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Flux balance analysis with or without molecular crowding fails to predict two thirds of experimentally observed epistasis in yeast.

Abstract：

:Computational predictions of double gene knockout effects by flux balance analysis (FBA) have been used to characterized genome-wide patterns of epistasis in microorganisms. However, it is unclear how in silico predictions are related to in vivo epistasis, as FBA predicted only a minority of experimentally observed genetic interactions between non-essential metabolic genes in yeast. Here, we perform a detailed comparison of yeast experimental epistasis data to predictions generated with different constraint-based metabolic modeling algorithms. The tested methods comprise standard FBA; a variant of MOMA, which was specifically designed to predict fitness effects of non-essential gene knockouts; and two alternative implementations of FBA with macro-molecular crowding, which account approximately for enzyme kinetics. The number of interactions uniquely predicted by one method is typically larger than its overlap with any alternative method. Only 20% of negative and 10% of positive interactions jointly predicted by all methods are confirmed by the experimental data; almost all unique predictions appear to be false. More than two thirds of epistatic interactions are undetectable by any of the tested methods. The low prediction accuracies indicate that the physiology of yeast double metabolic gene knockouts is dominated by processes not captured by current constraint-based analysis methods.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Alzoubi D,Desouki AA,Lercher MJ

doi

10.1038/s41598-019-47935-6

subject

Has Abstract

pub_date

2019-08-14 00:00:00

pages

11837

issue

1

issn

2045-2322

pii

10.1038/s41598-019-47935-6

journal_volume

9

pub_type

杂志文章

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