Learn Systems Biology Equations From Snapshot Single Cell Genomic Data
从快照单细胞基因组数据学习系统生物学方程
基本信息
- 批准号:10736507
- 负责人:
- 金额:$ 31.8万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-15 至 2027-06-30
- 项目状态:未结题
- 来源:
- 关键词:ATAC-seqAddressAlgorithmsBenchmarkingBinding SitesBiochemicalBiological ModelsBiologyBirthCRISPR interferenceCell Fate ControlCell physiologyCellsCellular biologyCessation of lifeCommunitiesComplementComplexComputersDNA BindingDNA sequencingDataData AnalysesData SetDevelopmentDimensionsDoseElementsEquationError SourcesEukaryotic CellGATA1 geneGene ExpressionGene Expression RegulationGenesGeneticGenetic CodeGenetic TranscriptionGenomic approachGenomicsGoalsGrantGraphInformaticsKnowledgeLMO2 geneLabelLearningMachine LearningMathematicsMeasurementMetabolicMethodsModalityModelingMutationNamesNaturePathologic ProcessesPatternPhysiological ProcessesProceduresProcessPublishingRNARNA SplicingRegulationResearchSamplingStochastic ProcessesSystems BiologySystems TheoryTAL1 geneTechniquesTestingTimeWorkcofactorcombinatorialcomputational pipelinescomputer frameworkcomputerized toolsdata acquisitiondata integrationdata spacedifferential geometrydynamic systemenvironmental changeexperimental studyextracellulargene regulatory networkgenome-widegenomic datahigh dimensionalityimprovedin silicoinformatics toolinsightmathematical modelmolecular dynamicsmultimodal datapredictive modelingreconstructionresponsesingle cell analysisstatisticssuccesstooltool developmenttranscription factortranscriptometranscriptomicsvector
项目摘要
Understanding how cells respond to environmental changes is a fundamental task in systems biology and has
profound biomedical implications. Mathematical modeling on small network motifs using dynamical systems
theories has been successful on providing mechanistic insight and guidance, but generalization to a genome-
wide intertwined gene regulatory network is challenging. Single cell genomics approaches emerge as powerful
tools for studying cellular processes, but the destructive nature of most single cell techniques makes it
unfeasible to extract dynamical information of cellular processes. In addition, a number of grand challenges
impede further development of the field, such as trajectory inference, effect of various sources of errors on
data analysis, and validating and benchmarking tools for single cell measurements and analyses. The goal of
this proposed research is to tackle these challenges through integrating dynamical systems modeling into
single cell genomics analyses. The proposed research is based on recent advances in the single cell genomics
field that one can extract both transcriptome (x) and estimation of RNA velocity (i.e., instant time derivatives of
transcriptome, dx/dt) from single cell genomics data. We further developed a unified theoretical framework that
allows estimating the velocity information from various types of single cell data, and a machine learning based
computational pipeline of reconstructing systems biology equations for genomewide regulatory networks,
together with a computer package, dynamo, released to the community. This integration between single cell
genomics analyses and systems biology modeling provides quantitative mechanistic and dynamics
information. We propose to further develop our package and computational framework to address several
limitations in our published work. In Aim 1, we will first develop dynamo to interface with other single cell
analysis and dynamics modeling packages, and expand the types of single cell data to be analyzed. Then we
will develop and test a discrete dynamical model for full stochastic cellular dynamics based on the graph
representation of discrete vector fields. In Aim 2, we will first develop a systematic pipeline of integrating data
of multi-modality (e.g., ATAC-seq, DNA sequencing and binding site analyses, etc) and dynamo to identify
genetic codes of combinatorial function of transcriptional factors, the so-called composite elements in genetics.
Eukaryotic cells use a combination of a finite number of transcription factors to generate a large number of
different target gene regulation patterns. Cracking the genetic code at the genome-wide level is fundamental to
cell biology but challenging despite extensive efforts. Then we will expand the pipeline to reconstruct biology-
informed systems biology models for the genomowide gene regulation. We will evaluate the in silico
predictions from the model against several Perturb-seq datasets.
