A mechanistic model of erythroblast growth inhibition: Optimising red blood cell manufacture

Manufacture of red blood cells (RBCs) from progenitors has been proposed as a method to reduce reliance on donors. Such a process would need to be extremely efficient for economic viability given a relatively low value product and high 2E12 cell dose. To achieve efficient process optimisation and scale-up an integrated approach comprising both experimentation and modelling is required. Using an automated stirred tank micro-bioreactor (ambr®, Sartorius Stedim, UK) we have shown that initially erythroblasts rapidly proliferate but then enter an inhibited growth phase. Experimentally we have confirmed that the conventional constraints on cell manufacturing efficiency, such as mass transfer, common metabolic limitations, or previously reported paracrine signals were not responsible for this inhibition. To further understand the mechanisms underlying the growth inhibition, we have used our own novel software interface, designed for the description, testing and manipulation of hypothetical dynamic mechanistic models. CD34+ cells derived from cord blood were grown in culture under erythroid expansion conditions. Cells were transferred to fresh culture medium and subject to different operating conditions. High time resolution growth curves were generated for each condition to distinguish between alternative models of growth and inhibition. The software, along with the experimental data, enabled a series of hypotheses regarding the mechanism of inhibition to be tested via the development of incrementally more complex mechanistic models based on the dominant phenomena involved in cell culture (e.g. substrate-dependent growth, cell death). Further experiments were performed under different operational conditions to test the predictive capabilities of the model and allow optimisation. These iterations produced a relatively simple deterministic mechanistic model based on inhibitor production and decay that could predict erythroblast growth behaviour as a consequence of medium provision and cell density strategy. We have described an experimentally efficient approach to model key cell growth behaviour and operational consequences for manufacturing, which has general relevance across therapeutic cell culture systems where feedback signals are prevalent. The approach supports a high degree of confidence in manufacturing control due to mechanistic underpinnings and is complimentary to a hypothesis driven approach to further understand influences of cell growth

Metabolomics in the Analysis of Inflammatory Diseases

Most infections and traumatic injuries are cleared or repaired relatively rapidly and metabolic homoeostasis is soon restored. However, there is a broad range of inflammatory diseases which involve chronic activation of the immune system and, as a result, chronic persistent inflammation. We have been studying the metabolic consequences of chronic inflammatory diseases with the aim of identifying metabolic fingerprints which may provide clues about why the localised tissue disease persists

The role of training in IBA implementation beyond primary health care settings in the UK

There has been a considerable drive to encourage a wide range of professional groups to incorporate alcohol screening (or identification) and brief advice (IBA) into their everyday practice. This article aims to examine the role of training in promoting IBA delivery in contexts outside primary care and other health settings. The data are drawn mainly from a structured online survey supplemented by illustrative material from nine qualitative interviews and insights from an expert workshop. Findings support the results from other research that issues relating to role relevance and role security continue to act as barriers to professional change. Furthermore, issues of organisational commitment and organisational barriers are insufficiently addressed in strategy to promote wider use of IBA. The article concludes that development of appropriate training for alcohol IBA needs to take account of the role of IBA within a complex interactive system of related services and help seeking pathways and consider how training can contribute to changing both professional attitudes and behaviours and organisational approaches to implementing and sustaining IBA in everyday professional practice

On your marks, get SET(D1A)::the race to protect stalled replication forks

We recently identified that methylation of lysine 4 of histone H3 (H3K4) by SETD1A (SET domain containing 1A) maintains genome stability by protecting newly-replicated DNA from degradation. Mechanistically, SETD1A-dependent histone methylation regulates nucleosome mobilisation by FANCD2 (FA complementation group D2), a crucial step in maintaining genome integrity with important implications in chemo-sensitivity

Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support

Individuals with type 1 diabetes have to monitor their blood glucose levels, determine the quantity of insulin required to achieve optimal glycaemic control and administer it themselves subcutaneously, multiple times per day. To help with this process bolus calculators have been developed that suggest the appropriate dose. However these calculators do not automatically adapt to the specific circumstances of an individual and require fine-tuning of parameters, a process that often requires the input of an expert. To overcome the limitations of the traditional methods this paper proposes the use of an artificial intelligence technique, case-based reasoning, to personalise the bolus calculation. A novel aspect of our approach is the use of temporal sequences to take into account preceding events when recommending the bolus insulin doses rather than looking at events in isolation. The in silico results described in this paper show that given the initial conditions of the patient, the temporal retrieval algorithm identifies the most suitable case for reuse. Additionally through insulin-on-board adaptation and postprandial revision, the approach is able to learn and improve bolus predictions, reducing the blood glucose risk index by up to 27% after three revisions of a bolus solution