75 results found
Brittain R, Jones N, Ouldridge T, 2018, Biochemical Szilard engine for memory limited inference
Code and data for figures in 'Biochemical Szilard engine for memory limited inference'
Sethi SS, Ewers RM, Jones NS, et al., 2018, Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device, METHODS IN ECOLOGY AND EVOLUTION, Vol: 9, Pages: 2383-2387, ISSN: 2041-210X
Garrod M, Jones NS, 2018, Large algebraic connectivity fluctuations in spatial network ensembles imply a predictive advantage from node location information, PHYSICAL REVIEW E, Vol: 98, ISSN: 2470-0045
Keogh MJ, Wei W, Aryaman J, et al., 2018, High prevalence of focal and multi-focal somatic genetic variants in the human brain, NATURE COMMUNICATIONS, Vol: 9, ISSN: 2041-1723
Wei W, Keogh MJ, Aryaman J, et al., 2018, Frequency and signature of somatic variants in 1461 human brain exomes., Genet Med
PURPOSE: To systematically study somatic variants arising during development in the human brain across a spectrum of neurodegenerative disorders. METHODS: In this study we developed a pipeline to identify somatic variants from exome sequencing data in 1461 diseased and control human brains. Eighty-eight percent of the DNA samples were extracted from the cerebellum. Identified somatic variants were validated by targeted amplicon sequencing and/or PyroMark® Q24. RESULTS: We observed somatic coding variants present in >10% of sampled cells in at least 1% of brains. The mutational signature of the detected variants showed a predominance of C>T variants most consistent with arising from DNA mismatch repair, occurred frequently in genes that are highly expressed within the central nervous system, and with a minimum somatic mutation rate of 4.25 × 10-10 per base pair per individual. CONCLUSION: These findings provide proof-of-principle that deleterious somatic variants can affect sizeable brain regions in at least 1% of the population, and thus have the potential to contribute to the pathogenesis of common neurodegenerative diseases.
Keogh MJ, Wei W, Aryaman J, et al., 2018, Oligogenic genetic variation of neurodegenerative disease genes in 980 postmortem human brains, JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY, Vol: 89, Pages: 813-816, ISSN: 0022-3050
Burgstaller JP, Kolbe T, Havlicek V, et al., 2018, Large-scale genetic analysis reveals mammalian mtDNA heteroplasmy dynamics and variance increase through lifetimes and generations, NATURE COMMUNICATIONS, Vol: 9, ISSN: 2041-1723
Lubba CH, Le Guen Y, Jarvis S, et al., 2018, PyPNS: Multiscale Simulation of a Peripheral Nerve in Python., Neuroinformatics
Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modelled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modelled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.
Pezet M, Gomez-Duran A, Aryaman J, et al., 2018, Understanding the mechanism underpinning the transmission of mtDNA mutations, 11th UK Neuromuscular Translational Research Conference, Publisher: PERGAMON-ELSEVIER SCIENCE LTD, Pages: S35-S35, ISSN: 0960-8966
McGrath T, Murphy KG, Jones NS, 2018, Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 15, ISSN: 1742-5689
The plant endoplasmic reticulum forms a network of tubules connected by three-way junctions or sheet-like cisternae. Although the network is three-dimensional, in many plant cells, it is constrained to a thin volume sandwiched between the vacuole and plasma membrane, effectively restricting it to a 2-D planar network. The structure of the network, and the morphology of the tubules and cisternae can be automatically extracted following intensity-independent edge-enhancement and various segmentation techniques to give an initial pixel-based skeleton, which is then converted to a graph representation. Collectively, this approach yields a wealth of quantitative metrics for ER structure and can be used to describe the effects of pharmacological treatments or genetic manipulation. The software is publicly available.
Aryaman J, Johnston IG, Jones NS, 2017, Mitochondrial DNA density homeostasis accounts for a threshold effect in a cybrid model of a human mitochondrial disease, BIOCHEMICAL JOURNAL, Vol: 474, Pages: 4019-4034, ISSN: 0264-6021
Fulcher BD, Jones NS, 2017, hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction, CELL SYSTEMS, Vol: 5, Pages: 527-+, ISSN: 2405-4712
Deshpande A, Gopalkrishnan M, Ouldridge TE, et al., 2017, Designing the optimal bit: balancing energetic cost, speed and reliability, PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, Vol: 473, ISSN: 1364-5021
Aryaman J, Hoitzing H, Burgstaller JP, et al., 2017, Mitochondrial heterogeneity, metabolic scaling and cell death, BIOESSAYS, Vol: 39, ISSN: 0265-9247
Fricker MD, Akita D, Heaton LLM, et al., 2017, Automated analysis of Physarum network structure and dynamics, JOURNAL OF PHYSICS D-APPLIED PHYSICS, Vol: 50, ISSN: 0022-3727
Brittain RA, Jones NS, Ouldridge TE, 2017, What we learn from the learning rate, JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, ISSN: 1742-5468
Colijn C, Jones N, Johnston IG, et al., 2017, Toward Precision Healthcare: Context and Mathematical Challenges, FRONTIERS IN PHYSIOLOGY, Vol: 8, ISSN: 1664-042X
McGrath T, Jones NS, ten Wolde PR, et al., 2017, Biochemical Machines for the Interconversion of Mutual Information and Work (vol 118, 028101, 2017), PHYSICAL REVIEW LETTERS, Vol: 118, ISSN: 0031-9007
McGrath T, Jones NS, Wolde PRT, et al., 2017, A biochemical machine for the interconversion of mutual information and work, Physical Review Letters, Vol: 118, ISSN: 1079-7114
We propose a physically-realisable biochemical device that is coupled to abiochemical reservoir of mutual information, fuel molecules and a chemicalbath. Mutual information allows work to be done on the bath even when the fuelmolecules appear to be in equilibrium; alternatively, mutual information can becreated by driving from the fuel or the bath. The system exhibits diversebehaviour, including a regime in which the information, despite increasingduring the reaction, enhances the extracted work. We further demonstrate that amodified device can function without the need for external manipulation,eliminating the need for a complex and potentially costly control.
