PubMed · 2349619
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B H Ackerman, K M Olsen, C B Padilla. 1990. Errors in assuming a one-compartment model for vancomycin.. https://pubmed.ncbi.nlm.nih.gov/2349619/
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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.
There is mounting evidence that Cu(I) is the most important oxidation state of copper in many physiological systems. Research into Cu(I)-thioamino acid complex formation serves not only to improve the chelation therapy for treating copper intoxication but may also provide a better understanding of many facets of normal copper metabolism. Formation constants for the ternary mixed ligand complexes of Cu(I) with cysteine (Cys), glutathione (GSH) and penicillamine (Pen) are reported here for the first time. Potentiometric titrations, using techniques specially developed for the stabilization of aqueous Cu(I), were performed at 25 degrees C in 1.00 M (Na)Cl. It was found that precipitation severely limits the experimentally accessible pH range and, consequently, the computer analysis of the binary metal-ligand systems; however, it is also found that this is less of a problem when two different ligands are present. This latter fact permitted better models of the binary systems to be developed. The formation constants of Cu(I)-thioamino acids determined in this work were used in an improved computer simulation of copper speciation in blood plasma which, for the first time, incorporates redox equilibria.