Biology subjects

Mac Aodha, O.

Publications and source records attributed to Mac Aodha, O..

9 recordsLinked to original sources

bioRxiv2025

Sustainable cattle management by communities supports African wildlife

Community-based conservation (CBC) initiatives aim to reconcile biodiversity protection with local livelihoods, yet their effectiveness in protecting wildlife remains uncertain, often hinging on local management1,2. We evaluated a globally significant CBC model in Kenyas Greater Maasai Mara Ecosystem (GME), where conservancies, run jointly by Maasai landowners and the tourism sector, employ rotational cattle grazing to support both wildlife and pastoralism3,4. Using a [~]1200 km2 grid of 180 camera traps across gradients of livestock pressure in Maasai Mara National Reserve and three conservancies in 2018, we collected and analysed over 2 million images with a customised AI-powered pipeline. We found a positive impact of observed cattle pressure on mammal community occupancy and species richness, except for at the highest levels of cattle grazing. However, sheep and goat grazing and proximity to infrastructure had a negative impact. These results provide evidence that wildlife and pastoralism can coexist under community-led stewardship5, but only with active management and targeted control of emerging threats. AI tools such as our image classifier may contribute to more adaptive community-led management of these areas6. As conservation policy shifts beyond formal protected areas, our findings support CBC as a scalable model for conserving biodiversity within working landscapes, offering a pathway to meet global targets while maintaining local livelihoods7.

ecology↗

bioRxiv2025

Accurate prediction of gene deletion phenotypes with Flux Cone Learning

Predicting the impact of gene deletions is crucial for biological discovery, biomedicine, and biotechnology. For example, identifying lethal deletions is key for new cancer therapies or antimicrobial treatments that bypass drug resistance. In biotechnology, non-lethal deletions are a powerful strategy to redirect chemical flux toward production of high-value compounds for the food, energy, and pharmaceutical sectors, using genetically engineered cells as an alternative to petrochemicals. Owing to the cost and complexity of large-scale deletion screens, there is a growing interest in computational models that can leverage such data for predictive modelling. Here, we present Flux Cone Learning, a general framework for predicting the impact of metabolic gene deletions on many phenotypes of interest. Flux Cone Learning is based on high-dimensional Monte Carlo sampling of the metabolic space in tandem with supervised learning of fitness scores from deletion screens. This strategy enables the method to learn correlations between the geometry of the metabolic space and a target deletion phenotype. We demonstrate best-in-class predictive accuracy for metabolic gene essentiality in organisms of varied complexity (Escherichia coli, Saccharomyces cerevisiae, Chinese Hamster Ovary cells), outperforming the gold standard predictions of Flux Balance Analysis. The method does not rely on an optimality principle and thus can be applied to a range of organisms where cellular objectives cannot be encoded as an optimization task. We showcase the versatility of Flux Cone Learning in other phenotypes by training a predictor of small molecule production from deletion screening data. Flux Cone Learning provides a widely applicable framework for phenotypic prediction and lays the groundwork for the development of metabolic foundation models across the kingdom of life.

systems biology↗

bioRxiv2025

Large language models possess some ecologicalknowledge, but how much?

Large Language Models (LLMs) have shown remarkable capabilities in question answering across various domains, yet their effectiveness in ecological knowledge remains underexplored. Understanding their potential to recall and synthesize ecological information is crucial as AI tools become increasingly integrated into scientific workflows. Here, we assess the ecological knowledge of two LLMs, Gemini 1.5 Pro and GPT-4o, across a suite of ecologically focused tasks. These tasks evaluate an LLMs ability to predict species presence, generate range maps, list critically endangered species, classify threats, and estimate species traits. We introduce a new benchmark dataset to quantify LLM performance against expert-derived data. While the LLMs tested outperform naive baselines, achieving around 20 percent-age points higher accuracy in species presence prediction, they reach only a third of the mean F1 score for range map generation and improve threat classification by just around 10 points over random guessing. These results highlight both the promise and challenges of applying LLMs in ecology. Our findings suggest that domain-specific fine-tuning is necessary to improve eco-logical knowledge in LLMs. By providing a repeatable evaluation framework, our benchmark dataset will facilitate future research in this area, helping to refine AI applications for ecological science.

