Towards the Design of Metamorphic Proteins using Ensemble-Based Energetic Information

Miralles, Enric; Tagliabue, BenedettaPrimer pla d'una part del Parc de Diagonal-Mar. Es pot veure el paisatge del parc, realitzat amb estructures d'acer i elements de trencadís ceràmic. Al fons, es veuen uns edificis de gran alçada

Ipl1/Aurora B kinase coordinates synaptonemal complex disassembly with cell cycle progression and crossover formation in budding yeast meiosis

Several protein kinases collaborate to orchestrate and integrate cellular and chromosomal events at the G2/M transition in both mitotic and meiotic cells. During the G2/M transition in meiosis, this includes the completion of crossover recombination, spindle formation, and synaptonemal complex (SC) breakdown. We identified Ipl1/Aurora B kinase as the main regulator of SC disassembly. Mutants lacking Ipl1 or its kinase activity assemble SCs with normal timing, but fail to dissociate the central element component Zip1, as well as its binding partner, Smt3/SUMO, from chromosomes in a timely fashion. Moreover, lack of Ipl1 activity causes delayed SC disassembly in a cdc5 as well as a CDC5-inducible ndt80 mutant. Crossover levels in the ipl1 mutant are similar to those observed in wild type, indicating that full SC disassembly is not a prerequisite for joint molecule resolution and subsequent crossover formation. Moreover, expression of meiosis I and meiosis II-specific B-type cyclins occur normally in ipl1 mutants, despite delayed formation of anaphase I spindles. These observations suggest that Ipl1 coordinates changes to meiotic chromosome structure with resolution of crossovers and cell cycle progression at the end of meiotic prophase

Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets

Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data. However, these techniques are ostensibly inapplicable for experimental systems where data is scarce or expensive to obtain. Here we introduce a strategy to resolve this impasse by augmenting the experimental dataset with synthetically generated data of a much simpler sister system. Specifically, we study spontaneously emerging local order in crease networks of crumpled thin sheets, a paradigmatic example of spatial complexity, and show that machine learning techniques can be effective even in a data-limited regime. This is achieved by augmenting the scarce experimental dataset with inexhaustible amounts of simulated data of rigid flat-folded sheets, which are simple to simulate and share common statistical properties. This significantly improves the predictive power in a test problem of pattern completion and demonstrates the usefulness of machine learning in bench-top experiments where data is good but scarce.Comment: 8 pages, 5 figures (+ Supplemental Materials: 5 pages, 6 figures