Synthetic multi-species consortia provide valuable insights into the ecological and structural dynamics of complex microbial biofilms. However, the specialized functional contributions of individual components under severe nutrient limitations remain poorly understood. This study investigated the population dynamics, matrix biogenesis and metabolic potential of a synthetic ‘protolichen biofilm’ model comprising Asterochloris microalgae, Gordonia bacteria, Thelebolus filamentous fungi and Occultifur yeast. The biofilms were cultivated under strict carbohydrate-deficient conditions for 30 days. Population changes, extracellular polymeric substance (EPS) matrix formation, and the concentrations of extracellular DNA (exDNA) and proteins (exProt), as well as potential dehydrogenase activity (via iodonitrotetrazolium reduction), were evaluated across monocultures, binary, ternary and quaternary consortia. Under carbon starvation, the photoautotrophic microalgae dominated the consortium, driving an 11-fold increase in population size in the four-component system and serving as the primary source of exDNA, which increased by up to three orders of magnitude by day 30. The Gordonia sp. exhibited a tenfold expansion by actively localizing to fungal hyphae and microalgal cell walls. This was directly correlated with a sharp increase in metabolic activity. By contrast, Thelebolus sp. initially provided the structural framework via EPS production, but exhibited limited metabolic activity over time. Meanwhile, the Occultifur sp. yeast population was severely suppressed, adopting a sit-and-wait ecological strategy. Spearman correlation analysis revealed that multi-species integration stabilized the community and triggered significant emergent effects in exDNA accumulation and metabolic potential, but only when microalgae were present. These findings demonstrate that microalgae and bacteria primarily drive metabolism and regulation within the protolichen consortia investigated, while fungi and yeast play structural or opportunistic roles. This provides a robust framework for understanding complex symbiotic interactions.
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.