Kulasiri Don, Liang Jingyi, He Yao, Samarasinghe Sandhya
Centre for Advanced Computational Solutions (C-fACS), Molecular Biosciences Department, Lincoln University, Christchurch, New Zealand.
Centre for Advanced Computational Solutions (C-fACS), Molecular Biosciences Department, Lincoln University, Christchurch, New Zealand.
J Theor Biol. 2017 Apr 21;419:116-136. doi: 10.1016/j.jtbi.2017.02.003. Epub 2017 Feb 9.
We investigate the epistemic uncertainties of parameters of a mathematical model that describes the dynamics of CaMKII-NMDAR complex related to memory formation in synapses using global sensitivity analysis (GSA). The model, which was published in this journal, is nonlinear and complex with Ca patterns with different level of frequencies as inputs. We explore the effects of parameter on the key outputs of the model to discover the most sensitive ones using GSA and partial ranking correlation coefficient (PRCC) and to understand why they are sensitive and others are not based on the biology of the problem. We also extend the model to add presynaptic neurotransmitter vesicles release to have action potentials as inputs of different frequencies. We perform GSA on this extended model to show that the parameter sensitivities are different for the extended model as shown by PRCC landscapes. Based on the results of GSA and PRCC, we reduce the original model to a less complex model taking the most important biological processes into account. We validate the reduced model against the outputs of the original model. We show that the parameter sensitivities are dependent on the inputs and GSA would make us understand the sensitivities and the importance of the parameters. A thorough phenomenological understanding of the relationships involved is essential to interpret the results of GSA and hence for the possible model reduction.
我们使用全局敏感性分析（GSA）研究了一个数学模型参数的认知不确定性，该模型描述了与突触中记忆形成相关的CaMKII-NMDAR复合物的动力学。该模型发表在本期刊上，是非线性且复杂的，以不同频率水平的Ca模式作为输入。我们使用GSA和偏排序相关系数（PRCC）探索参数对模型关键输出的影响，以发现最敏感的参数，并基于问题的生物学原理理解它们为何敏感而其他参数不敏感。我们还扩展了模型，添加了突触前神经递质囊泡释放，以不同频率的动作电位作为输入。我们对这个扩展模型进行GSA，以表明如PRCC景观所示，扩展模型的参数敏感性是不同的。基于GSA和PRCC的结果，我们在考虑最重要的生物学过程的情况下，将原始模型简化为一个不太复杂的模型。我们根据原始模型的输出对简化模型进行验证。我们表明参数敏感性取决于输入，GSA将使我们理解参数的敏感性和重要性。对所涉及关系的全面现象学理解对于解释GSA的结果以及因此对于可能的模型简化至关重要。
