Past Mathematical Biology and Ecology Seminar

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.

Please see https://www.eventbrite.co.uk/e/qbiox-colloquium-dunn-school-seminar-hil…

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:

• challenges in capturing dynamics of pathogens with multiple co-circulating strains
• untangling the 2009 influenza pandemic from medical insurance claims data from the US
• bioinformatic methods to detect viral packaging signals
• and a big science project (top secret until the talk!)

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.

Building on advancements in computer vision we now have an array of visual tracking methods that allow the reliable estimation of cellular motion in high-throughput settings as well as more complex biological specimens. In many cases the underlying assumptions of these methods are still not well defined and result in failures when analysing large scale experiments.

Using organotypic co-culture systems we can now mimic more physiologically relevant microenvironments in vitro.  The robust analysis of cellular dynamics in such complex biological systems remains an open challenge. I will attempt to outline some of these challenges and provide some very preliminary results on analysing more complex cellular behaviours.

In the first part of my presentation, I will briefly summarize a dynamic view of the cell cycle created in collaboration with Prof John Tyson over the past 25 years. 
In our view, the decisions a cell must make during DNA synthesis and mitosis are controlled by bistable switches, which provide abrupt and irreversible transition 
between successive cell cycle phases. In addition, bistability provides the foundation for 'checkpoints' that can stop cell proliferation if problems arise 
(e.g., DNA damage by UV irradiation). In the second part of my talk, I will highlight a few representative examples from our ongoing BBSRC Strategic LoLa grant 
(http://cellcycle.org.uk/) in which we are testing the predictions of our theoretical ideas in human cells in collaboration with four experimental groups.

Proteomics is seen as the next logical step after genomics to understand life processes at the molecular level. With increasing capabilities of modern mass spectrometers the deep proteome (>8000 proteins detected) has become widely accessible, only to be replaced recently by the "Ultra-deep proteome" with ~14000 proteins detected in a single cell line. Furthermore, new data search algorithms and sample preparation methods allow not only to achieve comprehensive sequence coverage for the majority of proteins, but also to detect protein variations and single amino acid polymorphisms in proteins, further linking genomic variation to protein phenotypes. The combination of genomic and proteomic information on individual (patient) level could mark the beginning of the "Post-Proteomic Era".

Please register via https://www.eventbrite.co.uk/e/qbiox-colloquium-trinity-term-2017-ticke…

The ability to study the transcriptome, proteome – and other aspects – of many individual cells represents one of the most important technical breakthroughs and tools in biology and medical science of the past few years. They are revolutionising study of biological systems and human disease, enabling for example: hypothesis-free identification of rare pathogenic (or protective) cell subsets in chronic diseases, routine monitoring of patient immune phenotypes and direct discovery of mole cular targets in rare cell populations. In parallel, new computational and analytical approaches are being intensively developed to analyse the vast data sets generated by these technologies. However, there is still a huge gap between our ability to generate the data, analyse their technical soundness and actually interpret them. The QBIOX network may provide for a unique opportunity to complement recent investments in Oxford technical capabilities in single-cell technologies with the development of revolutionary, visionary ways of interpreting the data that would help Oxford researchers to compete as leaders in this field.

Please register via https://www.eventbrite.co.uk/e/qbiox-colloquium-trinity-term-2017-ticke…

Biomedical research and clinical practice rely on complex and multimodality

datasets for the characterisation of human organs in health and disease. In

computational biomedicine, we often argue that multiscale computational

models are and will be increasingly required as tools for data integration,

for probing the established knowledge of physiological systems, and for

predictions of the effects of therapies and disease. But what has

computational biomedicine delivered so far? This presentation will describe

successes, failures and future directions of computational models in

cardiac research from basic to translational science.

All data intelligence processes are designed for processing a finite amount of data within a time period. In practice, they all encounter
some difficulties, such as the lack of adequate techniques for extracting meaningful information from raw data; incomplete, incorrect 
or noisy data; biases encoded in computer algorithms or biases of human analysts; lack of computational resources or human resources; urgency in 
making a decision; and so on. While there is a great enthusiasm to develop automated data intelligence processes, it is also known that
many of such processes may suffer from the phenomenon of data processing inequality, which places a fundamental doubt on the credibility of these 
processes. In this talk, the speaker will discuss the recent development of an information-theoretic measure (by Chen and Golan) for optimizing 
the cost-benefit ratio of a data intelligence process, and will illustrate its applicability using examples of data analysis and 
visualization processes including some in bioinformatics.

The management of natural resources, from fisheries and climate change to gut bacteria colonies, all require the development of ecological models that represent the full spectrum of population interactions, from competition, through mixotrophy and mutualism, to predation.

Mixotrophic plankton, that both photosynthesise and eat other plankton, underpin all marine food webs and help regulate climate by facilitating gas exchange between the ocean and atmosphere. We show the recent discovery that their feeding preferences change with increasing temperature implies climate change could dramatically alter the structure of marine food webs.

We describe a theoretical framework that reveals the key role of mixotrophy in facilitating transitions between trophic interactions. Mixotrophy smoothly and stably links competition to predation, and extends this linkage to include mutualism in both facultative and obligate forms. Such smooth stable transitions further allow the development of eco-evolutionary theory at the population level through quantitative trait modelling.