School of Mathematics and Statistics - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 14
-
ItemCharge Has a Marked Influence on Hyperbranched Polymer Nanoparticle Association in Whole Human Blood(AMER CHEMICAL SOC, 2017-06)In this study, we synthesize charge-varied hyperbranched polymers (HBPs) and demonstrate surface charge as a key parameter directing their association with specific human blood cell types. Using fresh human blood, we investigate the association of 5 nm HBPs with six white blood cell populations in their natural milieu by flow cytometry. While most cell types associate with cationic HBPs at 4 °C, at 37 °C phagocytic cells display similar (monocyte, dendritic cell) or greater (granulocyte) association with anionic HBPs compared to cationic HBPs. Neutral HBPs display remarkable stealth properties. Notably, these charge-association patterns are not solely defined by the plasma protein corona and are material and/or size dependent. As HBPs progress toward clinical use as imaging and drug delivery agents, the ability to engineer HBPs with defined biological properties is increasingly important. This knowledge can be used in the rational design of HBPs for more effective delivery to desired cell targets.
-
ItemExperimental and modelling evidence of shortening heat in cardiac muscle(WILEY, 2017-10-01)KEY POINTS: Heat associated with muscle shortening has been repeatedly demonstrated in skeletal muscle, but its existence in cardiac muscle remains contentious after five decades of study. By iterating between experiments and computational modelling, we show compelling evidence for the existence of shortening heat in cardiac muscle and reveal, mechanistically, the source of this excess heat. Our results clarify a long-standing uncertainty in the field of cardiac muscle energetics. We provide a revised partitioning of cardiac muscle energy expenditure to include this newly revealed thermal component. ABSTRACT: When a muscle shortens against an afterload, the heat that it liberates is greater than that produced by the same muscle contracting isometrically at the same level of force. This excess heat is defined as 'shortening heat', and has been repeatedly demonstrated in skeletal muscle but not in cardiac muscle. Given the micro-structural similarities between these two muscle types, and since we imagine that shortening heat is the thermal accompaniment of cross-bridge cycling, we have re-examined this issue. Using our flow-through microcalorimeter, we measured force and heat generated by isolated rat trabeculae undergoing isometric contractions at different muscle lengths and work-loop (shortening) contractions at different afterloads. We simulated these experimental protocols using a thermodynamically constrained model of cross-bridge cycling and probed the mechanisms underpinning shortening heat. Predictions generated by the model were subsequently validated by a further set of experiments. Both our experimental and modelling results show convincing evidence for the existence of shortening heat in cardiac muscle. Its magnitude is inversely related to the afterload or, equivalently, directly related to the extent of shortening. Computational simulations reveal that the heat of shortening arises from the cycling of cross-bridges, and that the rate of ATP hydrolysis is more sensitive to change of muscle length than to change of afterload. Our results clarify a long-standing uncertainty in the field of cardiac muscle energetics.
-
ItemAn analytical approach for quantifying the influence of nanoparticle polydispersity on cellular delivered dose(ROYAL SOC, 2018-07)Nanoparticles provide a promising approach for the targeted delivery of therapeutic, diagnostic and imaging agents in the body. However, it is not yet fully understood how the physico-chemical properties of the nanoparticles influence cellular association and uptake. Cellular association experiments are routinely performed in an effort to determine how nanoparticle properties impact the rate of nanoparticle-cell association. To compare experiments in a meaningful manner, the association data must be normalized by the amount of nanoparticles that arrive at the cells, a measure referred to as the delivered dose. The delivered dose is calculated from a model of nanoparticle transport through fluid. A standard assumption is that all nanoparticles within the population are monodisperse, namely the nanoparticles have the same physico-chemical properties. We present a semi-analytic solution to a modified model of nanoparticle transport that allows for the nanoparticle population to be polydisperse. This solution allows us to efficiently analyse the influence of polydispersity on the delivered dose. Combining characterization data obtained from a range of commonly used nanoparticles and our model, we find that the delivered dose changes by more than a factor of 2 if realistic amounts of polydispersity are considered.
