基于随机优化的二聚反应动力学研究
Stochastic Optimization Based Study of Dimerization Kinetics
摘要：
我们研究了潜在的数值算法破译的动力学参数参与多步化学反应。为此，我们研究了蛋白质的二聚动力学作为一个模型系统。我们遵循的二聚动力学使用随机模拟算法，并将其与三种不同的优化技术（遗传算法，模拟退火和并行回火），以获得在每个反应步骤中所涉及的速率常数。我们发现良好的收敛性的数值方案的过程中的速率常数。我们还进行了灵敏度测试的反应动力学参数，看看单体/二聚体分布的相关配置文件的参数的相对影响。
英文摘要：
We investigate the potential of numerical algorithms to decipher the kinetic parameters involved in multi-step chemical reactions. To this end we study a dimerization kinetics of protein as a model system. We follow the dimerization kinetics using a stochastic simulation algorithm and combine it with three different optimization techniques (Genetic Algorithm, Simulated Annealing and Parallel Tempering) to obtain the rate constants involved in each reaction step. We find good convergence of the numerical scheme to the rate constants of the process. We also perform a sensitivity test on the reaction kinetic parameters to see the relative effects of the parameters for the associated profile of the monomer/dimer distribution.
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