![]() Ribeiro, Mariana ![]() in bioRxiv (2022) Detailed reference viewed: 14 (0 UL)![]() ; ; et al in bioRxiv (2020) Detailed reference viewed: 87 (7 UL)![]() ; Sengupta, Anupam ![]() in bioRxiv (2020) Turbulence is an important determinant of phytoplankton physiology, often leading to cell stress and damage. Turbulence affects phytoplankton migration, both by transporting cells and by triggering ... [more ▼] Turbulence is an important determinant of phytoplankton physiology, often leading to cell stress and damage. Turbulence affects phytoplankton migration, both by transporting cells and by triggering switches in migratory behavior, whereby vertically migrating cells can invert their direction of migration upon exposure to turbulent cues. However, a mechanistic link between single-cell physiology and vertical migration of phytoplankton in turbulence is currently missing. Here, by combining physiological and behavioral experimentswith a mathematical model of stress accumulation and dissipation, we show that the mechanism responsible for the switch in the direction of migration in the marine raphidophyte Heterosigma akashiwo is the integration of reactive oxygen species (ROS) signaling generated by turbulent cues. Within timescales as short as tens of seconds, the emergent downward-migrating subpopulation exhibited a two-fold increase of ROS, an indicator of stress, 15% lower photosynthetic efficiency, and 35% lower growth rate over multiple generations compared to the upward-migrating subpopulation. The origin of the behavioral split in a bistable oxidative stress response is corroborated by the observation that exposure of cells to exogenous stressors (H2O2, UV-A radiation or high irradiance), in lieu of turbulence, caused comparable ROS accumulation and an equivalent split into the two subpopulations. By providing a mechanistic link between single-cell physiology, population-scale migration and fitness, these results contribute to our understanding of phytoplankton community composition in future ocean conditions. [less ▲] Detailed reference viewed: 61 (4 UL)![]() Vega Moreno, Carlos Gonzalo ![]() ![]() ![]() in bioRxiv (2020) Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual ... [more ▼] Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever increasing growth of domain literature. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).Competing Interest StatementThe authors have declared no competing interest. [less ▲] Detailed reference viewed: 225 (14 UL)![]() Sengupta, Anupam ![]() in bioRxiv (2018) Detailed reference viewed: 70 (8 UL)![]() Kane, Khalid ![]() ![]() ![]() in bioRxiv (2017) Detailed reference viewed: 197 (16 UL)![]() Hemmer, Kathrin ![]() ![]() ![]() in bioRxiv (2017) Detailed reference viewed: 272 (24 UL) |
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