Back

Growth-resolved genome-scale metabolic modeling of Priestia megaterium SR7 validated by chemostat and 13-C flux analysis

Chang, K. Y. W.; Song, Y.; Hing, N. Y. K.; Vethathirri, R. S.; Wang, Y.; Thompson, J. R.

2026-05-30 systems biology
10.64898/2026.05.27.728139 bioRxiv
Show abstract

Priestia megaterium SR7 is a promising candidate chassis for bioprocess engineering, but its development is limited by the availability of condition-grounded, mechanistic models that can translate experimental measurements into predictive design hypotheses. Here, we present PMSR7, a genome-scale metabolic model for SR7, and evaluate it under a growth-resolved chemostat framework spanning a dilution-rate series. Stable steady states were established across the growth regime, with the highest dilution rate (D = 1.1538 h-{superscript 1}) excluded from growth interpretation due to biomass collapse. Extracellular carbon fluxes were quantified by NMR and reported as mean {+/-} SD, providing an experimental basis for model comparison. PMSR7 was benchmarked using MEMOTE against representative reference reconstructions, supporting structural consistency suitable for constraint-based analyses. Under growth-resolved simulations, ATP demand scaled linearly with growth rate, enabling inference of maintenance-energy behavior across the regime. Growth-dependent feasibility and magnitude of overflow secretion were evaluated for acetate, lactate, and formate using feasible-space analyses, highlighting both agreement and regime-sensitive limitations. Finally, growth-resolved leucine and valine production was assessed in both raw and fold-change space, with experimental means compared against median-based summaries of sampled model distributions to account for feasible-space skew. Together, these results establish PMSR7 as a reproducible, quality-benchmarked platform for SR7 chassis development and provide a framework for iterative experimental integration in non-model organisms, where the dominant challenge is achieving congruence between measured physiology and model-feasible behavior.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
npj Systems Biology and Applications
125 papers in training set
Top 0.1%
19.0%
2
Metabolic Engineering
75 papers in training set
Top 0.1%
13.0%
3
mSystems
394 papers in training set
Top 0.7%
9.2%
4
PLOS Computational Biology
1863 papers in training set
Top 4%
8.1%
5
Molecular Systems Biology
162 papers in training set
Top 0.3%
5.6%
50% of probability mass above
6
Nature Communications
5641 papers in training set
Top 30%
4.5%
7
Communications Biology
993 papers in training set
Top 10%
2.2%
8
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 27%
1.8%
9
Frontiers in Bioengineering and Biotechnology
98 papers in training set
Top 1%
1.8%
10
eLife
5828 papers in training set
Top 51%
1.5%
11
Nucleic Acids Research
1281 papers in training set
Top 10%
1.5%
12
PLOS ONE
5266 papers in training set
Top 51%
1.5%
13
Scientific Reports
3612 papers in training set
Top 61%
1.4%
14
Computational and Structural Biotechnology Journal
242 papers in training set
Top 4%
1.4%
15
Bioinformatics
1204 papers in training set
Top 7%
1.2%
16
ACS Synthetic Biology
287 papers in training set
Top 2%
1.1%
17
iScience
1154 papers in training set
Top 28%
1.1%
18
Cell Systems
201 papers in training set
Top 4%
1.1%
19
Science Advances
1243 papers in training set
Top 27%
1.1%
20
Frontiers in Microbiology
427 papers in training set
Top 8%
1.0%
21
ISME Communications
120 papers in training set
Top 2%
0.9%
22
Genome Biology
637 papers in training set
Top 8%
0.9%
23
BMC Bioinformatics
457 papers in training set
Top 5%
0.9%
24
Microbiology Spectrum
469 papers in training set
Top 10%
0.9%
25
Life Science Alliance
285 papers in training set
Top 7%
0.9%
26
BMC Genomics
406 papers in training set
Top 9%
0.6%
27
Cell Reports Methods
165 papers in training set
Top 4%
0.6%
28
Molecular Microbiology
77 papers in training set
Top 1%
0.6%