Biology subjects

Jenner, A. L.

Publications and source records attributed to Jenner, A. L..

16 recordsLinked to original sources

bioRxiv2026

A computational investigation of pathways for ex vivo differentiation and dysfunction of healthy and patient T cells

Adoptive T cell therapy is a promising field in cancer immunotherapy that leverages the natural ability of T cells to target and kill cancer cells. A crucial step in this therapy involves the ex vivo expansion of memory-like and effector-like T cells, but limiting their differentiation into exhaustion-associated phenotypes remains a challenge. Mathematical model-informed strategies to optimise T cell expansion may be able to exploit our understanding of T cell differentiation, but there is a large amount of uncertainty and disagreement in the biological literature regarding the ways in which T cells differentiate and become exhausted. We therefore conduct a large-scale mathematical investigation to help further our understanding of T cell differentiation during short-term ex vivo expansion. Using observations from biological literature, we first construct thousands of potential pathways connecting distinct T cell phenotypes and then formulate corresponding ordinary differential equations to test hypotheses regarding T cell differentiation and dysfunction. We fit each model to experimental data describing the ex vivo expansion of T cells originating from healthy individuals and cancer patients and accept those that most closely describe the data. From our findings, we hypothesise that cancer may alter T cell differentiation kinetics rather than the structure of the differentiation pathway, which has significant potential to inform therapeutic strategies. Our analysis proposes that T cells may not differentiate unidirectionally ex vivo; most accepted models suggest that the cells dynamically switch between functional phenotypes. We postulate that effector-like cells may be prone to upregulating inhibitory receptors that drive exhaustion, and find that modelling effector-like proliferation is unnecessary to capture observed population dynamics. Our hypothesis-testing framework generates recommendations for promising future research avenues and may pave the way to improve the production of patient-tailored T cell populations for adoptive cell therapy.

immunology↗

bioRxiv2026

Linking Systemic Endotoxin Exposure to Retinal Microglia Migration Through Mathematical Modeling

Microglia are the resident immune cells of the central nervous system (CNS), including the brain, spinal cord, and retina, where they serve as the first line of defense against infection and inflammation. Dys-regulated microglia activity has been implicated in vision-threatening diseases, highlighting the need to understand how retinal microglia respond to inflammatory stimuli. Importantly, acute inflammation induces substantial redistribution of microglia across retinal layers, yet the mechanisms governing this migration remain poorly understood. Here, we develop the first mathematical model of retinal microglia migration during inflammation to determine how inflammatory exposure, administration route, and species-specific pharmacokinetics shape redistribution dynamics across the retina. The model couples lipopolysaccharide (LPS) pharmacokinetics with microglia migration between the outer plexiform layer (OPL), inner plexiform layer (IPL), and ganglion cell layer/nerve fiber layer (GCL/NFL). Model parameters are calibrated to retinal microglia density measurements from mice following LPS (bacterial endotoxin) challenge, before extending the framework to rats and rhesus macaques to investigate species-specific responses. Simulations also compare how administration route, i.e. intravenous or intraperitoneal injections, alter retinal LPS exposure and subsequent microglia redistribution. Our results suggest that redistribution patterns are driven primarily by LPS delivery route and species-specific pharmacokinetics, rather than the initial microglia distribution across retinal layers. Together, these findings provide new insight into immune cell reorganization in the inflamed retina and demonstrate how mechanistic mathematical modeling can be adapted across experimental designs, administration routes, and animal species.

immunology↗

bioRxiv2026

A New Paradigm for the Mathematical Modelling of Multiple Sclerosis

Multiple Sclerosis (MS) is an autoimmune disease that affects the central nervous system. It can lead to inflammation, neurodegeneration, and physical or cognitive disability. Currently, no cure for MS exists, but medications are available to slow its progression. To date, mathematical modelling of MS has focussed on a few aspects of the disease, but an overall modelling framework is missing. In this paper, we propose a multiscale paradigm for the mathematical modelling of MS. Based on the underlying biological scale, we propose six consecutive modelling levels and develop the first three model levels in this work using systems of ordinary differential equations. We test if these models can describe known effects related to MS disease risk, with particular focus on estrogen, vitamin D, Epstein-Barr virus (EBV) and HLA-DR mutations. We first show that periodic disease outbreaks are possible in this framework through interactions by antigen-presenting cells, regulatory cells and memory B cells. We show that the presence of Epstein-Barr virus infections can initiate the disease, low and high levels of estrogen and vitamin D deficiency can alleviate it, mutations in the HLA-DR gene can promote MS. We can identify a stable steady state of our model as the widely discussed MS prodrome, and we find that memory B-cells play a dominant role in the disease progression. We hope that this framework may serve as a reference for the development and comparative evaluation of future mathematical and computational models of MS.

