Data-driven investigation of bacterial growth dynamics in response to the medium-drug interplay

Bacterial responses to antimicrobial drugs are strongly influenced by environmental conditions, yet how drugs systematically reshape the contribution of individual nutrients across different growth phases remains poorly understood. Here, we investigated medium–drug interactions in Escherichia coli BW25113 using around 177 chemically defined media and six drug conditions, generating thousands of growth profiles. Lag time ( t ), growth rate ( r ), and carrying capacity ( K ) were analyzed to characterize responses across growth phases. By integrating large-scale growth profiling with machine learning, we quantitatively mapped how 41 medium components contributed to bacterial growth under different drug conditions. Drug exposure substantially reorganized these nutritional contributions and generally increased the number of components involved in determining growth, particularly for r and K . Glucose, isoleucine, K + , and PO₄ 3− emerged as common determinants, whereas their relative contributions changed with drug exposure. Importantly, drug-induced nutritional reorganization was both growth phase- and drug-specific: decreased contributions were enriched in amino acids, whereas increased contributions involved diverse nutritional categories, particularly metal ions. Thus, rather than viewing drug susceptibility as a fixed bacterial phenotype, our study provides a quantitative framework showing that antimicrobial responses emerge from dynamic interactions among drugs, nutrients, and growth phases.

Authors

Institutions

Publication Details

Journal
Frontiers in Microbiology
Published
2026-11-05
DOI
https://doi.org/10.3389/fmicb.2026.2016025
Primary Topic
Bacterial Genetics and Biotechnology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Data-driven investigation of bacterial growth dynamics in response to the medium-drug interplay

Taro Kawano, Bei‐Wen Ying, Shunpei Araki
Frontiers in Microbiology
Bacterial Genetics and Biotechnology
article

Data-driven investigation of bacterial growth dynamics in response to the medium-drug interplay

Taro Kawano, Bei‐Wen Ying, Shunpei Araki
article en

Abstract

Bacterial responses to antimicrobial drugs are strongly influenced by environmental conditions, yet how drugs systematically reshape the contribution of individual nutrients across different growth phases remains poorly understood. Here, we investigated medium–drug interactions in Escherichia coli BW25113 using around 177 chemically defined media and six drug conditions, generating thousands of growth profiles. Lag time ( t ), growth rate ( r ), and carrying capacity ( K ) were analyzed to characterize responses across growth phases. By integrating large-scale growth profiling with machine learning, we quantitatively mapped how 41 medium components contributed to bacterial growth under different drug conditions. Drug exposure substantially reorganized these nutritional contributions and generally increased the number of components involved in determining growth, particularly for r and K . Glucose, isoleucine, K + , and PO₄ 3− emerged as common determinants, whereas their relative contributions changed with drug exposure. Importantly, drug-induced nutritional reorganization was both growth phase- and drug-specific: decreased contributions were enriched in amino acids, whereas increased contributions involved diverse nutritional categories, particularly metal ions. Thus, rather than viewing drug susceptibility as a fixed bacterial phenotype, our study provides a quantitative framework showing that antimicrobial responses emerge from dynamic interactions among drugs, nutrients, and growth phases.

Frontiers in MicrobiologyVol. 17
University of Tsukuba (JP)
Openalex Percentile: Top 20%
Bacterial Genetics and Biotechnology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon