Biology subjects

Baker, R. E.

Publications and source records attributed to Baker, R. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Locus-specific spatial and transcriptional dynamics direct T cell lineage choice in the thymus

Decades of investigation have made the choice between CD4 and CD8 T cell lineages in the thymus a key paradigm for mammalian cell commitment and differentiation, yet how transcriptional diversification is coordinated with 3D genome organisation remains largely unexplored. Here, using single-molecule transcript quantification and region capture Pico-C (RCPC), we resolve distinct transcriptional and spatial chromatin dynamics at the lineage-defining loci Cd4 and Cd8. We find early Cd4/Cd8 transcriptional divergence driven by MHC-dependent bias in Cd8a transcript copy number, which unfolds gradually across both CCR7- and CCR7+ differentiation stages. Unexpectedly, Cd4 and Cd8 loci show fundamentally different spatial contact dynamics: transiently reduced mesoscale (10-100kb) contacts at Cd8 provide a permissive chromatin environment throughout the transcriptional diversification process. By contrast, Cd4 undergoes switch-like contact changes limited to the microscale (1kb) late in differentiation, associated with lineage stabilization by ThPOK and/or RUNX3. These results place distinct, locus-specific chromatin environments at the centre of transcriptional diversification during T cell lineage choice.

genetics↗

bioRxiv2026

RecA structure kinetics determine SOS dynamics and predict spontaneous cell death

Even under optimal growth conditions, bacterial cells experience endogenous stresses that threaten cellular function. Individual cells sporadically activate stress responses to restore homeostasis, but some cells ultimately die. The SOS response to DNA damage is particularly heterogeneous, but the origin of its spontaneous activation and its relationship to cell fate remain unclear. Here, we imaged fluorescently labelled RecA in single Escherichia coli cells under steady-state unperturbed growth using microfluidics. We find that RecA* structures assemble frequently near replication forks and precede SOS response pulses. RecA* structure lifetime quantitatively encodes the magnitude of SOS induction: longer-lived structures promote greater LexA degradation and larger SOS pulses. Thus, cell-to-cell variability in spontaneous SOS activation reflects differences in the repair kinetics of endogenous DNA damage events. By tracking cell lineages over time, we further show that RecA* structures are strong and early predictors of cell fate. Elevated RecA* structure presence emerges up to six generations before spontaneous cell death in over 75% of cases, before other physiological disturbances become detectable. Together, our results establish spontaneous SOS activation as a quantitative readout of endogenous DNA damage processing and identify failure to resolve such damage as a major route to spontaneous bacterial cell death.

biophysics↗

bioRxiv2026

Inferring Cell-Cell Interaction Dynamics from Cell Trajectory Data Using Deep Attention Networks

Interactions between nearby cells are a key driver of cell movement in many biological systems, including collective cell migration and the immune response to cancer. However, inferring the interaction rules in a given system in a manner that is both accurate and biologically interpretable remains a challenge. A valuable experimental method for analysing cell-cell interaction dynamics is the tracking of individual cell locations over a series of time-lapse images, and in this work we present a model, based on the theory of deep attention networks, that learns how cell-cell interactions affect cell movement directly from cell trajectory data. Our approach requires no a priori assumptions about the mechanisms governing cell behaviour, enabling its application to cell trajectory data originating from a diverse range of biological systems. In addition to the model, we develop a suite of tools that exploit the models attention-based structure to present the learned interaction dynamics in an interpretable manner. Our model extends previous applications of deep attention networks to cell movement by providing deeper insights into cell-cell interaction dynamics, moving beyond inferring whether cells interact to inferring how these interactions affect cell movement, and providing the ability to infer type dependent cell-cell interaction dynamics in multi-type cell movement systems. By combining data-driven learning and structural interpretability, our approach represents a highly general methodology for linking cell trajectory data to mechanistic hypotheses, showing that deep attention networks constitute a powerful exploratory tool for characterising the effect of cell-cell interactions on cell movement in complex cellular systems.