immunology↗

bioRxiv2025

A mathematical model for inflammation and demyelination in multiple sclerosis

Multiple sclerosis (MS) is an incurable, life-long disease caused by the demyelination of neurons in the brain and spine. MS is often characterised by relapses in inflammation and demyelination, that are then followed by periods of remittance. Symptoms can be highly debilitating and there are still many open questions about the origin and progression of the disease. Mathematical modelling is well-placed to capture the dynamics of MS and provide insight into disease aetiology. In this work, we present a minimal model for MS disease onset and progression driven by inflammation and demyelination. The model dynamics are capable of describing a typical evolution of the illness, with changes from a healthy state to a diseased scenario captured by certain ranges of parameter values. Our model also describes the non-uniform oscillatory nature of the disease, born from a Hopf bifurcation due to the strength of the inflammatory response. In particular, using experimental data for Contrast Enhancing Lesions (CELs) obtained from MS patients, we are able to reproduce some of the typical relapsing-remitting behaviours of this disease. We hope that the model presented here can serve as a baseline for more complex approaches and as a tool to predict possible evolutions of the disease.

bioinformatics↗

bioRxiv2025

A stochastic model of T cell expansion in activating micro-rod scaffolds and its continuum limit: Importance of IL-2 loading and scaffold homogeneity

T cells are immune cells that are known to be effective at killing cancer cells, however, an individual patients tumour-specific T cell counts are often insufficient to control cancer growths. Adoptive T cell therapy aims to address this by activating and expanding highly effective T cells ex vivo before injecting them into the patient to employ their cancer-killing functions. Recent experimental setups using activating micro-rod scaffolds have significantly improved T cell expansion over conventional methods, but there is still much to understand regarding the factors that maximise the expansion of functional T cells in these scaffolds. We present a stochastic agent-based model of T cell expansion alongside its continuum limit to simulate the average interactions between T cells and micro-rods, which enable us to explore several behaviours of the experimental system. Stochastic simulations demonstrate that T cell expansion is driven by activated cell clusters around micro-rods. Using our spatial models and a mean-field approximation, we discover that this cluster-driven expansion is most supported by scaffolds with initially homogeneous micro-rod concentrations. Our simulations also reveal that loading the T cell growth factor, interleukin-2 (IL-2), into micro-rod pores for secretion significantly prolongs expansion compared to the more conventional method of IL-2 supplementation.

immunology↗

bioRxiv2025

Therapeutic targeting of oligodendrocytes in an agent-based model of multiple sclerosis

Multiple sclerosis (MS) is a neurodegenerative disease in which misdirected, persistent activity of the immune system degrades the protective myelin sheaths of nerve axons. Historically, treatment of MS has relied on disease-modifying therapies that involve immunosuppression, such as targeting of the blood-brain barrier (BBB) to restrict lymphocyte movement. New therapeutic ideas in the development pipeline are instead designed to promote populations of myelin producing cells, oligodendrocytes, by exploiting their innate resilience to the stressors of MS or restoring their numbers. Given the significant advancements made in immunological disease understanding due to mathematical and computational modelling, we sought to develop a platform to (1) interrogate our understanding of the neuroimmunological mechanisms driving MS development and (2) examine the impact of different therapeutic strategies. To this end we propose a novel, open-source, agent-based model of lesion development in the CNS. Our model includes crucial populations of T cells, perivascular macrophages, and oligodendrocytes. We examine the sensitivity of the model to key parameters related to disease targets and conclude that lesion stabilisation can be achieved when targeting the integrated stress response of oligodendrocytes. Most significantly, complete prevention of lesion formation is observed when a combination of approved BBB-permeability targeting therapies and integrated-stress response targeting therapies is administered, suggesting the potential to strike a balance between a patients immune inflammation and their reparative capacity. Given that there are many open questions surrounding the etiology and treatment of MS, we hope that this malleable platform serves as a tool to test and generate further hypotheses regarding this disease. Author summaryMultiple sclerosis is a disease that is not yet fully understood and has no cure. Some patient phenotypes see little benefit from current therapeutic interventions besides symptomatic treatment. Typically, MS studies have focused on the prevention of damage to brain tissue. As such, there are unanswered questions about how to reverse the damage in the brain and spinal cord of MS patients caused by immune cells. In light of this there is an urgent need for mathematical modelling of new therapeutic strategies - some of which remain to be clinically examined - shifting the attention from the targeting of aberrant immune activity to the upregulation of resident, reparative cells called oligodendrocytes. Here, we have developed a mathematical model to probe the potential benefits of oligodendrocyte targeted therapies in silico. We focus on T cells as damaging agents and monitor oligodendrocyte function in response to their activity. We show that oligodendrocytes strongly influence the tissues ability to stabilise and even recover under persistent, harmful immune activity. In practice, these therapies could hold the potential to unlock neuroprotection by means of enhanced remyelination.