, 2017, Stochastic models for evolving cellular populations of mitochondria: Disease, development, and ageing, Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, Pages: 287-314, ISBN: 9783319626277
© Springer International Publishing AG 2017. Mitochondria are essential cellular organelles whose dysfunction is associated with ageing, cancer, mitochondrial diseases, and many other disorders. They contain their own genomes (mtDNA), of which thousands can be present in a single cell. These genomes are repeatedly replicated and degraded over time, and are prone to mutations. If the fraction of mutated genomes (heteroplasmy) exceeds a certain threshold, cellular defects can arise. The dynamics of mtDNAs over time and the accumulation of mutant genomes form a rich and vital stochastic process, the understanding of which provides important insights into disease progression. Numerous mathematical models have been constructed to provide a better understanding of how mitochondrial dysfunctions arise and, importantly, how clinical interventions can alleviate disease symptoms. For a given mean heteroplasmy, an increased variance-and thus a wider cell-to-cell heteroplasmy distribution-implies a higher probability of exceeding a given threshold value, meaning that stochastic models are essential to describe mtDNA disease. Mitochondria can undergo fusion and fission events with each other making the mitochondrial population a dynamic network that continuously changes its morphology, and allowing for the possibility of exchange of mtDNA molecules: coupled stochastic physical and genetic dynamics thus govern cellular mtDNA populations. Here, an overview is given of the kinds of stochastic mathematical models constructed describing mitochondria, their implications, and currently existing open problems.
Johnston IG, Jones NS, 2016, Evolution of Cell-to-Cell Variability in Stochastic, Controlled, Heteroplasmic mtDNA Populations, AMERICAN JOURNAL OF HUMAN GENETICS, Vol: 99, Pages: 1150-1162, ISSN: 0002-9297
Larson HJ, de Figueiredo A, Zhao X, et al., 2016, The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey, EBIOMEDICINE, Vol: 12, Pages: 295-301, ISSN: 2352-3964
de Figueiredo A, Johnston IG, Smith DMD, et al., 2016, Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years, LANCET GLOBAL HEALTH, Vol: 4, Pages: E726-E735, ISSN: 2214-109X
de Figueiredo A, Johnston IG, Smith DMD, et al., 2016, Forecasting time-series trends in vaccination coverage and their links with socio-economic factors: A global analysis over 30 years, Lancet Global Health, ISSN: 2214-109X
Background Incomplete immunisation coverage causes preventable illness and death in both the developing anddeveloped world. Identifying factors that may modulate coverage can inform effective immunisation programmes andpolicies.Methods We perform a data-driven analysis of unprecedented scale, examining time-varying trends in Diphtheriatetanus-pertussiscoverage across 190 countries over the past three decades. Gaussian process regression is employedto forecast future coverage rates and provide a Vaccine Performance Index: a summary measure of the strength ofimmunisation coverage in a country.Findings Overall vaccine coverage has increased in all five world regions between 1980 and 2010, with markedvariation in volatility and trends. Our Vaccine Performance Index identifies 53 countries with a less than 50% chanceof missing the Global Vaccine Action Plan (GVAP) target of 90% worldwide DTP3 coverage by 2015, in agreementwith recent immunisation data. These countries are mostly sub-Saharan and South Asian, but Austria and Ukraine inEurope also feature. Factors associated with DTP3 immunisation coverage vary by world-region: personal income(! = 0.66, ' < 0.001) and government health spending (! = 0.66, ' < 0.01) are particularly informative in theEastern Mediterranean between 1980 and 2010, whilst primary school completion is informative in Africa (! =0.56, ' < 0.001) over the same time. The fraction of births attended by skilled health staff is significantly informativeacross many world regionsInterpretation A Vaccine Performance Index can highlight countries at risk identifying the strength and resilience ofimmunisation programmes. Weakening correlations with socio-economic factors indicate a need to tackle vaccineconfidence whereas strengthening correlations points to clear factors to address.
Heaton LLM, Jones NS, Fricker MD, 2016, Energetic Constraints on Fungal Growth, AMERICAN NATURALIST, Vol: 187, Pages: E27-E40, ISSN: 0003-0147
Johnston IG, Jones NS, 2015, Closed-form stochastic solutions for non-equilibrium dynamics and inheritance of cellular components over many cell divisions, PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, Vol: 471, ISSN: 1364-5021
Johnston IG, Burgstaller JP, Havlicek V, et al., 2015, Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism, ELIFE, Vol: 4, ISSN: 2050-084X
Hoitzing H, Johnston IG, Jones NS, 2015, What is the function of mitochondrial networks? A theoretical assessment of hypotheses and proposal for future research, BIOESSAYS, Vol: 37, Pages: 687-700, ISSN: 0265-9247
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