cell biology↗

bioRxiv2026

Organ-specific fibroblast dynamics revealed via an integrated experimental-computational framework

Fibrosis is a progressive pathological process driven by dysregulated fibroblast activity and excessive extracellular matrix deposition, leading to scarring, functional decline and eventually organ failure. Although fibroblasts are key mediators of fibrotic remodelling, it remains unclear whether their behaviours are conserved across tissues or organ specific. Here, we combine experimental and computational approaches to dissect fibroblast dynamics underlying cardiac and pulmonary fibrosis. Using in vitro assays, we demonstrate that cardiac and lung fibroblasts differ in morphology, proliferation, and collagen matrix organisation, likely reflecting tissue-specific mechanical and biochemical demands. Lung fibroblasts display higher proliferative capacity and increased myofibroblast activation, forming condition-dependent collagen architectures, with fibres that are more aligned under proinflammatory and fibrotic conditions, but less so under anti-inflammatory conditions. In contrast, cardiac fibroblasts consistently generate diffuse, isotropic collagen matrices across all cytokine conditions. Integrating these data into a mechanistic computational model of fibroblast-collagen interactions, we show that fibroblast motility, rather than cell-cell interactions, dominates tissue dynamics, and that coupling between motility and local collagen density regulates matrix architecture. Simulations reveal that collagen-dependent motility and density-dependent collagen secretion prevent maladaptive matrix alignment, identifying potential mechanisms restraining fibrotic progression. Our findings uncover organ-specific fibroblast behaviours and highlight how integrating experimental and computational frameworks can illuminate the dynamic rules governing fibrosis and inform targeted antifibrotic strategies.

cell biology↗

bioRxiv2025

Mathematical modelling predicts novel mechanisms of stream confinement from Trail/Colec12/Dan in the collective migration of cranial neural crest cells

BackgroundIn vertebrate embryogenesis, cranial neural crest cells (CNCCs) migrate along discrete pathways. Analyses in the chick have identified key molecular candidates for the confinement of CNCC migration to stereo-typical pathways as Colec12, Trail, and Dan. The effects of these factors on CNCCs in vitro are known, but how they confine migration to discrete streams in vivo remains poorly-understood. Here, we propose and test several hypothetical mechanisms by which these factors confine cell streams and maintain coherent migration, simulating an expanded agent-based model for collective CNCC migration. ResultsModel simulations suggest that Trail enhances adhesion between CNCCs, facilitating movement towards stereotypical migratory pathways, whereas Colec12 confines CNCCs by inducing longer, branched filopodia that facilitate movement down Colec12 gradients and re-connections with streams. Moreover, we find that Trail and Colec12 facilitate the exchange of CNCCs and the formation of CNCC-bridges between adjacent streams that are observed in vivo but poorly-understood mechanistically. Finally, we predict that Dan increases the coherence of streams by modulating the speed of CNCCs at the leading edge of collectives to prevent escape. ConclusionsOur work highlights the importance of Trail, Colec12, and Dan in CNCC migration and predicts novel mechanisms for the confinement of CNCCs to stereotypical pathways in vivo.

developmental biology↗

bioRxiv2024

Optimal control of immune checkpoint inhibitor therapy in a heart-tumour model

Autoimmune myocarditis, or cardiac muscle inflammation, is a rare but frequently fatal side-effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite the dangers that side-effects such as these pose to patients, they are rarely, if ever, included explicitly when mechanistic mathematical modelling of cancer therapy is used for optimization of treatment. In this paper, we develop a two-compartment mathematical model which incorporates the impact of ICIs on both the heart and the tumour. Such a model can be used to inform the conditions under which autoimmune myocarditis may develop as a consequence of treatment. We use this model in an optimal control framework to design optimized dosing schedules for three types of ICI therapy that balance the positive and negative effects of treatment. We show that including the negative side-effects of ICI treatment explicitly within the mathematical framework significantly impacts the predictions for the optimized dosing schedule, thus stressing the importance of a holistic approach to optimizing cancer therapy regimens.

