Biology subjects

van Eunen, K.

Publications and source records attributed to van Eunen, K..

3 recordsLinked to original sources

bioRxiv2025

Collective parameter estimation of related models with an initial stability constraint

Parameterisation of dynamic biochemical network models is a challenging aspect of systems biology. Especially when the parameter space is large and data is semi quantitive but comparable across different experimental conditions. Here, we present a set of command line tools utilising Pycotools (COPASI) that leverages the power of high-performance computing to facilitate parameter estimation of large models with many unknown parameters. In particular, we expand upon the abilities of Pycotools to address two particular issues. Firstly, the difficulty of constraining a models parameterisation to assume the system begins in a steady state (prior to a perturbative stimulation). And secondly, parameterising against relative quantitative time series data that have no absolute scale. Our software operates on the SLURM workload manager system and can be applied to any parameter estimation against time series data produced by applying a single perturbation at time zero to an equilibrated system. We validate that our technique can produce a parameterised model of the MTOR (mechanistic target of rapamycin) network based on semi-quantitative time-series data from 2 breast cancer cell lines, stimulated with insulin and amino acids. We also show our model can make reasonable predictions on distinct signaling dynamics in one breast cancer cell line based on the other by adjusting the initial protein quantities only. In conclusion, models should fit both the initial steady state and the dynamics following stimulation, given that stabilising systems prior to stimulation is a common experimental protocol in signaling research. By expanding standard tools, commonly used in the field, we have developed a widely applicable method, which can easily be evaluated and is amenable to wide general use.

systems biology↗

bioRxiv2024

Phenylketonuria: modelling cerebral amino acid and neurotransmitter metabolism

ObjectivePhenylketonuria (PKU) is a metabolic disorder characterised by deficient hepatic phenylalanine hydroxylase activity, leading to elevated phenylalanine levels. Despite adherence to a phenylalanine-restricted diet, many adult PKU patients continue to experience executive function deficits, likely linked to high cerebral phenylalanine concentrations and deficiencies in monoaminergic neurotransmitters. Given the complexity of the interaction between diet and brain neurotransmitter metabolism, we employed computational metabolic modelling alongside experimental data from dietary intervention studies in PKU mice to identify key metabolic drivers underlying these deficits. Our goal was to provide a mechanistic, model-based foundation to support and optimise dietary therapies in PKU. MethodWe developed a computational model simulating large neutral amino acid (LNAA) transport across the blood-brain barrier and the subsequent metabolism of cerebral amino acids and monoaminergic neurotransmitters. The model was validated using direct measurements of brain amino acid concentrations in PKU mice subjected to various dietary regimens. ResultsThe model predicts that cerebral amino acid levels are primarily influenced by their plasma concentrations and, to a lesser extent, by competition among LNAAs for transport mechanisms. Notably, it suggests that cerebral monoaminergic neurotransmitter levels are more significantly affected by elevated phenylalanine levels, likely through non-competitive inhibition of hydroxylase enzymes, than by the availability of precursor amino acids. Consequently, the model indicates that reducing phenylalanine levels, in conjunction with supplementing tyrosine and tryptophan, is more effective in restoring neurotransmitter levels than precursor supplementation alone. ConclusionThis study presents the first comprehensive model integrating LNAA transport and cerebral neurotransmitter metabolism in PKU. The model enhances our understanding of the diseases pathophysiology and highlights the importance of combined therapeutic strategies that target both phenylalanine reduction and precursor amino acid supplementation. Furthermore, it identifies knowledge gaps in LNAA transport mechanisms and offers a framework applicable to other neurological disorders involving diet-gene-neurotransmitter interactions.

systems biology↗

bioRxiv2021

A yeast with muscle does not run faster: full humanization of the glycolytic pathway in Saccharomyces cerevisiae

While transplantation of single genes in yeast plays a key role in elucidating gene functionality in metazoans, technical challenges hamper the humanization of full pathways and processes. Empowered by advances in synthetic biology, this study demonstrates the feasibility and implementation of full humanization of glycolysis in yeast. Single gene and full pathway transplantation revealed the remarkable conservation of both glycolytic and moonlighting functions and, combined with evolutionary strategies, brought to light novel, context-dependent responses. Remarkably, human hexokinase 1 and 2, but not 4, required mutations in their catalytic or allosteric sites for functionality in yeast, while hexokinase 3 was unable to complement its yeast ortholog. Comparison with human tissues cultures showed the preservation of turnover numbers of human glycolytic enzymes in yeast and human cell cultures. This demonstration of transplantation of an entire, essential pathway paves the way to the establishment of species, tissue and disease-specific metazoan models. One Sentence SummaryThis work demonstrates the successful humanization of an entire pathway in Saccharomyces cerevisiae and establishes an attractive strategy to study (human) glycolysis architecture and regulation. HighlightsO_LIThe successful humanization of the entire glycolytic pathway in yeast offers new microbial models for both fundamental and applied studies. C_LIO_LIBoth glycolytic and moonlighting functions and turnover numbers of glycolytic enzymes are highly conserved between yeast and human. C_LIO_LIFunctionality of human hexokinases 1 and 2 in yeast requires mutations in the catalytic or allosteric binding sites. C_LIO_LICombination of single gene and full transplantation with laboratory evolution reveals context-dependent activity and evolution of glycolytic enzymes. C_LI

systems biology↗