Biology subjects

Shanley, D. P.

Publications and source records attributed to Shanley, D. P..

5 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

Modelling the spatiotemporal dynamics of senescent cells in wound healing, chronic wounds, and fibrosis

Cellular senescence is known to drive age-related pathology through the senescence-associated secretory phenotype (SASP). However, it also plays important physiological roles such as cancer suppression, embryogenesis and wound healing. Wound healing is a tightly regulated process which when disrupted results in conditions such as fibrosis and chronic wounds. Senescent cells appear during the proliferation phase of the healing process where the SASP is involved in maintaining tissue homeostasis after damage. Interestingly, SASP composition and functionality was recently found to be temporally regulated, with distinct SASP profiles involved: a fibrogenic, followed by a fibrolytic SASP, which could have important implications for the role of senescent cells in wound healing. Given the number of factors at play a full understanding requires addressing the multiple levels of complexity, pertaining to the various cell behaviours, individually followed by investigating the interactions and influence each of these elements have on each other and the system as a whole. Here, a systems biology approach was adopted whereby a multi-scale model of wound healing that includes the dynamics of senescent cell behaviour and corresponding SASP composition within the wound microenvironment was developed. The model was built using the software CompuCell3D, which is based on a Cellular Potts modelling framework. We used an existing body of data on healthy wound healing to calibrate the model and validation was done on known disease conditions. The model provides understanding of the spatiotemporal dynamics of different senescent cell phenotypes and the roles they play within the wound healing process. The model also shows how an overall disruption of tissue-level coordination due to age-related changes results in different disease states including fibrosis and chronic wounds. Further specific data to increase model confidence could be used to explore senolytic treatments in wound disorders.

systems biology↗

bioRxiv2023

Systems analysis of miR-199a/b-5p and multiple miR-199a/b-5p targets during chondrogenesis

Changes in chondrocyte gene expression can contribute to the development of osteoarthritis (OA), and so recognition of the regulative processes during chondrogenesis can lead to a better understanding of OA. microRNAs (miRNAs) are key regulators of gene expression in chondrocytes/OA and we have used a combined experimental, bioinformatic, and systems biology approach to explore the multiple miRNA-mRNA interactions that regulate chondrogenesis. A longitudinal chondrogenesis bioinformatic analysis identified paralogues miR-199a-5p and miR-199b-5p as pro-chondrogenic regulators. Experimental work demonstrated alteration of miR-199a-5p or miR-199b-5p expression led to significant inverse modulation of key chondrogenic genes and extracellular matrix production. miR-199a/b-5p targets FZD6, ITGA3 and CAV1 were identified by inhibition experiments and verified as direct targets by luciferase assay. The experimental work was used to generate and parameterize a multi-miRNA 14-day chondrogenesis kinetic model to be used as a repository for the experimental work and as a resource for further investigation of this system. This is the first multi-miRNA model of a chondrogenesis-based system, and highlights the complex relationships between regulatory miRNAs, and their target mRNAs.

systems biology↗

bioRxiv2022

Using Machine Learning to identify microRNA biomarkers for predisposition to Juvenile Onset Huntington's Disease

BackgroundHuntingtons disease (HD) is an autosomal dominant disease which is triggered by a large expansion of CAG nucleotides in the HTT gene. While the CAG expansion linearly correlates with the age of disease onset in HD, twin-studies and cohorts of Juvenile Onset HD (JOHD) patients have shown other factors influence the progression of HD. Thus, it would be of interest to identify molecular biomarkers which indicate predisposition to the development of HD, and as microRNAs (miRNAs) circulate in bio-fluids they would be particularly useful biomarkers. We explored a large HD miRNA-mRNA expression dataset (GSE65776) to establish appropriate questions that could be addressed using Machine Learning (ML). We sought sets of features (mRNAs or miRNAs) to predict HD or WT samples from aged or young mouse cortex samples, and we asked if a set of features could predict predisposition to HD or WT genotypes by training models on aged samples and testing the models on young samples. Several models were created using ADAboost, ExtraTrees, GaussianNB and Random Forest, and the best performing models were further analysed using AUC curves and PCA plots. Finally, genes used to train our miRNA-based predisposition model were mined from HD patient bio-fluid samples. ResultsOur testing accuracies were between 66-100% and AUC scores were between 31-100%. We generated several excellent models with testing accuracies >80% and AUC scores >90%. We also identified homologues of mmu-miR-154-5p, mmu-miR-181a-5p, mmu-miR-212-3p, mmu-miR-378b, mmu-miR-382-5p and mmu-miR-770-5p from our miRNA-based predisposition model to be circulating in HD patient blood samples at p.values of <0.05. ConclusionsWe generated several age-based models which could differentiate between HD and WT samples, including an aged mRNA-based model with a 100% AUC score, an aged miRNA-based model with a 92% AUC score and an aged miRNA-based model with a 96% AUC score. We also identified several miRNAs used to train our miRNA-based predisposition model which were detectable in HD patient blood samples, which suggests they could be potential candidates for use as non-invasive biomarkers for HD research.

bioinformatics↗

bioRxiv2019

Understanding biological ageing in terms of constitutive signals: Convergence to an average decrease in cellular sensitivity and information transmission

Biological ageing is a process that encompasses observations often too heterogeneous to draw coherent conceptual frameworks that may shed light into the generality of the underlying gradual loss of function. Whilst the concept of stochastic damage is often invoked as the driver of the ageing process, this can be too abstract to understand ageing at a higher mechanistic resolution. However, there do exist general mechanisms that describe how stochastic damage interferes with biological function, such as through genetic mutations. In a similar manner, we argue that a molecular habituation phenomenon occurs during biological ageing where constitutive signals arising from damage accumulation drive an average decrease in network sensitivity and information transmission, as well as an increase in noise, across cells and tissue.

systems biology↗