Westerhoff Hans V
Department of Molecular Cell Biology, Vrije Universiteit Amsterdam, A-Life, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands; Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, the Netherlands; School of Biological Sciences, Medicine and Health, University of Manchester, Manchester, United Kingdom; Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, 7600, South Africa.
Biosystems. 2023 Nov;233:104998. doi: 10.1016/j.biosystems.2023.104998. Epub 2023 Aug 15.
In Microbiology it is often assumed that growth rate is maximal. This may be taken to suggest that the dependence of the growth rate on every enzyme activity is at the top of an inverse-parabolic function, i.e. that all flux control coefficients should equal zero. This might seem to imply that the sum of these flux control coefficients equals zero. According to the summation law of Metabolic Control Analysis (MCA) the sum of flux control coefficients should equal 1 however. And in Flux Balance Analysis (FBA) catabolism is often limited by a hard bound, causing catabolism to fully control the fluxes, again in apparent contrast with a flux control coefficient of zero. Here we resolve these paradoxes (apparent contradictions) in an analysis that uses the 'Edinburgh pathway', the 'Amsterdam pathway', as well as a generic metabolic network providing the building blocks or Gibbs energy for microbial growth. We review and show that (i) optimization depends on so-called enzyme control coefficients rather than the 'catalytic control coefficients' of MCA's summation law, (ii) when optimization occurs at fixed total protein, the former differ from the latter to the extent that they may all become equal to zero in the optimum state, (iii) in more realistic scenarios of optimization where catalytically inert biomass is compensating or maintenance metabolism is taken into consideration, the optimum enzyme concentrations should not be expected to equal those that maximize the specific growth rate, (iv) optimization may be in terms of yield rather than specific growth rate, which resolves the paradox because the sum of catalytic control coefficients on yield equals 0, (v) FBA effectively maximizes growth yield, and for yield the summation law states 0 rather than 1, thereby removing the paradox, (vi) furthermore, FBA then comes more often to a 'hard optimum' defined by a maximum catabolic flux and a catabolic-enzyme control coefficient of 1. The trade-off between maintenance metabolism and growth is highlighted as worthy of further analysis.
在微生物学中,通常假定生长速率是最大的。这可能意味着生长速率对每种酶活性的依赖性处于反抛物线函数的顶部,即所有通量控制系数都应等于零。这似乎意味着这些通量控制系数的总和等于零。然而,根据代谢控制分析(MCA)的求和定律,通量控制系数的总和应该等于1。而且在通量平衡分析(FBA)中,分解代谢通常受到严格限制,导致分解代谢完全控制通量,这再次与通量控制系数为零明显矛盾。在此,我们在一项分析中解决了这些悖论(明显的矛盾),该分析使用了“爱丁堡途径”“阿姆斯特丹途径”以及一个为微生物生长提供构件或吉布斯自由能的通用代谢网络。我们回顾并表明:(i)优化取决于所谓的酶控制系数,而非MCA求和定律中的“催化控制系数”;(ii)当在固定总蛋白水平下进行优化时,前者与后者不同,以至于在最佳状态下它们可能都等于零;(iii)在更现实的优化场景中,考虑到催化惰性生物量的补偿或维持代谢,不应期望最佳酶浓度等于使比生长速率最大化的酶浓度;(iv)优化可能是基于产量而非比生长速率,这解决了悖论,因为产量的催化控制系数之和等于0;(v)FBA有效地使生长产量最大化,对于产量,求和定律规定为0而非1,从而消除了悖论;(vi)此外,FBA更常达到由最大分解代谢通量和分解代谢酶控制系数为1所定义的“硬最优”。维持代谢与生长之间的权衡被强调为值得进一步分析。