Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Temporal Topology Optimization (TTO) presents a novel approach to biological system optimization, moving beyond static parameterization to model the dynamic evolution of topological states. This paper introduces a system leveraging network theory and evolutionary dynamics to predict emergent behaviors within biological systems, specifically focusing on protein folding and gene regulatory network design. We propose a framework that translates biological complexity into a network of interconnected topological states, enabling a predictive model of system behavior. The core mechanism centers around utilizing temporal evolution to iteratively refine these topological states, ultimately generating novel and optimized configurations. This work addresses a key limitation of existing evolutionary modeling – the reliance on fixed parameters – and offers a framework for understanding and manipulating complex biological systems through the lens of topology.
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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.
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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.
