26 research outputs found

    Dynamics of Genetic Circuits with Molecule Partitioning Errors in Cell Division and RNA-RNA Interactions

    Get PDF
    Many signaling and regulatory molecules within cells exist in very few copies per cell. Any process affecting even limited numbers of these molecules therefore has the potential to affect the dynamics of the biochemical networks of which they are a part. This sensitivity to small copy-number changes is what allows stochasticity in gene expression to introduce a degree of randomness in what cells do. While this randomness can be suppressed, it does not appear to be so in many biological systems, at least not to the maximum degree possible. This suggests that this randomness is not necessarily detrimental to cell populations, as it can produce qualitatively new behaviours in genetic networks which may be utilized by cells.In this thesis, two other mechanisms are investigated which, through their interaction with low copy-number molecules, are able to produce qualitatively different dynamics in genetic networks: the stochastic partitioning of molecules in cell division, and the direct interaction of two low copy-number molecules. For this, a novel simulator of chemical kinetics is first presented, designed to simulate the dynamics of genetic circuits inside growing populations of cells. It is then used to study a genetic switch where one repressive link is formed by direct interaction between RNA molecules. This arrangement was found to decouple the stability of the two noisy attractors of the network and the speeds of the state transitions. In other words, it allows the network to have two equally-stable noisy attractors, but differing state transition speeds.Next, the cell-to-cell diversity in RNA numbers (as quantified by the normalized variance) of a single gene over time in a growing model cell population was studied as a function of the division synchrony. In the model, synchronous cell divisions introduce transient increases in the cell-to-cell diversity in RNA numbers of the population, a prediction which was verified using single-molecule measurements of RNA numbers. Finally, the effects of the stochastic partitioning of regulatory molecules in cell division on the dynamics of two genetic circuits, a switch and a clock, were studied. Of these two circuits, the switch has the most dramatic changes in its dynamics, brought on by the inevitable negative correlation in molecule numbers that sister cells inherit. This negative correlation can allow a cell population to partition the phenotypes of the individual cells with less variance than a binomial distribution.These results advance our understanding of the different behaviours that can be produced in genetic circuits due to these two mechanisms. Since they produce unique behaviours, these mechanisms, and combinations thereof, are expected to be used for specialized purposes in natural genetic circuits. Further, since the downstream effects of these mechanisms may be more predictable than, e.g., modifying promoter sequences, they may also be useful in the design and implementation of future synthetic genetic circuits with specific behaviours.<br/

    Recipes for calibration and validation of agent-based models in cancer biomedicine

    Full text link
    Computational models and simulations are not just appealing because of their intrinsic characteristics across spatiotemporal scales, scalability, and predictive power, but also because the set of problems in cancer biomedicine that can be addressed computationally exceeds the set of those amenable to analytical solutions. Agent-based models and simulations are especially interesting candidates among computational modelling strategies in cancer research due to their capabilities to replicate realistic local and global interaction dynamics at a convenient and relevant scale. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature to explore strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on validation approached as simulation calibration. We argue that simulation calibration goes beyond parameter optimization by embedding informative priors to generate plausible parameter configurations across multiple dimensions

