Ross Brian C, Boguslav Mayla, Weeks Holly, Costello James C
Computational Bioscience Program, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., Aurora, CO, 80045, USA.
Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12800 E. 19th Ave., Aurora, CO, 80045, USA.
BMC Syst Biol. 2018 Jun 7;12(1):64. doi: 10.1186/s12918-018-0591-9.
Certain biological processes, such as the development of cancer and immune activation, can be controlled by rare cellular events that are difficult to capture computationally through simulations of individual cells. Information about such rare events can be gleaned from an attractor analysis, for which a variety of methods exist (in particular for Boolean models). However, explicitly simulating a defined mixed population of cells in a way that tracks even the rarest subpopulations remains an open challenge.
Here we show that when cellular states are described using a Boolean network model, one can exactly simulate the dynamics of non-interacting, highly heterogeneous populations directly, without having to model the various subpopulations. This strategy captures even the rarest outcomes of the model with no sampling error. Our method can incorporate heterogeneity in both cell state and, by augmenting the model, the underlying rules of the network as well (e.g., introducing loss-of-function genetic alterations). We demonstrate our method by using it to simulate a heterogeneous population of Boolean networks modeling the T-cell receptor, spanning ∼ 10 distinct cellular states and mutational profiles.
We have developed a method for using Boolean models to perform a population-level simulation, in which the population consists of non-interacting individuals existing in different states. This approach can be used even when there are far too many distinct subpopulations to model individually.
某些生物学过程，如癌症的发展和免疫激活，可由罕见的细胞事件控制，而通过单个细胞模拟在计算上难以捕捉这些事件。关于此类罕见事件的信息可从吸引子分析中获取，对此存在多种方法（特别是针对布尔模型）。然而，以追踪甚至最罕见亚群的方式明确模拟定义的混合细胞群体仍然是一个悬而未决的挑战。
在此我们表明，当使用布尔网络模型描述细胞状态时，无需对各个亚群进行建模，就可以直接精确模拟非相互作用、高度异质群体的动态。该策略能够捕捉模型中最罕见的结果，且不存在采样误差。我们的方法可以在细胞状态以及通过扩展模型在网络的潜在规则方面纳入异质性（例如，引入功能丧失的基因改变）。我们通过使用该方法模拟一个模拟T细胞受体的布尔网络异质群体来展示我们的方法，该群体跨越约10种不同的细胞状态和突变谱。
我们开发了一种使用布尔模型进行群体水平模拟的方法，其中群体由处于不同状态的非相互作用个体组成。即使存在太多不同的亚群而无法单独建模，这种方法也可以使用。
