Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Constraint Satisfaction and Optimization
Abstract
This research investigates the application of non-linear constraint optimization to biological systems, specifically focusing on optimization of gene expression and protein folding. Traditional optimization methods often struggle with the inherent complexity and dynamic nature of biological systems, necessitating novel approaches that can effectively capture evolutionary principles and adapt to changing conditions. This paper proposes a hybrid method integrating evolutionary algorithms with constraint optimization, incorporating feedback from the system's own adaptive behavior. We demonstrate the effectiveness of this approach in optimizing a simplified biological model, highlighting its potential for broader applicability in the analysis and control of complex biological processes.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
