ORCID Profile
0000-0001-6569-9543
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Biological Mathematics | Nanomaterials | Nanomedicine | Applied Mathematics | Nanotechnology | Nanobiotechnology
Expanding Knowledge in the Biological Sciences | Expanding Knowledge in the Chemical Sciences | Expanding Knowledge in Engineering |
Publisher: Springer Science and Business Media LLC
Date: 2020
Publisher: The Royal Society
Date: 08-2021
Abstract: Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here, we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermodynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli and published experimental data.
Publisher: Cold Spring Harbor Laboratory
Date: 26-08-2021
DOI: 10.1101/2021.08.25.457323
Abstract: The advent of next-generation sequencing revealed extensive transcription beyond protein-coding genes, identifying tens of thousands of long non-coding RNAs (lncRNAs). Selected functional ex les raised the possibility that lncRNAs, as a class, may maintain broad regulatory roles. Compellingly, lncRNA expression is strongly linked with adjacent protein-coding gene expression, suggesting a potential cis -regulatory function. Evidence for these regulatory roles may be obtained through careful examination of the precise timing of lncRNA expression relative to adjacent protein-coding genes. Where causal cis -regulatory relationships exist, lncRNA activation is expected to precede changes in adjacent target gene expression. Using an RNA-seq time course of uniquely high temporal resolution, we profiled the expression dynamics of several thousand lncRNAs and protein-coding genes in synchronized, transitioning human cells. Our findings reveal lncRNAs are expressed synchronously with adjacent protein-coding genes. Analysis of lipopolysaccharide-activated mouse dendritic cells revealed the same temporal relationship observed in transitioning human cells. Our findings suggest broad-scale cis -regulatory roles for lncRNAs are not common. The strong association between lncRNAs and adjacent genes may instead indicate an origin as transcriptional by-products from active protein-coding gene promoters and enhancers.
Publisher: IOP Publishing
Date: 15-01-2018
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D0NA00774A
Abstract: We review mathematical models and experimental reporting standards for quantification of interactions between nano-engineered particles and biological systems.
Publisher: Cold Spring Harbor Laboratory
Date: 16-05-2017
DOI: 10.1101/138024
Abstract: Epithelial-mesenchymal transition (EMT) is a process whereby cells undergo reversible phenotypic change, losing epithelial characteristics and acquiring mesenchymal attributes. While EMT underlies normal, physiological programs in embryonic tissue development and adult wound healing, it also contributes to cancer progression by facilitating metastasis and altering drug sensitivity. Using a cell model of EMT (human mammary epithelial (HMLE) cells), we show that miRNAs act as an additional regulatory layer over and above the activity of the transcription factors with which they are closely associated. In this context, miRNAs serve to both enhance expression changes for genes with EMT function, whilst simultaneously reducing transcriptional noise in non-EMT genes. We find that members of the polycistronic miR-200c~141 and miR-183~182 clusters (which are decreased during HMLE cell EMT and are associated with epithelial gene expression in breast cancer patients) co-regulate common targets and pathways to enforce an epithelial phenotype. We demonstrate their combinatorial effects are apparent much closer to endogenous expression levels (and orders of magnitude lower than used in most studies). Importantly, the low levels of combinatorial miRNAs that are required to exert biological function ameliorate the “off-target” effects on gene expression that are a characteristic of supra-physiologic miRNA manipulation. We argue that high levels of over-expression characteristic of many miRNA functional studies have led to an over-estimation of the effect of many miRNAs in EMT regulation, with over 130 in idual miRNAs directly implicated as drivers of EMT. We propose that the functional effects of co-regulated miRNAs that we demonstrate here more-accurately reflects the endogenous post-transcriptional regulation of pathways, networks and processes, and illustrates that the post-transcriptional miRNA regulatory network is fundamentally cooperative.