immunology↗

bioRxiv2024

Spatial heterogeneity in collective electrotaxis: continuum modelling and applications to optimal control

Collective electrotaxis is a phenomenon that occurs when a cellular collective, for example an epithelial monolayer, is subjected to an electric field. Biologically, it is well known that the velocity of migration during the collective electrotaxis of large epithelia exhibits significant spatial heterogeneity. In this work, we demonstrate that the heterogeneity of velocities in the electrotaxing epithelium can be accounted for by a continuum model of cue competition in different tissue regions. Having established a working model of competing migratory cues in the migrating epithelium, we develop and validate a reaction-convection-diffusion model that describes the movement of an epithelial monolayer as it undergoes electrotaxis. We use the model to predict how tissue size and geometry affect the collective migration of MDCK monolayers, and to propose several ways in which electric fields can be designed such that they give rise to a desired spatial pattern of collective migration. We conclude with two examples that demonstrate practical applications of the method in designing bespoke stimulation protocols.

biophysics↗

bioRxiv2024

Modularity of the segmentation clock and morphogenesis

Vertebrates have evolved great diversity in the number of segments dividing the trunk body, however the developmental origin of the evolvability of this trait is poorly understood. The number of segments is thought to be determined in embryogenesis as a product of morphogenesis of the pre-somitic mesoderm (PSM) and the periodicity of a molecular oscillator active within the PSM known as the segmentation clock. Here we explore whether the clock and PSM morphogenesis exhibit developmental modularity, as independent evolution of these two processes may explain the high evolvability of segment number. Using a computational model of the clock and PSM parameterised for zebrafish, we find that the clock is broadly robust to variation in morphogenetic processes such as cell ingression, motility, compaction, and cell division. We show that this robustness is in part determined by the length of the PSM and the strength of phase coupling in the clock. As previous studies report no changes to morphogenesis upon perturbing the clock, we suggest that the clock and morphogenesis of the PSM exhibit developmental modularity.

developmental biology↗

bioRxiv2023

A mathematical framework for the emergence of winners and losers in cell competition

Cell competition is a process in multicellular organisms where cells interact with their neighbours to determine a "winner" or "loser" status. The loser cells are eliminated through programmed cell death, leaving only the winner cells to populate the tissue. Cell competition is context-dependent; the same cell type can win or lose depending on the cell type it is competing against. Hence, winner/loser status is an emergent property. A key question in cell competition is: how do cells acquire their winner/loser status? In this paper, we propose a mathematical framework for studying the emergence of winner/loser status based on a set of quantitative criteria that distinguishes competitive from non-competitive outcomes. We apply this framework in a cell-based modelling context, to both highlight the crucial role of active cell death in cell competition and identify the factors that drive cell competition. HighlightsO_LIEmergent winner/loser status tests for competitive outcomes in cell-based models C_LIO_LIDifferences in biomechanical properties alone are not sufficient for cell competition C_LIO_LIWinners have both higher tolerance and higher emission of death signals than losers C_LI

developmental biology↗

bioRxiv2022

Efficient Bayesian inference for mechanistic modelling with high-throughput data

Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent, we propose a minibatch approach to approximate Bayesian computation. Through a case study of a high-throughput imaging scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional Bayesian inference scheme. By applying a detailed mathematical model of single cell motility, proliferation and death to a data set of 118 gene knockdowns, we characterise functional subgroups of gene knockdowns, each displaying its own typical combination of local cell density-dependent and -independent motility and proliferation patterns. By comparing these patterns to experimental measurements of cell counts and wound closure, we find that density-dependent interactions play a crucial role in the process of wound healing.

bioinformatics↗

bioRxiv2022

Reliable and efficient parameter estimation using approximate continuum limit descriptions of stochastic models

