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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Continuous-Time, Discrete-State Method for Simulating the Dynamics of Biochemical Systems
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A Continuous-Time, Discrete-State Method for Simulating the Dynamics of Biochemical Systems

机译：一种连续时间离散状态方法，用于模拟生化系统的动力学

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摘要

Computational systems biology is largely driven by mathematical modeling and simulation of biochemical networks, via continuous deterministic methods or discrete event stochastic methods. Although the deterministic methods are efficient in predicting the macroscopic behavior of a biochemical system, they are severely limited by their inability to represent the stochastic effects of random molecular fluctuations at lower concentration. In this work, we have presented a novel method for simulating biochemical networks based on a deterministic solution with a modification that permits the incorporation of stochastic effects. To demonstrate the feasibility of our approach, we have tested our method on three previously reported biochemical networks. The results, while staying true to their deterministic form, also reflect the stochastic effects of random fluctuations that are dominant as the system transitions into a lower concentration. This ability to adapt to a concentration gradient makes this method particularly attractive for systems biology-based applications.
机译：计算系统生物学在很大程度上由生化网络的数学建模和仿真驱动，通过连续确定性方法或离散事件随机方法进行。尽管确定性方法可有效预测生化系统的宏观行为，但由于它们无法表示较低浓度下随机分子波动的随机效应，因此受到严重限制。在这项工作中，我们提出了一种基于确定性解决方案的生化网络模拟方法，该方法经过修改，可以纳入随机效应。为了证明我们方法的可行性，我们已经在三个先前报道的生化网络上测试了我们的方法。这些结果在保持其确定性形式不变的同时，还反映了随系统转变为较低浓度而占主导地位的随机波动的随机效应。这种适应浓度梯度的能力使该方法对于基于系统生物学的应用特别有吸引力。

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