Publisher: Cold Spring Harbor Laboratory
Date: 09-03-2018
DOI: 10.1101/279406
Abstract: Identifying the progeny of many single progenitor cells simultaneously can be achieved by tagging progenitors with unique heritable DNA barcodes, and allows inferences of lineage relationships, including longitudinally. While this approach has shed new light on single cell fate heterogeneity, data interpretation remains a major challenge. In this study, we applied our developmental interpolated t-Distributed Stochastic Neighbor Embedding (DiSNE) movie approach to visualize the clonal dynamics of hematopoietic reconstitution in primates and identify novel developmental patterns, namely a potential cluster of hematopoietic progenitors with early T cell and later granulocyte production. Complex single cell haematopoietic fate heterogeneity can be visualized and assessed with tSNE pie maps DiSNE movie visualization of in vivo haematopoiesis allows “play back” of the waves of haematopoiesis Identification of novel hematopoietic progenitors with early T cell and later granulocyte production
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 10-2022
Publisher: Cold Spring Harbor Laboratory
Date: 14-08-2020
DOI: 10.1101/2020.08.13.249144
Abstract: Calcium (Ca 2+ ) plays a critical role in the excitation contraction coupling (ECC) process that governs the contraction of cardiomyocytes during each heartbeat. While ryanodine receptors (RyRs) are the primary Ca 2+ channels responsible for mediating cell-wide Ca 2+ transients during ECC, Ca 2+ release via inositol 1,4,5-trisphosphate (IP 3 ) receptors (IP 3 Rs) have been reported to elicit ECC-modulating effects. Recent studies suggest that the proximal localization of IP 3 Rs at dyads grants their ability to modify the occurrence of Ca 2+ sparks (elementary Ca 2+ release events that constitute ECC-associated Ca 2+ transients) which may underlie the modulatory effects on ECC. Here, we aim to uncover the mechanism by which IP 3 Rs affect Ca 2+ spark dynamics. To this end, we developed a mathematical model of the dyad that incorporates IP 3 Rs to reveal their impact on local Ca 2+ handling and corresponding Ca 2+ spark formation. Consistent with published experimental data, our model predicts that the propensity for Ca 2+ spark formation increases with IP 3 R activity. Our simulations support the hypothesis that IP 3 R activity elevates Ca 2+ within the dyad, sensitizing proximal RyRs for future release. However, this lowers Ca 2+ in the JSR available for release and thus results in Ca 2+ sparks with the same duration but lower litudes.
Publisher: The Royal Society
Date: 12-2015
Abstract: The bond graph approach to modelling biochemical networks is extended to allow hierarchical construction of complex models from simpler components. This is made possible by representing the simpler components as thermodynamically open systems exchanging mass and energy via ports. A key feature of this approach is that the resultant models are robustly thermodynamically compliant: the thermodynamic compliance is not dependent on precise numerical values of parameters. Moreover, the models are reusable owing to the well-defined interface provided by the energy ports. To extract bond graph model parameters from parameters found in the literature, general and compact formulae are developed to relate free-energy constants and equilibrium constants. The existence and uniqueness of solutions is considered in terms of fundamental properties of stoichiometric matrices. The approach is illustrated by building a hierarchical bond graph model of glycogenolysis in skeletal muscle.
Publisher: Cold Spring Harbor Laboratory
Date: 27-07-2017
DOI: 10.1101/167635
Abstract: A thorough understanding of cellular development is incumbent on assessing the complexities of fate and kinetics of in idual clones within a population. Here, we develop a system for robust periodical assessment of lineage outputs of thousands of transient clones and establishment of bona fide cellular trajectories. We appraise the development of dendritic cells (DCs) from barcode-labeled hematopoietic stem and progenitor cells (HSPCs) by serially measuring barcode signatures, and visualize this multidimensional data using novel developmental interpolated t-distributed stochastic neighborhood embedding (Di-SNE) time-lapse movies. We identify multiple cellular trajectories of DC development that are characterized by distinct fate bias and expansion kinetics, and determine that these are intrinsically programmed. We demonstrate that conventional DC and plasmacytoid DC trajectories are largely separated already at the HSPC stage. This framework allows systematic evaluation of clonal dynamics and can be applied to other steady-state or perturbed developmental systems.
Publisher: Public Library of Science (PLoS)
Date: 05-12-2018
Publisher: Cold Spring Harbor Laboratory
Date: 27-06-2022
Abstract: The advent of massively parallel sequencing revealed extensive transcription beyond protein-coding genes, identifying tens of thousands of long noncoding RNAs (lncRNAs). Selected functional ex les raised the possibility that lncRNAs, as a class, may maintain broad regulatory roles. Expression of lncRNAs is strongly linked with adjacent protein-coding gene expression, suggesting potential cis -regulatory functions. A more detailed understanding of these regulatory roles may be obtained through careful examination of the precise timing of lncRNA expression relative to adjacent protein-coding genes. Despite the ersity of reported lncRNA regulatory mechanisms, where causal cis -regulatory relationships exist, lncRNA transcription is expected to precede changes in target gene expression. Using a high temporal resolution RNA-seq time course, we profiled the expression dynamics of several thousand lncRNAs and protein-coding genes in synchronized, transitioning human cells. Our findings reveal that lncRNAs are expressed synchronously with adjacent protein-coding genes. Analysis of lipopolysaccharide-activated mouse dendritic cells revealed the same temporal relationship observed in transitioning human cells. Our findings suggest broad-scale cis -regulatory roles for lncRNAs are not common. The strong association between lncRNAs and adjacent genes may instead indicate an origin as transcriptional by-products from active protein-coding gene promoters and enhancers.
