Forthcoming events in this series
Since the invention of the microscope, scientists have known that pond-dwelling algae can actually swim – powering their way through the fluid using tiny limbs called cilia and flagella. Only recently has it become clear that the very same structure drives important physiological and developmental processes within the human body. Motivated by this connection, we explore flagella-mediated swimming gaits and stereotyped behaviours in diverse species of algae, revealing the extent to which control of motility is driven intracellularly. These insights suggest that the capacity for fast transduction of signal to peripheral appendages may have evolved far earlier than previously thought.
DNA computing is emerging as a versatile technology that promises a vast range of applications, including biosensing, drug delivery and synthetic biology. DNA logic circuits can be achieved in solution using strand displacement reactions, or by decision-making molecular robots-so called 'walkers'-that traverse tracks placed on DNA 'origami' tiles.
Similarly to conventional silicon technologies, ensuring fault-free DNA circuit designs is challenging, with the difficulty compounded by the inherent unreliability of the DNA technology and lack of scientific understanding. This lecture will give an overview of computational models that capture DNA walker computation and demonstrate the role of quantitative verification and synthesis in ensuring the reliability of such systems. Future research challenges will also be discussed.
Classical work of Jeffery from 1922 established how at low Reynolds number, ellipsoids in steady shear flow undergo periodic motion with non-uniform rotation rate, termed 'Jeffery orbits'. I will present two problems where Jeffery orbits play a critical role in understanding the transport and aggregation of rod-shaped organisms. I will discuss the trapping of motile chemotactic bacteria in high shear, and the sedimentation rate of negatively buoyant plankton.
Many symptoms of Parkinson’s disease are connected with abnormally high levels of synchrony in neural activity. A successful and established treatment for a drug-resistant form of the disease involves electrical stimulation of brain areas affected by the disease, which has been shown to desynchronize neural activity. Recently, a closed-loop deep brain stimulation has been developed, in which the provided stimulation depends on the amplitude or phase of oscillations that are monitored in patient’s brain. The aim of this work was to develop a mathematical model that can capture experimentally observed effects of closed-loop deep brain stimulation, and suggest how the stimulation should be delivered on the basis of the ongoing activity to best desynchronize the neurons. We studied a simple model, in which individual neurons were described as coupled oscillators. Analysis of the model reveals how the therapeutic effect of the stimulation should depend on the current level of synchrony in the network. Predictions of the model are compared with experimental data.
The derivation of so-called `effective descriptions' that explicitly incorporate microscale physics into a macroscopic model has garnered much attention, with popular applications in poroelasticity, and models of the subsurface in particular. More recently, such approaches have been applied to describe the physics of biological tissue. In such applications, a key feature is that the material is active, undergoing both elastic deformation and growth in response to local biophysical/chemical cues.
Here, two new macroscale descriptions of drug/nutrient-limited tissue growth are introduced, obtained by means of two-scale asymptotics. First, a multiphase viscous fluid model is employed to describe the dynamics of a growing tissue within a porous scaffold (of the kind employed in tissue engineering applications) at the microscale. Secondly, the coupling between growth and elastic deformation is considered, employing a morpho-elastic description of a growing poroelastic medium. Importantly, in this work, the restrictive assumptions typically made on the underlying model to permit a more straightforward multiscale analysis are relaxed, by considering finite growth and deformation at the pore scale.
In each case, a multiple scales analysis provides an effective macroscale description, which incorporates dependence on the microscale structure and dynamics provided by prototypical `unit cell-problems'. Importantly, due to the complexity that we accommodate, and in contrast to many other similar studies, these microscale unit cell problems are themselves parameterised by the macroscale dynamics.
In the first case, the resulting model comprises a Darcy flow, and differential equations for the volume fraction of cells within the scaffold and the concentration of nutrient, required for growth. Stokes-type cell problems retain multiscale dependence, incorporating active cell motion [1]. Example numerical simulations indicate the influence of microstructure and cell dynamics on predicted macroscale tissue evolution. In the morpho-elastic model, the effective macroscale dynamics are described by a Biot-type system, augmented with additional terms pertaining to growth, coupled to an advection--reaction--diffusion equation [2].
