Statistical mechanical modeling of phase behavior and structure properties of associative biomacromolecules under crowding conditions: beyond the Flory-Huggins

01.10.2025 bis 30.09.2027

This project outlines a groundbreaking research project that aims to advance our understanding of complex biological
systems, particularly protein solutions and biomolecular condensates. The proposed investigation is built on fundamental ideas
from statistical physics. What sets this proposal apart is its potential to bridge the gap between theoretical modeling and practical
applications in biological and chemical engineering.
The unique aspect of this project lies in its use of classical density functional theory (cDFT) to predict thermodynamic and
structural properties of protein solutions, which is computationally much more efficient than computer simulations. This approach
can be used to analyze fluid interfaces and multiphase organization, making it a valuable tool for understanding complex
biological systems.
The proposed work has several important contributions, including the potential to create new computational and
experimental strategies for the development of therapeutic proteins, drug delivery, and equilibrium colloidal gels. Additionally,
the project's focus on protein-regulator mixtures and the impact of additional components on phase separation can provide
valuable insights into the behavior of biomolecular condensates, which are crucial for understanding various biological processes.
Advantages of modern liquid state theories, in particular Wertheim’s theory for associating fluid, are proposed to overcome
disadvantages of polymer mean field lattice theories (Flory-Huggins theory), that are widely used in theoretical descriptions of
phase separation within cells.
Overall, this proposal has the potential to make significant contributions to our understanding of complex biological
systems and their applications. Its unique blend of theoretical modeling and practical applications makes it an exciting and
promising area of study.