Simulating Iron Deficiency in Plant Plastids With a Flexible Physics-Informed Neural Network Approach

Flux balance analysis has proven to be a successful approach in metabolic engineering and systems biology, for predicting intracellular fluxes of large genome-scale networks and the essentiality of genes encoding enzymes and regulatory factors. Flux balance analysis (FBA) relies on a key assumption of a metabolic state being persistent (“steady”) over a given time frame. This assumption works well for microbial growth because of the ease with which microbial media can be fixed, biomass can be decomposed, and growth rates can be measured. However, the assumption is far less tenable for the cells and tissues of complex multicellular organisms, particularly if any integrated data is sampled from a heterogeneous collection of developing cells continually interacting between and across tissues. These will likely exhibit transient metabolic states equilibrating over varying timescales, and many FBA studies in complex organisms typically either ignore time as a parameter, or integrate data taken over long timescales (days/weeks). In this work, we adopt and modify a previously published machine learning approach that hybridized several aspects of a constraint-based approach with machine-learning in order to predict growth. This study introduces a Machine Learning-FBA framework for plant tissues that accommodates transient state dynamics, at the cost of violating the steady-state assumption, in order to enable more accurate flux estimation in plant tissues. For our case study we reconstruct the metabolism of the plastid of Poplar and Sorghum, integrating data sampled from leaf tissue under varying levels of iron bioavailability. We show that the approach gives us more realistic insights into plastidial metabolism, indicates where our metabolic reconstruction could be improved, and still allows us to draw novel hypotheses on the impact of metal bioavailability on plant leaves.