The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy.
Department of Computer Science, Aalto University, Finland.
Brief Bioinform. 2020 Mar 23;21(2):527-540. doi: 10.1093/bib/bbz014.
With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). We consider two methods in the deterministic approach, namely the direct differential method and the finite difference method, and five methods in the stochastic approach, namely the Girsanov transformation, the independent random number method, the common random number method, the coupled finite difference method and the rejection-based finite difference method. The reviewed methods are compared in terms of sensitivity values and computational time to identify differences in outcome that can highlight conditions in which one approach performs better than the other.
随着数学模型在计算系统生物学领域的应用日益增多，人们对敏感性分析方法的兴趣也有所增加。基于化学主方程的随机方法和基于常微分方程（ODE）的确定性方法是分析生化系统数学模型的两种主要方法。在这项工作中，当随机模拟和确定性模拟可能提供不同的结果（具有不稳定稳态的系统、具有种群灭绝的振荡器和双稳态系统）时，研究了这些方法计算敏感性系数的性能。我们在确定性方法中考虑了两种方法，即直接微分法和有限差分法，在随机方法中考虑了五种方法，即吉布斯变换、独立随机数法、公共随机数法、耦合有限差分法和基于拒绝的有限差分法。根据敏感性值和计算时间对所审查的方法进行了比较，以确定结果中的差异，这些差异可以突出一种方法比另一种方法表现更好的条件。
