Biology subjects

Barnes, C. P.

Publications and source records attributed to Barnes, C. P..

33 records · Page 2Linked to original sources

bioRxiv2021

Reconstruction of Contemporary Human Stem Cell Dynamics with Oscillatory Molecular Clocks

Molecular clocks record cellular ancestry. However, currently used clocks tick too slowly to measure the short-timescale dynamics of cellular renewal in adult tissues. Here we develop rapidly oscillating DNA methylation clocks where ongoing (de)methylation causes the clock to tick-tock back-and-forth between methylated and unmethylated states like a pendulum. We identify oscillators using standard methylation arrays and develop a mathematical modelling framework to quantitatively measure human adult stem cell dynamics from these data. Small intestinal crypts were inferred to contain slightly more stem cells than colon (6.5 {+/-} 1.0 vs 5.8 {+/-} 1.7 stem cells/crypt) with slower stem cell replacement in small intestine (0.79 {+/-} 0.5 vs 1.1 {+/-} 0.8 replacements/stem cell/year). Germline APC mutation increased the number of replacements per crypt (13.0 {+/-} 2.4 replacements/crypt/year vs 6.9 {+/-} 4.6 for healthy colon). In blood, we measure rapid expansion of acute leukaemia and slower growth of chronic disease. Rapidly oscillating molecular clocks are a new methodology to quantitatively measure human somatic cell dynamics.

bioinformatics↗

bioRxiv2020

Automated design of synthetic microbial communities

In naturally occurring microbial systems, species rarely exist in isolation. There is strong ecological evidence for a positive relationship between species diversity and the functional output of communities. The pervasiveness of these communities in nature highlights that there may be advantages for engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigates issues often found in engineering a monoculture, especially when functional complexity is increasing. Here, we demonstrate a methodology for designing robust synthetic communities that use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We explore model spaces for two and three strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and tuning the community composition.

synthetic biology↗

bioRxiv2020

FlopR: An open source software package for calibration and normalization of plate reader and flow cytometry data

The measurement of gene expression using fluorescence markers has been a cornerstone of synthetic biology for the last two decades. However, the use of arbitrary units has limited the usefulness of this data for many quantitative purposes. Calibration of fluorescence measurements from flow cytometry and plate reader spectrophotometry has been implemented previously but the tools are disjointed. Here we pull together, and in some cases improve, extant methods into a single software tool, written as a package in the R statistical framework. The workflow is validated using Escherichia coli engineered to express GFP from a set of commonly used constitutive promoters. We then demonstrate its power by identifying the time evolution of distinct subpopulations of bacteria from bulk plate reader data, a task previously reliant on laborious flow cytometry experiments. Along with standardized parts and experimental methods, the development and dissemination of usable tools for quantitative measurement and data analysis will benefit the synthetic biology community by improving interoperability.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗

bioRxiv2019

Killing in response to competition stabilises synthetic microbial consortia

The scale of the biological systems we can engineer is limited by the burden that host cells can bear. Division-of-labour can spread that burden across a community of cells but competitive exclusion inevitably leads to the removal of less fit community members over time. Here, we leverage amensalism and competitive exclusion to stabilise multi-species communities by engineering a strain of Escherichia coli which secretes a toxin in response to competition. We show mathematically and experimentally that such a system can produce stable populations with a composition that is tunable by easily controllable parameters. This is the first system to use competitive exclusion to create a stable two-species consortia and the first to only require the engineering of a single strain.

synthetic biology↗

bioRxiv2019

Detecting changes in the Caenorhabditis elegans intestinal environment using an engineered bacterial biosensor

Caenorhabditis elegans has become a key model organism within biology. In particular, the transparent gut, rapid growing time and ability to create a defined gut microbiota make it an ideal candidate organism for understanding and engineering the host microbiota. Here we present the development of an experimental model which can be used to characterise whole-cell bacterial biosensors in vivo. A dual-plasmid sensor system responding to isopropyl {beta}-D-1-thiogalactopyranoside was developed and fully characterised in vitro. Subsequently, we show the sensor was capable of detecting and reporting on changes in the intestinal environment of C. elegans after introducing exogenous inducer into the environment. The protocols presented here may be used for aiding the rational design of engineered bacterial circuits, primarily for diagnostic applications. In addition, the model system may serve to reduce the use of current animal models and aid in the exploration of complex questions within general nematode and host-microbe biology.

