Biology subjects

Simpson, I.

Publications and source records attributed to Simpson, I..

7 recordsLinked to original sources

bioRxiv2026

Large-Scale Assessment of Animal-to-Human Drug Translation Using Natural Language Processing

BackgroundLarge-scale estimates of animal-to-human drug translation and the study characteristics associated with successful translation remain limited. The expanding preclinical literature also challenges manual evidence synthesis. We developed a natural language processing (NLP) pipeline to structure and link preclinical and clinical evidence at scale. MethodsIn this retrospective meta-research study, we analysed more than 500,000 neuroscience-related animal drug studies from PubMed and linked them to clinical trial and regulatory approval data. NLP methods extracted drug, disease, and experimental design characteristics from abstracts and full texts. Translation was defined as progression to completed phase III/IV trials or regulatory approval. Logistic regression assessed associations between preclinical study characteristics and successful translation. FindingsAmong 291,624 drug entities identified in animal studies, 6{middle dot}7% entered clinical development and 3{middle dot}1% reached phase III/IV trials or regulatory approval. At the drug-disease level, 4{middle dot}4% entered clinical development and 1{middle dot}9% achieved translation. Restricting analyses to successfully linked ontology entities increased estimates to 11{middle dot}3% and 4{middle dot}1%, respectively. Male-only animal studies predominated, whereas reporting of randomisation, blinding, and sample size calculations remained limited. Testing across multiple species and reporting blinding were associated with higher odds of successful translation. InterpretationOnly a minority of interventions tested in animals progress to advanced clinical development or regulatory approval. Greater species diversity and blinding were associated with improved translational success. NLP-based evidence synthesis may support scalable evaluation of translational research and identification of potentially modifiable research practices. FundingSwiss National Science Foundation, UZH Digital Entrepreneurship Fellowship, Universities Federation for Animal Welfare. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched the literature for studies quantifying large-scale animal-to-human translation and factors associated with successful translation. Existing work was mainly limited to specific diseases, interventions, or manually curated datasets, and large-scale linkage of animal and clinical evidence remained limited. Added value of this studyWe developed a natural language processing pipeline linking more than 500,000 animal studies to clinical trial and regulatory approval data. The study provides large-scale estimates of translation and identifies experimental characteristics associated with successful translation. Implications of all the available evidenceThe findings suggest that only a minority of interventions tested in animals progress to advanced clinical development or regulatory approval. Greater species diversity and reporting of blinding were associated with improved translation. Automated evidence synthesis may support more systematic evaluation of translational research practices.

bioinformatics↗

bioRxiv2026

BatchVaria: a variance-aware framework for evaluating batch correction in high-dimensional omics data

SummaryBatch effects and other unwanted technical sources of variation remain a persistent challenge in the integrative analysis of high-dimensional-omics data. Although established methods such as ComBat effectively mitigate batch-associated signal, their impact on biologically meaningful variation is frequently evaluated in an ad hoc and non-quantitative manner. This is particularly problematic in heterogeneous disease contexts, such as breast cancer transcriptomics, where technical and biological sources of variation may be partially confounded. We present BatchVaria, an R package that implements a variance-aware framework for batch correction and post-adjustment evaluation. BatchVaria integrates variance component modelling, batch adjustment, and systematic re-profiling within a unified analysis container, enabling iterative quantification and reassessment of technical and biological variance contributions while preserving analytical provenance. By supporting multiple variance profiling engines and structured storage of intermediate results, BatchVaria facilitates transparent and reproducible evaluation of batch correction strategies. We demonstrate the utility of BatchVaria using a publicly available breast cancer transcriptomic dataset with known covariate-driven structure, illustrating how iterative variance profiling can guide responsible batch correction without erosion of subtype-associated biological signal.

bioinformatics↗

bioRxiv2023

BioNAR: An Integrated Biological Network Analysis Package in Bioconductor

Biological function in protein complexes emerges from more than just the sum of their parts: Molecules interact in a range of different subcomplexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone. Here we present the Bioconductor package BioNAR which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the networks vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a proteins impact within multiple complexes, and enables estimation of the co-occurrence of meta-data, i.e., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms. The package is available from Bioconductor release 3.16: https://bioconductor.org/packages/release/bioc/html/BioNAR.html Author BiographiesColin McLean holds a PhD in Experimental Particle Physics from the University of Edinburgh. He is currently a Senior Research Fellow in Health Economics and Data Science at the Institute for Genetics and Cancer at the University of Edinburgh. His research interests include applied network and data science in the biomedical domain. Anatoly Sorokin holds PhD Degree in Biophysics and is a Senior computational biologist in the Biological Systems Unit, Okinawa Institute of Science and Technology. His research interests include graph-based analysis, constraint-base, dynamics and rule-based modelling and application areas include systems biology, bioinformatics and microbiomics. Ian Simpson has a DPhil in Genetics (Oxford 2000) and is currently Director of the UKRI Centre for Doctoral Training in Biomedical Artificial Intelligence and a Reader in Biomedical Informatics in the School of Informatics at The University of Edinburgh. His research interests lie at the boundary between Informatics and Biomedicine and focus on jointly modelling molecular and clinical data to improve our understanding of genetic disease. J Douglas Armstrong holds a PhD in Molecular Genetics (Glasgow 1995) and is currently Professor of Systems Neurobiology at the School of Informatics at Edinburgh University. His research interests focus on structure/function mapping in the brains of model organisms. Oksana Sorokina holds a PhD in Systems Biology (Edinburgh 2010) and is a Senior Researcher at the School of Informatics at Edinburgh University. Her expertise is in the computational analysis of complex datasets primarily proteomics and the integration of genetic and other omic data types to understand molecular complexes at the systems biology level.

