Biology subjects

Rand, D. A.

Publications and source records attributed to Rand, D. A..

9 recordsLinked to original sources

bioRxiv2026

A robust negative association between estimated tumour circadian clock function and survival in early stage breast cancer

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of chronic conditions including cardio-metabolic diseases and cancer. For the latter, they also suggest that chronotherapy offers the potential to refine current treatments and improve the development of future anti-cancer medicines. Therefore, there is a need for tools to measure the functional state of the tumoural circadian clock in patients. We have previously developed a model-led machine-learning algorithm called TimeTeller which has the potential to provide such a tool. Here we demonstrate its potential for clinical relevance by a study of 1286 breast cancer patients in which we characterise the nature of the circadian clock disruption in their tumours and demonstrate a strong nonlinear association between 10-year survival and TimeTellers tumoural clock disfunction score {Theta}. We find that good tumour clock function is antagonistic to survival.

cancer biology↗

bioRxiv2026

Measuring developmental information encoded by a dynamicallandscape

During embryogenesis, as cells proliferate and assemble into tissues, they undergo a sequence of transitions between distinct molecular states eventually giving rise to a cellular population consisting of an appropriate distribution of specific functional cell types. Recent progress on the dynamics underlying decision-making in developmental landscape makes it feasible to start analysing the amount of information involved in constructing such systems. To explore this we introduce the notion of potency of a developmental landscape and attempt to calculate it for two development systems of current interest, in-vitro differentiation of epiblast-like cells into neural and mesodermal progenitors and the worm vulva patterning system. Our approach integrates concepts from developmental biology, information theory and dynamical systems to estimate both the number and identity of signalling regimes that give rise to distinguishable temporal response patterns.

developmental biology↗

bioRxiv2025

Dynamic Landscape Analysis of Cell Fate Decisions: Predictive Models of Neural Development From Single-Cell Data

Building a mechanistic understanding of cell fate decisions remains a fundamental goal of developmental biology, with implications for stem cell therapies, regenerative medicine and understanding disease mechanisms. Single-cell transcriptomics provides a detailed picture of the cellular states observed during these decisions, but building dynamic and predictive models from these data remains a challenge. Here, we present dynamic landscape analysis (DLA), a framework that applies dynamical systems theory to identify stable cell states, map transition pathways, and generate a predictive cell fate decision landscape from single-cell data. Applying this framework to vertebrate neural tube development, revealed that progenitor specification by Sonic Hedgehog (Shh) can be captured in a landscape with an unexpected topology in which initially divergent lineages converge to the same fate through multiple distinct routes. The model accurately predicted cellular responses and cell fate allocation for unseen dynamic signalling regimes. Cross-species validation using human embryonic organoid data demonstrated conservation of this decision-making architecture. By modelling the dynamic responses that drive cell fate decisions, the DLA framework provides a quantitative and generative framework for extracting mechanistic insights from high-dimensional single-cell data.

developmental biology↗

bioRxiv2023

TimeTeller: a tool to probe the circadian clock as a multigene dynamical system

AO_SCPLOWBSTRACTC_SCPLOWRecent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a system. In this paper we consider the methodology behind Time-Teller, a machine learning tool that analyses the clock as a system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine.

systems biology↗

bioRxiv2021

Hyperinflammation evokes different antiviral TMPRSS2 and ADAM17 expression responses in human gut xenograft versus host mouse gut although overall genomic responses are similar

The global spread of the newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the pandemic outbreak of coronavirus disease 2019 (COVID-19), an inflammatory disease that is primarily affecting the respiratory system. However, gastrointestinal symptoms in COVID-19 patients suggests that the gut may present another viral target organ. Disease development and severity is dependent on viral interaction with two cell surface human proteins, ACE2 and TMPRSS2, and on antiviral response which may lead to systemic hyperinflammatory syndrome and multiorgan dysfunction. Understanding the host response to SARS-CoV-2 infection and the pathology of the disease will be greatly enhanced by the development of appropriate animal models. Laboratory mice have been the mainstay of therapeutic and vaccine development, however, the virus does not grow in wild type mice and only induced mild disease in transgenic animals expressing human ACE2. As there are known differences between immune response in laboratory mice and humans we evaluated the response of human gut developed as xenografts and host mouse gut following systemic LPS injections as a hyperinflammation model system. The orthologous gene expression levels in the mouse and human gut were highly correlated (Spearmans rank correlation coefficient: 0.28-0.76) and gene set enrichment analysis of significantly upregulated human and mouse genes revealed that a number of inflammatory and immune response pathways are commonly regulated in the two species. However, species differences were also observed, most importantly, in the inflamed human gut but not in the mouse gut, there was clear upregulation of mRNAs coding for TMPRSS2, ADAM17 and for RIG-I-like receptors, which are involved in the recognition of viruses and in antiviral innate immune response. Moreover, using species-specific immunofluorescence microscopy, we demonstrated the expression and localization of human ACE2 and TMPRSS2 proteins, which are essential elements of the molecular machinery that enables SARS-CoV-2 to infect and replicate in human gut cells. Our findings demonstrate that the intestinal immune response to inflammation in humans and mice are generally very similar. However, certain human-specific diseases, such as COVID-19, can only be successfully studied in an experimental model of human tissue, such as the gut xenograft.

