A machine learning based analysis to probe the relationship between odorant structure and olfactory behaviour inC. elegans

Abstract

The chemical basis of smell remains an unsolved problem, with ongoing studies mapping perceptual descriptor data from human participants to the chemical structures using computational methods. These approaches are, however, limited by linguistic capabilities and inter-individual differences in participants. We use olfactory behaviour data from the nematode C. elegans, which has isogenic populations in a laboratory setting, and employ machine learning approaches for a binary classification task predicting whether or not the worm will be attracted to a given monomolecular odorant. Among others, we use architectures based on Natural Language Processing methods on the SMILES representation of chemicals for molecular descriptor generation and show that machine learning algorithms trained on the descriptors give robust prediction results. We further show, by data augmentation, that increasing the number of samples increases the accuracy of the models. From this detailed analysis, we are able to achieve accuracies comparable to that in human studies and infer that there exists a non trivial relationship between the features of chemical structures and the nematode's behaviour.

Deep-Worm-Tracker: Deep Learning Methods for Accurate Detection and Tracking for Behavioral Studies in C. elegans

“…C. elegans , a free-living nematode, is an important model organism for behavioral studies because of the complex behavioral patterns it exhibits. Researchers have long been attempting to understand the attraction or repulsion of these nematodes (roundworms) to external stimuli such as temperature gradients, food, and chemical signals in studies termed ‘taxis’ [1–7]. In particular, chemotaxis assays have gained importance in understanding the underlying neurological circuits and signalling pathways that govern worm movement towards a particular chemical stimulus [8, 9].…”

“…stimuli such as temperature gradients, food, and chemical signals in studies termed 'taxis' [1][2][3][4][5][6][7]. In particular, chemotaxis assays have gained importance in understanding the underlying neurological circuits and signalling pathways that govern worm movement towards a particular chemical stimulus [8,9].…”

“…The first, worm detection dataset, is used for training the YOLOv5 object detection model. We extracted individual frames from 30s video recordings and selected frames containing multiple worms(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) as positive instances for manual annotation. We employed Roboflow, an online annotation tool for generating annotation files for the YOLOv5 model used in Deep-Worm-Tracker.…”

Deep-worm-tracker: Deep learning methods for accurate detection and tracking for behavioral studies in C. elegans

“…stimuli such as temperature gradients, food, and chemical signals in studies termed 'taxis' [1][2][3][4][5][6][7]. In particular, chemotaxis assays have gained importance in understanding the underlying neurological circuits and signalling pathways that govern worm movement towards a particular chemical stimulus [8,9].…”

“…The first, worm detection dataset, is used for training the YOLOv5 object detection model. We extracted individual frames from 30s video recordings and selected frames containing multiple worms(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) as positive instances for manual annotation. We employed Roboflow, an online annotation tool for generating annotation files for the YOLOv5 model used in Deep-Worm-Tracker.…”