Browsing by Author "Zhang, Hang"

Now showing 1 - 4 of 4
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    DynaCut: A Framework for Dynamic and Adaptive Program Customization
    Mahurkar, Abhijit; Wang, Xiaoguang; Zhang, Hang; Ravindran, Binoy (ACM, 2023-11-27)
    Software is becoming increasingly complex and feature-rich, yet only part of any given codebase is frequently used. Existing software customization and debloating approaches target static binaries, focusing on feature discovery, control-flow analysis, and binary rewriting. As a result, the customized program binary has a smaller attack surface as well as less available functionality. This means that once a software’s use scenario changes, the customized binary may not be usable. This paper presents DynaCut, for dynamic software code customization. DynaCut can disable “not being used” code features during software runtime and re-enable them when required again. DynaCut works at the binary level; no source code is needed. To achieve its goal, DynaCut includes a dynamic process rewriting technique that seamlessly and transparently updates the image of a running process, with specific code features blocked or re-enabled. To help identify potentially unused code, DynaCut employs an execution trace-based differential analysis to pinpoint the code related to specific software features, which can be dynamically turned on/off based on user configuration. We also develop automatic methods to locate code that is only temporally used (e.g., initialization code), which can be dropped in a timely manner (e.g., after the initialization phase). We prototype DynaCut and evaluate it using 3 widely used server applications and the SPECint2017_speed benchmark suite. The result shows that, compared to existing static binary customization approaches, DynaCut removes an additional 10% of code on average and up to 56% of temporally executed code due to the dynamic code customization.
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    A strategy to study pathway cross-talks of cells under repetitive exposure to stimuli
    Fu, Yan; Jiang, Xiaoshan; Zhang, Hang; Xing, Jianhua (BMC Systems Biology, 2012-12-17)
    Background Cells are subject to fluctuating and multiple stimuli in their natural environment. The signaling pathways often crosstalk to each other and give rise to complex nonlinear dynamics. Specifically repetitive exposure of a cell to a same stimulus sometime leads to augmented cellular responses. Examples are amplified proinflammatory responses of innate immune cells pretreated with a sub-threshold then a high dose of endotoxin or cytokine stimulation. This phenomenon, called priming in the literature, has important pathological and clinical significances. Results In a previous study, we enumerated possible mechanisms for priming using a three-node network model. The analysis uncovered three mechanisms. Based on the results, in this work we developed a straightforward procedure to identify molecular species candidates contributing to the priming effect and the corresponding mechanisms. The procedure involves time course measurements, e.g., gene expression levels, or protein activities under low, high, and low + high dose of stimulant, then computational analysis of the dynamics patterns, and identification of functional roles in the context of the regulatory network. We applied the procedure to a set of published microarray data on Inteferon- priming of human macrophages. The analysis identified a number of network motifs possibly contributing to Interferon- priming. A further detailed mathematical model analysis further reveals how combination of different mechanisms leads to the priming effect. Conclusions One may perform systematic screening using the proposed procedure combining with high throughput measurements, at both transcriptome and proteome levels. It is applicable to various priming phenomena.
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    Theoretical and Computational Studies on the Dynamics and Regulation of Cell Phenotypic Transitions
    Zhang, Hang (Virginia Tech, 2016-04-18)
    Cell phenotypic transitions, or cell fate decision making processes, are regulated by complex regulatory networks composed of genes, RNAs, proteins and metabolites. The regulation can take place at the epigenetic, transcriptional, translational, and post-translational levels to name a few. Epigenetic histone modification plays an important role in cell phenotype maintenance and transitions. However, the underlying mechanism relating dynamical histone modifications to stable epigenetic cell memory remains elusive. Incorporating key pieces of molecular level experimental information, we built a statistical mechanics model for the inheritance of epigenetic histone modifications. The model reveals that enzyme selectivity of different histone substrates and cooperativity between neighboring nucleosomes are essential to generate bistability of the epigenetic memory. We then applied the epigenetic modeling framework to the differentiation process of olfactory sensory neurons (OSNs), where the observed 'one-neuron-one-allele' phenomenon has remained as a long-standing puzzle. Our model successfully explains this singular behavior in terms of epigenetic competition and enhancer cooperativity during the differentiation process. Epigenetic level events and transcriptional level events cooperate synergistically in the OSN differentiation process. The model also makes a list of testable experimental predictions. In general, the epigenetic modeling framework can be used to study phenotypic transitions when histone modification is a major regulatory element in the system. Post-transcriptional level regulation plays important roles in cell phenotype maintenance. Our integrated experimental and computational studies revealed such a motif regulating the differentiation of definitive endoderm. We identified two RNA binding proteins, hnRNPA1 and KSRP, which repress each other through microRNAs miR-375 and miR-135a. The motif can generate switch behavior and serve as a noise filter in the stem cell differentiation process. Manipulating the motif could enhance the differentiation efficiency toward a specific lineage one desires. Last we performed mathematical modeling on an epithelial-to-mesenchymal transition (EMT) process, which could be used by tumor cells for their migration. Our model predicts that the IL-6 induced EMT is a stepwise process with multiple intermediate states. In summary, our theoretical and computational analyses about cell phenotypic transitions provide novel insights on the underlying mechanism of cell fate decision. The modeling studies revealed general physical principles underlying complex regulatory networks.
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    Tumor-Specific Chromosome Mis-Segregation Controls Cancer Plasticity by Maintaining Tumor Heterogeneity
    Hu, Yuanjie; Ru, Ning; Xiao, Huasheng; Chaturbedi, Abhishek; Hoa, Neil T.; Tian, Xiao-Jun; Zhang, Hang; Ke, Chao; Yan, Fengrong; Nelson, Jodi; Li, Zhenzhi; Gramer, Robert; Yu, Liping; Siegel, Eric; Zhang, Xiaona; Jia, Zhenyu; Jadus, Martin R.; Limoli, Charles L.; Linskey, Mark E.; Xing, Jianhua; Zhou, Yi-Hong (PLOS, 2013-09-25)
    Aneuploidy with chromosome instability is a cancer hallmark. We studied chromosome 7 (Chr7) copy number variation (CNV) in gliomas and in primary cultures derived from them. We found tumor heterogeneity with cells having Chr7-CNV commonly occurs in gliomas, with a higher percentage of cells in high-grade gliomas carrying more than 2 copies of Chr7, as compared to low-grade gliomas. Interestingly, all Chr7-aneuploid cell types in the parental culture of established glioma cell lines reappeared in single-cell-derived subcultures. We then characterized the biology of three syngeneic glioma cultures dominated by different Chr7-aneuploid cell types. We found phenotypic divergence for cells following Chr7 mis-segregation, which benefited overall tumor growth in vitro and in vivo. Mathematical modeling suggested the involvement of chromosome instability and interactions among cell subpopulations in restoring the optimal equilibrium of tumor cell types. Both our experimental data and mathematical modeling demonstrated that the complexity of tumor heterogeneity could be enhanced by the existence of chromosomes with structural abnormality, in addition to their mis-segregations. Overall, our findings show, for the first time, the involvement of chromosome instability in maintaining tumor heterogeneity, which underlies the enhanced growth, persistence and treatment resistance of cancers.