Stochastic individual-based mathematical models are attractive for modelling biological phenomena because they naturally capture the stochasticity and variability that is often evident in biological data. Such models also allow us to track the motion of individuals within the population of interest. Unfortunately, capturing this microscopic detail means that simulation and parameter inference can become computationally expensive. One approach for overcoming this computational limitation is to coarse-grain the stochastic model to provide an approximate continuum model that can be solved using far less computational effort. However, coarse-grained continuum models can be biased or inaccurate, particularly for certain parameter regimes. In this work, we combine stochastic and continuum mathematical models in the context of lattice-based models of two-dimensional cell biology experiments by demonstrating how to simulate two commonly used experiments: cell proliferation assays and barrier assays. Our approach involves building a simple statistical model of the discrepancy between the expensive stochastic model and the associated computationally inexpensive coarse-grained continuum model. We form this statistical model based on a limited number of expensive stochastic model evaluations at design points sampled from a user-chosen distribution, corresponding to a computer experiment design problem. With straightforward design point selection schemes, we show that using the statistical model of the discrepancy in tandem with the computationally inexpensive continuum model allows us to carry out prediction and inference while correcting for biases and inaccuracies due to the continuum approximation. We demonstrate this approach by simulating a proliferation assay, where the continuum limit model is the well-known logistic ordinary differential equation, as well as a barrier assay where the continuum limit model is closely related to the well-known Fisher-KPP partial differential equation. We construct an approximate likelihood function for parameter inference, both with and without discrepancy correction terms. Using maximum likelihood estimation, we provide point estimates of the unknown parameters, and use the profile likelihood to characterise the uncertainty in these estimates and form approximate confidence intervals. For the range of inference problems considered, working with the continuum limit model alone leads to biased parameter estimation and confidence intervals with poor coverage. In contrast, incorporating correction terms arising from the statistical model of the model discrepancy allows us to recover the parameters accurately with minimal computational overhead. The main tradeoff is that the associated confidence intervals are typically broader, reflecting the additional uncertainty introduced by the approximation process. All algorithms required to replicate the results in this work are written in the open source Julia language and are available at GitHub.

biophysics↗

bioRxiv2021

Progressive polysomal association of Upf1, Upf2, and Upf3 as ribosomes traverse mRNA coding regions

Upf1, Upf2, and Upf3 are the central regulators of nonsense-mediated mRNA decay (NMD), the eukaryotic mRNA quality control pathway generally triggered when a premature termination codon is recognized by the ribosome. The NMD-related functions of the Upf proteins likely commence while these factors are ribosome-associated, but little is known of the timing of their ribosome binding, their specificity for ribosomes translating NMD substrates, or the nature and role of any ribosome:Upf complexes. Here, we have elucidated details of the ribosome-associated steps of NMD. By combining yeast genetics with selective ribosome profiling and co-sedimentation analyses of polysomes with wild-type and mutant Upf proteins, our approaches have identified distinct states of ribosome:Upf association. All three Upf factors manifest progressive polysome association as mRNA translation proceeds, but these events appear to be preceded by formation of a Upf1:80S complex as mRNAs initiate translation. This complex is likely executing an early mRNA surveillance function.

molecular biology↗

bioRxiv2021

Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors

Autoimmune myocarditis is a rare, but frequently fatal, side effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite extensive experimental work on the causes, development and progression of this disease, much still remains unknown about the importance of the different immunological pathways involved. We present a mathematical model of autoimmune myocarditis and the effects of ICIs on its development and progression to either resolution or chronic inflammation. From this, we gain a better understanding of the role of immune cells, cytokines and other components of the immune system in driving the cardiotoxicity of ICIs. We parameterise the model using existing data from the literature, and show that qualitative model behaviour is consistent with disease characteristics seen in patients in an ICI-free context. The bifurcation structures of the model show how the presence of ICIs increases the risk of developing autoimmune myocarditis. This predictive modelling approach is a first step towards determining treatment regimens that balance the benefits of treating cancer with the risk of developing autoimmune myocarditis.

