Impact and consequences of climate change and global warming for the environment and increasing demand for biobased chemicals and materials promoted the conversion of lignocellulosic biomass (LB) from plants as a natural renewable resource into bioproducts. Plants have evolved to resist pathogen attacks and deconstruction. This natural resistance known as LB recalcitrance is responsible for the relatively high cost of LB transformation. Over the past decades, extensive research has been conducted to identify and overcome the chemical and structural parameters governing LB recalcitrance. Nonetheless, the identified parameters are restricted to biomass species and pretreatment type therefore lack universality.  To identify the structural parameters governing LB recalcitrance, we set up a computational imaging pipeline to capture the dynamics of LB deconstruction during enzymatic hydrolysis. The pipeline includes 4D (space + time) imaging, segmentation, tracking, and quantification computational tools to study the LB hydrolysis with unprecedented detail. Using this pipeline on pretreated poplar wood sections, we computed dynamics of several cellular and tissular scale structural parameters while measuring enzymatic hydrolysis. The results revealed a nonlinear relationship across scales allowing to quantitatively link key parameters. Coupling the computational pipeline with machine learning tools (artificial neural networks) will provide a predictive tool to optimize the conversion of LB. The qualifications obtained using the computational pipeline provides initial dataset for the neural network algorithms which will be confronted and enriched with the dataset from transgenic dataset and different biomass species. This approach will allow us to propose a computational tool to predict LB conversion and will be in the long run a fundamental first step in the identification of parameters common to different biomass species and pretreatment types to optimize LB transformation.

Image-based computational approach to optimize lignocellulosic biomass conversion

ACS Spring Meeting 2021

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