[1] HOLDEN, COLLIS, BROOK and O'DEA. (2018). A multiphase multiscale model for nutrient limited tissue growth, ANZIAM (In press)
[2] COLLIS, BROWN, HUBBARD and O'DEA. (2017). Effective Equations Governing an Active Poroelastic Medium, Proceedings of the Royal Society A. 473, 20160755
Dr Nicola Beer heads up the Department of Stem Cell Engineering at the new Novo Nordisk Research Centre Oxford. Her team will use human stem cells to derive metabolically-relevant cells and tissues such as islets, hepatocytes, and adipocytes todiscover novel secreted factors and corresponding signalling pathways which modify cell function, health, and viability. Bycombining in vitro-differentiated human stem cell-derived models with CRISPR and other genomic targeting techniques, the teamassay cell function from changes in a single gene up to a genome-wide scale. Understanding the genes and pathways underlying cell function (and dysfunction) highlights potential targets for new Type 2 Diabetes therapeutics. Dr Beer will talk about the work ongoing in her team, as well as more broadly about the role of human stem cells in drug discovery and patient treatment.
A wide range of chronic degenerative diseases of mankind result from the accumulation of altered forms of self proteins, resulting in cell toxicity, tissue destruction and chronic inflammatory processes in which the body’s immune system contributes to further cell death and loss of function. A hallmark of these conditions, which include major disease burdens such as Alzheimer’s Disease and type II diabetes, is the formation of long fibrillar polymers that are deposited in expanding tangled masses called plaques. Recently, similarities between these pathological accumulations and physiological mechanisms for organising intracellular space have been recognised, and formal demonstrations that amyloid accumulations form hydrogels have confirmed this link. We are interested in the pathological consequences of amyloid hydrogel formation and in order to study these processes we combine modelling of the assembly process with biophysical measurement of gelation and its cellular consequences.
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T cells stimulation by antigen (peptide-MHC, pMHC) initiates adaptive immunity, a major factor contributing to vertebrate fitness. The T cell antigen receptor (TCR) present on the surface of T cells is the critical sensor for the recognition of and response to “foreign" entities, including microbial pathogens and transformed cells. Much is known about the complex molecular machine physically connected to the TCR to initiate, propagate and regulate signals required for cellular activation. However, we largely ignore the physical distribution, dynamics and reaction energetics of this machine before and after TCR binding to pMHC. I will illustrate a few basic notions of TCR signalling and potent quantitative in-cell approaches used to interpret TCR signalling behaviour. I will provide two examples where mathematical formalisation will be welcome to better understand the TCR signalling process.
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Bacteria swim by rotating semi-rigid helical flagellar filaments, using an ion driven rotary motor embedded in the membrane. Bacteria are too small to sense a spatial gradient and therefore sense changes in time, and use the signals to bias their direction changing pattern to bias overall swimming towards a favourable environment. I will discuss how interdisciplinary research has helped us understand both the mechanism of motor function and its control by chemosensory signals.
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for details.
Please note that this seminar will take place at the Physical and Theoretical Chemistry Laboratory within the
Department of Chemistry, room, PTCL lecture theatre.
The ability of cells to sense and respond to the mechanical properties of their environments is fundamental to cellular behaviour, with stiffness found to be a key control parameter. The physical mechanisms underpinning mechanosensing are, however, not well understood. I here consider the key physical cellular behaviours of active contractility of the internal cytoskeleton and cell growth, coupling these into mechanical models. These models suggest new distinct mechanisms of mechanotransduction in cells and tissues.
I will discuss a new theoretical approach to information and decisions in signalling systems and relate this to new experimental results about the NF-kappaB signalling system. NF-kappaB is an exemplar system that controls inflammation and in different contexts has varying effects on cell death and cell division. It is commonly claimed that it is information processing hub, taking in signals about the infection and stress status of the tissue environment and as a consequence of the oscillations, transmitting higher amounts of information to the hundreds of genes it controls. My aim is to develop a conceptual and mathematical framework to enable a rigorous quantifiable discussion of information in this context in order to follow Francis Crick's counsel that it is better in biology to follow the flow of information than those of matter or energy. In my approach the value of the information in the signalling system is defined by how well it can be used to make the "correct decisions" when those "decisions" are made by molecular networks. As part of this I will introduce a new mathematical method for the analysis and simulation of large stochastic non-linear oscillating systems. This allows an analytic analysis of the stochastic relationship between input and response and shows that for tightly-coupled systems like those based on current models for signalling systems, clocks, and the cell cycle this relationship is highly constrained and non-generic.
This will be a whistle-stop tour of a few topics on infectious disease modelling, mainly influenza. Topics to include:
Julia will be visiting the Mathematical Institute on sabbatical this term, and hopes this talk will help us find areas of overlapping interests.
Image use continues to increase in both biomedical sciences and clinical practice. State of the art acquisition techniques allow characterisation from subcellular to whole organ scale, providing quantitative information of structure and function. In the heart, for example, images acquired from a single modality (cardiac MRI) can characterise micro- and macrostructure, describe mechanical function and measure blood flow. In the lungs, new contrast agents can be used to visualise the flow of gas in free breathing subjects. This provides rich new sources of information as well as new challenges to extract data in a way that is useful to clinicians as well as computer modellers.
