Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This paper presents a novel geometric simulation method, termed Wave Function Collapse (WFC), designed to model complex biological processes. Traditional computational methods often struggle to effectively represent the intricate dynamics of biological systems due to their inherent complexity. WFC utilizes quantum-inspired representations and collapse mechanisms to achieve a more streamlined and insightful simulation of emergent properties, offering a potential paradigm shift in biological modeling. The core of the method involves representing the system's state as a wave function, collapsing it through a controlled process to generate output behavior. We explore the application of WFC to simulating protein folding and enzyme kinetics, demonstrating its potential for capturing essential aspects of these processes. This work aims to provide a robust and mathematically rigorous framework for visualizing and analyzing complex biological phenomena.
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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This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
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This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
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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.
