Biology subjects

Sturrock, M.

Publications and source records attributed to Sturrock, M..

11 recordsLinked to original sources

bioRxiv2026

Overinflation and overconcentration: why Cauchy perturbation kernels are the right choice for ABC-SMC

Approximate Bayesian computation sequential Monte Carlo (ABC-SMC) propagates its particles with a perturbation kernel, and with the standard Normal kernel it degrades sharply as the parameter dimension grows, a failure usually attributed to dimension itself. We show instead that it is governed by the quality of the summary statistics, with dimension entering only through a separate and milder mechanism, and that the two must act together for the Normal kernel to break. The first ingredient is covariance overinflation: the kernel covariance, estimated from the particle cloud, overshoots the true posterior covariance by a factor set by information loss in the summary statistics. We derive this overscaling factor in closed form for a Gaussian model with sufficient statistics and show that it stays modest at any dimension, shrinking toward its baseline value as the tolerance tightens; the extreme values seen in practice (of order 103) are a signature of insufficient summaries, not of dimension. The second ingredient is perturbation overconcentration: the normalised Normal step size concentrates around one as the dimension grows, so every proposal overshoots by the same factor. Either ingredient alone is harmless; only their combination breaks the Normal kernel. A Cauchy kernel (multivariate t with one degree of freedom) removes the concentration, keeping a positive acceptance rate under arbitrary overscaling at a bounded worst-case cost of 1.87x in expected squared jump distance. In a Metropolis-Hastings framework we derive closed-form acceptance rates for both kernels that illustrate the advantage of the Cauchy kernel in this limit. A series of full ABC-SMC computational experiments on five problems at d = 12, including a hierarchical gene-expression model, show the Cauchy reducing the sliced Wasserstein distance to the reference posterior by factors of up to 50 with the same simulation budget. Since the summary statistics are commonly insufficient for the models that require ABC, overinflation is structural and the Cauchy perturbation kernel is the right default for problems in higher dimensions.

systems biology↗

bioRxiv2026

An analytical framework for phenotypic selection of fitness-conferring genes

Phenotypic selection can cause the transient, selective upregulation of fitness-conferring genes in isogenic cell populations under stress, producing selective enrichment of the fitness gene relative to a neutral reference gene. While computational models have shown that such enrichment requires noisy gene expression and a cellular memory linking growth rate to gene expression (Ciechonska et al., 2022), the precise mechanistic requirements and the analytical principles governing enrichment have remained unclear. Here, we present an exact analytical framework that unifies enrichment mechanisms across both growth-driven and death-driven selection regimes. By analysing a stochastic model of explicit mRNA and protein dynamics, we prove that when selection acts via cell division, the fitness advantage of faster growth is exactly cancelled by the penalty of faster protein dilution. We show this symmetry is broken by translational feedback but not by transcriptional feedback alone; for genes with regulated (switching) promoters, the promoter-state memory provides an independent route to enrichment without translational feedback. Conversely, when selection acts via cell death, this exact cancellation is bypassed, allowing selective enrichment to emerge from baseline gene expression noise without any assumptions about growth-related feedback loops or regulated vs constitutive expression. We derive an exact fluctuation-response relation demonstrating that, in all cases, enrichment scales with the super-Poissonian component of unperturbed protein noise times the relevant memory timescale. All analytical predictions are corroborated by stochastic simulations of a finitepopulation Moran model. These results have implications for the emergence of drug resistance: by transiently enriching survival-conferring phenotypes, phenotypic selection can extend the window during which cell division occurs under stress, increasing the opportunity for permanent genetic mutations to arise.

systems biology↗

bioRxiv2026

A neural network model delivers a highly prognostic protein signature in cancer stem cells that identifies relapse in stage III colorectal cancer patients.