I will describe efforts in my group to use the latest advances in machine learning to analyse images, and explain how we are applying these to the development of accurate computer models of the heart.
Hypoxia, i.e. a reduction in dissolved oxygen concentration below physiologically normal levels, has been identified as playing a critical role
in the progression of many types of disease and as a key determinant of the success of cancer treatment. It poses a particular challenge for treatments
such as radiotherapy, photodynamic and sonodynamic therapy which rely on the production of reactive oxygen species. Strategies for treating hypoxia have
included the development of hypoxia-selective drugs as well as methods for directly increasing blood oxygenation, e.g. hyperbaric oxygen therapy, pure
oxygen or carbogen breathing, ozone therapy, hydrogen peroxide injections and administration of suspensions of oxygen carrier liquids. To date, however,
these approaches have delivered limited success either due to lack of proven efficacy and/or unwanted side effects. Gas microbubbles, stabilised by a
biocompatible shell have been used as ultrasound contrast agents for several decades and have also been widely investigated as a means of promoting drug
delivery. This talk will present our recent research on the use of micro and nanobubbles to deliver both drug molecules and oxygen simultaneously to a
tumour to facilitate treatment.
We adopt the paradigm of understanding how the heart develops during pregnancy as a first principal to inform on adult heart repair and regeneration. Our target for cell-based repair is the epicardium and epicardium-derived cells (EPDCs) which line the outside of the forming heart and contribute vascular endothelial and smooth muscle cells to the coronary vasculature, interstitial fibroblasts and cardiomyocytes. The epicardium can also act as a source of signals to condition the growth of the underlying embryonic heart muscle. In the adult heart, whilst the epicardium is retained, it is effectively quiescent. We have sought to extrapolate the developmental potential of the epicardium to the adult heart following injury by stimulating dormant epicardial cells to give rise to new muscle and vasculature. In parallel, we seek to modulate the local environment into which the new cells emerge: a cytotoxic mixture of inflammation and fibrosis which prevents cell engraftment and integration with survived heart tissue. To this end we manipulate the lymphatic vessels in the heart given that, elsewhere in the body, the lymphatics survey the immune system and modulate inflammation at peripheral injury sites. We recently described the development of the cardiac lymphatic vasculature and revealed in the adult heart that they undergo increased vessel sprouting (lymphangiogenesis) in response to injury, to improve function, remodelling and fibrosis. We are currently investigating whether increased lymphangiogenesis functions to clear immune cells and constrain the reparative response for optimal healing.
Feedback control is found extensively in many natural and technological systems. Indeed, many biological processes use feedback
to regulate key processes – examples include bacterial chemotaxis and negative autoregulation in genetic circuits. Despite the prevalence of
feedback in natural systems, its design and implementation in a Synthetic Biological context is much harder. In this talk I will give
examples of how we implemented feedback systems in three different biological systems. The first one concerns the design of a synthetic
recombinase-based feedback loop, which results into robust expression. The second describes the use of small RNAs to post-transcriptionally
regulate gene expression through interaction with messenger RNA (mRNA). The third involves the introduction of negative feedback in a
two-component signalling system through a controllable phosphatase. Closing, I will outline the challenges posed by the design of such
systems, both theoretical and on their implementation.
Current medical practice is driven by the experience of clinicians, by the difficulties of integrating enormous amounts of complex and heterogeneous static and dynamic data and by clinical guidelines designed for the “average” patient. In this talk, I will describe some of my research on developing novel, specially-crafted machine learning theories, methods and systems aimed at extracting actionable intelligence from the wide variety of information that is becoming available (in electronic health records and elsewhere) and enabling every aspect of medical care to be personalized to the patient at hand. Because of the unique and complex characteristics of medical data and medical questions, many familiar machine-learning methods are inadequate. My work therefore develops and applies novel machine learning theory and methods to construct risk scores, early warning systems and clinical decision support systems for screening and diagnosis and for prognosis and treatment. This work achieves enormous improvements over current clinical practice and over existing state-of-the-art machine learning methods. By design, these systems are easily interpretable and so allow clinicians to extract from data the necessary knowledge and representations to derive data-driven medical epistemology and to permit easy adoption in hospitals and clinical practice. My team has collaborated with researchers and clinicians in oncology, emergency care, cardiology, transplantation, internal medicine, etc. You can find more information about our past research in this area at: http://medianetlab.ee.ucla.edu/MedAdvance.