immunology↗

bioRxiv2021

Parameter identifiability and model selection for sigmoid population growth models

Sigmoid growth models, such as the logistic, Gompertz and Richards models, are widely used to study population dynamics ranging from microscopic populations of cancer cells, to continental-scale human populations. Fundamental questions about model selection and parameter estimation are critical if these models are to be used to make practical inferences. However, the question of parameter identifiability - whether a data set contains sufficient information to give unique or sufficiently precise parameter estimates - is often overlooked. We use a profile-likelihood approach to explore practical parameter identifiability using data describing the re-growth of hard coral. With this approach, we explore the relationship between parameter identifiability and model misspecification, finding that the logistic growth model does not suffer identifiability issues for the type of data we consider whereas the Gompertz and Richards models encounter practical non-identifiability issues. This analysis of parameter identifiability and model selection is important because different growth models are used within areas of the biological modelling literature without necessarily considering whether parameters are identifiable, or checking statistical assumptions underlying model adequacy. Standard practices that do not consider parameter identifiability can lead to unreliable or imprecise parameter estimates and potentially misleading mechanistic interpretations. While tools developed here focus on three standard sigmoid growth models only, our theoretical developments are applicable to any sigmoid growth model and any relevant data set. MATLAB implementations of all software are available on GitHub.

ecology↗

bioRxiv2021

Chemical-genetic interaction mapping links carbon metabolism and cell wall structure to tuberculosis drug efficacy

Current chemotherapy against Mycobacterium tuberculosis (Mtb), an important human pathogen, requires a multidrug regimen lasting several months. While efforts have been made to optimize therapy by exploiting drug-drug synergies, testing new drug combinations in relevant host environments remains arduous. In particular, host environments profoundly affect the bacterial metabolic state and drug efficacy, limiting the accuracy of predictions based on in vitro assays alone. In this study, we utilize conditional Mtb knockdown mutants of essential genes as an experimentally-tractable surrogate for drug treatment, and probe the relationship between Mtb carbon metabolism and chemical-genetic interactions (CGI). We examined the anti-tubercular drugs isoniazid, rifampicin and moxifloxacin, and found that CGI are differentially responsive to the metabolic state, defining both environment-independent and -dependent interactions. Specifically, growth on the in vivo-relevant carbon source, cholesterol, reduced rifampicin efficacy by altering mycobacterial cell surface lipid composition. We report that a variety of perturbations in cell wall synthesis pathways restore rifampicin efficacy during growth on cholesterol, and that both environment-independent and cholesterol-dependent in vitro CGI could be leveraged to enhance bacterial clearance in the mouse infection model. Our findings present an atlas of novel chemical-genetic-environmental interactions that can be used to optimize drug-drug interactions as well as provide a framework for understanding in vitro correlates of in vivo efficacy. SignificanceEfforts to improve tuberculosis therapy include optimizing multi-drug regimens to take advantage of drug-drug synergies. However, the complex host environment has a profound effect on bacterial metabolic state and drug activity, making predictions of optimal drug combinations difficult. In this study, we leverage a newly developed library of conditional knockdown Mycobacterium tuberculosis mutants in which genetic depletion of essential genes mimics the effect of drug therapy. This tractable system allowed us to assess the effect of growth condition on predicted drug-drug interactions. We found that these interactions can be differentially sensitive to the metabolic state and select in vitro-defined interactions can be leveraged to accelerate bacterial killing during infection. These findings suggest new strategies for optimizing tuberculosis therapy.