-
ItemSystems analysis identifies miR-29b regulation of invasiveness in melanoma(BMC, 2016-11-16)BACKGROUND: In many cancers, microRNAs (miRs) contribute to metastatic progression by modulating phenotypic reprogramming processes such as epithelial-mesenchymal plasticity. This can be driven by miRs targeting multiple mRNA transcripts, inducing regulated changes across large sets of genes. The miR-target databases TargetScan and DIANA-microT predict putative relationships by examining sequence complementarity between miRs and mRNAs. However, it remains a challenge to identify which miR-mRNA interactions are active at endogenous expression levels, and of biological consequence. METHODS: We developed a workflow to integrate TargetScan and DIANA-microT predictions into the analysis of data-driven associations calculated from transcript abundance (RNASeq) data, specifically the mutual information and Pearson's correlation metrics. We use this workflow to identify putative relationships of miR-mediated mRNA repression with strong support from both lines of evidence. Applying this approach systematically to a large, published collection of unique melanoma cell lines - the Ludwig Melbourne melanoma (LM-MEL) cell line panel - we identified putative miR-mRNA interactions that may contribute to invasiveness. This guided the selection of interactions of interest for further in vitro validation studies. RESULTS: Several miR-mRNA regulatory relationships supported by TargetScan and DIANA-microT demonstrated differential activity across cell lines of varying matrigel invasiveness. Strong negative statistical associations for these putative regulatory relationships were consistent with target mRNA inhibition by the miR, and suggest that differential activity of such miR-mRNA relationships contribute to differences in melanoma invasiveness. Many of these relationships were reflected across the skin cutaneous melanoma TCGA dataset, indicating that these observations also show graded activity across clinical samples. Several of these miRs are implicated in cancer progression (miR-211, -340, -125b, -221, and -29b). The specific role for miR-29b-3p in melanoma has not been well studied. We experimentally validated the predicted miR-29b-3p regulation of LAMC1 and PPIC and LASP1, and show that dysregulation of miR-29b-3p or these mRNA targets can influence cellular invasiveness in vitro. CONCLUSIONS: This analytic strategy provides a comprehensive, systems-level approach to identify miR-mRNA regulation in high-throughput cancer data, identifies novel putative interactions with functional phenotypic relevance, and can be used to direct experimental resources for subsequent experimental validation. Computational scripts are available: http://github.com/uomsystemsbiology/LMMEL-miR-miner.
-
ItemDistributed gene expression modelling for exploring variability in epigenetic function(BMC, 2016-11-05)BACKGROUND: Predictive gene expression modelling is an important tool in computational biology due to the volume of high-throughput sequencing data generated by recent consortia. However, the scope of previous studies has been restricted to a small set of cell-lines or experimental conditions due an inability to leverage distributed processing architectures for large, sharded data-sets. RESULTS: We present a distributed implementation of gene expression modelling using the MapReduce paradigm and prove that performance improves as a linear function of available processor cores. We then leverage the computational efficiency of this framework to explore the variability of epigenetic function across fifty histone modification data-sets from variety of cancerous and non-cancerous cell-lines. CONCLUSIONS: We demonstrate that the genome-wide relationships between histone modifications and mRNA transcription are lineage, tissue and karyotype-invariant, and that models trained on matched -omics data from non-cancerous cell-lines are able to predict cancerous expression with equivalent genome-wide fidelity.
-
ItemInformation theoretic approaches for inference of biological networks from continuous-valued data(BMC, 2016-09-06)BACKGROUND: Characterising programs of gene regulation by studying individual protein-DNA and protein-protein interactions would require a large volume of high-resolution proteomics data, and such data are not yet available. Instead, many gene regulatory network (GRN) techniques have been developed, which leverage the wealth of transcriptomic data generated by recent consortia to study indirect, gene-level relationships between transcriptional regulators. Despite the popularity of such methods, previous methods of GRN inference exhibit limitations that we highlight and address through the lens of information theory. RESULTS: We introduce new model-free and non-linear information theoretic measures for the inference of GRNs and other biological networks from continuous-valued data. Although previous tools have implemented mutual information as a means of inferring pairwise associations, they either introduce statistical bias through discretisation or are limited to modelling undirected relationships. Our approach overcomes both of these limitations, as demonstrated by a substantial improvement in empirical performance for a set of 160 GRNs of varying size and topology. CONCLUSIONS: The information theoretic measures described in this study yield substantial improvements over previous approaches (e.g. ARACNE) and have been implemented in the latest release of NAIL (Network Analysis and Inference Library). However, despite the theoretical and empirical advantages of these new measures, they do not circumvent the fundamental limitation of indeterminacy exhibited across this class of biological networks. These methods have presently found value in computational neurobiology, and will likely gain traction for GRN analysis as the volume and quality of temporal transcriptomics data continues to improve.
-
ItemMathematical modelling indicates that lower activity of the haemostatic system in neonates is primarily due to lower prothrombin concentration(NATURE PORTFOLIO, 2019-03-08)Haemostasis is governed by a highly complex system of interacting proteins. Due to the central role of thrombin, thrombin generation and specifically the thrombin generation curve (TGC) is commonly used as an indicator of haemostatic activity. Functional characteristics of the haemostatic system in neonates and children are significantly different compared with adults; at the same time plasma levels of haemostatic proteins vary considerably with age. However, relating one to the other has been difficult, both due to significant inter-individual differences for individuals of similar age and the complexity of the biochemical reactions underlying haemostasis. Mathematical modelling has been very successful at representing the biochemistry of blood clotting. In this study we address the challenge of large inter-individual variability by parameterising the Hockin-Mann model with data from individual patients, across different age groups from neonates to adults. Calculating TGCs for each patient of a specific age group provides us with insight into the variability of haemostatic activity across that age group. From our model we observe that two commonly used metrics for haemostatic activity are significantly lower in neonates than in older patients. Because both metrics are strongly determined by prothrombin and prothrombin levels are considerably lower in neonates we conclude that decreased haemostatic activity in neonates is due to lower prothrombin availability.
