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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Combinatorial decision-making driven by multicomponent surface condensates.

Aidan Zentner1, Ethan V Halingstad2,3,4, Cameron Chalk5

  • 1School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.

Proceedings of the National Academy of Sciences of the United States of America
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

Biomolecular condensates can classify surfaces by recruiting distinct molecular compartments. This programmable system, inspired by machine learning, enables adaptive information processing beyond simple compartmentalization.

Keywords:
biophysicscondensatesmolecular computationself-organization

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Area of Science:

  • Biophysics
  • Systems Biology
  • Molecular Biology

Background:

  • Living organisms utilize molecular networks for information processing and decision-making.
  • Biomolecules self-organize into spatial compartments called condensates via phase transitions.
  • Condensates form on cellular surfaces like chromatin and membranes, influencing biological functions.

Purpose of the Study:

  • To demonstrate surface classification using distinct biomolecular condensates.
  • To explore the analogy between condensate behavior and multidimensional classification in machine learning.
  • To investigate the reprogramming potential of molecular ensembles for adaptive information processing.

Main Methods:

  • Designing multicomponent fluids to recruit specific condensates to surfaces.
  • Utilizing phase transitions for self-organization of biomolecular condensates.
  • Drawing analogies to machine learning concepts like hidden nodes to analyze condensate behavior.

Main Results:

  • Achieved surface classification by differential recruitment of condensates.
  • Demonstrated that hidden species expand the capacity for complex decision boundaries.
  • Showcased reprogramming of the same molecular repertoire to perform new tasks by altering species levels.

Conclusions:

  • Physical processes in biomolecular condensates can encode and drive adaptive information processing.
  • Condensate behavior offers a framework for understanding biological decision-making beyond simple compartmentalization.
  • This work suggests a new paradigm for designing programmable molecular systems for information processing.