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Ingalls, B.

Publications and source records attributed to Ingalls, B..

3 recordsLinked to original sources

A Keller-Segel model for C elegans L1 aggregation

We describe a mathematical model for the aggregation of starved first-stage C elegans larvae (L1s). We propose that starved L1s produce and respond chemotactically to two labile diffusible chemical signals, a short-range attractant and a longer range repellent. This model takes the mathematical form of three coupled partial differential equations, one that describes the movement of the worms and one for each of the chemical signals. Numerical solution of these equations produced a pattern of aggregates that resembled that of worm aggregates observed in experiments. We also describe the identification of a sensory receptor gene, srh-2, whose expression is induced under conditions that promote L1 aggregation. Worms whose srh-2 gene has been knocked out form irregularly shaped aggregates. Our model suggests this phenotype may be explained by the mutant worms slowing their movement more quickly than the wild type. Author summaryAmong the most complex of animal behaviors are collective behaviors, in which animals interact with each other so as to produce large-scale organization. Starved first-stage larvae of the nematode Caenorhabditis elegans exhibit such a behavior: they come together to form aggregates of several hundred worms. How and why they do this are unknown. To address these questions, we developed a mathematical model of starved L1 aggregation. This model reproduced the main features of the behavior.

animal behavior and cognition↗

Challenges in Undergraduate Synthetic Biology Training: Insights from a Canadian iGEM Student Perspective

The last two decades have seen vigorous activity in synthetic biology research and ever-increasing applications of synthetic biology technologies. However, pedagogical research on synthetic biology is scarce, especially when compared to some scientific and engineering disciplines. Within Canada, there are only three universities that formally teach synthetic biology programs; two of which are at the undergraduate level. Many Canadian undergraduate students are instead introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition where they work in design teams to conceive of and execute a synthetic biology project that they present at an international jamboree. We surveyed the Canadian landscape of synthetic biology education through the experience of students from the Canadian iGEM teams of 2019. Using a thematic codebook analysis, we gathered insights to generate recommendations that could empower future iGEM team operations and inform educators about best practices in teaching undergraduate synthetic biology.

scientific communication and education↗

Successful forecasting of harmful cyanobacteria blooms with high frequency lake data

Cyanobacterial blooms are causing increasing issues across the globe. Bloom forecasting can facilitate adaptation to blooms. Most bloom forecasting models depend on weekly or fortnightly sampling, but these sparse measurements can miss important dynamics. Here we develop forecasting models from five years of high frequency summer monitoring in a shallow lake (which serves as an important regional water supply). A suite of models were calibrated to predict cyanobacterial fluorescence (a biomass proxy) using measurements of: cyanobacterial fluorescence, water temperature, light, and wind speed. High temporal autocorrelation contributed to relatively strong predictive power over 1, 4 and 7 day intervals. Higher order derivatives of water temperature helped improve forecasting accuracy. While traditional monitoring and modelling have supported forecasting on longer timescales, we show high frequency monitoring combined with telemetry allows forecasting over timescales of 1 day to 1 week, supporting early warning, enhanced monitoring, and adaptation of water treatment processes.

ecology↗