Constrained hybrid modelling to predict microbial dynamics and organic matter turnover in soil systems

2026-06-18

AI Summary

This study addresses the challenge of efficiently inferring soil microbial kinetic parameters from metagenomic data to enhance the predictive capability of process-based models for organic matter turnover and carbon cycling. We propose the first hybrid modeling framework that integrates neural networks, ecological theory constraints, and a process-based soil organic matter model. In this approach, neural networks learn unobservable biological kinetic parameters from metagenomic functional annotations, while ecological theory imposes physically meaningful constraints to ensure simulation plausibility. Our method achieves, for the first time, effective coupling between DNA sequencing data and process-based models under limited sample sizes. It significantly outperforms existing baselines on both synthetic and real-world datasets, substantially improving the accuracy of predicting microbially driven carbon processes.

Abstract

Soil microorganisms control organic matter cycling and largely determine how soil systems can cope with and mitigate climate change and environmental threats. Representing microbial dynamics in process-based soil models is therefore critical to predict carbon cycling in soils, albeit highly challenging to inform from data. One promising approach to improve their parametrisation is the integration of genomic data, yet modelling the complex and unknown relationship between genomes and the processes the microbes are driving is an unsolved problem. In this work, we present the first hybrid modeling framework for deriving biokinetic parameter values of a process-based soil organic matter turnover model from metagenome-inferred functional traits based on DNA sequencing data. Our model predicts biokinetic parameters of the process-based model from genomic trait data with a neural network and integrates constraints from ecological theory and literature to ensure realistic behavior, even of non-observed state variables. We evaluate our method on synthetic genomic trait datasets of varying complexity and on real data, showing that our approach improves performance over multiple baselines and learns the dynamics of unmeasurable components of the process-based model effectively, even for small training datasets.

Problem

• microbial dynamics
• soil organic matter turnover
• biokinetic parameters
• metagenomic data
• process-based modeling

Innovation

• hybrid modeling
• metagenomics
• soil organic matter turnover
• biokinetic parameters
• neural networks