Publisher: Cold Spring Harbor Laboratory
Date: 25-03-2021
DOI: 10.1101/2021.03.24.436792
Abstract: Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermo-dynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli ( E. coli ) and published experimental data.
Publisher: Cold Spring Harbor Laboratory
Date: 10-03-2021
DOI: 10.1101/2021.03.09.434672
Abstract: Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an ex le, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition. Biological and physiological systems usually involve multiple underlying processes, mechanisms, structures, and phenomena, referred to here as sub-systems. Modelling the whole system every time from scratch requires a huge amount of effort. An alternative is to model each sub-system in a modular fashion, i.e ., containing meaningful interfaces for connecting to other modules. Such modules are readily combined to produce a whole-system model. For the combined model to be consistent, modules must be described using the same modelling scheme. One way to achieve this is to use energy-based models that are consistent with the conservation laws of physics. Here, we present an approach that achieves this using bond graphs, which allows modules to be combined faster and more efficiently. First, physically plausible modules are generated using a small number of template modules. Then a meaningful interface is added to each module to automate connection. This approach is illustrated by applying this method to an existing model of the circulatory system and verifying the results against the reference model.
Publisher: Springer Science and Business Media LLC
Date: 09-2018
Publisher: Cold Spring Harbor Laboratory
Date: 28-10-2021
DOI: 10.1101/2021.10.26.465839
Abstract: Nanoparticles hold great preclinical promise in cancer therapy but continue to suffer attrition through clinical trials. Advanced, three dimensional (3D) cellular models such as tumor spheroids can recapitulate elements of the tumor environment and are considered the superior model to evaluate nanoparticle designs. However, there is an important need to better understand nanoparticle penetration kinetics and determine how different cell characteristics may influence this nanoparticle uptake. A key challenge with current approaches for measuring nanoparticle accumulation in spheroids is that they are often static, losing spatial and temporal information which may be necessary for effective nanoparticle evaluation in 3D cell models. To overcome this challenge, we developed an analysis platform, termed the Determination of Nanoparticle Uptake in Tumor Spheroids (DONUTS), which retains spatial and temporal information during quantification, enabling evaluation of nanoparticle uptake in 3D tumor spheroids. Outperforming linear profiling methods, DONUTS was able to measure silica nanoparticle uptake to 10 µm accuracy in both isotropic and irregularly shaped cancer cell spheroids. This was then extended to determine penetration kinetics, first by a forward-in-time, center-in-space model, and then by mathematical modelling, which enabled the direct evaluation of nanoparticle penetration kinetics in different spheroid models. Nanoparticle uptake was shown to inversely relate to particle size and varied depending on the cell type, cell stiffness and density of the spheroid model. The automated analysis method we have developed can be applied to live spheroids in situ , for the advanced evaluation of nanoparticles as delivery agents in cancer therapy.
Publisher: Elsevier BV
Date: 11-2019
DOI: 10.1016/J.JTBI.2018.09.034
Abstract: Membrane transporters contribute to the regulation of the internal environment of cells by translocating substrates across cell membranes. Like all physical systems, the behaviour of membrane transporters is constrained by the laws of thermodynamics. However, many mathematical models of transporters, especially those incorporated into whole-cell models, are not thermodynamically consistent, leading to unrealistic behaviour. In this paper we use a physics-based modelling framework, in which the transfer of energy is explicitly accounted for, to develop thermodynamically consistent models of transporters. We then apply this methodology to model two specific transporters: the cardiac sarcoplasmic/endoplasmic Ca
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.JCONREL.2019.06.027
Abstract: 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 (bionano.xyz/estimator), which allows other researchers to easily apply this modeling approach to their experimental results.
Publisher: Institution of Engineering and Technology (IET)
Date: 10-2017
Publisher: Public Library of Science (PLoS)
Date: 13-05-2021
DOI: 10.1371/JOURNAL.PCBI.1008859
Abstract: Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an ex le, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.
Publisher: American Chemical Society (ACS)
Date: 18-04-2019
Abstract: 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.
Location: Switzerland
Start Date: 02-2017
End Date: 06-2020
Amount: $316,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 12-2018
Amount: $639,369.00
Funder: Australian Research Council
View Funded Activity