Chaste — Cancer, Heart And Soft Tissue Environment — is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high-performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cell-based simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs). Re-use of these components avoids the need for researchers to ‘re-invent the wheel’ with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular test-driven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at http://www.cs.ox.ac.uk/chaste, together with details of a mailing list and links to documentation and tutorials.

The coordinated behaviour of populations of cells plays a central role in tissue growth and renewal. Cells react to their microenvironment by modulating processes such as movement, growth and proliferation, and signalling. Alongside experimental studies, computational models offer a useful means by which to investigate these processes. To this end a variety of cell-based modelling approaches have been developed, ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes. However, it remains unclear how these approaches compare when applied to the same biological problem, and what differences in behaviour are due to different model assumptions and abstractions. Here, we exploit the availability of an implementation of five popular cell-based modelling approaches within a consistent computational framework, Chaste (http://www.cs.ox.ac.uk/chaste). This framework allows one to easily change constitutive assumptions within these models. In each case we provide full details of all technical aspects of our model implementations. We compare model implementations using four case studies, chosen to reflect the key cellular processes of proliferation, adhesion, and short- and long-range signalling. These case studies demonstrate the applicability of each model and provide a guide for model usage. In combination with molecular and live-imaging techniques, computational modelling plays an increasingly important role in the study of tissue growth and renewal. To this end a variety of cell-based modelling approaches have been developed, ranging in complexity from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes. However, it remains unclear how these approaches compare when applied to the same biological problem, and under which circumstances each approach is valid. Here we implement five classes of such model in a consistent computational framework, Chaste. We apply each model to four simulation studies, chosen to illustrate how the cellular processes such as proliferation, adhesion, and short- and long-range signalling may be implemented in each model. These case studies demonstrate the applicability of each model and highlight where one may expect to see qualitative differences between model behaviours. Taken together, these findings provide a guide for model usage.

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy. Increasing antibiotic resistance, coupled with the slowing rate of discovery of novel antibiotic agents, is a public health threat which could soon reach crisis point. Indeed, the last decade has seen the emergence of deadly, highly resistant forms of pathogens, such as Escherichia coli, Acenitobacter baumanii, Klebsiella pneumoniae, Enterococcus and Staphylococcus aureus as well as non–bacterial pathogens including malaria and viruses such as HIV. Here, we develop a mathematical model of an evolving bacterial population, which allows us to predict the probability of resistant strains emerging. Using this model we show how sequences of drugs can be prescribed in order to prevent resistance where each drug alone may fail. These model predictions suggest a novel treatment strategy: using sequences of antibiotics to ‘steer’ the evolution of a pathogen to a configuration from which resistance to a final antibiotic cannot emerge. Further, we test the likelihood of resistance emerging when arbitrary sequences of antibiotics are prescribed, finding that approximately 70% of arbitrary sequences of 2–4 drugs promote resistance to the final drug. This result serves as a cautionary warning that we may be inadvertently promoting resistance through careless (or random) prescription of drugs.