BackgroundStage III colorectal cancer poses a significant threat of metastasis development, as tumour resection and adjuvant chemotherapy do not guarantee prolonged disease-free survival. ObjectiveThe spatial, quantitative, and qualitative characteristics of various cell types within tumour tissues could be key to developing accurate prognostic AI models. DesignTissue microarrays created from primary tumour tissues collected during surgical resection from a cohort of 493 stage III colorectal cancer (CRC) patients were analysed for 61 protein markers at the single-cell level using multiplexed immunofluorescence imaging via the Cell DIVE platform. Subsequent cell-type classification enabled quantitative cell-type analyses, co-localisation neighbourhood assessments, and cell-type-specific protein signature discoveries that distinguish between early and late/non-recurring patient samples. ResultsThis study identifies a stem cell protein profile that drives tumour relapse. A deep neural network (DNN) model, based on a stem cell protein signature composed of BAX, MLKL, FLIP, GLUT1, and CDX2, provided accurate prognosis for stage III CRC patients in both discovery and validation cohorts and in an independent validation cohort. Nodal count-based metric further increased prognosis accuracy. Our study also revealed distinct spatial arrangements of immune, endothelial, and stem cells that were linked to early tumour recurrence. ConclusionOur findings propose a clinically promising prognostic tool based on a five-protein stem cell signature. These markers not only predict chemotherapy resistance in cancer stem cells but also suggest potential therapeutic strategies such as combinatorial treatments incorporating small molecule inhibitors targeting FLIP and GLUT1. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIMore than 20% of stage III colorectal cancer patients will experience early tumour recurrence within the first 3 years post treatment that includes surgery and adjuvant 5-FU based chemotherapy treatment. C_LIO_LISeveral studies pointed towards involvements of number of cell type specific spatial neighbourhoods in tumour progression where some immune tumour microenvironment promoting angiogenesis and intravasation events, some may provide immunosuppression. C_LIO_LICancer stem cells could be responsible for metastatic tumour spread, early recurrence and chemoresistance. C_LI What this study addsO_LISpatial single cell quantitative multiplex profiling of 45 cancer hallmark proteins and 15 cell identity markers in 493 stage III CRC patients tissue samples demonstrated significant differences in cellular proximity neighbourhoods, cell type specific abundance and expression between the early and late recurrence samples. C_LIO_LIWe discover that macrophages show spatial association with the blood vessels in early recurrence samples. Moreover, we observed conglomeration of B cells and macrophages with Tregulatory, Thelper and Tcytotoxic cells in association with early recurrences. C_LIO_LIWe showed that stromal abundance of Tregulatory, Thelper, Tcytotoxic cells and monocytes are significantly in late, and no recurrence samples compared to early recurrence samples. C_LIO_LIThe most differential expression profile that differentiates late and no recurrence samples from the early recurrence samples is related to the stem cell population. Particularly, we found overexpression of GLUT1, FLIP and downregulation of BAX, BAK, MLKL and CDX2 proteins in the cancer stem cell of early recurrence samples. C_LIO_LIWe built a neural network based on the cancer stem cell protein signature (BAX, MLKL, FLIP, GLUT1 and CDX2 proteins) that delivers a high-performance prognostic classifier. C_LI How this study might affect research, practice or policyO_LIOur results propose a clinically promising prognostic tool based on a five-protein stem cell signature that outperforms existing clinical and proposed transcriptomic based signatures for separation between risk groups. C_LIO_LIMoreover, our five-protein signature markers not only predict stem cell chemotherapy resistance and therefore tumour recurrence but also suggest potential therapeutic strategies. For instance, this approach could guide combinatorial treatments at high risk of chemoresistance, such as incorporating small molecule inhibitors targeting FLIP (currently in discovery phase) and GLUT1 (already under preclinical trial evaluation). C_LI

cancer biology↗

bioRxiv2023

Bayesian model discovery for reverse-engineering biochemical networks from data

The reverse engineering of gene regulatory networks based on gene expression data is a challenging inference task. A related problem in computational systems biology lies in identifying signalling networks that perform particular functions, such as adaptation. Indeed, for many research questions, there is an ongoing search for efficient inference algorithms that can identify the simplest model among a larger set of related models. To this end, in this paper, we introduce SLIO_SCPLOWNC_SCPLOWG, a Bayesian sparse likelihood-free inference method using Gibbs sampling. We demonstrate that SLIO_SCPLOWNC_SCPLOWG can reverse engineer stochastic gene regulatory networks from single-cell data with high accuracy, outperforming state-of-the-art correlation-based methods. Furthermore, we show that SLIO_SCPLOWNC_SCPLOWG can successfully identify signalling networks that execute adaptation. Sparse hierarchical Bayesian inference thus provides a versatile tool for model discovery in systems biology and beyond.

systems biology↗

bioRxiv2021

Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

Single cell RNA-sequencing (scRNA-seq) has very rapidly become the new workhorse of modern biology providing an unprecedented global view on cellular diversity and heterogeneity. In particular, the structure of gene-gene expression correlation contains information on the underlying gene regulatory networks. However, interpretation of scRNA-seq data is challenging due to specific experimental error and biases that are unique to this kind of data including drop-out (or technical zeros). To deal with this problem several methods for imputation of zeros for scRNA-seq have been developed. However, it is not clear how these processing steps affect inference of genetic networks from single cell data. Here, we introduce Biomodelling.jl, a tool for generation of synthetic scRNA-seq data using multiscale modelling of stochastic gene regulatory networks in growing and dividing cells. Our tool produces realistic transcription data with a known ground truth network topology that can be used to benchmark different approaches for gene regulatory network inference. Using this tool we investigate the impact of different imputation methods on the performance of several network inference algorithms. Biomodelling.jl provides a versatile and useful tool for future development and benchmarking of network inference approaches using scRNA-seq data.

