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Journal articlePeach R, Yaliraki S, Lefevre D, et al., 2019,
Data-driven unsupervised clustering of online learner behaviour 
, npj Science of Learning, Vol: 4, ISSN: 2056-7936The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here we introduce a mathematical framework for the analysis of time series of online learner engagement, which allows the identification of clusters of learners with similar online temporal behaviour directly from the raw data without prescribing a priori subjective reference behaviours. The method uses a dynamic time warping kernel to create a pairwise similarity between time series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to identify groups of learners with similar temporal behaviour. To showcase our approach, we analyse task completion data from a cohort of learners taking an online post-graduate degree at Imperial Business School. Our analysis reveals clusters of learners with statistically distinct patterns of engagement, from distributed to massed learning, with different levels of regularity, adherence to pre-planned course structure and task completion. The approach also reveals outlier learners with highly sporadic behaviour. A posteriori comparison against student performance shows that, whereas high performing learners are spread across clusters with diverse temporal engagement, low performers are located significantly in the massed learning cluster, and our unsupervised clustering identifies low performers more accurately than common machine learning classification methods trained on temporal statistics of the data. Finally, we test the applicability of the method by analysing two additional datasets: a different cohort of the same course, and time series of different format from another university.
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Book chapterAltuncu MT, Sorin E, Symons JD, et al., 2019,
Extracting information from free text through unsupervised graph-based clustering: an application to patient incident records
The large volume of text in electronic healthcare records often remainsunderused due to a lack of methodologies to extract interpretable content. Herewe present an unsupervised framework for the analysis of free text thatcombines text-embedding with paragraph vectors and graph-theoretical multiscalecommunity detection. We analyse text from a corpus of patient incident reportsfrom the National Health Service in England to find content-based clusters ofreports in an unsupervised manner and at different levels of resolution. Ourunsupervised method extracts groups with high intrinsic textual consistency andcompares well against categories hand-coded by healthcare personnel. We alsoshow how to use our content-driven clusters to improve the supervisedprediction of the degree of harm of the incident based on the text of thereport. Finally, we discuss future directions to monitor reports over time, andto detect emerging trends outside pre-existing categories.
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Journal articleAryaman J, Bowles C, Jones NS, et al., 2019,
Mitochondrial network state scales mtDNA genetic dynamics
, Genetics, Vol: 212, Pages: 1429-1443, ISSN: 0016-6731Mitochondrial DNA (mtDNA) mutations cause severe congenital diseases but may also be associated with healthy aging. MtDNA is stochastically replicated and degraded, and exists within organelles which undergo dynamic fusion and fission. The role of the resulting mitochondrial networks in the time evolution of the cellular proportion of mutated mtDNA molecules (heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy variance), remains incompletely understood. Heteroplasmy variance is particularly important since it modulates the number of pathological cells in a tissue. Here, we provide the first wide-reaching theoretical framework which bridges mitochondrial network and genetic states. We show that, under a range of conditions, the (genetic) rate of increase in heteroplasmy variance and de novo mutation are proportionally modulated by the (physical) fraction of unfused mitochondria, independently of the absolute fission-fusion rate. In the context of selective fusion, we show that intermediate fusion/fission ratios are optimal for the clearance of mtDNA mutants. Our findings imply that modulating network state, mitophagy rate and copy number to slow down heteroplasmy dynamics when mean heteroplasmy is low could have therapeutic advantages for mitochondrial disease and healthy aging.
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Journal articleKuntz Nussio J, Thomas P, Stan GB, et al., 2019,
Bounding the stationary distributions of the chemical master equation via mathematical programming
, Journal of Chemical Physics, Vol: 151, ISSN: 0021-9606The stochastic dynamics of biochemical networks are usually modelled with the chemical master equation (CME). The stationary distributions of CMEs are seldom solvable analytically, and numerical methods typically produce estimates with uncontrolled errors. Here, we introduce mathematical programming approaches that yield approximations of these distributions with computable error bounds which enable the verification of their accuracy. First, we use semidefinite programming to compute increasingly tighter upper and lower bounds on the moments of the stationary distributions for networks with rational propensities. Second, we use these moment bounds to formulate linear programs that yield convergent upper and lower bounds on the stationary distributions themselves, their marginals and stationary averages. The bounds obtained also provide a computational test for the uniqueness of the distribution. In the unique case, the bounds form an approximation of the stationary distribution with a computable bound on its error. In the non unique case, our approach yields converging approximations of the ergodic distributions. We illustrate our methodology through several biochemical examples taken from the literature: Schl¨ogl’s model for a chemical bifurcation, a two-dimensional toggle switch, a model for bursty gene expression, and a dimerisation model with multiple stationary distributions.
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Journal articleJohnston I, Hoffmann T, Greenbury S, et al., 2019,
Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data
, npj Digital Medicine, Vol: 2, ISSN: 2398-6352More than 400,000 deaths from severe malaria (SM) are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicates important differences in disease pathogenesis that require specific treatment, and this clinical heterogeneity of SM remains poorly understood. Here, we apply tools from machine learning and model-based inference to harness large-scale data and dissect the heterogeneity in patterns of clinical features associated with SM in 2904 Gambian children admitted to hospital with malaria. This quantitative analysis reveals features predicting the severity of individual patient outcomes, and the dynamic pathways of SM progression, notably inferred without requiring longitudinal observations. Bayesian inference of these pathways allows us assign quantitative mortality risks to individual patients. By independently surveying expert practitioners, we show that this data-driven approach agrees with and expands the current state of knowledge on malaria progression, while simultaneously providing a data-supported framework for predicting clinical risk.
