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Roadmap for Condensates in Cell Biology

Dilimulati Aierken, Sebastian Aland, Stefano Bo, Steven Boeynaems, Danfeng Cai, Serena Carra, Lindsay B. Case, Hue Sun Chan, Jorge R. Espinosa, Trevor K. GrandPre, Alexander Y. Grosberg, Ivar S. Haugerud, William M. Jacobs, Jerelle A. Joseph, Frank Jülicher, Kurt Kremer, Guido Kusters, Liedewij Laan, Keren Lasker, Katrin S. Laxhuber, Hyun O. Lee, Kathy F. Liu, Dimple Notani, Yicheng Qiang, Paul Robustelli, Leonor Saiz, Omar A. Saleh, Helmut Schiessel, Jeremy Schmit, Meng Shen, Krishna Shrinivas, Antonia Statt, Andres R. Tejedor, Tatjana Trcek, Christoph A. Weber, Stephanie C. Weber, Ned S. Wingreen, Huaiying Zhang, Yaojun Zhang, Huan Xiang Zhou, David Zwicker

TL;DR

Biomolecular condensates are a unifying physical principle in cells, organizing molecules without membranes through phase coexistence and multicomponent interactions. The paper provides a physics-based roadmap that reframes condensation as an input-output framework with controllable knobs and measurable responses, then outlines how condensates influence cellular space, signaling, and mechanics. It surveys biological roles, potential applications, and a set of outstanding challenges—conceptual, methodological, and social—that must be addressed to achieve predictive, experimentally anchored understanding. By advocating quantitative, cross-disciplinary collaboration and careful perturbation studies, the work sketches a path toward engineering and therapeutically targeting condensates in development, disease, and environmental contexts.

Abstract

Biomolecular condensates govern essential cellular processes yet elude description by traditional equilibrium models. This roadmap, distilled from structured discussions at a workshop and reflecting the consensus of its participants, clarifies key concepts for researchers, funding bodies, and journals. After unifying terminology that often separates disciplines, we outline the core physics of condensate formation, review their biological roles, and identify outstanding challenges in nonequilibrium theory, multiscale simulation, and quantitative in-cell measurements. We close with a forward-looking outlook to guide coordinated efforts toward predictive, experimentally anchored understanding and control of biomolecular condensates.

Roadmap for Condensates in Cell Biology

TL;DR

Biomolecular condensates are a unifying physical principle in cells, organizing molecules without membranes through phase coexistence and multicomponent interactions. The paper provides a physics-based roadmap that reframes condensation as an input-output framework with controllable knobs and measurable responses, then outlines how condensates influence cellular space, signaling, and mechanics. It surveys biological roles, potential applications, and a set of outstanding challenges—conceptual, methodological, and social—that must be addressed to achieve predictive, experimentally anchored understanding. By advocating quantitative, cross-disciplinary collaboration and careful perturbation studies, the work sketches a path toward engineering and therapeutically targeting condensates in development, disease, and environmental contexts.

Abstract

Biomolecular condensates govern essential cellular processes yet elude description by traditional equilibrium models. This roadmap, distilled from structured discussions at a workshop and reflecting the consensus of its participants, clarifies key concepts for researchers, funding bodies, and journals. After unifying terminology that often separates disciplines, we outline the core physics of condensate formation, review their biological roles, and identify outstanding challenges in nonequilibrium theory, multiscale simulation, and quantitative in-cell measurements. We close with a forward-looking outlook to guide coordinated efforts toward predictive, experimentally anchored understanding and control of biomolecular condensates.
Paper Structure (32 sections, 5 figures)

This paper contains 32 sections, 5 figures.

Table of Contents

  1. Introduction
  2. What are condensates?
  3. Physics of condensates
  4. Control Parameters (“Knobs”)
  5. Condensate Properties (“Responses”)
  6. Effects of condensation on the cellular space
  7. Ways condensation may be used to control cellular processes
  8. How researchers can apply what we learn about condensates
  9. Challenges
  10. Conceptual challenges
  11. Sensitivity to conditions and perturbations.
  12. Multicomponent nature of condensates.
  13. Linking condensate properties to function and dysfunction.
  14. De novo functional condensates.
  15. Methodological challenges
  16. ...and 17 more sections

Figures (5)

  • Figure 1: The strong rise in the number of publications in recent years (obtained from searching PubMed for “phase separation” and “cell”) indicates a growing interest in using the concept of phase separation in cell biology.
  • Figure 2: Historical development of condensates in cell biology. The timeline highlights selected landmark papers that influenced biomolecular condensates research substantially. Future directions are discussed in detail in section \ref{['sec:outlook']}.
  • Figure 3: Pictures of blackboards summarizing weekly discussion sessions at the KITP Workshop “Physical Principles Shaping Biomolecular Condensates” in 2025. The three shown examples are related to sections \ref{['sec:terminology']}, \ref{['sec:physics']}, and \ref{['sec:challenges']}.
  • Figure 4: The physical framework of condensation describes how various control parameters affect condensate properties. Cells can use the control parameters as knobs to tune condensate properties. Alternatively, these properties serve as proxies for environmental parameters to perform sensing tasks.
  • Figure 5: Understanding biomolecular condensates and their biological consequences will require integrating theoretical, numerical, and experimental approaches. Each approach offers extensive tools, such as chemically non-specific, composition-dependent, and sequence-specific theories, as well as exploring similarities and variability across different size and time scales (e.g., evolution) in experiments. Figure generated using BioRender.