synthetic biology↗

bioRxiv2019

Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios

The distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells, however the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that yields the selective coefficients from individual driver mutations from dN/dS estimates, and then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1-5%). Accurate measurement of the per-gene DFE in cancer evolution is precluded by the quality of currently available sequencing data. This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and reveals the DFE for mutations in human tissues.

bioinformatics↗

bioRxiv2019

Model-based tumor subclonal reconstruction

The vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors.

bioinformatics↗

bioRxiv2019

Measuring single cell divisions in human cancers from multi-region sequencing data

Cancer is driven by complex evolutionary dynamics involving billions of cells. Increasing effort has been dedicated to sequence single tumour cells, but obtaining robust measurements remains challenging. Here we show that multi-region sequencing of bulk tumour samples contains quantitative information on single-cell divisions that is accessible if combined with evolutionary theory. Using high-throughput data from 16 human cancers, we measured the in vivo per-cell point mutation rate (mean: 1.69x10-8 bp per cell division) and per-cell survival rate (mean: 0.57) in individual patient tumours from colon, lung and renal cancers. Per-cell mutation rates varied 50-fold between individuals, and per-cell survival rates were between nearly-homeostatic and almost perfect cell doublings, equating to tumour ages between 1 and 19 years. Furthermore, reanalysing a recent dataset of 89 whole-genome sequenced healthy haematopoietic stem cells, we find 1.14 mutations per genome per cell division and near perfect cell doublings (per-cell survival rate: 0.96) during early haematopoietic development. Our analysis measures in vivo the most fundamental properties of human cancer and healthy somatic evolution at single-cell resolution within single individuals.

cancer biology↗

bioRxiv2019

Evolutionary dynamics of neoantigens in growing tumours

Cancer evolution is driven by the acquisition of somatic mutations that provide cells with a beneficial phenotype in a changing microenvironment. However, mutations that give rise to neoantigens, novel cancer-specific peptides that elicit an immune response, are likely to be disadvantageous. Here we show how the clonal structure and immunogenotype of growing tumours is shaped by negative selection in response to neoantigenic mutations. We construct a mathematical model of neoantigen evolution in a growing tumour, and verify the model using genomic sequencing data. The model predicts that, in the absence of active immune escape mechanisms, tumours either evolve clonal neoantigens (antigen- hot), or have no clonally- expanded neoantigens at all (antigen- cold), whereas antigen- warm tumours (with high frequency subclonal neoantigens) form only following the evolution of immune evasion. Counterintuitively, strong negative selection for neoantigens during tumour formation leads to an increased number of antigen- warm or - hot tumours, as a consequence of selective pressure for immune escape. Further, we show that the clone size distribution under negative selection is effectively- neutral, and moreover, that stronger negative selection paradoxically leads to more neutral- like dynamics. Analysis of antigen clone sizes and immune escape in colorectal cancer exome sequencing data confirms these results. Overall, we provide and verify a mathematical framework to understand the evolutionary dynamics and clonality of neoantigens in human cancers that may inform patient- specific immunotherapy decision- making.

