The Hyman group has made significant contributions to molecular and cell biology, particularly in understanding the mechanisms of cell division and cell compartmentalization. Perhaps the two most notable achievements include utilizing RNA interference to map various cytoplasmic processes¹ and discovering that compartments in cells can form via phase separation². Their expansive body of work has also illuminated fundamental interworking of protein complexes and cellular structures like the mitotic spindle, centrioles, and P granules^3,4. Currently, their research is aimed at exploring how phase separation of intrinsically disordered proteins may drive neurodegenerative diseases including ALS.

The Grill group bridges experiment with theory in the physics of life, in particular active matter physics, morphogenesis, the actomyosin cytoskeleton, and biomolecular condensates. Key research questions addressed concern the force balance that underlies actomyosin cortical flows¹, symmetry breaking in active matter systems^2–4, active chiral matter and left/right symmetry breaking⁴. In collaboration with the Jülicher group in Dresden the Grill group has, through its long-lasting expertise in using optical tweezers in a single molecule and a mesoscale context⁵, investigated protein-DNA co-condensation⁶, discovered a dynamic instability of cortical condensates in the context of actomyosin cortex activation⁷, and together with the Hyman and Jülicher groups discovered a DNA-sequence-dependent prewetting transition towards droplet of general transcription factors on DNA⁸.

The Toth-Petroczy group comprises an interdisciplinary team of computational biologists, computer scientists, molecular biologists, biochemists and biophysicists. The group aims to understand protein sequence space in the light of evolution and erroneous protein production with focus on intrinsically disordered proteins regions (IDRs).

The two main research directions in the lab include:

1) Sequence-to-function paradigm of intrinsically disordered regions and condensate forming proteins.

2) Phenotypic mutations in evolution and diseases¹.

The group uses an interdisciplinary approach by developing novel mechanistic^2,3 and machine learning/deep learning algorithms focusing on evolutionary and/or structural information⁴, and performing experimental validation and high-throughput screenings⁵. Specifically, the group has designed an alignment-free algorithm to assess homology between unalignable IDR sequences (SHARK-dive)⁶ and developed a method to identify conserved motifs within a set of homologous IDR sequences (SHARK-capture). The group has developed the CrowDsourcing Condensate Database and Encyclopedia (CD-CODE.org)⁷, that catalogues known condensates and their constituent proteins, and trained a predictor of proteins involved in condensates (PICNIC.cd-code.org) across organisms⁸.