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A whole-cell computational model predicts phenotype from genotype

机译:全细胞计算模型可根据基因型预测表型

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Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.
机译:理解单个分子如何产生复杂的表型以及它们之间的相互作用是生物学中计算方法准备应对的主要挑战。我们报告了人类病原体生殖器生殖器生命周期的全细胞计算模型,其中包括其所有分子成分及其相互作用。结合多种数学原理的综合建模方法可以同时包含根本不同的细胞过程和实验测量结果。我们的全细胞模型考虑了所有带注释的基因功能,并针对大量数据进行了验证。该模型提供了许多以前未曾观察到的细胞行为的见解,包括蛋白质-DNA结合的体内速率以及DNA复制起始和复制的持续时间之间的反比关系。此外,由模型预测指导的实验分析确定了先前未检测到的动力学参数和生物学功能。我们得出结论,可以使用全面的全细胞模型促进生物学发现。

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