    Investigating the Evolutionary Dynamics of Drug Resistance in Colorectal Cancer

    Get PDF
    PhD ThesesCancer resistance evolution was presumed to result from either a pre-existing or acquired mutation that survives treatment, re-populating the tumour following therapy. However, it appears cancer cells can adopt both genetic and non-genetic mechanisms to evade treatment, and a much broader range of evolutionary scenarios could drive resistance evolution. Here, I first develop models that explicitly capture both genetic and non-genetic sources of phenotypic variation in cell populations evolving resistance to therapy. I show that, given different parameters controlling the change in a resistance phenotype per division and the relative fitness cost of resistance, I can distinguish between various evolutionary scenarios, including those that lead to the same proportion of resistance. I subsequently combine these theoretical models with a long-term drug-treatment experiment in vitro: I employ a high-resolution lineage tracing technique and metronomic chemotherapy exposure in two colorectal cancer cell models. In one cell-line - HCT116 - the lineage distributions are consistent with a resistance phenotype being held at a low frequency by a high reversion phenotypic switching rate, or a high relative fitness cost. The other cell-line – SW620 – exhibits a response that is consistent with a broad range of evolutionary scenarios, all of which have relatively lower switching rates and fitness costs, whilst maintaining the resistant phenotype at a higher frequency within the population. My data show a role for either plasticity or a high fitness cost in the evolution of drug resistance in these colorectal cancer cell models. These results highlight the importance of including the diverse evolutionary scenarios that produce phenotypic differences within the population when modelling cancer cells' response to therapy. As stymieing resistance requires hampering a tumour's evolution, I argue that designing more effective treatment strategies will depend on accurately describing these diverse routes to resistance

    Statistical Modelling for Cell Reprogramming

    Get PDF
    Cells generally begin their lives as a pluripotent stem cell that gradually differentiates into specialised cell fates over time. However, recent advances in cell reprogramming have successfully converted differentiated cells to other cell types, by overexpressing a combination of transcription factors, fundamentally altering our view of cell identity. This could have large implications for the field of regenerative medicine as cell reprogramming offers the potential to regrow, repair or replace tissues and organs which have been damaged from age or disease. Despite much attention, combinations of transcription factors to drive reprogramming were mostly determined by trial and error, taking up considerable time and resources. To this end, computational methods have been developed to simulate the reprogramming process in silico with the goal of guiding the hypotheses to be experimentally validated. These models have provided a variety of perspectives to the process of cell reprogramming, however they all suffer from limitations in generalisability and scalability. Here, we categorise the existing computational approaches for cell reprogramming and critically evaluate their applicability in a broader context. We propose a novel method which leverages emerging multimodal single cell data. By integrating these different modes of data, we can create a more holistic model of a cell's regulatory system which provides a more accurate and scalable model for reprogramming. We demonstrate the applicability our method by recapitulating known properties of cell differentiation and reprogramming in both simulated and experimental data. We hope that this thesis will contribute to our understanding of the role of gene regulation in cell reprogramming by synthesising the existing computational models. Furthermore, our novel method may be a starting point for future computational models to integrate data from multiple modalities to create more comprehensive models for cell regulation

    3D in vitro cancer models for drug screening: A study of glucose metabolism and drug response in 2D and 3D culture models

    Get PDF
    Current drug screening protocols use in vitro cancer cell panels grown in 2D to evaluate drug response and select the most promising candidates for further in vivo testing. Most drug candidates fail at this stage, not showing the same efficacy in vivo as seen in vitro. An improved first screening that is more translatable to the in vivo tumor situation could aid in reducing both time and cost of cancer drug development. 3D cell cultures are an emerging standard for in vitro cancer cell models, being more representative of in vivo tumour conditions. To overcome the translational challenges with 2D cell cultures, 3D systems better model the more complex cell-to-cell contact and nutrient levels present in a tumour, improving our understanding of cancer complexity. Furthermore, cancer cells exhibit altered metabolism, a phenomenon described a century ago by Otto Warburg, and possibly related to changes in nutrient access. However, there are few reports on how 3D cultures differ metabolically from 2D cultures, especially when grown in physiological glucose conditions. Along with this, metabolic drug targeting is considered an underutilized and poorly understood area of cancer therapy. Therefore, the aim of this work was to investigate the effect of culture conditions on response to metabolic drugs and study the metabolism of 3D spheroid cultures in detail. To achieve this, multiple cancer cell lines were studied in high and low glucose concentrations and in 2D and 3D cultures. We found that glucose concentration is important at a basic level for growth properties of cell lines with different metabolic phenotypes and it affects sensitivity to metformin. Furthermore, metformin is able to shift metabolic phenotype away from OXPHOS dependency. There are significant differences in glucose metabolism of 3D cultures compared to 2D cultures, both related to glycolysis and oxidative phosphorylation. Spheroids have higher ATP-linked respiration in standard nutrient conditions and higher non-aerobic ATP production in the absence of supplemented glucose. Multi-round treatment of spheroids is able to show more robust response than standard 2D drug screening, including resistance to therapy. Results from 2D cultures both over and underestimate drug response at different concentrations of 5-fluorouracil (5-FU). A higher maximum effect of 5-FU is seen in models with lower OCR/ECAR ratios, an indication of a more glycolytic metabolic phenotype. In conclusion, both culture method and nutrient conditions are important consideration for in vitro cancer models. There is good reason to not maintain in vitro cultures in artificially high glucose conditions. It can have downstream affects on drug response and likely other important metrics. If possible, assays should also be implemented in 3D. If not in everyday assays, at least as a required increase in complexity to validate 2D results. Finally, metabolism even in the small scope presented here, is complex in terms of phenotypic variation. This shows the importance of metabolic screening in vitro to better understand the effects of these small changes and to model how a specific tumor may behave based on its complex metabolism

