Browsing by Author "Wang, Zhipeng"

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    Modeling the therapeutic efficacy of NFκBsynthetic decoy oligodeoxynucleotides (ODNs)
    (BMC, 2018) Wang, Zhipeng; Potoyan, Davit A.; Wolynes, Peter G.; Center for Theoretical Biological Physics
    Background: Transfection of NF κB synthetic decoy Oligodeoxynucleotides (ODNs) has been proposed as a promising therapeutic strategy for a variety of diseases arising from constitutive activation of the eukaryotic transcription factor NF κB. The decoy approach faces some limitations under physiological conditions notably nuclease-induced degradation. Results: In this work, we show how a systems pharmacology model of NF κB regulatory networks displaying oscillatory temporal dynamics, can be used to predict quantitatively the dependence of therapeutic efficacy of NF κB synthetic decoy ODNs on dose, unbinding kinetic rates and nuclease-induced degradation rates. Both deterministic mass action simulations and stochastic simulations of the systems biology model show that the therapeutic efficacy of synthetic decoy ODNs is inversely correlated with unbinding kinetic rates, nuclease-induced degradation rates and molecular stripping rates, but is positively correlated with dose. We show that the temporal coherence of the stochastic dynamics of NF κB regulatory networks is most sensitive to adding NF κB synthetic decoy ODNs having unbinding time-scales that are in-resonance with the time-scale of the limit cycle of the network. Conclusions: The pharmacokinetics/pharmacodynamics (PK/PD) predicted by the systems-level model should provide quantitative guidance for in-depth translational research of optimizing the thermodynamics/kinetic properties of synthetic decoy ODNs.
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    Molecular stripping, targets and decoys as modulators of oscillations in the NF-κB/IκBα/DNA genetic network
    (The Royal Society, 2016) Wang, Zhipeng; Potoyan, Davit A.; Wolynes, Peter G.; Center for Theoretical Biological Physics
    Eukaryotic transcription factors in the NF-κB family are central components of an extensive genetic network that activates cellular responses to inflammation and to a host of other external stressors. This network consists of feedback loops that involve the inhibitor IκBα, numerous downstream functional targets, and still more numerous binding sites that do not appear to be directly functional. Under steady stimulation, the regulatory network of NF-κB becomes oscillatory, and temporal patterns of NF-κB pulses appear to govern the patterns of downstream gene expression needed for immune response. Understanding how the information from external stress passes to oscillatory signals and is then ultimately relayed to gene expression is a general issue in systems biology. Recently, in vitro kinetic experiments as well as molecular simulations suggest that active stripping of NF-κB by IκBα from its binding sites can modify the traditional systems biology view of NF-κB/IκBα gene circuits. In this work, we revise the commonly adopted minimal model of the NF-κB regulatory network to account for the presence of the large number of binding sites for NF-κB along with dissociation from these sites that may proceed either by passive unbinding or by active molecular stripping. We identify regimes where the kinetics of target and decoy unbinding and molecular stripping enter a dynamic tug of war that may either compensate each other or amplify nuclear NF-κB activity, leading to distinct oscillatory patterns. Our finding that decoys and stripping play a key role in shaping the NF-κB oscillations suggests strategies to control NF-κB responses by introducing artificial decoys therapeutically.
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    Robust Multiple Regression
    (MDPI, 2021) Scott, David W.; Wang, Zhipeng
    As modern data analysis pushes the boundaries of classical statistics, it is timely to reexamine alternate approaches to dealing with outliers in multiple regression. As sample sizes and the number of predictors increase, interactive methodology becomes less effective. Likewise, with limited understanding of the underlying contamination process, diagnostics are likely to fail as well. In this article, we advocate for a non-likelihood procedure that attempts to quantify the fraction of bad data as a part of the estimation step. These ideas also allow for the selection of important predictors under some assumptions. As there are many robust algorithms available, running several and looking for interesting differences is a sensible strategy for understanding the nature of the outliers.
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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.