Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.
Allen Discovery Center at Tufts University, and Department of Biology, Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA.
Sci Rep. 2017 Jan 27;7:41339. doi: 10.1038/srep41339.
Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation. We developed a Machine Learning method which inferred a model explaining this complex, stochastic all-or-none dataset. We then used this model to ask how a new phenotype could be generated: animals in which only some of the melanocytes converted. Systematically performing in silico perturbations, the model predicted that a combination of altanserin (5HTR2 inhibitor), reserpine (VMAT inhibitor), and VP16-XlCreb1 (constitutively active CREB) would break the all-or-none concordance. Remarkably, applying the predicted combination of three reagents in vivo revealed precisely the expected novel outcome, resulting in partial conversion of melanocytes within individuals. This work demonstrates the capability of automated analysis of dynamic models of signaling networks to discover novel phenotypes and predictively identify specific manipulations that can reach them.
再生医学的进展需要对细胞控制网络进行逆向工程，以推断出具有所需系统水平结果的干扰。这种动态模型允许对新型干扰进行表型预测，并在计算机上快速评估。在这里，我们分析了一种仅以前以全有或全无的方式观察到的黑色素细胞向转移性表型转化的爪蟾模型。先前的体内遗传和药理学实验表明，在给定的干扰后，个体动物要么完全转化，要么以一定的特征频率保持正常。我们开发了一种机器学习方法，该方法推断了一个可以解释这种复杂、随机的全有或全无数据集的模型。然后，我们使用该模型来询问如何产生新的表型：只有一些黑色素细胞转化的动物。通过系统地进行计算机模拟干扰，该模型预测，altanserin（5HTR2 抑制剂）、利血平（VMAT 抑制剂）和 VP16-XlCreb1（组成型激活 CREB）的组合将打破全有或全无的一致性。值得注意的是，在体内应用预测的三种试剂组合后，准确地揭示了预期的新结果，导致个体内部黑色素细胞的部分转化。这项工作证明了自动分析信号网络动态模型以发现新表型并预测性地识别可以达到这些表型的特定操作的能力。
