
Dr Carina Dunlop
Academic and research departments
Department of Mathematics, Centre for Mathematical and Computational Biology.About
Biography
My research focuses on the modelling of biological systems with a particular focus on integrating mechanical signalling and spatial effects into our understanding of biology. I work closely with collaborators across molecular biology, biophysics, medicine and the pharmaceutical industry.
I joined the University of Surrey in 2012, following time as a postdoctoral researcher in the University of Heidelberg, Germany. In Germany, I was hosted in the group of Prof. Ulrich Schwarz (Institute of Theoretical Physics) and worked on integrated mechanical modelling of cell and tissues. Prior to this position, I was both a PhD student and postdoctoral researcher at the University of Oxford in the Oxford Centre for Industrial and Applied Mathematics and the Centre for Mathematical Biology.
Areas of specialism
University roles and responsibilities
- Admissions Tutor
Affiliations and memberships
ResearchResearch interests
I use mathematical and computational approaches to solve a broad range of problems in developmental biology, tissue morphogenesis and cancer modelling. A particular research focus is on the cell as a physical object, incorporating an understanding of the role of mechanical forces into models of biological processes. I draw on a diverse range of concepts in pursuing this research ranging from population modelling to the theories of fluid dynamics and elasticity theory.
Research projects
Open positions PhD positions available within the group - please do get in touch.
Mechanobiology The importance of the cell as a physical object and of mechanical interactions in directing cellular behaviours is now clear. We use mathematical modelling to investigate how physical forces internally generated within cells can direct cellular behaviour through - and in particular on the role of cell-substrate (in vitro) and cell-ECM interactions in this process.mechanotransduction PKPD modelling for cancer treatments Quantitative systems pharmacology is being increasingly recognised as being of high value in the pharma sector. We here focus on PKPD models of drug effects in tissue within the context of mathematical oncology. (A collaboration with AstraZeneca) Ovarian DevelopmentIt is becoming increasingly clear across tissues that mechanical interactions play a crucial role in coordinating cell behaviours within tissues. These mechanical signals must be then integrated with other biochemical signals to determine cell and tissue response. A mechanical perspective is integral to e.g. bioengineering approaches to tissue engineering across tissue types. In reproductive biology our understanding of the role of mechanics in tissue behaviour is, however, much less developed despite their importance. Specifically in the case of the ovary, the tissue undergoes large and rapid changes and remodelling over monthly cycles with consequently large mechanical effects expected to play a role. This is a collaboration with Prof. Kate Hardy and Prof. Stephen Franks (both Institute of Reproductive and Developmental Biology, Imperial College).
Modelling for biophysical experiment The range of techniques available for interrogating the biophysics of cells and tissues has exploded over recent years. These new experiments have illuminated the physical nature of cells. We use mathematical modelling of cellular mechanics to inform the interpretation of what these experimental observations could imply for cell cytoskeletal dynamics and force generation. This is particularly important, where cells are seen to interact with each other in multicellular systems so that isolating individual cell behaviours experimentally is difficult.
Research interests
I use mathematical and computational approaches to solve a broad range of problems in developmental biology, tissue morphogenesis and cancer modelling. A particular research focus is on the cell as a physical object, incorporating an understanding of the role of mechanical forces into models of biological processes. I draw on a diverse range of concepts in pursuing this research ranging from population modelling to the theories of fluid dynamics and elasticity theory.
Research projects
It is becoming increasingly clear across tissues that mechanical interactions play a crucial role in coordinating cell behaviours within tissues. These mechanical signals must be then integrated with other biochemical signals to determine cell and tissue response. A mechanical perspective is integral to e.g. bioengineering approaches to tissue engineering across tissue types. In reproductive biology our understanding of the role of mechanics in tissue behaviour is, however, much less developed despite their importance. Specifically in the case of the ovary, the tissue undergoes large and rapid changes and remodelling over monthly cycles with consequently large mechanical effects expected to play a role. This is a collaboration with Prof. Kate Hardy and Prof. Stephen Franks (both Institute of Reproductive and Developmental Biology, Imperial College).
