Spatiotemporal modeling of microbial metabolism.
Spatiotemporal modeling of microbial metabolism.
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DOI：
10.1186/s12918-016-0259-2
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发表时间：
2016-03-01
影响因子：
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通讯作者：
Henson MA
中科院分区：
文献类型：
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作者：
Chen J;Gomez JA;Höffner K;Phalak P;Barton PI;Henson MA
在自然界和工程应用中，胞外环境在空间和时间上都是变化的微生物系统是非常常见的。虽然使用基因组规模的代谢重建的稳态通量平衡分析（FBA）和动态FBA的扩展是常见的，时空代谢模型的发展很少受到关注。我们提出了一个一般的方法时空代谢建模的基础上结合基因组规模的重建与基本的运输方程，管理相关的对流和/或扩散过程中的时间和空间变化的环境。我们的解决方案包括偏微分方程模型的空间离散化，然后使用DFBAlab（一种执行可靠和高效动态FBA模拟的MATLAB代码）将所得常微分方程系统与嵌入式线性程序进行数值积分。我们证明了我们的方法，通过解决时空代谢模型的两个系统的相当大的实际利益：（1）气泡塔反应器与合成气发酵细菌杨氏梭菌;和（2）慢性伤口生物膜与人类病原体铜绿假单胞菌。尽管离散化模型的复杂性，其中包括900 ODE/600 LP和250 ODE/250 LP，分别，我们表明，建议的计算框架允许有效和强大的模型解决方案。我们的研究为制定和解决具有时间和空间变化的基因组尺度代谢模型建立了一个新的范式，并对自然和工程微生物系统具有广泛的适用性。本文的在线版本（doi：10.1186/s12918-016-0259-2）包含补充材料，可供授权用户使用。
Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention. We present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution. Our study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems. The online version of this article (doi:10.1186/s12918-016-0259-2) contains supplementary material, which is available to authorized users.