systems biology↗

bioRxiv2021

Efficient inference for agent-based models of real-world phenomena

The modelling of many real-world problems relies on computationally heavy simulations. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue based on machine learning methods. One approach is to construct lightweight surrogate models to substitute the simulations used in inference. Alternatively, one might altogether circumnavigate the need for Bayesian sampling schemes and directly estimate the posterior distribution. We focus on stochastic simulations that track autonomous agents and present two case studies of real-world applications: tumour growths and the spread of infectious diseases. We demonstrate that good accuracy in inference can be achieved with a relatively small number of simulations, making our machine learning approaches orders of magnitude faster than classical simulation-based methods that rely on sampling the parameter space. However, we find that while some methods generally produce more robust results than others, no algorithm offers a one-size-fits-all solution when attempting to infer model parameters from observations. Instead, one must choose the inference technique with the specific real-world application in mind. The stochastic nature of the considered real-world phenomena poses an additional challenge that can become insurmountable for some approaches. Overall, we find machine learning approaches that create direct inference machines to be promising for real-world applications. We present our findings as general guidelines for modelling practitioners. Author summaryComputer simulations play a vital role in modern science as they are commonly used to compare theory with observations. One can thus infer the properties of a observed system by comparing the data to the predicted behaviour in different scenarios. Each of these scenarios corresponds to a simulation with slightly different settings. However, since real-world problems are highly complex, the simulations often require extensive computational resources, making direct comparisons with data challenging, if not insurmountable. It is, therefore, necessary to resort to inference methods that mitigate this issue, but it is not clear-cut what path to choose for any specific research problem. In this paper, we provide general guidelines for how to make this choice. We do so by studying examples from oncology and epidemiology and by taking advantage of developments in machine learning. More specifically, we focus on simulations that track the behaviour of autonomous agents, such as single cells or individuals. We show that the best way forward is problem-dependent and highlight the methods that yield the most robust results across the different case studies. We demonstrate that these methods are highly promising and produce reliable results in a small fraction of the time required by classic approaches that rely on comparisons between data and individual simulations. Rather than relying on a single inference technique, we recommend employing several methods and selecting the most reliable based on predetermined criteria.

bioinformatics↗

bioRxiv2021

A pipeline for systematic comparison of model levels and parameter inference settings applied to negative feedback gene regulation

Quantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order to get accurate and representative results. The choice is important, because models of successively higher fidelity come at a rapidly increasing computational cost. In some situations, the level of detail is clearly motivated by the question under study. In many situations however, many model options could qualitatively agree with available data, depending on the amount of data and the nature of the observations. Here, an important distinction is whether we are interested in inferring the true (but unknown) physical parameters of the model or if it is sufficient to be able to capture and explain available data. The situation becomes complicated from a computational perspective because inference and model selection need to be approximate. Most often it is based on likelihood-free Approximate Bayesian Computation (ABC) and here determining which summary statistics to use, as well as how much data is needed to reach the desired level of accuracy, are difficult tasks. Ultimately, all of these aspects - the model fidelity, the available data, and the numerical choices for inference and model selection - interplay in a complex manner. In this paper we develop a computational pipeline designed to systematically evaluate inference accuracy for a wide range of true known parameters. We then use it to explore inference settings for negative feedback gene regulation. In particular, we compare a spatial stochastic model, a coarse-grained multiscale model, and a simple well-mixed model for several data-scenarios and for multiple numerical options for parameter inference. Practically speaking, this pipeline can be used as a preliminary step to guide modelers prior to gathering experimental data. By training Gaussian processes to approximate the distance metric, we are able to significantly reduce the computational cost of running the pipeline.

systems biology↗

bioRxiv2021

A push-pull system of repressors matches levels of glucose transporters to extracellular glucose in budding yeast