Supervision
Postgraduate research supervision
Kieran Boniface, PhD, Oct. 2019 - present, Mathematical models for tissue growth and development with applications to organoids and tissue eningeering
Josephine Solowiej-Wedderburn, PhD, Oct. 2017 - present, Mechanical models of cell-substrate interactions
Adam Nasim, PhD, Jan. 2018 - present, Mathematical modelling of pharmacological approaches to cancer treatment (collaboration with Dr James Yates, AstraZeneca)
Teaching
I am lecturing this year on
MATM040 - Mathematical Biology and Physiology
MAT2050 - Inviscid Fluid Dynamics
Publications
Follicle development in the ovary must be tightly regulated to ensure cyclical release of oocytes (ovulation). Disruption of this process is a common cause of infertility, for example via polycystic ovary syndrome (PCOS) and premature ovarian insufficiency (POI). Recent ex vivo studies suggest that follicle growth is mechanically regulated, however, crucially, the actual mechanical properties of the follicle microenvironment have remained unknown. Here we use atomic force microscopy (AFM) spherical probe indentation to map and quantify the mechanical microenvironment in the mouse ovary, at high resolution and across the entire width of the intact (bisected) ovarian interior. Averaging over the entire organ, we find the ovary to be a fairly soft tissue comparable to fat or kidney (mean Young’s Modulus 3.3 +/-2.5 kPa). This average, however, conceals substantial spatial variations, with the overall range of tissue stiffnesses from c. 0.5 –10 kPa, challenging the concept that a single Young’s Modulus can effectively summarize this complex organ. Considering the internal architecture of the ovary, we find that stiffness is low at the edge and centre which are dominated by stromal tissue, and highest in an intermediate zone that is dominated by large developmentally-advanced follicles, confirmed by comparison with immunohistology images. These results suggest that largefollicles are mechanically dominant structures in the ovary, contrasting with previous expectations that collagen-rich stroma would dominate. Extending our study to the highest resolutions (c. 5 μm) showed substantial mechanical variations within the larger zones, even over very short (sub-100 μm) lengths, and especially within the stiffer regions of the ovary. Taken together, our results provide a new, physiologically accurate, framework for ovarian biomechanics and follicle tissue engineering.
A 2‐day meeting was held by members of the UK Quantitative Systems Pharmacology Network (http://www.qsp‐uk.net/) in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: • Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. • Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. • Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. • Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.
There is increasing experimental interest in mechanotransduction in multi-cellular tissues as opposed to single cells. This is driven by a growing awareness of the importance of physiologically relevant three-dimensional culture and of cell-cell and cell-gel interactions in directing growth and development. The paradigm biophysical technique for investigating tissue level mechanobiology in this context is to grow model tissues in artificial gels with well-defined mechanical properties. These studies often indicate that the sti↵ness of the encapsulating gel can significantly alter cellular behaviours. We demonstrate here potential mechanisms linking tissue growth with sti↵ness-mediated mechanotransduction. We show how tissue growth in gel systems generates points at which there is a significant qualitative change in the cellular stress and strain experienced. We show analytically how these potential switching points depend on the mechanical properties of the constraining gel and predict when they will occur. Significantly, we identify distinct mechanisms that act separately in each of the stress and strain fields at di↵erent times. These observations suggest growth as a potential physical mechanism coupling gel sti↵ness with cellular mechanotransduction in three-dimensional tissues. We additionally show that non-proliferating areas, in the case that the constraining gel is soft compared with the tissue, will expand and contract passively as a result of growth. Central compartment size is thus seen to not be a reliable indicator on its own for growth initiation or active behaviour.
The ability of cells to sense and respond to the mechanical properties of their environments is fundamental to a range of cellular behaviours, with substrate stiffness increasingly being found to be a key signalling factor. Although active contractility of the cytoskeleton is clearly necessary for stiffness sensing in cells, the physical mechanisms connecting contractility with mechanosensing and molecular conformational change are not well understood. Here we present a contractility-driven mechanism for linking changes in substrate stiffness with internal conformational changes. Cellular contractility is often assumed to imply an associated compressive strain. We show, however, that where the contractility is non-uniform, localized areas of internal stretch can be generated as stiffer substrates are encountered. This suggests a physical mechanism for the stretch-activation of mechanotransductive molecules on stiffer substrates. Importantly, the areas of internal stretch occur deep within the cell and not near the cellular perimeter, which region is more traditionally associated with stiffness sensing through e.g. focal adhesions. This supports recent experimental results on whole-cell mechanically-driven mechanotransduction. Considering cellular shape we show that aspect ratio acts as an additional control parameter, so that the onset of positive strain moves to higher stiffness values in elliptical cells.
