Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland; Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin, Ireland; Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland; Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin, Ireland.
Trends Cell Biol. 2023 Nov;33(11):913-923. doi: 10.1016/j.tcb.2023.04.004. Epub 2023 May 30.
Acquisition of omics data advances at a formidable pace. Yet, our ability to utilize these data to control cell phenotypes and design interventions that reverse pathological states lags behind. Here, we posit that cell states are determined by core networks that control cell-wide networks. To steer cell fate decisions, core networks connecting genotype to phenotype must be reconstructed and understood. A recent method, cell state transition assessment and regulation (cSTAR), applies perturbation biology to quantify causal connections and mechanistically models how core networks influence cell phenotypes. cSTAR models are akin to digital cell twins enabling us to purposefully convert pathological states back to physiologically normal states. While this capability has a range of applications, here we discuss reverting oncogenic transformation.
组学数据的获取进展迅速。然而,我们利用这些数据来控制细胞表型和设计逆转病理状态的干预措施的能力却落后了。在这里,我们假设细胞状态由控制全细胞网络的核心网络决定。为了引导细胞命运决定,连接基因型和表型的核心网络必须被重建和理解。最近的一种方法,细胞状态转变评估和调控(cSTAR),应用扰动生物学来量化因果关系,并从机制上模拟核心网络如何影响细胞表型。cSTAR 模型类似于数字细胞双胞胎,使我们能够有目的地将病理状态转换回生理正常状态。虽然这种能力有广泛的应用,但在这里我们讨论逆转致癌转化。