Recent developments in parameter estimation and structure identification of biochemical and genomic systems.
Recent developments in parameter estimation and structure identification of biochemical and genomic systems.
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DOI：
10.1016/j.mbs.2009.03.002
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发表时间：
2009-06
影响因子：
4.3
通讯作者：
Voit EO
中科院分区：
文献类型：
--
作者：
Chou IC;Voit EO
在许多情况下，生物系统的组织、调节和动态响应过于复杂，无法进行直观预测，需要数学模型的支持来进行定量评估和对系统功能的可靠理解。构建生物系统数学模型的所有步骤都具有挑战性，但可以说其中最困难的任务是模型参数的估计以及底层生物网络的结构和调节的识别。现代高通量技术的最新进展已经允许生成时间序列数据，这些数据表征基因组、蛋白质组、代谢和生理反应的动态，并使我们至少在原则上能够使用“自上而下”或“逆向”方法来处理估计和识别任务。虽然成功的逆向估计或识别的回报是巨大的，但提取结构和监管信息的过程在技术上却很困难。挑战一般可以分为四个方面，即与数据、模型、系统的数学结构以及优化和支持算法相关的问题。最近的许多文章都解决了生化系统理论 (BST) 建模框架内的逆问题。选择 BST 来完成这些任务是因为它具有独特的结构灵活性，并且生物系统的结构和调节基本上一对一地映射到描述模型的参数上。所提出的方法主要集中于各种优化算法，但也涉及支持技术，包括避免微分方程组耗时的数值积分、平滑噪声过多的数据、估计时间序列的斜率、降低推理任务的复杂性以及约束参数搜索空间的方法。其他方法针对数据预处理、模型冗余检测和改进以及无模型或基于模型的结构识别等问题。到目前为止，提出的方法及其应用的总数已超过一百种，这使得新手和专家都很难全面了解可用的算法选项和局限性。为了促进进入 BST 和相关建模领域内的逆向建模领域，本文回顾了该领域并提出了一个可操作的“工作流程”，指导用户完成估计过程，识别可能有问题的步骤，并根据各种可用算法的具体特征提出相应的解决方案。本文最后讨论了当前的技术水平并描述了悬而未决的问题。
The organization, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterize the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using “top-down” or “inverse” approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorized into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimization and support algorithms. Many recent articles have addressed inverse problems within the modeling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational “work-flow” that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.
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影响因子：
9.8
作者：
Bozdech, Zbynek;Llinas, Manuel;Pulliam, Brian Lee;Wong, Edith D;Zhu, Jingchun;DeRisi, Joseph L
通讯作者：
DeRisi, Joseph L
影响因子：
7
作者：
Bono, H;Ogata, H;Kanehisa, M
通讯作者：
Kanehisa, M
DOI：
10.1049/sb:20045005
发表时间：
2004-06-01
期刊：
SYSTEMS BIOLOGY
影响因子：
--
作者：
通讯作者：
--
影响因子：
--
作者：
CHEVALIER, T;SCHREIBER, I;ROSS, J
通讯作者：
ROSS, J
影响因子：
16
作者：
Cho, RJ;Campbell, MJ;Davis, RW
通讯作者：
Davis, RW