microbiology↗

bioRxiv2021

Quantifying the impact of electric fields on single-cell motility

Cell motility in response to environmental cues forms the basis of many developmental processes in multicellular organisms. One such environmental cue is an electric field (EF), which induces a form of motility known as electrotaxis. Electrotaxis has evolved in a number of cell types to guide wound healing, and has been associated with different cellular processes, suggesting that observed electrotactic behaviour is likely a combination of multiple distinct effects arising from the presence of an EF. In order to determine the different mechanisms by which observed electrotactic behaviour emerges, and thus to design EFs that can be applied to direct and control electrotaxis, researchers require accurate quantitative predictions of cellular responses to externally-applied fields. Here, we use mathematical modelling to formulate and parametrise a variety of hypothetical descriptions of how cell motility may change in response to an EF. We calibrate our model to observed data using synthetic likelihoods and Bayesian sequential learning techniques, and demonstrate that EFs bias cellular motility through only one of a selection of hypothetical mechanisms. We also demonstrate how the model allows us to make predictions about cellular motility under different EFs. The resulting model and calibration methodology will thus form the basis for future data-driven and model-based feedback control strategies based on electric actuation. SIGNIFICANCEElectrotaxis is attracting much interest and development as a technique to control cell migration due to the precision of electric fields as actuation signals. However, precise control of electrotactic migration relies on an accurate model of how cell motility changes in response to applied electric fields. We present and calibrate a parametrised stochastic model that accurately replicates experimental single-cell data and enables the prediction of input-output behaviour while quantifying uncertainty and stochasticity. The model allows us to elucidate and quantify how electric fields perturb the motile behaviour of the cell. This model and the associated simulation-based calibration methodology will be central to future developments in the control of electrotaxis.

biophysics↗

bioRxiv2020

The role of mechanical interactions in EMT

The detachment of cells from the boundary of an epithelial tissue and the subsequent invasion of these cells into surrounding tissues is important for cancer development and wound healing, and is strongly associated with the epithelial-mesenchymal transition (EMT). Chemical signals, such as TGF-{beta}, produced by surrounding tissue can be up-taken by cells and induce EMT. In this work, we present a novel cell-based discrete mathematical model of mechanical cellular relaxation, cell proliferation, and cell detachment driven by chemically-dependent EMT in an epithelial tissue. A continuum description of the model is then derived in the form of a novel nonlinear free boundary problem. Using the discrete and continuum models we explore how the coupling of chemical transport and mechanical interactions influences EMT, and postulate how this could be used to help control EMT in pathological situations.

biophysics↗

bioRxiv2020

A Budding Yeast Model for Human Disease Mutations in the EXOSC2 Cap Subunit of the RNA Exosome

RNA exosomopathies, a growing family of tissue-specific diseases, are linked to missense mutations in genes encoding the structural subunits of the conserved 10-subunit exoribonuclease complex, the RNA exosome. Such mutations in the cap subunit gene EXOSC2 cause the novel syndrome SHRF (Short stature, Hearing loss, Retinitis pigmentosa and distinctive Facies). In contrast, exosomopathy mutations in the cap subunit gene EXOSC3 cause pontocerebellar hypoplasia type 1b (PCH1b). Though having strikingly different disease pathologies, EXOSC2 and EXOSC3 exosomopathy mutations result in amino acid substitutions in similar, conserved domains of the cap subunits, suggesting that these exosomopathy mutations have distinct consequences for RNA exosome function. We generated the first in vivo model of the SHRF pathogenic amino acid substitutions using budding yeast by introducing the EXOSC2 mutations in the orthologous S. cerevisiae gene RRP4. The resulting rrp4 mutant cells have defects in cell growth and RNA exosome function. We detect significant transcriptomic changes in both coding and non-coding RNAs in the rrp4 variant, rrp4-G226D, which models EXOSC2 p.Gly198Asp. Comparing this rrp4-G226D mutant to the previously studied S. cerevisiae model of EXOSC3 PCH1b mutation, rrp40-W195R, reveals that these mutants have disparate effects on certain RNA targets, providing the first evidence for different mechanistic consequences of these exosomopathy mutations. Congruently, we detect specific negative genetic interactions between RNA exosome cofactor mutants and rrp4-G226D but not rrp40-W195R. These data provide insight into how SHRF mutations could alter the function of the RNA exosome and allow the first direct comparison of exosomopathy mutations that cause distinct pathologies.

genetics↗