PubMed · 14508539
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Charlotte Haug. 2003-08-28. [Virtual realities and real changes].. https://pubmed.ncbi.nlm.nih.gov/14508539/
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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.
To test a hydrophobic core-directed protein design approach, we previously have used phage-display and proteolysis to select stably folded proteins from a library of mutants of apocytochrome b562. The consensus sequence of the selected mutants has hydrophilic residues at two of the three positions that are designed to form a hydrophobic core. To understand this unexpected result, we determined the high-resolution structure of one of the selected mutants using multi-dimensional nuclear magnetic resonance (NMR). The structure shows that the two hydrophilic residues in the consensus sequence were on the surface of the structure. Instead, two of their neighboring hydrophobic residues reorganized their side-chain conformations and formed the hydrophobic core. This result suggests that the hydrophobic core-directed protein design by phage-display and proteolysis is a valid method in general but alternative hydrophobic packing needs to be considered in the initial design. The unexpected repacking of the hydrophobic residues also highlights the plastic nature of protein structures.