A common cellular task is to match gene expression dynamically to a range of concentrations of a regulatory molecule. Studying glucose transport in budding yeast, we determine mechanistically how such matching occurs for seven hexose transporters. By combining time-lapse microscopy with mathematical modelling, we find that levels of transporters are history-dependent and are regulated by a push-pull system comprising two types of repressors. Repression by these two types varies with glucose in opposite ways, and not only matches the expression of transporters by their affinity to a range of glucose concentrations, but also the expression of some to how glucose is changing. We argue that matching is favoured by a rate-affinity trade-off and that the regulatory system allows yeast to import glucose rapidly enough to starve competitors. Matching expression to a pattern of input is fundamental, and we believe that push-pull repression is widespread.

systems biology↗

bioRxiv2019

Ohm’s Law for emergent gene expression under fitness pressure

Natural selection relies on genotypic and phenotypic adaptation in response to fluctuating environmental conditions and is the key to predicting and preventing drug resistance. Whereas classic persistence is all-or-nothing, here we show for the first time that an antibiotic resistance gene displays linear dose-responsive selection for increased expression in proportion to rising antibiotic concentration in E. coli. Furthermore, we observe the general nature of an instantaneous phenotypic selection process upon bactericidal and bacteriostatic antibiotic treatment, as well as an amino acid synthesis pathway enzyme under a range of auxotrophic conditions. To explain this phenomenon, we propose an analogy to Ohms law in electricity (V=IR) where fitness pressure acts similarly to voltage (V), gene expression to current (I), and resistance (R) to cellular machinery constraints. Lastly, mathematical modelling approaches reveal that the emergent gene expression mechanism requires variation in mRNA and protein production within an isogenic population, and cell memory from positive feedbacks between growth and expression of any fitness-inducing gene.

systems biology↗

bioRxiv2019

Transcriptional signatures of invasiveness in Meloidogyne incognita populations from sub-Saharan Africa

Meloidogyne incognita is an economically important plant parasitic nematode. Here we demonstrate substantial variation in the invasiveness of four M. incognita populations relative to tomato. Infective (J2) stage transcriptomes reveal significant variation in the expression of protein-coding and non-coding RNAs between populations. We identify 33 gene expression markers (GEMs) that correlate with invasiveness, and which map to genes with predicted roles in host-finding and invasion, including neuropeptides, ion channels, GPCRs, cell wall-degrading enzymes and microRNAs. These data demonstrate a surprising diversity in microRNA complements between populations, and identify GEMs for invasiveness of M. incognita for the first time.

animal behavior and cognition↗

bioRxiv2019

ABC transporters alter plant-microbe-parasite interactions in the rhizosphere

Plants are master regulators of rhizosphere ecology, secreting a complex mixture of compounds into the soil, collectively termed plant root exudate. Root exudate composition is highly dynamic and functional, mediating interactions between plants and a wide range of beneficial / harmful soil organisms. Exudate composition is under selective pressure to diversify in response to pathogen perception, whilst maintaining interactions with beneficial organisms. However, crop domestication has exerted significant and unintended changes to crop root exudate composition, and we know very little about genotype - phenotype linkages that pertain to root exudates and rhizosphere interactions. Better understanding could enable the modulation of root exudate composition for crop improvement by promoting positive, and impeding negative, interactions. Root expressed transporters modulate exudate composition and could be manipulated towards the rational design of beneficial root exudate profiles. Using Virus Induced Gene silencing (VIGS), we demonstrate that knockdown of two root-expressed ABC transporter genes in tomato cv. Moneymaker, ABC-G33 and ABC-C6, alters the composition of semi-volatile compounds in collected root exudates. Root exudate chemotaxis assays demonstrate that knockdown of each transporter gene triggers the repulsion of economically relevant Meloidogyne and Globodera spp. plant parasitic nematodes, which are attracted to control treatment root exudates. Knockdown of ABC-C6 inhibits egg hatching of Meloidogyne and Globodera spp., relative to controls. Knockdown of ABC-G33 has no impact on egg hatching of Meloidogyne spp. but has a substantial inhibitory impact on egg hatching of G. pallida. ABC-C6 knockdown has no impact on the attraction of the plant pathogen Agrobacterium tumefaciens, or the plant growth promoting Bacillus subtilis, relative to controls. Silencing ABC-G33 induces a statistically significant reduction in attraction of B. subtilis, with no impact on attraction of A. tumefaciens. ABC-C6 represents a promising target for breeding or biotechnology intervention strategies as gene knockdown (-64.9%) leads to the repulsion of economically important plant parasites and retains attraction of the beneficial rhizobacterium B. subtilis. This study exposes the link between ABC transporters, root exudate composition, and ex planta interactions with agriculturally and economically relevant rhizosphere organisms, paving the way for an entirely new approach to rhizosphere engineering and crop protection.

plant biology↗