Biology subjects

Barker, C. G.

Publications and source records attributed to Barker, C. G..

6 recordsLinked to original sources

bioRxiv2025

MAGELLAN: Automated Generation of Interpretable Computational Models for Biological Reasoning

Computational models have become essential tools for understanding signalling networks and their non-linear dynamics. However, these models are typically constructed manually using prior knowledge and can be over-reliant on study bias. These limitations hinder their ability to make accurate predictions and incorporate new evidence. Scaling up the construction of models to take advantage of increasingly abundant omics data can bridge these gaps by providing a comprehensive view of signalling events and how they influence cellular phenotypes. In this study, we present MAGELLAN, a method leveraging message passing graph neural networks to build computational models directly from pathway data and discrete rules representing experimental results. We used this to construct a computational model of breast cancer signalling and re-parameterize a previously published non-small cell lung cancer (NSCLC) model, showing that MAGELLAN can predict genetic dependencies and achieve comparable model quality to expert-curated and manually trained models. Our approach enables the integration of prior knowledge networks and experimental data to build predictive models that are mechanistically interpretable. This approach simplifies model creation, making it more accessible and practical for experimentalists, and supports broader applications in drug discovery and biological research.

systems biology↗

bioRxiv2025

Decoding MASLD Progression: A Molecular Trajectory-Based Framework for Modelling Disease Dynamics

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) has emerged as a silent pandemic, affecting nearly one-third of the global population. MASLD encompasses a spectrum of liver disorders, ranging from simple steatosis to Metabolic Dysfunction-Associated Steatohepatitis (MASH), characterised by lipotoxicity, hepatocellular injury, inflammation, and fibrosis, which can eventually progress to cirrhosis and hepatocellular carcinoma. Despite the progressive nature of MASLD/MASH, current research and clinical practice primarily rely on static, histopathology-defined stages that fail to capture the continuous nature of disease progression. Here, we present an integrative framework that combines patient pseudo-temporal ordering, network analysis, and cell-type deconvolution to reconstruct the continuous MASLD/MASH trajectory. By analysing patient liver transcriptomic profiles, we position patients along this data-driven trajectory, moving beyond conventional stage-based classifications. This approach reveals the sequence of critical molecular events underlying MASLD/MASH progression, providing mechanistic insights into the diseases pathophysiology. By integrating these findings with plasma proteomics data, we identify novel trajectory-specific plasma biomarkers that predict disease stage (and trajectory position) independently of histology. Together, these findings demonstrate the value of trajectory-based frameworks for understanding MASLD pathophysiology and highlight new opportunities for precision diagnosis and therapeutic target prioritisation across the disease spectrum.

systems biology↗

bioRxiv2025

Predicting Personalised Therapeutic Combinations in Non-Small Cell Lung Cancer Using In Silico Modelling

The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with around a million new cases diagnosed globally each year, and a 5-year survival rate of less than 20%. A lack of therapeutic options personalized to individual patient genetics, and the targeted therapies that exist quickly succumbing to resistance, leads to high variation in survival. Patient stratification combined with greater personalisation of therapies have the potential to improve outcomes, however, the wide variation in mutations found in NSCLC adenocarcinoma patients mean that experimentally determining suitable treatment combinations is time-consuming and expensive. Here we present an in silico model encompassing tumour intrinsic key oncogenic signalling pathways, including EGFR, AKT, JAK/STAT and WNT for efficiently predicting rational drug-drug and drug-radiotherapy combination therapies in NSCLC. Using this model, we simulate diverse genetic profiles and test over 10,000 therapeutic combinations to identify optimal strategies to overcome resistance mechanisms specific to genetic profiles and p53 status. Our in silico model reproduces drug additivity experiments, predicts radio-sensitising genes validated in a CRISPR screen and identifies 53BP1 as a potential drug target that improves the therapeutic window during radiotherapy, as well as potential to use ATM inhibitors to overcome p53 loss-of-function driven radiotherapy resistance. We further use the in silico model to identify a 19-gene signature to stratify patients most likely to benefit from radiotherapy and validated this using TCGA data. These results further demonstrate the utility of in silico mechanistic modelling and present a bespoke computational resource for large-scale screening of personalised therapies applied to NSCLC.

cancer biology↗

bioRxiv2024

ARID1A-induced transcriptional reprogramming rewires signalling responses to drug treatment in melanoma