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Journal articleMaes A, Barahona M, Clopath C, 2019,
Learning spatiotemporal signals using a recurrent spiking network that discretizes time
, PLOS Computational Biology, Vol: 16, Pages: e1007606-e1007606<jats:title>Abstract</jats:title><jats:p>Learning to produce spatiotemporal sequences is a common task the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the recurrent network is constrained to encode time while the read-out neurons encode space. Space is then linked with time through plastic synapses that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on a timescale that is behaviourally relevant. Learned sequences are robustly replayed during a regime of spontaneous activity.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>The brain has the ability to learn flexible behaviours on a wide range of time scales. Previous studies have successfully build spiking network models that learn a variety of computational tasks. However, often the learning involved is not local. Here, we investigate a model using biological-plausible plasticity rules for a specific computational task: spatiotemporal sequence learning. The architecture separates time and space into two different parts and this allows learning to bind space to time. Importantly, the time component is encoded into a recurrent network which exhibits sequential dynamics on a behavioural time scale. This network is then used as an engine to drive spatial read-out neurons. We demonstrate that the model can learn complicated spatiotemporal spiking dynamics, such as the song of a bird, and replay the song robustly.</jats:p></jats:sec>
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Conference paperChrysostomou S, Roy R, Prischi F, et al., 2019,
Abstract 1775: Targeting RSK4 prevents both chemoresistance and metastasis in lung cancer
, AACR Annual Meeting on Bioinformatics, Convergence Science, and Systems Biology, Publisher: American Association for Cancer Research, Pages: 1-2, ISSN: 0008-5472Lung cancer is the commonest cause of cancer death worldwide with a five-year survival rate of less than five percent for metastatic tumors. Non-small cell lung cancer (NSCLC) accounts for 80% of lung cancer cases of which adenocarcinoma prevails. Patients almost invariably develop metastatic drug-resistant disease and this is responsible for our failure to provide curative therapy. Hence, a better understanding of the mechanisms underlying these biological processes is urgently required to improve clinical outcome.The 90-kDa ribosomal S6 kinases (RSKs) are downstream effectors of the RAS/MAPK cascade. RSKs are highly conserved serine/threonine protein kinases implicated in diverse cellular processes, including cell survival, proliferation, migration and invasion. Four isoforms exist in humans (RSK1-4) and are uniquely characterized by the presence of two non-identical N- and C-terminal kinase domains. RSK isoforms are 73-80% identical at protein level and this has been thought to suggest overlapping functions.However, through functional genomic kinome screens, we show that RSK4, contrary to RSK1, promotes both drug resistance and metastasis in lung cancer. This kinase is overexpressed in the majority (57%) of NSCLC biopsies and this correlates with poor overall survival in lung adenocarcinoma patients. Genetic silencing of RSK4 sensitizes lung cancer cells to chemotherapy and prevents their migration and invasiveness in vitro and in vivo. RSK4 downregulation decreases the anti-apoptotic proteins Bcl2 and cIAP1/2 which correlates with increased apoptotic signalling, whilst it also induces mesenchymal-epithelial transition (MET) through inhibition of NFκB activity. A small-molecule inhibitor screen identified several floxacins, including trovafloxacin, as potent allosteric inhibitors of RSK4 activation. Trovafloxacin reproduced all biological and molecular effects of RSK4 silencing in vitro and in vivo, and is predicted to bind a novel allosteric site revealed
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Conference paperPrischi F, Chrysostomou S, Roy R, et al., 2019,
Targeting RSK4 prevents both chemoresistance and metastasis in lung and bladder cancer
, FEBS Open Bio, Publisher: WILEY, Pages: 330-330, ISSN: 2211-5463 -
Journal articleBurgstaller J, Kolbe T, Havlicek V, et al., 2019,
Large-scale genetic analysis reveals mammalian mtDNA heteroplasmy dynamics and variance increase through lifetimes and generations
, Nature Communications, Vol: 9, ISSN: 2041-1723Vital mitochondrial DNA (mtDNA) populations exist in cells and may consist of heteroplasmic mixtures of mtDNA types. The evolution of these heteroplasmic populations through development, ageing, and generations is central to genetic diseases, but is poorly understood in mammals. Here we dissect these population dynamics using a dataset of unprecedented size and temporal span, comprising 1947 single-cell oocyte and 899 somatic measurements of heteroplasmy change throughout lifetimes and generations in two genetically distinct mouse models. We provide a novel and detailed quantitative characterisation of the linear increase in heteroplasmy variance throughout mammalian life courses in oocytes and pups. We find that differences in mean heteroplasmy are induced between generations, and the heteroplasmy of germline and somatic precursors diverge early in development, with a haplotype-specific direction of segregation. We develop stochastic theory predicting the implications of these dynamics for ageing and disease manifestation and discuss its application to human mtDNA dynamics.
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Journal articleHoitzing H, Gammage PA, Haute LV, et al., 2019,
Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations
, PLoS Computational Biology, Vol: 15, ISSN: 1553-734XThe dynamics of the cellular proportion of mutant mtDNA molecules is crucial for mitochondrial diseases. Cellular populations of mitochondria are under homeostatic control, but the details of the control mechanisms involved remain elusive. Here, we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations, the cost to the cell of different mtDNA states, and the optimisation of therapeutic control of mtDNA populations. This formalism yields a wealth of biological results, including that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue, that intermediate levels of heteroplasmy can be more detrimental than homoplasmy even for a dysfunctional mutant, that heteroplasmy distribution (not mean alone) is crucial for the success of gene therapies, and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations.
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