-
ItemNo Preview AvailableRevisiting cell-particle association in vitro: A quantitative method to compare particle performance.(Elsevier, 2019-08-10)Nanoengineering has the potential to revolutionize medicine by designing drug delivery systems that are both efficacious and highly selective. Determination of the affinity between cell lines and nanoparticles is thus of central importance, both to enable comparison of particles and to facilitate prediction of in vivo response. Attempts to compare particle performance can be dominated by experimental artifacts (including settling effects) or variability in experimental protocol. Instead, qualitative methods are generally used, limiting the reusability of many studies. Herein, we introduce a mathematical model-based approach to quantify the affinity between a cell-particle pairing, independent of the aforementioned confounding artifacts. The analysis presented can serve as a quantitative metric of the stealth, fouling, and targeting performance of nanoengineered particles in vitro. We validate this approach using a newly created in vitro dataset, consisting of seven different disulfide-stabilized poly(methacrylic acid) particles ranging from ~100 to 1000 nm in diameter that were incubated with three different cell lines (HeLa, THP-1, and RAW 264.7). We further expanded this dataset through the inclusion of previously published data and use it to determine which of five mathematical models best describe cell-particle association. We subsequently use this model to perform a quantitative comparison of cell-particle association for cell-particle pairings in our dataset. This analysis reveals a more complex cell-particle association relationship than a simplistic interpretation of the data, which erroneously assigns high affinity for all cell lines examined to large particles. Finally, we provide an online tool (http://bionano.xyz/estimator), which allows other researchers to easily apply this modeling approach to their experimental results.
-
ItemCombinatorial Targeting by MicroRNAs Co-ordinates Post-transcriptional Control of EMT(CELL PRESS, 2018-07-25)MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression, functioning in part by facilitating the degradation of target mRNAs. They have an established role in controlling epithelial-mesenchymal transition (EMT), a reversible phenotypic program underlying normal and pathological processes. Many studies demonstrate the role of individual miRNAs using overexpression at levels greatly exceeding physiological abundance. This can influence transcripts with relatively poor targeting and may in part explain why over 130 different miRNAs are directly implicated as EMT regulators. Analyzing a human mammary cell model of EMT we found evidence that a set of miRNAs, including the miR-200 and miR-182/183 family members, co-operate in post-transcriptional regulation, both reinforcing and buffering transcriptional output. Investigating this, we demonstrate that combinatorial treatment altered cellular phenotype with miRNA concentrations much closer to endogenous levels and with less off-target effects. This suggests that co-operative targeting by miRNAs is important for their physiological function and future work classifying miRNAs should consider such combinatorial effects.
-
ItemLink between Low-Fouling and Stealth: A Whole Blood Biomolecular Corona and Cellular Association Analysis on Nanoengineered Particles(American Chemical Society, 2019-05-28)Upon exposure to human blood, nanoengineered particles interact with a multitude of plasma components, resulting in the formation of a biomolecular corona. This corona modulates downstream biological responses, including recognition by and association with human immune cells. Considerable research effort has been directed toward the design of materials that can demonstrate a low affinity for various proteins (low-fouling materials) and materials that can exhibit low association with human immune cells (stealth materials). An implicit assumption common to bio–nano research is that nanoengineered particles that are low-fouling will also exhibit stealth. Herein, we investigated the link between the low-fouling properties of a particle and its propensity for stealth in whole human blood. High-fouling mesoporous silica (MS) particles and low-fouling zwitterionic poly(2-methacryloyloxyethyl phosphorylcholine) (PMPC) particles were synthesized, and their interaction with blood components was assessed before and after precoating with serum albumin, immunoglobulin G, or complement protein C1q. We performed an in-depth proteomics characterization of the biomolecular corona that both identifies specific proteins and measures their relative abundance. This was compared with observations from a whole blood association assay that identified with which cell type each particle system associates. PMPC-based particles displayed reduced association both with cells and with serum proteins compared with MS-based particles. Furthermore, the enrichment of specific proteins within the biomolecular corona was found to correlate with association with specific cell types. This study demonstrates how the low-fouling properties of a material are indicative of its stealth with respect to immune cell association.