Resistance to BRAF and MAPK inhibitors is a significant challenge in melanoma treatment, driven by adaptive and acquired mechanisms that allow tumour cells to evade therapy. Here, we examined early signalling responses to single and combined BRAF and MAPK inhibition in a BRAFV600E, drug-sensitive melanoma cell line and a drug-resistant ARID1A-knockout (KO) derivative. ARID1A, frequently mutated in melanoma, is associated with resistance and immune evasion. Using an innovative systems biology approach that integrates transcriptomics, proteomics, phosphoproteomics, and functional kinomics through matrix factorization and network analysis, we identified key signalling alterations and resistance mechanisms. We found that ARID1A-KO cells exhibited transcriptional rewiring, sustaining MAPK1/3 and JNK activity post-treatment, bypassing feedback sensitivity observed in parental cells. This rewiring suppressed PRKD1 activation, increased JUN activity--a central resistance network node--and disrupted PKC dynamics through elevated basal RTKs (e.g., EGFR, ROS1) and Ephrin receptor activity post-treatment. ARID1A mutations also reduced HLA-related protein expression and enriched extracellular matrix components, potentially limiting immune infiltration and reducing immunotherapy efficacy. Our graph-theoretical multi-omics approach uncovered novel resistance-associated signalling pathways, identifying PRKD1, JUN, and NCK1 as critical nodes. While receptor activation redundancies complicate single-target therapies, they also present opportunities for combination strategies. This study highlights ARID1As role in reshaping signalling and immune interactions, offering new insights into melanoma resistance mechanisms. By identifying actionable targets, including JUN and immune pathways, we provide a foundation for developing integrated therapeutic strategies to overcome resistance in BRAF/MAPK inhibitor-treated melanoma. One sentence summaryThis study reveals how ARID1A-mediated transcriptional rewiring drives resistance to MAPK inhibitors in melanoma by altering signalling pathways, immune interactions, and receptor dynamics, highlighting potential targets for combinatorial therapies.

systems biology↗

bioRxiv2022

Inter-gastruloid heterogeneity revealed by single cell transcriptomics time course: implications for organoid based perturbation studies

Recent advances in organoid and genome editing technologies are allowing for perturbation experiments at an unprecedented scale. However, before doing such experiments it is important to understand the gene expression profile in each of the organoids cells, as well as how much heterogeneity there is between individual organoids. Here we characterise an organoid model of mouse gastrulation called gastruloids using single cell RNA-sequencing of individual organoids at half-day intervals between day 3 and day 5 of differentiation (roughly corresponding to E6.5-E8.75 in vivo). Our study reveals multiple differentiation trajectories that have hitherto not been characterised in gastruloids. Intriguingly, we observe that individual gastruloids displayed a strong bias towards producing either mesodermal (largely somitic) or ectodermal (specifically neural) cell types. This bifurcation is already seen at the earliest sampled time point, and is characterised by increased activity of WNT-associated pathways in mesodermally-biased gastruloids as compared to neurally-biased gastruloids. Notably, at day 5, mesodermal gastruloids show an increase in the proportion of neural cells, while neural gastruloids do not produce notably more mesodermal cells. This is in line with previous studies on how the balance between these cell types is regulated. We demonstrate using in silico simulations that without proper understanding of the inter-organoid heterogeneity, perturbation experiments have either very high false positive or negative rates, depending on the statistical model used. Thus in future studies, modelling of inter-organoid heterogeneity will be crucial when designing organoid-based perturbation studies. HighlightsO_LIA single cell RNA-sequencing time course of day 3 to day 5 mouse gastruloids reveals multiple mesodermal and neural differentiation trajectories hitherto uncharacterised in gastruloids C_LIO_LISingle gastruloid, single cell RNA-sequencing of mouse gastruloids reveals that gastruloids are either mesodermally- or neurally-biased C_LIO_LIThe two classes of gastruloid arise from differences in response strength to the WNT-agonist chiron C_LIO_LIAt day 5, mesodermal gastruloids start making more neural cells, while neural gastruloids do not make more mesodermal cells, aligning with previously studied in vivo feedback loops C_LIO_LIWe show using simulations that understanding interorganoid heterogeneity is a crucial consideration in the design and analysis of well-powered organoid-based perturbation studies C_LI

genomics↗

bioRxiv2021

Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

The morphology of breast cancer cells is often used as an indicator of tumour severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterised. In this study, we employ a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allows the discovery of unbiased and context-specific gene expression signatures and cell signaling sub-networks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch as well as evidence for the fine control of NFkB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NFkB activity, with specific relevance to cell shape in breast cancer.

systems biology↗