    Development of decellularised porcine osteochondral scaffolds as matrices for cell implantation

    Get PDF
    Osteoarthritis currently affects 8.75 million people in the UK alone. This can cause major issues for those living with the disease, such as immobility and pain, which are often accompanied with psychological distress due to a loss in quality of life. One cause of osteoarthritis is damage to the articular cartilage which triggers inflammation and progressive degeneration. Early intervention strategies are employed to prevent disease progression such as microfracture, mosaicplasty and more recently autologous chondrocyte implantation. However, these all have their limitations in either, insufficient quality of repair material, donor site morbidity or limited biomechanical function prior to tissue regeneration. This study first aimed to investigate the applicability of decellularised porcine osteochondral scaffolds in the treatment of large shallow cartilage lesions. This project built upon previous work, with an aim of enhancing these scaffolds through application with chondrocytes and self-assembling peptide hydrogel with chondroitin sulphate (P11-8/CS) incorporated. The hypothesis was that the resultant scaffold would be an ideal tissue replacement due to the retained native extracellular matrix structure, the increased regenerative potential offered by the cells and the enhanced biomechanical function from the addition of SAP-CS. These benefits, would ideally allow faster restoration of the healthy biomechanical function of the joint. Potential for cost-effectiveness versus matrix assisted chondrocyte implantation was observed. The dimensions of the decellularised scaffolds were adapted to dimensions which are clinically appropriate for the treatment of large shallow lesions. The resultant decellularisation quality, cytocompatibility and mechanical properties were all conserved, despite larger dimensions. Following this, a recellularization process was established for these decellularised scaffolds based using lyophilisation to increase cell penetration. These scaffolds were evaluated in a natural knee joint simulation model, which indicated viability of recellularised chondrocytes at Day 7. Following this, the ability of the P11-8/CS hydrogel alone to support chondrocyte cell proliferation and survival over a 14-day timecourse was demonstrated, whilst chondrogenic gene expression of encapsulated primary porcine chondrocytes was shown. The lyophilisation method was then developed to deliver SAP-GAG to the osteochondral scaffolds, which showed a trend for improved biomechanical properties. Overall, this work has shown the potential for both recellularised decellularised scaffolds and self-assembling peptides, as devices to support chondrocyte implantation to aid the regeneration of large shallow cartilage lesions and early stage lesions respectively

    The nature of growth in the biofuel feedstock and bloom-forming green macroalga Ulva

