expression. In addition, definition of modularity and maximization algorithm for bipartite, directed, and weighted networks are proposed and subsequently tested on a genome-scale bacterial metabolic network under different levels of survival stress. Overall, results presented here support the hypothesis of modularity’s role as a performance predictor for complex systems. The existing theory of modularity has been validated in numerous scenarios and expanded with the concept of ”network fragmentation”. Modularity can be applied to clinical settings for risk evaluation, and even contribute to individualized therapy. It can also help understand the mechanism of biological processes that are currently poorly understood. Of course, future research is needed to further the understanding of the emergence of modularity in complex systems and its application. Better definition of modularity, faster and more functionally appropriate clustering algorithm, and the collection of larger amount of higher quality data are crucial for the advancement of the field.
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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.