Biology subjects

Tiukova, I. A.

Publications and source records attributed to Tiukova, I. A..

3 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↗

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

Genome-scale model of Rhodotorula toruloides metabolism

The basidiomycete red yeast Rhodotorula toruloides is a promising platform organism for production of biooils. We present rhto-GEM, the first genome-scale model of R. toruloides metabolism, that was largely reconstructed using RAVEN toolbox. The model includes 4869 genes, 897 reactions, and 3334 metabolites. Lipid metabolism was described using the SLIMEr formalism, which allows direct integration of lipid class and acyl chain experimental distribution data. The simulation results confirmed that the R. toruloides model provides valid growth predictions on glucose, xylose and glycerol, while prediction of genetic engineering targets to increase production of linolenic acid and triacylglycerols highlighted genes that have previously been successfully used to increase lipid production. This renders rtho-GEM useful for future studies to improve the production of other industrially important oleochemicals including both value-added fatty acids and carotenoids, while it will also be valuable tool for system-wide omics-data analysis in R. toruloides. Expanding the portfolio of GEMs for lipid accumulating fungi contributes to both understanding of metabolic mechanisms of the oleaginous phenotype but also uncover particularities of lipid production machinery in R. toruloides.

systems biology↗