Epistasis predicted by enzyme-constrained metabolic models is computed at a single value of the protein available to metabolism, although that amount varies two- to three-fold with growth rate. We ask how epistatic interactions are affected by computing every single and pairwise gene deletion in an enzyme-constrained model of Saccharomyces cerevisiae while sweeping protein availability, with and without a bound on nutrient uptake. Protein availability changes relative fitness only under co-limitation, when the protein pool and a nutrient constraint limit growth together. The pool alone, the configuration in which these models are calibrated, makes protein a mere scale factor: no interaction changes sign. Under co-limitation, one genetic interaction in three is gained or lost across the physiological range. Most of this is inherited from single mutants of the respiratory machinery, which is recruited as protein becomes available. The epistasis between a glycolytic lesion with a protein-expensive bypass and the respiratory chain changes sign where the optimum switches from fermentation to respiration. We also find protein-priced interactions: the network supplies a bypass around each deletion and the pool gives it a price, so two genes with no stoichiometric coupling interact whenever the bypass around one competes with the route of the other for the protein pool. Isozyme pairs predicted neutral by flux balance analysis show negative epistasis for the same reason. Relative fitness depends on protein availability and nutrient supply only through their ratio. Thus, nutrient supply is a directly accessible experimental parameter to test epistatic interactions. Sign inversion, isozyme pairs, and interactions with cofactor-consuming branches are predictions a condition-resolved genetic interaction screen can test.
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 study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
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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.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
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 seque...
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.