PubMed · 2648380
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W G Richards, P M King, C A Reynolds. 1989. Solvation effects.. https://doi.org/10.1093/protein%2F2.5.319
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Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.
The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.
It was shown recently [Goussias, C., Ioannidis, N., and Petrouleas, V. (1997) Biochemistry 36, 9261-9266] that incubation of photosystem II preparations with NO at -30 degrees C in the dark results in the formation of a new intermediate of the water-oxidizing complex. This is characterized by an EPR signal centered at g = 2 with prominent manganese hyperfine structure. We have examined the detailed structure of the signal using difference EPR spectroscopy. This is facilitated by the observations that NO can be completely removed without decrease or modification of the signal, and illumination at 0 degree C eliminates the signal. The signal spans 1600 G and is characterized by sharp hyperfine structure. 14NO and 15NO cw EPR combined with pulsed ENDOR and ESEEM studies show no detectable contributions of the nitrogen nucleus to the spectrum. The spectrum bears similarities to the experimental spectrum of the Mn(II)-Mn(III) catalase [Zheng, M., Khangulov, S. V., Dismukes, G. C., and Barynin, V. V. (1994) Inorg. Chem. 33, 382-387]. Simulations allowing small variations in the catalase-tensor values result in an almost accurate reproduction of the NO-induced signal. This presents strong evidence for the assignment of the latter to a magnetically isolated Mn(II)-Mn(III) dimer. Since the starting oxidation states of Mn are higher than II, we deduce that NO acts effectively as a reductant, e.g., Mn(III)-Mn(III) + NO--> Mn(II)-Mn(III) + NO+. The temperature dependence of the nonsaturated EPR-signal intensity in the range 2-20 K indicates that the signal results from a ground state. The cw microwave power saturation data in the range 4-8 K can be interpreted assuming an Orbach relaxation mechanism with an excited state at delta = 42 K. Assuming antiferromagnetic coupling, -2JS1.S2, between the two manganese ions, J is estimated to be 10 cm-1. The finding that an EPR signal from the Mn cluster of PSII can be clearly assigned to a magnetically isolated Mn(II)-Mn(III) dimer bears important consequences in interpreting the structure of the Mn cluster. Although the signal is not currently assigned to a particular S state, it arises from a state lower than S1, possibly lower than S0, too.