immunology↗

bioRxiv2024

Identifiability of heterogeneous phenotype adaptation from low-cell-count experiments and a stochastic model

Phenotypic plasticity contributes significantly to treatment failure in many cancers. Despite the increased prevalence of experimental studies that interrogate this phenomenon, there remains a lack of applicable quantitative tools to characterise data, and importantly to distinguish between resistance as a discrete phenotype and a continuous distribution of phenotypes. To address this, we develop a stochastic individual-based model of plastic phenotype adaptation through a continuously-structured phenotype space in low-cell-count proliferation assays. That our model corresponds probabilistically to common partial differential equation models of resistance allows us to formulate a likelihood that captures the intrinsic noise ubiquitous to such experiments. We apply our framework to assess the identifiability of key model parameters in several population-level data collection regimes; in particular, parameters relating to the adaptation velocity and cell-to-cell heterogeneity. Significantly, we find that cell-to-cell heterogeneity is practically non-identifiable from both cell count and proliferation marker data, implying that population-level behaviours may be well characterised by homogeneous ordinary differential equation models. Additionally, we demonstrate that population-level data are insufficient to distinguish resistance as a discrete phenotype from a continuous distribution of phenotypes. Our results inform the design of both future experiments and future quantitative analyses that probe phenotypic plasticity in cancer. Author SummaryMany cancers adaptively and reversibly develop resistance to treatment, adding complexity to predictive model development and, by extension, treatment design. While so-called drug challenge experiments are now commonly employed to interrogate phenotypic plasticity, there are very few quantitative tools available to interpret the biological data that arises. In particular, it remains unclear what is needed from drug challenge experiments in order to identify the phenotypic structure of a population that responds adaptively to treatment. In this work, we develop a new individual-level mathematical model of phenotypic plasticity in parallel with a structured model calibration process. Applying our framework to various existing and potential experimental designs reveals that experiments that yield only population-level data cannot distinguish between drug resistance as a distinct cell state, or drug resistance as a continuum of cell states. Consequentially, at the population-level, we demonstrate that common mathematical models that assume a set of distinct cell states can characterise the behaviour of cell populations that, in actuality, respond through a continuum of states. Importantly, our results shed light on both the mathematical models and experiments required to capture phenotypic plasticity in cancer.

cancer biology↗

bioRxiv2024

Growth rate-driven modelling reveals how phenotypic adaptation drives drug resistance in BRAFV600E-mutant melanoma

Phenotypic adaptation, the ability of cells to change phenotype in response to external pressures, has been identified as a driver of drug resistance in cancer. To quantify phenotypic adaptation in BRAFV600E-mutant melanoma, we develop a theoretical model informed by growthrate data of WM239A-BRAFV600E cells challenged with the BRAF-inhibitor encorafenib. We use an individual-based model (IBM) in which each cell is described by one of multiple discrete and plastic phenotype states that are directly linked to drug-dependent net growth rates and, by extension, drug resistance. To describe how cells transition between phenotype states, we explore a gamut of candidate models common in the mathematical biology literature. Comparing these on their ability to reproduce in vitro growth curves, data-matched simulations suggest that phenotypic adaptation is directed towards states of high net growth rates, enabling the evasion of drug-effects. The model subsequently provides an explanation for when and why intermittent treatments outperform continuous treatments in the studied system, and demonstrates the benefits of not only targeting, but also leveraging, phenotypic adaptation in treatment protocols. Building on the IBM, we present a flexible mathematical methodology based on ordinary differential equations to compare responses to continuous and intermittent treatments through long-term effective net growth rates.

