Biology subjects

King, R. D.

Publications and source records attributed to King, R. D..

5 recordsLinked to original sources

bioRxiv2025

Self-driven Biological Discovery through Automated Hypothesis Generation and Experimental Validation

Automation is transforming scientific discovery by enabling systematic exploration of complex hypotheses. Large language models (LLMs) perform well across diverse tasks and promise to accelerate research, but often struggle to interact with logical structures. Here we present a framework integrating LLM-based agents with laboratory automation, guided by a logical scaffold incorporating symbolic relational learning, structured vocabularies, and experimental constraints. This integration reduces output incoherence and improves reliability in automated workflows. We couple this AI-driven approach to automated cell-culture and metabolomics platforms, enabling hypothesis validation and refinement, yielding a flexible system for scientific discovery. We validate the system in Saccharomyces cerevisiae, identifying novel interactions, including glutamate-induced synergistic growth inhibition in spermine-treated cells and aminoadipates partial rescue of formic-acid stress. All hypotheses, experiments, and data are captured in a graph database employing controlled vocabularies. Existing ontologies are extended, and a novel representation of scientific hypotheses is presented using description logics. This work enables a more reliable, machine-driven discovery process in systems biology.

systems biology↗

bioRxiv2025

Deletion of Nudt19 Increases Albuminuria in Mice Fed a High Fat Diet

Nudix hydrolase 19 (NUDT19) is a peroxisomal enzyme that hydrolyzes CoA species at the phosphodiester bond and has been linked to peroxisomal dysfunction in the context of diabetic kidney disease. Despite its predominant expression in mouse kidneys, the physiological role of NUDT19 remains poorly understood. To investigate its function under metabolic stress, we fed Nudt19-/- mice a high fat diet (HFD) for 15 weeks. Nudt19 deletion exacerbated HFD-induced albuminuria, suggesting a previously unrecognized role in kidney function. This phenotype was associated with altered lipid metabolism in the kidneys, including reduced levels of non-esterified fatty acids and specific mono-acyl lipids, as well as differential expression of proteins involved in lipid metabolism. These included ECH1, THIKB, and ECHD2, enzymes involved in peroxisomal and mitochondrial {beta}-oxidation; C19orf12, a lipid droplet-associated protein; and the lipolysis-stimulated lipoprotein receptor (LSR). These findings highlight NUDT19 as a key regulator of renal lipid homeostasis and suggest that its loss contributes to kidney dysfunction under conditions of dietary lipid overload.

physiology↗

bioRxiv2021

A genetic trap in yeast for inhibitors of SARS-CoV-2 main protease

The ongoing COVID-19 pandemic urges searches for antiviral agents that can block infection or ameliorate its symptoms. Using dissimilar search strategies for new antivirals will improve our overall chances of finding effective treatments. Here, we have established an experimental platform for screening of small molecule inhibitors of SARS-CoV-2 main protease in Saccharomyces cerevisiae cells, genetically engineered to enhance cellular uptake of small molecules in the environment. The system consists of a fusion of the E. coli toxin MazF and its antitoxin MazE, with insertion of a protease cleavage site in the linker peptide connecting the MazE and MazF moieties. Expression of the viral protease confers cleavage of the MazEF fusion, releasing the MazF toxin from its antitoxin, resulting in growth inhibition. In the presence of a small molecule inhibiting the protease, cleavage is blocked and the MazF toxin remains inhibited, promoting growth. The system thus allows positive selection for inhibitors. The engineered yeast strain is tagged with a fluorescent marker protein, allowing precise monitoring of its growth in the presence or absence of inhibitor. We detect an established main protease inhibitor down to 10 M by a robust growth increase. The system is suitable for robotized large-scale screens. It allows in vivo evaluation of drug candidates, and is rapidly adaptable for new variants of the protease with deviant site specificities. IMPORTANCEThe COVID-19 pandemic may continue several years before vaccination campaigns can put an end it globally. Thus, the need for discovery of new antiviral drug candidates will remain. We have engineered a system in yeast cells for detection of small molecule inhibitors of one attractive drug target of SARS-CoV-2, its main protease which is required for viral replication. To detect inhibitors in live cells brings the advantage that only compounds capable of entering the cell and remain stable there, will score in the system. Moreover, by its design in yeast, the system is rapidly adaptable for tuning of detection level, eventual modification of protease cleavage site in case of future mutant variants of the SARS-CoV-2 main protease, or even for other proteases.

synthetic biology↗

bioRxiv2019

Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability

To secure the worlds food supply it is essential that we improve our knowledge of the genetic underpinnings of complex agronomic traits. In this paper, we report our findings from performing trait prediction and association mapping using marker stability in diverse rice landraces. We used the least absolute shrinkage and selection operator as our marker selection algorithm, and considered twelve real agronomic traits and a hundred simulated traits using a population with approximately a hundred thousand markers. For trait prediction, we considered several statistical/machine learning methods. We found that some of the methods considered performed best when preselected markers using marker stability were used. However, our results also show that one might need to make a trade-off between model size and performance for some learning methods. For association mapping, we compared marker stability to the genome-wide efficient mixed-model analysis (GEMMA), and for the simulated traits, we found that marker stability significantly outperforms GEMMA. For the real traits, marker stability successfully identifies multiple associated markers, which often entail those selected by GEMMA. Further analysis of the markers selected for the real traits using marker stability showed that they are located in known quantitative trait loci (QTL) using the QTL Annotation Rice Online database. Furthermore, co-functional network prediction of the selected markers using RiceNet v2 also showed association to known controlling genes. We argue that a wide adoption of the marker stability approach for the prediction of agronomic traits and association mapping could improve global rice breeding efforts.

bioinformatics↗

bioRxiv2017

An Evaluation of Machine-learning for Predicting Phenotype: studies in yeast and wheat

In phenotype prediction, the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods (elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM)), with two state-of-the-art classical statistical genetics methods (including genomic BLUP). Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all phenotypes considered standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. When applied to the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure, which suggests one way to improve standard machine learning methods when population structure is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise.

bioinformatics↗