Biology subjects

Ingalls, B.

Publications and source records attributed to Ingalls, B..

4 recordsLinked to original sources

bioRxiv2022

Deep Reinforcement Learning for Optimal Experimental Design in Biology

The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence--reinforcement learning--to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty. 1 Author summaryBiological systems are often complex and typically exhibit non-linear behaviour, making accurate model parametrisation difficult. Optimal experimental design tools help address this problem by identifying experiments that are predicted to provide maximally accurate parameter estimates. In this work we use reinforcement learning, an artificial intelligence method, to determine such experiments. Our simulation studies show that this approach allows uncertainty in model parameterisation to be directly incorporated into the search for optimal experiments, opening a practical avenue for training an experimental controller without confident knowledge of the systems parameter values. We present this method as complementary to existing optimisation approaches and we anticipate that artificial intelligence has a fundamental role to play in the future of optimal experimental design.

synthetic biology↗

bioRxiv2021

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↗

bioRxiv2020

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↗

bioRxiv2019

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↗