Department of Electrical Engineering and Electronics, Technical University of Bari, Via E. Orabona 4, Bari, Italy.
BMC Bioinformatics. 2009 Oct 15;10 Suppl 12(Suppl 12):S4. doi: 10.1186/1471-2105-10-S12-S4.
Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments.
We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification.
We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich--data poor' paradox in Systems Biology.
机制模型在系统生物学中越来越受欢迎；鉴定和控制肿瘤学中感兴趣的生化途径的模型是该领域的主要目标。不幸的是，数据的稀缺性仍然限制了我们对癌症等复杂病理固有特征的理解：获取系统理解复杂反应网络的信息既耗时又昂贵。刺激反应实验（SRE）已被用于更深入地了解细胞生命和功能的生化机制细节。然而，由于问题的复杂性及其与系统生物学相关实验中的信息检索任务的相关性，优化输入时间分布仍然是一个主要的研究领域。
我们使用 Fisher 信息矩阵来量化与实验相关的信息，并提出了一种基于进化算法的最优实验设计策略，以应对系统生物学中信息收集的问题。基于控制系统理论领域获得的理论结果，我们研究了用于细胞刺激的信号的动态特性。这项研究的结果被用于开发一种用于细胞刺激过程自动化的微流控设备，以进行系统识别。
我们将提出的方法应用于表皮生长因子受体途径，观察到它最小化了与所识别模型相关的参数不确定性的数量。基于不确定性椭球的蒙特卡罗估计的统计框架证实了最优设计实验优于典型输入。该方法可以很容易地扩展到多目标公式，也可以利用可识别性分析。此外，专门为生化模型识别任务开发的全自动微流控平台的可用性有望减少系统生物学中“数据丰富-数据匮乏”悖论的影响。