cancer biology↗

bioRxiv2018

A Bayesian framework for the analysis of systems biology models of the brain

Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain a more complete understanding of the parameter space as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a healthy and impaired state. By analysing both of these datasets, we show that different parameter spaces can be distinguished and compared between different physiological states or conditions. Finally, we analyse experimental data using the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the Bayesian approach provides a more complete understanding of the parameter space.\n\nAuthor summarySystems biology models are mathematical representations of biological processes that reproduce the overall behaviour of a biological system. They are comprised by a number of parameters representing biological information. We use them to understand the behaviour of biological systems, such as the brain. We do this by fitting the models parameter to observed or simulated data; and by looking at how these values change during the fitting process we investigate the mechanisms controlling the behaviour of our system. We are interested in understanding differences between a healthy and an injured brain. Here we outline a statistical framework that uses a Bayesian approach during the fitting process that can provide us with a distribution of parameters rather than single parameter number. We apply this method when simulating the physiological responses between a healthy and a vascular compromised brain to a drop in oxygenation. We then use experimental data that demonstrates the healthy brain response to an increase in arterial CO2 and fit our brain model predictions to the measurements. In both instances we show that our approach provides more information about the overlap between healthy and unhealthy brain states than a fitting process that provides a single value parameter estimate.

systems biology↗

bioRxiv2018

Deep reinforcement learning for the control of bacterial populations in bioreactors

Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity (in comparison with pure cultures) and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence - reinforcement learning - in the control of co-cultures within continuous bioreactors. We confirm that feedback via reinforcement learning can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment, by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.

synthetic biology↗

bioRxiv2018

Two new plasmid post-segregational killing mechanisms for the implementation of synthetic gene networks in E. coli

Plasmids are the workhorse of both industrial biotechnology and synthetic biology, but ensuring they remain in bacterial cells is a challenge. Antibiotic selection, commonly used in the laboratory, cannot be used to stabilise plasmids in most real-world applications, and inserting dynamical gene networks into the genome is difficult. Plasmids have evolved several mechanisms for stability, one of which, post-segregational killing (PSK), ensures that plasmid-free cells do not grow or survive. Here we demonstrate the plasmid-stabilising capabilities of the axe/txe two component system and the microcin-V system in the probiotic bacteria Escherichia coli Nissle 1917 and show they can outperform the hok/sok system commonly used in biotechnological applications. Using plasmid stability assays, automated flow cytometry analysis, mathematical models and Bayesian statistics we quantified plasmid stability in vitro. Further, we used an in vivo mouse cancer model to demonstrate plasmid stability in a real-world therapeutic setting. These new PSK systems, plus the developed Bayesian methodology, will have wide applicability in clinical and industrial biotechnology.

synthetic biology↗

bioRxiv2018

Reply: Neutral tumor evolution?

Mutation, selection and neutral drift shape the cancer evolutionary process1. The role of selection has received particular interest, but inferring the presence and strength of selection during tumour growth remains challenging. Recently, we analysed the frequency distribution of subclonal mutations in many cancers and found that in approximately 30% of cases the observed distribution was entirely consistent with a simple model of neutral evolution2. Thus, we concluded that neutral evolution, perhaps surprisingly, provides an adequate explanation of the intra-tumour heterogeneity present in a significant proportion of cancers.\n\nTarabichi and colleagues [bioRxiv: 2017/06/30/158006] question the robustness of the method we presented in Williams et al. 20162 to identify neutral cancer evolution from variant allele frequency (VAF) distributions. Their critique has four main points that we addre ...

cancer biology↗

bioRxiv2017

A perturbation model of the gut microbiome’s response to antibiotics

Treatment with antibiotics is one of the most extreme perturbations to the human microbiome. Even standard courses of antibiotics dramatically reduce the microbiomes diversity and can cause transitions to dysbiotic states. Conceptually, this is often described as a stability landscape: the microbiome sits in a landscape with multiple stable equilibria, and sufficiently strong perturbations can shift the microbiome from its normal equilibrium to another state. However, this picture is only qualitative and has not been incorporated in previous mathematical models of the effects of antibiotics. Here, we outline a simple quantitative model based on the stability landscape concept and demonstrate its success on real data. Our analytical impulse-response model has minimal assumptions with three parameters. We fit this model in a Bayesian framework to previously published data on the year-long effects of four common antibiotics (ciprofloxacin, clindamycin, minocycline, and amoxicillin) on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes. Furthermore, using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of ciprofloxacin and clindamycin in both the gut and salivary microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling.

microbiology↗

bioRxiv2016

A computational method for the investigation of multistable systems and its application to genetic switches

Genetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology. Here we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour. Our results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks.

synthetic biology↗