Multi-objective Optimisation of Gene Regulatory Networks: Insights from a Boolean Circadian Clock Model-猫眼课题宝

DOI：

10.29007/bvbj

发表时间：

2020-03

期刊：

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通讯作者：

中科院分区：

其他

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作者：

O. Akman;J. Fieldsend

包括生物钟的基因调控网络在一系列尺度上调节生物功能，从基因表达到表现和适应行为。这些计时器通过产生内源性节律来发挥作用，这些节律可以被带入外部的24小时昼夜循环，使生物体能够相对于黎明和黄昏优化生化过程的时间。近年来，基于微分方程的计算模型，以及最近基于布尔逻辑的计算模型，已经成为解剖和量化时钟振荡动力学背后的复杂调节关系的有用工具。然而，根据实验数据优化这些模型的参数是不平凡的。搜索空间是连续的，并随着系统大小呈指数增长，禁止穷举搜索过程，这通常是通过网格搜索或随机探索参数空间来模拟。此外，为了简化搜索过程，通常将表示与单个实验数据集拟合的目标函数进行汇总，这意味着其中包含的信息没有得到充分利用。在这里，我们研究铸造这个问题作为一个多目标，并说明如何使用进化优化算法-多目标进化策略（MOES）-可以显着加快参数搜索过程。作为一个测试案例，我们考虑了一个基于布尔延迟方程的范例生物钟模型-状态离散但时间连续的动态模型。该模型的离散性质使我们能够直接将优化器的性能与基于以固定分辨率枚举参数空间的网格搜索进行比较。我们发现，MOES在计算时间内产生接近最优的参数化，比网格搜索快几个数量级。作为这项调查的一部分，我们还表明，有一个明显的贸易之间的时钟电路的性能在自由运行和夹带的光环境。重要的是，运行时结果表明，使用多目标进化优化算法将使调查更大，更复杂的模型计算上易于处理

The gene regulatory networks that comprise circadian clocks modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. These timekeepers function by generating endogenous rhythms that can be entrained to the external 24-hour day-night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on diﬀerential equations, and more recently on Boolean logic, have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock’s oscillatory dynamics. Optimising the parameters of these models to experimental data is, however, non-trivial. The search space is continuous and increases exponentially with sys-tem size, prohibiting exhaustive search procedures, which are often emulated instead via grid-searching or random explorations of parameter space. Furthermore, to simplify the search procedure, objective functions representing ﬁts to individual experimental datasets are often aggregated, meaning the information contained within them is not fully utilised. Here, we examine casting this problem as a multi-objective one, and illustrate how the use of an evolutionary optimisation algorithm — the multi-objective evolution strategy (MOES) — can signiﬁcantly accelerate the parameter search procedure. As a test case, we consider an exemplar circadian clock model based on Boolean delay equations — dynamic models that are discrete in state but continuous in time. The discrete nature of the model enables us to directly compare the performance of our optimiser to grid searches based on enumeration of the parameter space at a ﬁxed resolution. We ﬁnd that the MOES generates near-optimal parameterisations in computation times which are several orders of magnitude faster than the grid search. As part of this investigation, we also show that there is a distinct trade-oﬀ between the performance of the clock circuit in free-running and entrained photic environments. Importantly, runtime results indicate that the use of multi-objective evolutionary optimisation algorithms will make the investigation of larger and more complex models computationally tractable

Multi-objective Optimisation of Gene Regulatory Networks: Insights from a Boolean Circadian Clock Model

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