A two-day meeting was held by members of the UK Quantitative Systems Pharmacology Network (http://www.qsp-uk.net/) in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modelling applications in non-clinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: - Evaluate the predictivity and reproducibility of animal cancer models through pre-competitive collaboration - Apply mechanism of action (MoA) based mechanistic model derived from nonclinical data to clinical trial data - Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions - Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design
Syncytial embryos develop through cycles of nuclear division and rearrangement within a common cytoplasm. A paradigm example is Drosophila melanogaster in which nuclei form an ordered array in the embryo surface over cell cycles 10-13. This ordering process is assumed to be essential for subsequent cellularisation. Using quantitative tissue analysis, it has previously been shown that the regrowth of actin and microtubule networks after nuclear division generates reordering forces that counteract its disordering effect (Kanesaki et al., 2011). We present here an individual-based computer simulation modelling the nuclear dynamics. In contrast to similar modelling approaches e.g. epithelial monolayers or tumour spheroids, we focus not on the spatial dependence, but rather on the time-dependence of the interaction laws. We show that appropriate phenomenological inter-nuclear force laws reproduce the experimentally observed dynamics provided that the cytoskeletal network regrows sufficiently quickly after mitosis. Then repulsive forces provided by the actin system are necessary and sufficient to regain the observed level of order in the system, after the strong disruption resulting from cytoskeletal network disassembly and spindle formation. We also observe little mixing of nuclei through cell cycles. Our study highlights the importance of the dynamics of cytoskeletal forces during this critical phase of syncytial development and emphasises the need for real-time experimental data at high temporal resolution. © 2014 Elsevier Ltd.
It is known that cells grown in 3D are more tolerant to drug treatment than those grown in dispersion but the mechanism for this is still not clear; cells grown in 3D have opportunities to develop inter-cell communication, but are also closely packed which may impede diffusion. In this study we examine methods for dielectrophoresis-based cell aggregation of both suspension and adherent cell lines and compare the effect of various drugs on cells grown in 3D and 2D. Comparing viability of pharmacological interventions on 3D cell clusters against both suspension cells and adherent cells grown in monolayer, as well as against a unicellular organism with no propensity for intracellular communication, we suggest that 3D aggregates of adherent cells, compared to suspension cells, show a substantially different drug response to cells grown in monolayer, which increases as the IC50 is approached. Further, a mathematical model of the system for each agent demonstrates that changes to drug response are due to inherent changes in the system of adherent cells from the 2D to 3D state. Finally, differences within electrophysiological membrane properties of the adherent cell type suggest this parameter plays an important role in the differences found in the 3D drug response.
Maintenance and activation of the limited supply of primordial follicles in the ovary are important determinants of reproductive lifespan. Currently, the molecular programme that maintains the primordial phenotype and the early events associated with follicle activation are not well defined. Here we have systematically analysed these events using microscopy and detailed image analysis. Using the immature mouse ovary as a model, we demonstrate that the onset of granulosa cell (GC) proliferation results in increased packing density on the oocyte surface and consequent GC cuboidalisation. These events precede oocyte growth and nuclear translocation of FOXO3a, a transcription factor important in follicle activation. Immunolabelling of the TGF signalling mediators and transcription factors, SMAD2/3, revealed a striking expression pattern specific to GCs of small follicles. SMAD2/3 was expressed in the nuclei of primordial GCs but was mostly excluded in early growing follicles. In activated follicles, GC nuclei lacking SMAD2/3 generally expressed Ki67. These findings suggest that the first phenotypic changes during follicle activation are observed in GCs, and that TGF signalling is fundamental for regulating GC arrest and the onset of proliferation.