ecology↗

bioRxiv2024

Modelling dynamic host-pathway interactions at the genome scale

Pathway engineering offers a promising avenue for sustainable chemical production. The design of efficient production systems requires understanding complex host-pathway interactions that shape the metabolic phenotype. While genome-scale metabolic models are widespread tools for studying static host-pathway interactions, it remains a challenge to predict dynamic effects such as metabolite accumulation or enzyme overexpression during the course of fermentation. Here, we propose a novel strategy to integrate kinetic pathway models with genome-scale metabolic models of the production host. Our method enables the simulation of the local nonlinear dynamics of pathway enzymes and metabolites, informed by the global metabolic state of the host as predicted by Flux Balance Analysis (FBA). To reduce computational costs, we make extensive use of surrogate machine learning models to replace FBA calculations, achieving simulation speed-ups of at least two orders of magnitude. Through case studies on two production pathways in Escherichia coli, we demonstrate the consistency of our simulations and the ability to predict metabolite dynamics under genetic perturbations and various carbon sources. We showcase the utility of our method for screening dynamic control circuits through large-scale parameter sampling and mixed-integer optimization. Our work links together genome-scale and kinetic models into a comprehensive framework for computational strain design.

synthetic biology↗

bioRxiv2023

Machine learning for optimization of multiscale biological circuits

Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signalling pathways and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian Optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space towards an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and hence provides a feasible approach to solve a highly non-convex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.

synthetic biology↗

bioRxiv2022

Towards a General Approach for Bat Echolocation Detection and Classification

O_LIAcoustic monitoring is a scalable approach for assessing bat populations, yet automating the detection and classification of bat echolocation calls remains challenging, particularly in data-scarce regions. Although deep learning (DL) is increasingly applied to this task, most existing approaches repurpose computer-vision architectures and generate a single prediction per spectrogram clip, offering limited robustness to variable background noise and potentially constraining generality across regions and species assemblages. C_LIO_LIHere, we develop BatDetect2, an open-source DL pipeline for the joint detection and classification of bat echolocation calls. BatDetect2 builds on a 2D convolutional architecture and incorporates two targeted modifications: (i) a temporal self-attention layer designed to capture long-range structure across call sequences, and (ii) convolutional layers augmented with frequency coordinates to explicitly encode frequency information directly. We evaluate model generality using five diverse datasets from four different regions: UK, Mexico, Australia, and Brazil, and conduct ablation analyses using a UK dataset spanning 17 bat species. We further assess whether a trained model can detect echolocation calls from species absent from the training data. C_LIO_LIBatDetect2 consistently outperforms a traditional call-parameter extraction baseline across all datasets and evaluation metrics. Ablation analyses show that the inclusion of temporal self-attention yields a substantial species classification performance gain, increasing mean Average Precision (mAP) from 0.83 to 0.88, while frequency-coordinate augmentation provides no measurable benefit. When applied to novel species assemblages without retraining, model detection performance varies across datasets, with Average Precision ranging from 0.60 to 0.98. C_LIO_LIOverall, BatDetect2 demonstrates strong and transferable performance across acoustically and taxonomically diverse regions. By jointly detecting and classifying all bat calls present in each input clip, the pipeline provides a practical and extensible tool for passive acoustic monitoring. The full training pipeline and a pretrained UK model are released through the open-source Python package batdetect2, enabling practitioners to develop and deploy models using their own data. C_LI

