Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil

Area of Science:

• Environmental microbiology
• Computational biology
• Agricultural science

Background:

• Extensive agrochemical use pollutes soil and water, necessitating sustainable remediation strategies.
• Bioremediation using biostimulation shows promise for pollutant removal by supporting indigenous microbes but faces variable success rates.
• Current commercial agricultural practices rarely integrate biostimulation due to inconsistent efficacy.

Purpose of the Study:

• To apply metabolic-based community modeling for simulating and prioritizing biostimulant supplements for agricultural soil remediation.
• To investigate the efficacy of biostimulants in enhancing the degradation activity of indigenous soil bacteria, including *Paenarthrobacter*.
• To develop a data-guided approach for optimizing soil microbiome function for efficient biodegradation of agrochemicals.

Main Methods:

• Metabolic-based community modeling was used to simulate the effects of various supplements on indigenous bacterial communities.
• Biostimulant efficacy was ranked via simulation and subsequently validated through pot experiments.
• A simulation matrix predicted biostimulant effects on key degrader taxa (*Paenarthrobacter*, *Pseudomonas*, *Clostridium*, *Geobacter*), validated against experimental data.

Main Results:

• Computational models successfully predicted compounds acting as taxa-selective biostimulants.
• Simulations guided the prioritization of effective biostimulants for enhancing indigenous microbial degradation.
• Experimental validation confirmed the model's ability to predict biostimulant efficacy in enhancing soil bioremediation.

Conclusions:

• Computational algorithms can effectively guide the *in situ* manipulation of soil microbiomes for biodegradation.
• Metabolic modeling provides a pathway for the rational design of biostimulation strategies in agriculture.
• This study lays the foundation for ecologically sound methods to optimize microbiome functioning for sustainable agriculture.