Evidence map›Paper›PMID 41424553›Full record

ReviewFrontiers in plant science2025

Biophysical modelling as a tool for advancing plant science and engineering.

Debanik Deb, Bandan Chakrabortty

Abstract readReview
In one paragraph

Review in Frontiers in plant science, 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. Review
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.

Debanik DebTheoretical and Computational Biology Lab, School of Biology, Indian Institute of Science Education and Research (IISER) Thiruvananthapuram, Thiruvananthapuram, Kerala, India.
Bandan ChakraborttyTheoretical and Computational Biology Lab, School of Biology, Indian Institute of Science Education and Research (IISER) Thiruvananthapuram, Thiruvananthapuram, Kerala, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plant growth, development, and physiology are governed by complex processes operating across scales spanning from molecular, cellular, tissue, and whole organism. Experimental studies have uncovered key mechanisms underlying hormone signalling, gene regulation, metabolism, and environmental responses. However, integrating this information into a unified framework to understand the mechanistic origin, evolution, and robustness of plant form and function remains challenging. Mathematical and computational modelling provides powerful tools to address this challenge, where simulation and predictions offer strategic guidance for efficient experimental search of missing information underlying plant processes. In this review, we discuss a broad spectrum of modelling approaches, including reaction-kinetic and Boolean network models for molecular and genetic regulation; mechanical and geometry-based models for tissue growth and morphogenesis; metabolic and constraint-based models for resource allocation; and hydraulic and electrophysiological models for physiological transport processes. We also propose that by combining different modelling strategies, researchers can develop predictive tools for biotechnological applications, including enhancement of stress tolerance, efficient use of nutrients, regenerative tissue engineering, and biomass productivity.

Indexed as

biophysical modellingcomputational plant biologyplant developmental biologyplant engineeringplant mechanobiologyplant systems biologysynthetic biology

Identifiers

PMID41424553
PMCPMC12713686

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.