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Dynamic modeling of enzyme controlled metabolic networks using a receding time horizon

机译：使用后退时间范围的酶控制代谢网络的动态建模

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Microorganisms have developed complex regulatory features controlling their reaction and internal adaptation to changing environments. When modeling these organisms we usually do not have full understanding of the regulation and rely on substituting it with an optimization problem using a biologically reasonable objective function. The resulting constraint-based methods like the Flux Balance Analysis (FBA) and Resource Balance Analysis (RBA) have proven to be powerful tools to predict growth rates, by-products, and pathway usage for fixed environments. In this work, we focus on the dynamic enzyme-cost Flux Balance Analysis (deFBA), which models the environment, biomass products, and their composition dynamically and contains reaction rate constraints based on enzyme capacity. We extend the original deFBA formalism to include storage molecules and biomass-related maintenance costs. Furthermore, we present a novel usage of the receding prediction horizon as used in Model Predictive Control (MPC) in the deFBA framework, which we call the short-term deFBA (sdeFBA). This way we eliminate some mathematical artifacts arising from the formulation as an optimization problem and gain access to new applications in MPC schemes. A major contribution of this paper is a systematic approach for choosing the prediction horizon and identifying conditions to ensure solutions grow exponentially. We showcase the effects of using the sdeFBA with different horizons through a numerical example.
机译：微生物已经开发了复杂的监管特征，控制其反应和内部适应改变环境。在建模这些生物体时，我们通常没有充分了解规定，并依赖于使用生物学上合理的目标函数用优化问题取代。由助焊剂平衡分析（FBA）和资源平衡分析（RBA）等产生的基于约束的方法已经证明是强大的工具，以预测固定环境的增长率，副产品和通路使用。在这项工作中，我们专注于动态酶成本通量平衡分析（DEFBA），其动态地模拟环境，生物质产品及其组合物，并含有基于酶容量的反应速率约束。我们将原始DEFBA形式主义扩展到包括储存分子和生物量相关的维护成本。此外，我们在DEFBA框架中展示了模型预测控制（MPC）中使用的后退预测地平线的新颖使用，我们称之为短期defba（SDEFBA）。这样，我们消除了从制剂中产生的一些数学伪像作为优化问题，并获得MPC方案中的新应用。本文的主要贡献是选择预测地平线和识别条件以确保解决方案呈指数增长的系统方法。我们通过数值示例展示使用SDEFBA使用不同视野的效果。

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