cancer biology↗

bioRxiv2024

Using virtual patient cohorts to uncover immune response differences in cancer and immunosuppressed COVID-19 patients

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in millions of deaths globally. Adults with immunosuppression (e.g., solid organ transplant recipients) and those undergoing active cancer treatments experience worse infections and more severe COVID-19. It is difficult to conduct clinical studies in these populations, resulting in a restricted amount of data that can be used to relate mechanisms of immune dysfunction to COVID-19 outcomes in these vulnerable groups. To study immune dynamics after infection with SARS-CoV-2 and to investigate drivers of COVID-19 severity in individuals with cancer and immunosuppression, we adapted our mathematical model of the immune response during COVID-19 and generated virtual patient cohorts of cancer and immunosuppressed patients. The cohorts of plausible patients recapitulated available longitudinal clinical data collected from patients in Montreal, Canada area hospitals. Our model predicted that both cancer and immunosuppressed virtual patients with severe COVID-19 had decreased CD8+ T cells, elevated interleukin-6 concentrations, and delayed type I interferon peaks compared to those with mild COVID-19 outcomes. Additionally, our results suggest that cancer patients experience higher viral loads (however, with no direct relation with severity), likely because of decreased initial neutrophil counts (i.e., neutropenia), a frequent toxic side effect of anti-cancer therapy. Furthermore, severe cancer and immunosuppressed virtual patients suffered a high degree of tissue damage associated with elevated neutrophils. Lastly, parameter values associated with monocyte recruitment by infected cells were found to be elevated in severe cancer and immunosuppressed patients with respect to the COVID-19 reference group. Together, our study highlights that dysfunction in type I interferon and CD8+ T cells are key drivers of immune dysregulation in COVID-19, particularly in cancer patients and immunosuppressed individuals.

immunology↗

bioRxiv2023

Impact of resistance on therapeutic design: a Moran model of cancer growth

Resistance of cancers to treatments, such as chemotherapy, largely arise due to cell mutations. These mutations allow cells to resist apoptosis and inevitably lead to recurrence and often progression to more aggressive cancer forms. Sustained-low dose therapies are being considered as an alternative over maximum tolerated dose treatments, whereby a smaller drug dosage is given over a longer period of time. However, understanding the impact that the presence of treatment-resistant clones may have on these new treatment modalities is crucial to validating them as a therapeutic avenue. In this study, a Moran process is used to capture stochastic mutations arising in cancer cells, inferring treatment resistance. The model is used to predict the probability of cancer recurrence given varying treatment modalities. The simulations predict that sustained-low dose therapies would be virtually ineffective for a cancer with a non-negligible probability of developing a sub-clone with resistance tendencies. Furthermore, calibrating the model to in vivo measurements for breast cancer treatment with Herceptin, the model suggests that standard treatment regimens are ineffective in this mouse model. Using a simple Moran model, it is possible to explore the likelihood of treatment success given a non-negligible probability of treatment resistant mutations and suggest more robust therapeutic schedules.

cancer biology↗

bioRxiv2023

Deconstructing the contributions of heterogeneity to combination treatment of hormone sensitive breast cancer

Combination therapies are fundamental to cancer treatments, including in breast cancer the most common invasive malignancy in women. Breast cancer treatment is determined based on molecular subtypes, and since 2016, combination palbociclib and fulvestrant has been used to treat hormone receptor-positive breast cancer. However, the impact of heterogeneity of the tumour landscape and tumour composition dynamics on scheduling decisions remains poorly understood. To elucidate the contributions of variability at multiple scales to treatment outcomes in hormone receptor-positive breast cancer, we developed a simple mathematical model of two unique estrogen receptor positive (ER+) breast cancer cell types and their response to combination treatment with palbociclib and fulvestrant. We used this model to understand how the initial fraction of either cell type may impact the fraction remaining after treatment and examined how heterogeneity in pharmacokinetics and pharmacodynamics result in a large distribution of outcomes. Our results suggest that the pharmacokinetics and pharmacodynamics of fulvestrant were the major drivers of final tumour size and composition. We then leveraged our model to guide therapeutic scheduling of combination palbociclib and fulvestrant, demonstrating the use of mathematical modelling to improve our understanding of cancer biology and treatments.

