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EAGER: Towards Understanding the Information-Theoretic Nature of the Human Epigenome

EAGER: Towards Understanding the Information-Theoretic Nature of the Human Epigenome
渴望:了解人类表观基因组的信息理论本质
批准号:
1656201
负责人:
John Goutsias
金额:
$20.08万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-01 至 2018-08-31

项目摘要

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中文摘要
翻译
细胞使用一个复杂的系统来确定特定基因在发育和分化过程中以及对环境条件和刺激的反应中何时何地表达。该系统以可遗传的表观遗传标记的形式覆盖在DNA上,通过修改基因组的结构组织而不改变DNA序列来控制基因表达。表观遗传标记可以通过使染色质(一种在真核细胞核内形成染色体的DNA/蛋白质复合物)适应抑制或刺激基因活性的不同状态来沉默或激活基因,这可以驱动遗传相同的细胞彼此表现不同。现在有充分的证据表明,异常的表观遗传调节可以导致疾病。然而,与基因突变形成鲜明对比的是,表观遗传改变可以逆转。因此,人们相信,了解人类表观基因组的研究可以导致许多人类疾病,如癌症,糖尿病和阿尔茨海默氏症的新的和高效的治疗策略?S.与目前文献中使用的相当粗糙的数学和计算方法相比,对所提出的研究的正式追求将为开发用于表观遗传信息的建模、量化和分析的根本不同的方法提供坚实的基础。这可能对表观遗传学领域以及整个医学和社会产生重大影响,这可能导致新的生物学发现,以了解表观遗传学在发展中的作用,本研究的主要目标是开发一种新的方法来理解人类表观基因组的信息结构和特性,有根据的生物学假设和统计物理学和信息论的原理。研究人员将开发量化表观遗传随机性的方法,以及识别和分析生物样本之间的表观遗传不一致性,为研究表观遗传调节在疾病和衰老中的作用提供新的和令人兴奋的方法。通过将细胞分裂过程中传递表观遗传信息的过程视为通信系统,引入了甲基化通道的概念,其特征在于其容量、耗散能量和输入/输出熵。使用真实的表观遗传学数据的初步结果表明,染色质组织和甲基化通道的信息特性之间存在有趣的联系。这表明,表观遗传生物学,统计物理学和信息理论的合并可能会导致对表观基因组的信息属性和正常发育和疾病中的核组织之间的关系的基本见解。拟议工作的成功完成将改变表观遗传信息建模,量化和分析的方式,并导致强大的方法来理解人类表观基因组的信息理论内容及其在发育,疾病和衰老中的作用。
英文摘要
Cells use an elaborate system to determine when and where specific genes will be expressed during development and differentiation as well as in response to environmental conditions and stimuli. This system is overlaid on the DNA in the form of heritable epigenetic marks that control gene expression by modifying the structural organization of the genome without changing the DNA sequence. Epigenetic marks can silence genes or activate them by adapting chromatin (a DNA/protein complex which forms chromosomes within the nucleus of eukaryotic cells) to distinct states that repress or stimulate gene activity and this can drive genetically identical cells to behave differently from each other. There is now ample evidence that aberrant epigenetic regulation can lead to disease. However, and in sharp contrast to gene mutations, epigenetic alterations can be reversed. It is therefore believed that research in understanding the human epigenome can lead to novel and highly effective therapeutic strategies for many human diseases, such as cancer, diabetes, and Alzheimer?s. A formal pursuit of the proposed research will provide a solid foundation for developing fundamentally different methods for the modeling, quantification, and analysis of epigenetic information, as compared to rather crude mathematical and computational methods currently used in the literature. This could potentially have a major impact on the area of epigenetic science, as well as on medicine and society at large, which could lead to new biological discoveries towards understanding the role of epigenetics in development, disease and aging.The main goal of this research is to develop a novel approach for understanding the informational structure and properties of the human epigenome by using well-grounded biological assumptions and principles of statistical physics and information theory. The investigators will develop methods for quantifying epigenetic stochasticity, as well as discern and analyze epigenetic discordance between biological samples, providing new and exciting ways for studying the role of epigenetic regulation in disease and aging. By viewing the process of transmitting epigenetic information during cell division as a communication system, the concept of a methylation channel is introduced, which can be characterized by its capacity, dissipated energy, and input/output entropy. Preliminary results using real epigenetic data have demonstrated an intriguing connection between chromatin organization and informational properties of methylation channels. This shows that a merger of epigenetic biology, statistical physics and information theory may lead to fundamental insights into the relationship between the informational properties of the epigenome and nuclear organization in normal development and disease. Successful completion of the proposed work will transform the way epigenetic information is modeled, quantified, and analyzed, and lead to powerful methodologies for understanding the information theoretic content of the human epigenome and its role in development, disease, and aging.
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CIF: Small: Understanding Complexity in Markovian Interaction Networks: Self-Organization, Functional Stability, Robustness, and Evolutionary Behavior
  • 批准号:
    1217213
  • 项目类别:
    Standard Grant
  • 资助金额:
    $41.35万
  • 财政年份:
    2012
  • 负责人:
    John Goutsias
  • 依托单位:
Identification and Sensitivity Analysis of Complex Interaction Networks
  • 批准号:
    0830128
  • 项目类别:
    Standard Grant
  • 资助金额:
    $18.22万
  • 财政年份:
    2008
  • 负责人:
    John Goutsias
  • 依托单位:
Workshop on Genomic Signal Processing and Statistics (GENSIPS); May 26-28, 2004; Baltimore, MD
  • 批准号:
    0352769
  • 项目类别:
    Standard Grant
  • 资助金额:
    $3.4万
  • 财政年份:
    2004
  • 负责人:
    John Goutsias
  • 依托单位:
CISE Research Instrumentation: Computational Techniques for 3-D Modeling and Analysis of Infrared and MRI Image Sequences
  • 批准号:
    9729576
  • 项目类别:
    Standard Grant
  • 资助金额:
    $11.0万
  • 财政年份:
    1998
  • 负责人:
    John Goutsias
  • 依托单位:
海外基金