MANET: Maximum Entropy Neural Networks for Mechanistic Modeling of Single Cell Behavior

项目总结/摘要 尽管最近单细胞技术取得了实验进展，并且同时开发了 统计方法，我们预测单细胞动力学和识别生物化学 决定细胞间变异性的过程仍然是基本的。我们已经找到了 在实现单细胞行为的机械理解的障碍：我们没有 将单细胞数据与机械信号网络模型整合的计算方法。 基于我们以前的工作，并利用神经网络的前沿发展， 我们提出了一个全面的研究计划，以弥补这一差距。 单细胞数据与机械模型整合的中心问题是，即使是大的- 比例数据仅部分约束模型，导致模型族与数据拟合相等 好.那么，我们如何从模型中进行选择呢？我们的策略是使用最大熵（Max Ent）方法，该方法推断出最不复杂的模型：一个不会不利于任何结果的模型 除非由数据和机械约束保证。在过去的十年里，我们 开创了使用Max Ent来模拟生物网络动态的新方法。未来五 我们计划有两个主要的研究目标：（1）建立和验证计算 将单个单元数据与模型集成所需的架构，以及（2）与 实验学家，使用开发的框架来研究两个重要的变化 信号转导通路;丝裂原活化蛋白激酶（MAPK）通路和机械转导。我们 设想这一框架将是不可或缺的探索机制的起源细胞到 细胞变异性在广泛的信号网络。值得注意的是，欠约束模型 在定量生物学的许多领域中无处不在，包括该实验室的其他两项研究 焦点：代谢和微生物组动力学。这里提出的方案将直接受益于 大规模数据与机械模型的整合以及对其他方法的原则性探索 隐藏的假设

Project Summary/Abstract Despite recent experimental advances in single cell techniques and a concurrent development in statistical methods, our ability to predict single cell dynamics and identify the biochemical processes that dictate cell-to-cell variability remains rudimentary. We have identified the key roadblock in achieving mechanistic understanding of single cell behavior: we do not have computational methods to integrate single cell data with mechanistic signaling network models. Building upon our previous work and leveraging cutting-edge developments in neural networks, we propose a comprehensive research program to bridge this gap. The central problem in integration of single cell data with mechanistic models is that even large- scale data only partially constrain the models, leading to a family of models that fit the data equally well. How do we then choose from the models? Our strategy is to use the Maximum Entropy (Max Ent) approach which infers the least complex model: one that does not disfavor any outcome unless warranted by the data and the mechanistic constraints. Over the past decade, we have pioneered the novel use of Max Ent to model dynamics of biological networks. In the next five years, we plan to have two main research goals; (1) to build and validate the computational architecture required to integrate single cell data with models and (2) in close collaboration with experimentalists, use the developed framework to study the variability in two important pathways; the mitogen activated protein kinase (MAPK) pathway and mechanotransduction. We envision that this framework will be indispensable in exploring the mechanistic origins of cell-to- cell variability across a broad range of signaling networks. Notably, under-constrained models are ubiquitous in many areas of quantitative biology, including two of the laboratory’s other research foci: metabolism and microbiome dynamics. The program proposed here will directly benefit integration of large-scale data with mechanistic models and a principled exploration of otherwise hidden hypotheses.