Functional annotation of proteins for signaling network inference in non-model species.

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence.

Genetic control of tolerance to drought stress in soybean.

Background: Drought stress limits the production of soybean [Glycine max (L.) Merr.], which is the most grown high-value legume crop worldwide.

Response of a Diverse European Soybean Collection to "Short Duration" and "Long Duration" Drought Stress.

Drought causes significant damage to a high value crop of soybean. Europe has an increasing demand for soybean and its own production is insufficient. Selection and breeding of cultivars adapted to European growth conditions is therefore urgently needed.

A Genome-Wide Genetic Diversity Scan Reveals Multiple Signatures of Selection in a European Soybean Collection Compared to Chinese Collections of Wild and Cultivated Soybean Accessions.

Targeted and untargeted selections including domestication and breeding efforts can reduce genetic diversity in breeding germplasm and create selective sweeps in crop genomes. The genomic regions at which selective sweeps are detected can reveal important information about signatures of selection. We have analyzed the genetic diversity within a soybean germplasm collection relevant for breeding in Europe (the EUCLEG collection), and have identified selective sweeps through a genome-wide scan comparing that collection to Chinese soybean collections.

Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality.

Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model.