Gianchandani Erwin P, Oberhardt Matthew A, Burgard Anthony P, Maranas Costas D, Papin Jason A
Department of Biomedical Engineering University of Virginia Box 800759, Health System Charlottesville, VA 22908 USA.
BMC Bioinformatics. 2008 Jan 24;9:43. doi: 10.1186/1471-2105-9-43.
Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For example, methods are emerging to engineer cells to optimally produce byproducts of commercial value, such as bioethanol, as well as molecular compounds for disease therapy. Flux balance analysis (FBA) is an optimization framework that aids in this interrogation by generating predictions of optimal flux distributions in cellular networks. Critical features of FBA are the definition of a biologically relevant objective function (e.g., maximizing the rate of synthesis of biomass, a unit of measurement of cellular growth) and the subsequent application of linear programming (LP) to identify fluxes through a reaction network. Despite the success of FBA, a central remaining challenge is the definition of a network objective with biological meaning.
We present a novel method called Biological Objective Solution Search (BOSS) for the inference of an objective function of a biological system from its underlying network stoichiometry as well as experimentally-measured state variables. Specifically, BOSS identifies a system objective by defining a putative stoichiometric "objective reaction," adding this reaction to the existing set of stoichiometric constraints arising from known interactions within a network, and maximizing the putative objective reaction via LP, all the while minimizing the difference between the resultant in silico flux distribution and available experimental (e.g., isotopomer) flux data. This new approach allows for discovery of objectives with previously unknown stoichiometry, thus extending the biological relevance from earlier methods. We verify our approach on the well-characterized central metabolic network of Saccharomyces cerevisiae.
We illustrate how BOSS offers insight into the functional organization of biochemical networks, facilitating the interrogation of cellular design principles and development of cellular engineering applications. Furthermore, we describe how growth is the best-fit objective function for the yeast metabolic network given experimentally-measured fluxes.
优化理论已应用于复杂的生物系统，以探究网络特性并开发和完善代谢工程策略。例如，正在出现一些方法来改造细胞，以最优地生产具有商业价值的副产品，如生物乙醇，以及用于疾病治疗的分子化合物。通量平衡分析（FBA）是一种优化框架，通过生成细胞网络中最优通量分布的预测来辅助这种探究。FBA的关键特征是定义一个生物学相关的目标函数（例如，最大化生物量的合成速率，生物量是细胞生长的一种度量单位），以及随后应用线性规划（LP）来确定通过反应网络的通量。尽管FBA取得了成功，但一个核心的遗留挑战是定义具有生物学意义的网络目标。
我们提出了一种名为生物目标解决方案搜索（BOSS）的新方法，用于从生物系统的基础网络化学计量以及实验测量的状态变量推断其目标函数。具体而言，BOSS通过定义一个假定的化学计量“目标反应”来识别系统目标，将此反应添加到由网络内已知相互作用产生的现有化学计量约束集中，并通过LP最大化假定的目标反应，同时最小化所得的计算机模拟通量分布与可用实验（例如，同位素异构体）通量数据之间的差异。这种新方法允许发现具有先前未知化学计量的目标，从而扩展了早期方法的生物学相关性。我们在特征明确的酿酒酵母中心代谢网络上验证了我们的方法。
我们说明了BOSS如何深入了解生化网络的功能组织，促进对细胞设计原则的探究和细胞工程应用的开发。此外，我们描述了根据实验测量的通量，生长是酵母代谢网络最合适的目标函数。