immunology↗

bioRxiv2021

A quantitative landscape of cell fate transitions identifies principles of cellular decision-making

Fate decisions in developing tissues involve cells transitioning between a set of discrete cell states, each defined by a distinct gene expression profile. Geometric models, often referred to as Waddington landscapes, in which developmental paths are given by the gradient and cell states by the minima of the model, are an appealing way to describe differentiation dynamics and developmental decisions. To construct and validate accurate dynamical landscapes, quantitative methods based on experimental data are necessary. To this end we took advantage of the differentiation of neural and mesodermal cells from pluripotent mouse embryonic stem cells exposed to different combinations and durations of signalling factors. We developed a principled statistical approach using flow cytometry data to quantify differentiating cell states. Then, using a framework based on Catastrophe Theory and approximate Bayesian computation, we constructed the corresponding dynamical landscape. The result was a quantitative model that accurately predicted the proportions of neural and mesodermal cells differentiating in response to specific signalling regimes. Analysis of the geometry of the landscape revealed two distinct ways in which cells make a binary choice between one of two fates. We discuss the biological relevance of these mechanisms and suggest that they represent general archetypal designs for developmental decisions. Taken together, the approach we describe is broadly applicable for the quantitative analysis of differentiation dynamics and for determining the logic of developmental cell fate decisions.

developmental biology↗

bioRxiv2021

Quantifying cell transitions in C. elegans with data-fitted landscape models

Increasing interest has emerged in new mathematical approaches that simplify the study of complex differentiation processes by formalizing Waddingtons landscape metaphor. However, a rational method to build these landscape models remains an open problem. Building on pioneering work by Corson and Siggia (2012, 2017) we study vulval development in C. elegans by developing a framework based on Catastrophe Theory (CT) and approximate Bayesian computation (ABC) to build data-fitted landscape models. We first identify the candidate qualitative landscapes, and then use CT to build the simplest model consistent with the data, which we quantitatively fit using ABC. The resulting model suggests that the underlying mechanism is a quantifiable two-step decision controlled by EGF and Notch-Delta signals, where a non-vulval/vulval decision is followed by a bistable transition to the two vulval states. This new model fits a broad set of data and makes several novel predictions.

developmental biology↗

bioRxiv2019

Multiplexing information flow through dynamic signalling systems

We consider how a signalling system can act as an information hub by multiplexing information arising from multiple signals. We formally define multiplexing, mathematically characterise which systems can multiplex and how well they can do it. While the results of this paper are theoretical, to motivate the idea of multiplexing, we provide experimental evidence that tentatively suggests that the NF-{kappa}B transcription factor can multiplex information about changes in multiple signals. We believe that our theoretical results may resolve the apparent paradox of how a system like NF-{kappa}B that regulates cell fate and inflammatory signalling in response to diverse stimuli can appear to have the low information carrying capacity suggested by recent studies on scalar signals. In carrying out our study, we introduce new methods for the analysis of large, nonlinear stochastic dynamic models, and develop computational algorithms that facilitate the calculation of fundamental constructs of information theory such as Kullback-Leibler divergences and sensitivity matrices, and link these methods to new theory about multiplexing information. We show that many current models such as those of the NF-{kappa}B system cannot multiplex effectively and provide models that overcome this limitation using post-transcriptional modifications.

systems biology↗

bioRxiv2019

TimeTeller: a New Tool for Precision Circadian Medicine and Cancer Prognosis

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart disease. Therefore, there is a need for tools to measure the functional state of the circadian clock and its downstream targets in patients. We provide such a tool and demonstrate its clinical relevance by an application to breast cancer where we find a strong link between survival and our measure of clock dysfunction. We use a machine-learning approach and construct an algorithm called TimeTeller which uses the multi-dimensional state of the genes in a transcriptomics analysis of a single biological sample to assess the level of circadian clock dysfunction. We demonstrate how this can distinguish healthy from malignant tissues and demonstrate that the molecular clock dysfunction metric is a potentially new prognostic and predictive breast cancer biomarker that is independent of the main established prognostic factors.

cancer biology↗