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Epigenetics is coming to the fore as a key process which underpins health. In particular emerging experimental evidence has associated alterations to DNA methylation status with healthspan and aging. Mammalian DNA methylation status is maintained by an intricate array of biochemical and molecular processes. It can be argued changes to these fundamental cellular processes ultimately drive the formation of aberrant DNA methylation patterns, which are a hallmark of diseases, such as cancer, Alzheimer’s disease and cardiovascular disease. In recent years mathematical models have been used as effective tools to help advance our understanding of the dynamics which underpin DNA methylation. In this paper we present linear and nonlinear models which encapsulate the dynamics of the molecular mechanisms which define DNA methylation. Applying a recently developed Bayesian algorithm for parameter estimation and model selection, we are able to estimate distributions of parameters which include nominal parameter values. Using limited noisy observations, the method also identified which methylation model the observations originated from, signaling that our method has practical applications in identifying what models best match the biological data for DNA methylation.

Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics., Royal Society Open Science, 8 (1), ISSN:2054-5703

Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient- specific parameter fitting for haemodynamical flow models., Royal Society Open Science, 6 (10), ISSN:2054-5703

Samples collected from urban streams in the cities of Sacramento and Stockton, California, USA, during the precipitation season were analyzed for diazinon and chlorpyrifos. Concentrations were determined with enzyme-linked immunosorbent assays specific for each pesticide. Two hundred thirty-one samples from the two cities were analyzed for diazinon; 85% exceeded California Department of Fish and Game water-quality criteria for this pesticide. Chlorpyrifos was measured in 90 of the samples collected from Sacramento and Stockton; 80% exceeded the California Department of Fish and Game criterion for this pesticide. Thirty-six of 47 samples (76.6%) tested for toxicity produced total mortality within 72 h with Ceriodaphnia dubia. Toxicity identification evaluations on selected samples confirmed that toxicity was primarily due to one or both of these pesticides. Uses of diazinon and chlorpyrifos in urban areas include dormant sprays on fruit trees, professional landscape and maintenance uses, and structural pest control. Pesticide concentrations were lower in a catchment favoring commercial and industrial activities compared with a catchment receiving largely residential inputs. Aerial drift from agricultural applications may play a role in storm-water concentrations.

The HIV-1 nucleocapsid (NC) protein is a small, basic protein containing two retroviral zinc fingers. It is a highly active nucleic acid chaperone; because of this activity, it plays a crucial role in virus replication as a cofactor during reverse transcription, and is probably important in other steps of the replication cycle as well. We previously reported that NC binds with high-affinity to the repeating sequence d(TG)n. We have now analyzed the interaction between NC and d(TG)4 in considerable detail, using surface plasmon resonance (SPR), tryptophan fluorescence quenching (TFQ), fluorescence anisotropy (FA), isothermal titration calorimetry (ITC) and electrospray ionization Fourier transform mass spectrometry (ESI-FTMS). Our results show that the interactions between these two molecules are surprisngly complex: while the K(d) for binding of a single d(TG)4 molecule to NC is only approximately 5 nM in 150 mM NaCl, a single NC molecule is capable of interacting with more than one d(TG)4 molecule, and conversely, more than one NC molecule can bind to a single d(TG)4 molecule. The strengths of these additional binding reactions are quantitated. The implications of this multivalency for the functions of NC in virus replication are discussed.