标题： 在多尺度设置中翻译和评估单细胞布尔网络干预 显示英文标题

标题： Translating and evaluating single-cell Boolean network interventions in the multiscale setting

摘要： 细胞内网络处理细胞水平的信息并控制细胞命运。 它们可以使用布尔网络进行计算建模，布尔网络是离散二进制事件的隐式时间因果模型。 这些网络可以嵌入计算代理中以驱动细胞行为。 为了探索这种集成，我们构建了一组候选干预措施，利用穷举搜索模拟、稳定基序控制和基于个体的平均场方法（IBMFA），在一种罕见白血病的细胞存活网络中诱导细胞凋亡。 由于固有的算法限制，这些干预措施最适合用于细胞级决策，而不是更现实的多细胞环境。 为了解决这些限制，我们将目标控制解决方案视为潜在的治疗干预目标，并开发了一个管道，将它们转化为连续时间的多细胞、基于代理的模型。 我们通过阈值化设置布尔网络和多细胞模型之间的离散到连续转换，并生成简单的计算模拟，旨在模仿实验和转化生物学中的情况。 这些包括一系列模拟：恒定底物梯度、全局底物脉冲和随时间变化的边界条件。 我们发现，在隐式时间单细胞环境中表现相同的干预措施，在多尺度环境中在影响种群增长和空间分布的能力上是可分离的。 进一步分析表明，种群和空间分布的差异源于内部动态的差异（稳定基序控制与目标控制）以及干预节点与输出节点之间的网络距离。 这一概念验证工作展示了在多细胞模拟中考虑内部动态的重要性，以及对布尔网络控制理解的影响。 显示英文摘要

摘要： Intracellular networks process cellular-level information and control cell fate. They can be computationally modeled using Boolean networks, which are implicit-time causal models of discrete binary events. These networks can be embedded in computational agents to drive cellular behavior. To explore this integration, we construct a set of candidate interventions that induce apoptosis in a cell-survival network of a rare leukemia using exhaustive search simulation, stable motif control, and an individual-based mean field approach (IBMFA). Due to inherent algorithmic limitations, these interventions are most suitable for cell-level determinations, not the more realistic multicellular setting. To address these limitations, we treat the target control solutions as putative targets for therapeutic interventions and develop a pipeline to translate them to continuous-time multicellular, agent-based models. We set the discrete-to-continuous transitions between the Boolean network and multicellular model via thresholding and produce simple computational simulations designed to emulate situations in experimental and translational biology. These include a series of simulations: constant substrate gradients, global substrate pulses, and time-varying boundary conditions. We find that interventions that perform equally well in the implicit-time single-cell setting are separable in the multiscale setting in ability to impact population growth and spatial distribution. Further analysis shows that the population and spatial distribution differences arise from differences in internal dynamics (stable motif controls versus target controls) and network distance between the intervention and output nodes. This proof of concept work demonstrates the importance of accounting for internal dynamics in multicellular simulations as well as impacts on understanding of Boolean network control.