PubMed · 15144159
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Karen Kraco. 2004. Lifelike learning.. https://pubmed.ncbi.nlm.nih.gov/15144159/
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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.
The concentration of phytoplankton in the sea is affected by biological processes, such as growth/mortality rates, predatory zooplankton concentrations and nutrient levels. Phytoplankton concentrations are also influenced by physical processes, in particular the mixing properties of the local fluid environment. On planktonic scales (approximately 10-1000 microm) one can assume the local turbulent flow is isotropic, with no distinction between horizontal and vertical mixing. However, agglomerations of phytoplankton into patches are observed on larger scales of up to hundreds of metres, whose formation will be influenced by the anisotropic advection/mixing properties and large-eddy structures prevalent in the surface mixed layer. This paper presents the results of the coupling of a large-eddy simulation (LES) model of the mixed layer with an advection-diffusion system of coupled equations for nitrate-phytoplankton-zooplankton (NPZ) concentration, incorporating sub-grid parameterizations of the biological processes. Typically these include phytoplankton growth due to light levels and ambient nitrate concentration, offset by grazing losses due to the presence of zooplankton. The primary goal of this work is to investigate how the characteristics of the mixed layer turbulence influence the observed distribution of phytoplankton. One novel feature is the incorporation of a 'vortex-force' term in the LES code in order to generate Langmuir circulations. It has been speculated that the enhanced mixing rates associated with 'Langmuir turbulence' play a significant role in regulating planktonic activity. Results derived from the coupled LES-NPZ model, run with and without the presence of Langmuir circulations, are presented in order to investigate these ideas.