The advent of deep learning algorithms for protein folding opened a new era in the ability of pre- dicting and optimizing the function of proteins once the sequence is known. The task is more intricate when cofactors like metal ions or small ligands are essential to functioning. In this case, the combined use of traditional simulation methods based on interatomic force fields and deep learning...
Over the last decade, the combined development of accurate time- resolved experimental tech- niques and advanced algorithms for computer simulations has opened the possibility of investigat- ing biological mechanisms at atomic resolution with physics-based models. In particular, combi- nation of experimental information and enhanced sampling techniques now allow the reconstruc- tion of the co-...
Gene regulation is a complex web across biological levels, and its intricacy often complicates pre- cise interventions, with off-target effects being a major hurdle. To transform this, we here propose a photoactivatable microRNA-based circuit that enables unmatched accuracy in gene targeting, po- tentially reducing off-target effects. Our approach leverages the concept of microRNAs (miRNAs) as...
I will review some concepts and applications of Reinforcement Learning to modeling of animal behavior
Unlike gas molecules at equilibrium, the spatial organization of self-propelled particles can be very sensitive to what happens at the boundaries of their container. Understanding the link between boundary phenomena and bulk stationary distributions could enable the design of optimized con- tainer shapes for the geometric control of confined active particles. Here we propose a boundary method...
