Schwab Julian D, Kestler Hans A
Medical Faculty, Institute of Medical Systems Biology Ulm University, Ulm, Germany.
International Graduate School of Molecular Medicine Ulm University, Ulm, Germany.
Front Physiol. 2018 Apr 24;9:431. doi: 10.3389/fphys.2018.00431. eCollection 2018.
A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior-so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.
在系统生物学中，解决生物学问题的一种常见方法是使用动态模型来模拟调控机制。其中，布尔网络可用于对生物学中调控过程的动态进行建模。布尔网络模型允许模拟所建模过程的定性行为。布尔网络模拟的一个核心目标是计算其长期行为，即所谓的吸引子。这些吸引子特别受关注，因为它们通常可以与生物学相关行为联系起来。改变内部和外部条件会影响布尔网络模型的长期行为。通过去除系统的一个组件或模拟另一个元素的过剩来对布尔网络进行扰动，可能会导致不同的吸引子。显然，可能的扰动数量以及扰动组合会随着网络规模呈指数增长。如果不是不可能的话，手动筛选一组可能的组件以寻找对长期行为有期望影响的组合可能会非常耗时。我们开发了一种方法来自动筛选导致网络功能发生用户指定变化的扰动。该方法在视觉模拟框架ViSiBool中实现，利用可满足性（SAT）求解器进行快速详尽的吸引子搜索。
