PubMed · 14647491
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Harald Brune. 2003. Surface diffusion: shifting strings.. https://doi.org/10.1038/nmat1027
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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.
In the present work, the current status of the hydrodynamic cavitation reactors has been reviewed discussing the bubble dynamics analysis, optimum design considerations, design correlations for cavitational intensity (in terms of collapse pressure)/cavitational yield and different successful chemical synthesis applications clearly illustrating the utility of these types of reactors. The theoretical discussion based on the modeling of the bubble dynamics equations aims at understanding the design information related to the dependency of the cavitational intensity on the operating parameters and recommendations have been made for the choice of the optimized conditions of operating parameters. The design information based on the theoretical analysis has also been supported with some experimental illustrations concentrating on the chemical synthesis applications. Assessment of the hydrodynamic cavitation reactors and comparison with the sonochemical reactors has been done by citing the different industrially important reactions (oxidation of toluene, o-xylene, m-xylene, p-xylene, mesitylene, o-nitrotoluene, p-nitrotoluene, m-nitrotoluene, o-chlorotoluene and p-chlorotoulene, and trans-esterification reaction i.e., synthesis of bio-diesel). Some recommendations have also been made for the future work to be carried out as well as the choice of the operating conditions for realizing the dream of industrial scale applications of the cavitational reactors.