bioinformatics↗

bioRxiv2022

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule-based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and results were acquired through model simulations. Conforming to previous reports, we confirm that the Kappa model recapitulated the general dynamics of its ODE counterpart with minor differences. These differences occur whenever molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one but can be corrected by revision of the rate constants used in the relevant rules. As in previous reports on other molecular systems, we find that, also in the case of the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, facilitating model reuse. In parallel with these analyses, this manuscript reports a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of a more complex interaction network to study this important molecular system.

systems biology↗

bioRxiv2021

SFARI Genes and where to find them; classification modelling to identify genes associated with Autism Spectrum Disorder from RNA-seq data

MotivationAutism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one that is gaining popularity is the combination of gene expression and clinical genetic data. For ASD, the SFARI-gene database comprises lists of curated genes in which presumed causative mutations have been identified in patients. In order to predict novel candidate SFARI-genes we built classification models combining differential gene expression data for ASD patients and unaffected individuals with a genes status in the SFARI-gene list. ResultsSFARI-genes were not found to be significantly associated with differential gene expression patterns, nor were they enriched in gene co-expression network modules that had a strong correlation with ASD diagnosis. However, network analysis and machine learning models that incorporate information from the whole gene co-expression network were able to predict novel candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We found a statistically significant bias related to the absolute level of gene expression for existing SFARI genes and their scores. It is essential that this bias be taken into account when studies interpret ASD gene expression data at gene, module and whole-network levels. AvailabilitySource code is available from GitHub (https://doi.org/10.5281/zenodo.4463693) and the accompanying data from The University of Edinburgh DataStore (https://doi.org/10.7488/ds/2980) Contactian.simpson@ed.ac.uk

genomics↗

bioRxiv2020

A configurable model of the synaptic proteome reveals the molecular mechanisms of disease co-morbidity.

Synapses contain highly complex proteomes which control synaptic transmission, cognition and behaviour. Genes encoding synaptic proteins are associated with neuronal disorders many of which show clinical co-morbidity. Our hypothesis is that there is mechanistic overlap that is emergent from the network properties of the molecular complex. To test this requires a detailed and comprehensive molecular network model. We integrated 57 published synaptic proteomic datasets obtained between 2000 and 2019 that describe over 7000 proteins. The complexity of the postsynaptic proteome is reaching an asymptote with a core set of ~3000 proteins, with less data on the presynaptic terminal, where each new study reveals new components in its landscape. To complete the network, we added direct protein-protein interaction data and functional metadata including disease association. The resulting amalgamated molecular interaction network model is embedded into a SQLite database. The database is highly flexible allowing the widest range of queries to derive custom network models based on meta-data including species, disease association, synaptic compartment, brain region, and method of extraction. This network model enables us to perform in-depth analyses that dissect molecular pathways of multiple diseases revealing shared and unique protein components. We can clearly identify common and unique molecular profiles for co-morbid neurological disorders such as Schizophrenia and Bipolar Disorder and even disease comorbidities which span biological systems such as the intersection of Alzheimers Disease with Hypertension.

systems biology↗

bioRxiv2018

Circadian protein regulation in the green lineage I. A phospho-dawn anticipates light onset before proteins peak in daytime.

Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. Here, we compare transcriptome data under light-dark cycles to partial proteome and phosphoproteome data, assayed using shotgun mass-spectrometry, from the alga Ostreococcus tauri, the smallest free-living eukaryote. 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Mathematical modelling showed that light-stimulated protein synthesis can account for the observed clustering of protein peaks in the daytime. Prompted by night-peaking and apparently dark-stable proteins, we also tested cultures under prolonged darkness, where the proteome changed less than under the diel cycle. The dark-stable, prasinophyte-specific proteins were also reported to accumulate when O. tauri formed lipid droplets. In the phosphoproteome, 39% of rhythmic phospho-sites reached peak levels just before dawn. This anticipatory phosphorylation suggests that a clock-regulated phospho-dawn prepares green cells for daytime functions. Acid-directed and proline-directed protein phosphorylation sites were regulated in antiphase, implicating the clock-related, casein kinases 1 and 2 in phase-specific regulation, alternating with the CMGC protein kinase family. Understanding the dynamic phosphoprotein network should be facilitated by the minimal kinome and proteome of O. tauri. The data are available from ProteomeXchange, with identifiers PXD001734, PXD001735 and PXD002909. This submission updates a previous version, posted on bioRxiv on 4th April 2018, as https://www.biorxiv.org/content/10.1101/287862v1 HighlightThe phosphorylation of most protein sites was rhythmic under light-dark cycles, and suggested circadian control by particular kinases. Day-peaking, rhythmic proteins likely reflect light-stimulated protein synthesis in this microalga.

plant biology↗