cancer biology↗

bioRxiv2022

Calibration of a Voronoi cell-based model for tumour growth using approximate Bayesian computation

Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.

systems biology↗

bioRxiv2022

Approaches to generating virtual patient cohorts with applications in oncology

Virtual clinical trials (VCTs) have gained popularity for their ability to rationalize the drug development process using mathematical and computational modelling, and to provide key insights into the mechanisms regulating patient responses to treatment. In this chapter, we cover approaches for generating virtual cohorts with applications in cancer biology and treatment. VCTs are an effective tool for predicting clinical responses to novel therapeutics and establishing effective treatment strategies. These VCTs allow us to capture inter-individual variability (IIV) which can lead to diversity in patient drug responses. Here we discuss three main methodologies for capturing IIV with a VCT. First, we highlight the use of population pharmacokinetic (PopPK) models, which extrapolate from empirical data population PK parameters that best fits the individual variability seen in drug disposition using non-linear mixed effects models. Next, we show how virtual patients may be sampled from a normal distribution with mean and standard deviation informed from experimental data to estimate parameters in a mechanistic model that regulates drug PKs. Lastly, we show how optimization techniques can be used to calibrate virtual patient parameter values and generate the VCT. Throughout, we compare and contrast these methods to provide a broader view of the generation of virtual patients, and to aid the decision-making process for those looking to leverage virtual clinical trials in their research.

cancer biology↗

bioRxiv2022

Establishing combination PAC-1 and TRAIL regimens for treating ovarian cancer based on patient-specific pharmacokinetic profiles using in silico clinical trails

Ovarian cancer is commonly diagnosed in its late stages, and new treatment modalities are needed to improve patient outcomes and survival. We have recently established the synergistic effects of combination tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) and procaspase activating compound (PAC-1) therapies in granulosa cell tumours (GCT) of the ovary, a rare form of ovarian cancer, using a mathematical model of the effects of both drugs in a GCT cell line. Here, to understand the mechanisms of combined TRAIL and PAC-1 therapy, study the viability of this treatment strategy, and accelerate preclinical translation, we leveraged our mathematical model in combination with population pharmacokinetics (PopPK) models of both TRAIL and PAC-1 to expand a realistic heterogeneous cohort of virtual patients and optimize treatment schedules. Using this approach, we investigated treatment responses in this virtual cohort and determined optimal therapeutic schedules based on patient-specific pharmacokinetic characteristics. Our results showed that schedules with high initial doses of PAC-1 were required for therapeutic efficacy. Further analysis of individualized regimens revealed two distinct groups of virtual patients within our cohort: one with high PAC-1 elimination, and one with normal PAC-1 elimination. In the high elimination group, high weekly doses of both PAC-1 and TRAIL were necessary for therapeutic efficacy, however virtual patients in this group were predicted to have a worse prognosis when compared to those in the normal elimination group. Thus, PAC-1 pharmacokinetic characteristics, particularly clearance, can be used to identify patients most likely to respond to combined PAC-1 and TRAIL therapy. This work underlines the importance of quantitative approaches in preclinical oncology.

cancer biology↗

bioRxiv2021

Identifying cell-to-cell variability in internalisation using flow cytometry

Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as internalisation. Given that internalisation is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalisation is of high biological interest. Yet, it is common for studies of internalisation to neglect cell-to-cell variability. We develop a simple mathematical model of internalisation that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data of internalisation of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. Our model reproduces experimental observations, identifies cell-to-cell variability in the internalisation and recycling rates, and, importantly, provides information relating to inferential uncertainty. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from internalisation assays and similar experiments that probe cellular dynamical processes.

cell biology↗

bioRxiv2021

COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results indicate that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. Author summaryUnderstanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.

immunology↗