Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

The goal of cancer immunotherapy is to boost a patient's immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor 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 modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers 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 tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society Interfac

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Understanding tuberculosis dynamics in the United Kingdom using mathematical modelling

In the UK, tuberculosis incidence has risen from the mid-1980s until recently, with the proportion of cases in foreign-born patients increasing to more than 70% of total cases today. Because several features of tuberculosis epidemiology in the UK are unclear, a simulation model was applied to better understand the epidemiology of tuberculosis in the UK. The model was first used to estimate age- and birthplace-dependent risks of disease progression for those infected via the different disease progression pathways-recent infection, reinfection, and latent infection-by fitting the model to incident cases in England and Wales from 1999 - 2009. Results showed that UK-born risks were lower than previous estimates, though foreign-born risks were an estimated 2.5 times higher than UK-born risks. Estimates for the proportion of disease due to recent transmission were higher than previous estimates, at around 46%. Simulations also identified plausible assumptions for the contact rate and the infection status of migrants upon entry to the UK. Results informed a model fitted to Variable Number Tandem Repeat (VNTR) genotyping data from cases in the West Midlands from 2007 - 2011 , which was used to estimate the proportion of disease due to recent transmission in the UK and compare estimates to those based on genotyping data. Results showed that a n estimated 45 - 63% of cases in the West Midlands were due to recenttransmission i n the UK, which was underestimated by genotyping data-derived estimates of 35%. Results also identified plausible mutation rates for VNTR profiles and plausible strain type distributions for UK-born and foreign-born individuals. This work suggests there is a large proportion of cases due to recent transmission in the UK, which is underestimated by genotyping data. The study also provides current disease risk estimates and shows a need for better data on migrants to the UK. This work may help focus prevention efforts

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order