Cross-species complementation reveals conserved functions for EARLY FLOWERING 3 between monocots and dicots

Plant responses to the environment are shaped by external stimuli and internal signaling pathways. In both the model plant Arabidopsis thaliana (Arabidopsis) and crop species, circadian clock factors are critical for growth, flowering, and circadian rhythms. Outside of Arabidopsis, however, little is known about the molecular function of clock gene products. Therefore, we sought to compare the function of Brachypodium distachyon (Brachypodium) and Setaria viridis (Setaria) orthologs of EARLY FLOWERING 3, a key clock gene in Arabidopsis. To identify both cycling genes and putative ELF3 functional orthologs in Setaria, a circadian RNA-seq dataset and online query tool (Diel Explorer) were generated to explore expression profiles of Setaria genes under circadian conditions. The function of ELF3 orthologs from Arabidopsis, Brachypodium, and Setaria was tested for complementation of an elf3 mutation in Arabidopsis. We find that both monocot orthologs were capable of rescuing hypocotyl elongation, flowering time, and arrhythmic clock phenotypes. Using affinity purification and mass spectrometry, our data indicate that BdELF3 and SvELF3 could be integrated into similar complexes in vivo as AtELF3. Thus, we find that, despite 180 million years of separation, BdELF3 and SvELF3 can functionally complement loss of ELF3 at the molecular and physiological level

Fast solvers for optimal control problems from pattern formation

The modelling of pattern formation in biological systems using various models of reaction-diffusion type has been an active research topic for many years. We here look at a parameter identification (or PDE-constrained optimization) problem where the Schnakenberg and Gierer-Meinhardt equations, two well-known pattern formation models, form the constraints to an objective function. Our main focus is on the efficient solution of the associated nonlinear programming problems via a Lagrange-Newton scheme. In particular we focus on the fast and robust solution of the resulting large linear systems, which are of saddle point form. We illustrate this by considering several two- and three-dimensional setups for both models. Additionally, we discuss an image-driven formulation that allows us to identify parameters of the model to match an observed quantity obtained from an image