Theoretical and Data-Driven Approaches for Biomolecular Condensates

Biomolecular condensation processes are increasinglyrecognizedas a fundamental mechanism that living cells use to organize biomoleculesin time and space. These processes can lead to the formation of membranelessorganelles that enable cells to perform distinct biochemical processesin controlled local environments, thereby supplying them with an additionaldegree of spatial control relative to that achieved by membrane-boundorganelles. This fundamental importance of biomolecular condensationhas motivated a quest to discover and understand the molecular mechanismsand determinants that drive and control this process. Within thismolecular viewpoint, computational methods can provide a unique angleto studying biomolecular condensation processes by contributing theresolution and scale that are challenging to reach with experimentaltechniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-drivensimulations and data-driven machine learning methods. We review recentprogress in using these tools for probing biomolecular condensationacross all three fields and outline the key advantages and limitationsof each of the approaches. We further discuss some of the key outstandingchallenges that we foresee the community addressing next in orderto develop a more complete picture of the molecular driving forcesbehind biomolecular condensation processes and their biological rolesin health and disease.