了解细胞如何响应环境变化是系统生物学的一项基本任务,
深刻的生物医学意义。基于动力系统的小网络模体的数学建模
理论已经成功地提供了机械的洞察力和指导,但推广到基因组-
广泛交织的基因调控网络是具有挑战性的。单细胞基因组学方法成为强大的
研究细胞过程的工具,但大多数单细胞技术的破坏性,
无法提取细胞过程的动力学信息。此外,一些重大挑战
阻碍了该领域的进一步发展,如轨迹推断,各种误差源对
数据分析、单细胞测量和分析的验证和基准测试工具。的目标
这项拟议的研究是通过将动态系统建模集成到
单细胞基因组学分析。这项研究是基于单细胞基因组学的最新进展
可以提取转录组(x)和RNA速度的估计(即,瞬时时间导数
转录组,dx/dt)。我们进一步发展了一个统一的理论框架,
允许从各种类型的单细胞数据估计速度信息,以及基于机器学习的
为全基因组调控网络重建系统生物学方程的计算管道,
连同一套名为“dynamo”的电脑软件,一并向市民发放。这种单细胞和单细胞之间的整合
基因组学分析和系统生物学建模提供了定量的机制和动力学
信息.我们建议进一步开发我们的软件包和计算框架,以解决几个
我们已发表作品的局限性。在目标1中,我们将首先开发与其他单细胞接口的发电机
分析和动力学建模软件包,并扩展了要分析的单细胞数据的类型。然后我们
我将开发和测试一个离散的动态模型,完全随机细胞动力学的基础上,图
离散向量场的表示。在目标2中,我们将首先开发一个集成数据的系统管道
多模态(例如,ATAC-seq、DNA测序和结合位点分析等)和dynamo来鉴定
转录因子的组合功能的遗传密码,在遗传学中被称为复合元件。
真核细胞使用有限数量的转录因子的组合来产生大量的转录因子。
不同的靶基因调控模式。在全基因组水平上破解遗传密码是
细胞生物学,但尽管付出了大量努力,仍具有挑战性。我们将扩大重建生物学的管道-
信息系统生物学模型的基因组的基因调控。我们将通过计算机模拟评估
该模型针对多个Perturb-seq数据集进行了预测。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jianhua Xing其他文献
Jianhua Xing的其他文献
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{{ truncateString('Jianhua Xing', 18)}}的其他基金
Role of the Snail1-Twist-p21 axis on cell cycle arrest and renal fibrosis development
Snail1-Twist-p21 轴在细胞周期停滞和肾纤维化发展中的作用
- 批准号:
10062964 - 财政年份:2018
- 资助金额:
$ 31.8万 - 项目类别:
Coupling between cell cycle arrest and epithelial-to-mesenchymal transition in renal fibrosis development
肾纤维化发展中细胞周期停滞与上皮间质转化之间的耦合
- 批准号:
10923257 - 财政年份:2018
- 资助金额:
$ 31.8万 - 项目类别:
Role of the Snail1-Twist-p21 axis on cell cycle arrest and renal fibrosis development
Snail1-Twist-p21 轴在细胞周期停滞和肾纤维化发展中的作用
- 批准号:
10300999 - 财政年份:2018
- 资助金额:
$ 31.8万 - 项目类别:
Dynamics and mechanism of mechanical regulation of bacterial flagellar motor swit
细菌鞭毛运动开关的机械调节动力学及机制
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8423015 - 财政年份:2012
- 资助金额:
$ 31.8万 - 项目类别:
Dynamics and mechanism of mechanical regulation of bacterial flagellar motor swit
细菌鞭毛运动开关的机械调节动力学及机制
- 批准号:
8269787 - 财政年份:2012
- 资助金额:
$ 31.8万 - 项目类别:
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