Enzyme-Constrained Metabolic Modeling Reveals CO₂-Dependent Growth Control in Picochlorum celeri

Picochlorum celeri grows 11.6 times faster at elevated CO₂ than in air, yet the abundance of its metabolic enzymes is only 17% higher. To explain this, we reconstructed iPiccel954, a genome-scale metabolic model with 3525 reactions, 2973 metabolites and 954 genes, and eciPiccel954, its enzyme-constrained version limited by the measured enzyme abundance in air and at elevated CO₂. Calibrating the enzyme-constrained models against the measured proteome showed that a similar protein budget supports very different growth depending on how it is allocated. With urea as the nitrogen source, urea carboxylase was the only step whose relaxation alone raised growth, by 627% in air and 78% at elevated CO₂. Bottleneck analysis showed that growth is limited by many enzymes spread across the network in both conditions, that the limiting enzymes differ between the two, and that control over starch metabolism seen in air disappears at elevated CO₂. Measured proteomes agreed with these predictions, since the predicted bottleneck enzymes were more abundant than other enzymes. Rapid growth in P. celeri therefore reflects how the protein budget is allocated more than how large it is. Therefore, eciPiccel954 provides a quantitative basis for engineering this alga for biotechnological application.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-11-03
DOI
https://doi.org/10.64898/2026.10.30.763171
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
preprint
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preprint

Enzyme-Constrained Metabolic Modeling Reveals CO₂-Dependent Growth Control in Picochlorum celeri

Yasin Torres-Tiji, Niaz Bahar Chowdhury, Angela Contolesi, Matthew Posewitz
bioRxiv (Cold Spring Harbor Laboratory)
Microbial Metabolic Engineering and Bioproduction
preprint

Enzyme-Constrained Metabolic Modeling Reveals CO₂-Dependent Growth Control in Picochlorum celeri

Yasin Torres-Tiji, Niaz Bahar Chowdhury, Angela Contolesi, Matthew Posewitz
preprint en

Abstract

Picochlorum celeri grows 11.6 times faster at elevated CO₂ than in air, yet the abundance of its metabolic enzymes is only 17% higher. To explain this, we reconstructed iPiccel954, a genome-scale metabolic model with 3525 reactions, 2973 metabolites and 954 genes, and eciPiccel954, its enzyme-constrained version limited by the measured enzyme abundance in air and at elevated CO₂. Calibrating the enzyme-constrained models against the measured proteome showed that a similar protein budget supports very different growth depending on how it is allocated. With urea as the nitrogen source, urea carboxylase was the only step whose relaxation alone raised growth, by 627% in air and 78% at elevated CO₂. Bottleneck analysis showed that growth is limited by many enzymes spread across the network in both conditions, that the limiting enzymes differ between the two, and that control over starch metabolism seen in air disappears at elevated CO₂. Measured proteomes agreed with these predictions, since the predicted bottleneck enzymes were more abundant than other enzymes. Rapid growth in P. celeri therefore reflects how the protein budget is allocated more than how large it is. Therefore, eciPiccel954 provides a quantitative basis for engineering this alga for biotechnological application.

bioRxiv (Cold Spring Harbor Laboratory)
Pacific Northwest National Laboratory (US), Colorado School of Mines (US)
Microbial Metabolic Engineering and Bioproduction
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