Optimal mutation rates in dynamic environments.

Martin Nilsson; Nigel Snoad

doi:10.1006/bulm.2002.0314

Home/Archive/PubMed

PubMed · 12508529

Optimal mutation rates in dynamic environments.

Martin Nilsson·Nigel Snoad

View original source ↗Export metadata ↓

Abstract

In this paper, we study the evolution of the mutation rate for simple organisms in dynamic environments. A model based on explicit population dynamics at the gene sequence level, with multiple fitness coding loci tracking a moving fitness peak in a random fitness background, is developed and an analytical expression for the optimal mutation rate is derived. The optimal mutation rate per genome is approximately independent of genome length, something that has been observed in nature. Furthermore, the optimal mutation rate is a function of the absolute, not relative, replication rate of the superior gene sequences. Simulations confirm the theoretical predictions.

Explore related subjects

Computer Simulation Environment Evolution, Molecular Genome Models, Genetic Point Mutation Population Dynamics Journal Article Research Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Martin Nilsson, Nigel Snoad. 2002. Optimal mutation rates in dynamic environments.. https://doi.org/10.1006/bulm.2002.0314

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

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.

Computer Simulation↗

PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

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.

Computer Simulation↗

PubMed2004

Determination of the kinetics of guanine nucleotide exchange on EF-Tu and EF-Ts: continuing uncertainties.

An analysis is made of the rate constants for the reactions involving the interactions of EF-Tu, EF-Ts, GDP, and GTP recently derived by Gromadski et al. [Biochemistry 41 (2002) 162]. Though their measured values appear to allow a reasonable rate of nucleotide exchange sufficient to support rates of protein synthesis in vivo, their data underestimate the thermodynamic barrier involved in nucleotide exchange and therefore cannot be considered definitive. A kinetic scheme consistent with the thermodynamic barrier can be achieved by modification of various rate constants, particularly of those involving the release of EF-Ts from EF-Tu.GTP.EF-Ts, but such constants are markedly different from what are experimentally observed. It thus remains impossible at present satisfactorily to model guanine nucleotide exchange on EF-Tu, catalysed by EF-Ts by a double displacement mechanism, with experimentally derived rate constants. Metabolic control analysis has been applied to determine the degree of flux control of the different steps in the pathway.

Computer Simulation↗