    Get PDF
    Ulva is a genus of multicellular green algae that is phylogenetically similar to uni- cellular green algae such as Chlamydomonas and Ostreococcus. Ulva is present in much of the coastal benthic zones worldwide, and is of great interest for three main reasons. Firstly, Ulva is an important feedstock for biofuels. Secondly, many Ulva species are massively proliferating organisms that cause Harmful Algal Blooms, which are ecologically devastating. Finally, Ulva is an important model organism that could elucidate the evolution of multicellularity. This thesis investigates the physiology of growth in Ulva in four sequential results chapters. The first establishes a statistical proof for the goodness of fit of gene family occupancy data to a discrete power law model. This was an assumption used in the only Ulva genome study, which found no genomic signature for multicellularity. This establishes the baseline for the in- vestigation of bottom-up morphogenesis in Ulva. The second is the investigation of differential growth, by identifying cell tessellation patterns in different morphologies of Ulva thalli, namely the “ribbon” and “leaf” morphotypes, with mathematical mod- els using Voronoi tessellations. The third expands investigates differential growth in the ribbon and leaf morphotypes with a focus on identifying potential mechanisms with further mathematical models using Centroidal Voronoi Tessellations. The fourth aims to develop experimental techniques to confirm the hypotheses arising from the mathematical modelling in the second and third chapters. The first part involves the use of EdU cellular proliferation assays. The remainder of the chapter will investigate the development of a live-imaging biomass monitoring system that aims to improve the accuracy, reliability and temporal resolution of aquatic biomass measurements. It can be concluded that Ulva does not show a genomic signature for multicellularity, and bottom-up mechanisms likely explain its morphogenesis and morphologies

    Integration of pharmacokinetic and intracellular models of interferon administration and induced responses

    Get PDF
    A thorough understanding of drug-target relationships is essential in preclinical and clinical translation studies. However, there is a gap of knowledge in the quantitative understanding of dose-response relationships at the target site. To fill that gap, a particularly promising approach is quantitative systems pharmacology (QSP) where, mechanistic and hence comprehensive models of dose-effect relationships are used to guide the design of clinical and translational studies. In this thesis, I present for the first time a QSP approach for a therapeutic protein, interferon alpha (IFN-a), by coupling physiologically based pharmacokinetic (PBPK) models at the whole-body level with intracellular models of signal transduction in the liver. Whole-body distribution models of an injected dose of IFN-a calibrated to quantitative measurements of the plasma concentration are established for humans and mice. They are then coupled to mechanistic intracellular models of the triggered JAK/STAT signalling cascade that describes the dynamic response in the expression of the antiviral mRNAc of IRF9 for humans and antiviral protein Mx2 for mice on the cellular scale. By doing so, I am able to establish the quantitative dose-effect relationship of the injected IFN-a dose to the responding interferon stimulated genes (ISGs) triggered at the target site, the liver. The established multi-scale physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of human predict a reduced response of IRF9 mRNAc to IFN-a under physiological in vivo conditions as compared to in vitro. The QSP model also elicits the large impact of the IFN-receptors on the clearance of IFN-a in the liver, thus, not only providing mechanistic insights into the pharmacodynamic (PD) response but also elucidating the influence of receptor variability on the response. Although IFN-a is specifically used in humans, in preclinical studies, it is also tested in mice for understanding the medical impact of IFN-a for other diseases. Therefore, I elaborate an analogous QSP model for the IFN-a response in mice to illustrate possibilities of model-based cross species translation. Like the human model, a whole body PBPK/PD mouse model was also established to follow the response of antiviral protein Mx2. The model clarified the differences between the pharmacokinetics of human and murine IFN-a injection in mice and will support quantitative crossspecies extrapolation in the future. Finally, as heterogeneity in ISGs reflects inter-cell variability in response to IFN-a, I study the impact of sources of this heterogeneity by implementing the mechanistic stochastic model of the JAK/STAT signalling pathway. The model was developed on the basis of time-resolved flow cytometry data of two ISGs, MxA and IFIT1, in Huh7.5 cells. The model analysed intrinsic variability in the concentration of the molecules of the pathway and generated a graded response of MxA and IFIT1 instead of an all-or-none response. Ultimately, the model concludes that the stochasticity in the initiation of the signalling pathway, i.e., at the receptor level, can be buffered by the system and a more robust response of ISGs, MxA and IFIT1 is induced
    corecore