Evidence map›Paper›PMID 40478826›Full record

ArticlePLoS computational biology2025

Resource acquisition in diel cycles and the cost of growing quickly.

Kevin J Flynn, Andrew Yu Morozov

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Kevin J FlynnPlymouth Marine Laboratory, Plymouth, United Kingdom.
Andrew Yu MorozovSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom.ORCID 0000-0002-6935-3563

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many organisms, notably phototrophs, routinely acquire resources over only a fraction of the day. They have to balance their main period of initial biosynthesis against cell cycle events. Because of their short generation times, this challenge is especially acute for the planktonic microalgae that perform 50% of global C-fixation. Empirical evidence indicates that microalgal day-average growth is a function of the ability to acquire resources rapidly when available, retaining initial products of assimilation to support growth. A fundamental question arises over the optimal physiological configuration to support such activity. Here, we applied computer simulations implementing a development of the quota concept, in which the internal limiting resource is itself C, ratioed against total organism C-biomass. The model comprises metabolite and core pools of carbon C (MC and CC, respectively), with growth modulated by MC/(MC + CC); MC supports growth of CC in the absence of concurrent resource acquisition. Dynamic feedback interactions from the relative size of MC controls resource acquisition. The model reproduces the general pattern of growth at different light:day fraction (LD), and of afternoon-depression of C-fixation. We explored the efficiency of the physiological cell configuration to locate optimal configurations at different combinations of maximum growth rates (Umax) and LD values across plausible parameter values for microalgae. While the optimum maximum resource acquisition rate deployed during the L phase scales with Umax/LD, the maximum size of the metabolite pool scales to LD/DV, where DV is division time (i.e. Umax/Ln(2)). Accordingly, we conclude that faster growing organisms carry a penalty limiting their geographic spread to latitudes and seasons where LD is high. Larger, vacuolated organisms (such as diatoms), having a bigger metabolite compartment, may be at an advantage in such situations.

Indexed as

Circadian RhythmMicroalgaeModels, BiologicalBiomassCarbonCarbon CycleComputational BiologyComputer SimulationCarbon

Identifiers

PMID40478826
PMCPMC12803028

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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.