: Cupriavidus necator is a metabolically versatile β-proteobacterium of growing interest for auto- and heterotrophic bioprocesses, yet the genetic determinants governing its biofilm formation remain largely uncharacterized, particularly under process-relevant heterotrophic conditions. Here, we applied a forward-genetics transposon-enrichment approach to identify loci which promote surface-associated growth. A high-density mini-Tn 5 mutant library (26,185 insertion clones, exceeding the >17,000 required for genome-wide coverage) was cultivated as a biofilm in a microfluidic flow-cell system on fructose for 168 h, and the surface-associated community was characterized by deep sequencing. Twelve genes showed significantly elevated insertion frequencies, several with documented links to biofilm formation in other bacteria, including the ferrous-iron uptake system ( feoA / feoB ), galU , and a GSDEF/EAL dual-domain protein. The gene B2043 (E6A55_RS29530), encoding this c-di-GMP-metabolizing protein, was selected for validation by markerless deletion. Under static conditions, the ΔB2043 mutant showed a 1.69 ± 0.06-fold increase in biofilm-associated biomass (p = 5.16 × 10 -15 ). Under flow-through conditions, the mutant attached faster, entered exponential growth ∼10 h earlier, reached its biovolume plateau ∼16 h earlier than the wild-type, and formed distinct tower-like structures. These results identify B2043 as a negative regulator of biofilm formation acting predominantly during attachment, provide the first experimental evidence for c-di-GMP-dependent biofilm regulation in C. necator H16, and establish a functional-genomics framework — together with eleven further candidate loci — for engineering productive biofilms in this organism.
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.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
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.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
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.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
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 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.