Verwoerd Wynand S, Mao Longfei
Centre for Advanced Computational Solutions, Faculty of Ag & Life Sciences, Lincoln University, Lincoln, 4647, New Zealand.
Department of Pharmacy, College of Biology, Hunan University, Changsha, China.
BMC Bioinformatics. 2025 Jul 17;26(1):182. doi: 10.1186/s12859-025-06216-y.
The solution space of an FBA-based model of cellular metabolism, can be characterised by extraction of a bounded, low dimensional kernel (the SSK) that facilitates perceiving it as a geometric object in multidimensional flux space. The aim is to produce an amenable description, intermediate between the single feasible extreme flux of FBA, and the intractable proliferation of extreme modes in conventional solution space descriptions. Fluxes that remain fixed are separated off while the focus of interest is put on the subset of variable fluxes that have a nonzero but finite range of values. For unbounded fluxes, a finite subrange that geometrically corresponds to the variable flux range is determined and is supplemented by a limited set of rays that encapsulates their unbounded aspects. In this way the kernel emphasises the realistic range of flux variation allowed in the interconnected biochemical network by e.g. limited nutrient uptake, an optimised objective and other model constraints. This work builds on the full presentation of the kernel approach in a research monograph.
Calculations are performed with the publicly available software package SSKernel, the source code and user manual of which is included as a supplementary file.
It is demonstrated how knowledge of the SSK and accompanying rays can be exploited to explore representative flux states of the metabolic network. Noting that bioengineering interventions such as gene knockouts modify the solution space, new tools based on the kernel analysis are presented here that predict the effects of such interventions on a target flux constructed to represent a desired metabolic output. A simple metabolic model is used first to demonstrate the special concepts and constructions needed to define and compute the SSK. The demonstration model is tweaked to produce typical behaviours of larger models, but with kernels in 1, 2 or 3 dimensions that are explicitly displayed to visualise the concepts. General applicability to models where visualisation is inaccessible, is illustrated by showing evaluation of potential bioengineering strategies for a genome scale model.
SSKernel is a flexible interactive tool that facilitates an overview of the FBA solution space as a multidimensional geometric object, in terms of a manageable number of parameters. It allows exploration of effects on this solution space from metabolic interventions and can be used to investigate bioengineering strategies to manipulate cellular metabolism.
基于通量平衡分析(FBA)的细胞代谢模型的解空间,可以通过提取一个有界的低维核(即稳态子空间核,SSK)来表征,这有助于将其视为多维通量空间中的几何对象。目的是生成一种易于处理的描述,介于FBA的单一可行极端通量和传统解空间描述中难以处理的极端模式增殖之间。固定不变的通量被分离出来,而关注点则放在具有非零但有限值范围的可变通量子集上。对于无界通量,确定一个在几何上对应于可变通量范围的有限子范围,并由一组封装其无界方面的有限射线进行补充。通过这种方式,核强调了相互连接的生化网络中例如有限养分吸收、优化目标和其他模型约束所允许的通量变化的实际范围。这项工作建立在一本研究专著中对核方法的完整阐述之上。
使用公开可用的软件包SSKernel进行计算,其源代码和用户手册作为补充文件包含在内。
展示了如何利用对SSK和伴随射线的了解来探索代谢网络的代表性通量状态。注意到诸如基因敲除等生物工程干预会改变解空间,本文提出了基于核分析的新工具,这些工具可预测此类干预对构建用于代表所需代谢输出的目标通量的影响。首先使用一个简单的代谢模型来演示定义和计算SSK所需的特殊概念和构建。对演示模型进行调整以产生更大模型的典型行为,但具有明确显示以可视化概念的1、2或3维核。通过展示对基因组规模模型的潜在生物工程策略的评估,说明了对无法进行可视化的模型的一般适用性。
SSKernel是一个灵活的交互式工具,有助于将FBA解空间作为一个多维几何对象,通过可管理数量的参数进行概述。它允许探索代谢干预对该解空间的影响,并可用于研究操纵细胞代谢的生物工程策略。