ecology↗

bioRxiv2021

From sequence to yield: deep learning for protein production systems

Many applications of synthetic biology involve engineering microbial strains to express high-value proteins. Thanks to advances in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain design and optimization. Such models, however, require large amounts of training data that are costly to acquire, which creates substantial entry barriers for many laboratories. Here, we study the relation between model accuracy and data efficiency in a large panel of machine learning models of varied complexity, from penalized linear regressors to deep neural networks. Our analysis is based on data from a large genotype-phenotype screen in Escherichia coli, which was generated with a design-of-experiments approach to balance coverage and depth of the genotypic space. We sampled these data to emulate scenarios with a limited number of DNA sequences for training, as commonly encountered in strain engineering applications. Our results suggest that classic, non-deep, models can achieve good prediction accuracy with much smaller datasets than previously thought, and provide robust evidence that convolutional neural networks further improve performance with the same amount of data. Using methods from Explainable AI and model benchmarking, we show that convolutional neural networks have an improved ability to discriminate between input sequences and extract sequence features that are highly predictive of protein expression. We moreover show that controlled sequence diversity leads to important gains in data efficiency, and validated this principle in a separate genotype-phenotype screen in Saccharomyces cerevisiae. These results provide practitioners with guidelines for designing experimental screens that strike a balance between cost and quality of training data, laying the groundwork for wider adoption of deep learning across the biotechnology sector.

synthetic biology↗

bioRxiv2018

VideoTagger: User-Friendly Software for Annotating Video Experiments of Any Duration

BackgroundScientific insight is often sought by recording and analyzing large quantities of video. While easy access to cameras has increased the quantity of collected videos, the rate at which they can be analyzed remains a major limitation. Often, bench scientists struggle with the most basic problem that there is currently no user-friendly, flexible, and open source software tool with which to watch and annotate these videos.\n\nResultsWe have created the VideoTagger tool to address these and many of the other associated challenges of video analysis. VideoTagger allows non-programming users to efficiently explore, annotate, and visualize large quantities of video data, within their existing experimental protocols. Further, it is built to accept programmed plugins written in Python, to enable seamless integration with other sophisticated computer-aided analyses.\n\nWe tested VideoTagger ourselves, and have a growing base of users in other scientific disciplines. Capitalising on the unique features of VideoTagger to play back infinite lengths of video footage at various speeds, we annotated 39h of a Drosophila melanogaster lifespan video, at approximately 10-15x faster than real-time. We then used these labels to train a machine-learning plugin, which we used to annotate an additional 538h of footage automatically. In this way, we found that flies fall over spontaneously with increasing frequency as they age, and also spend longer durations struggling to right themselves. Ageing in flies is typically defined by length of life. We propose that this new mobility measure of ageing could help the discovery of mechanisms in biogerontology, refining our definition of what healthy ageing means in this extremely small, but widely used, invertebrate.\n\nConclusionsWe show how VideoTagger is sufficiently flexible for studying lengthy and/or numerous video experiments, thus directly improving scientists productivity across varied domains.

animal behavior and cognition↗

bioRxiv2017

Bat Detective - Deep Learning Tools for Bat Acoustic Signal Detection

O_LIPassive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings.\nC_LIO_LIWe developed a convolutional neural network (CNN) based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats (BatDetect). Our deep learning algorithms (CNN FULL and CNN FAST) were trained on full-spectrum ultrasonic audio collected along road-transects across Romania and Bulgaria by citizen scientists as part of the iBats programme and labelled by users of www.batdetective.org. We compared the performance of our system to other algorithms and commercial systems on expert verified test datasets recorded from different sensors and countries. As an example application, we ran our detection pipeline on iBats monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system.\nC_LIO_LIHere, we show that both CNNFULL and CNNFAST deep learning algorithms have a higher detection performance (average precision, and recall) of search-phase echolocation calls with our test sets, when compared to other existing algorithms and commercial systems tested. Precision scores for commercial systems were reasonably good across all test datasets (>0.7), but this was at the expense of recall rates. In particular, our deep learning approaches were better at detecting calls in road-transect data, which contained more noisy recordings. Our comparison of CNNFULL and CNNFAST algorithms was favourable, although CNNFAST had a slightly poorer performance, displaying a trade-off between speed and accuracy. Our example monitoring application demonstrated that our open-source, fully automatic, BatDetect CNNFAST pipeline does as well or better compared to a commercial system with manual verification previously used to analyse monitoring data.\nC_LIO_LIWe show that it is possible to both accurately and automatically detect bat search-phase echolocation calls, particularly from noisy audio recordings. Our detection pipeline enables the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale, particularly when combined with automatic species identification. We release our system and datasets to encourage future progress and transparency.\nC_LI

ecology↗