Evidence map›Paper›PMID 40676517›Full record

ArticleBMC bioinformatics2025

The FBA solution space kernel: introduction and illustrative examples.

Wynand S Verwoerd, Longfei Mao

Abstract read
In one paragraph

Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wynand S VerwoerdCentre for Advanced Computational Solutions, Faculty of Ag & Life Sciences, Lincoln University, Lincoln, 4647, New Zealand. wynand.verwoerd@lincoln.ac.nz.
Longfei MaoDepartment of Pharmacy, College of Biology, Hunan University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsCalculations are performed with the publicly available software package SSKernel, the source code and user manual of which is included as a supplementary file.

resultsIt 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.

conclusionsSSKernel 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.

Indexed as

Metabolic Networks and PathwaysModels, BiologicalSoftwareAlgorithmsBioengineeringBiological networksFlux balance analysisMetabolic fluxesMetabolomicsNetwork analysisSolution space reduction

Identifiers

PMID40676517
PMCPMC12273201

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.