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Written by David Orrell As the physicist Richard Feynman famously observed in 1982: “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical.” And as he went on, “by golly it’s a wonderful problem, because it doesn’t look so easy.” Indeed, the configuration of something like a drug compound, and its interaction with other substances, is ultimately...

Recently, combination treatments involving immunotherapy have emerged as an exciting new way to tackle cancer [1].The spotlight has fallen especially on immune checkpoint inhibitors. In this post, we discuss how modelling can be used to unlock key insights to help optimise the promise of combination therapies.  Immunotherapy as a treatment approach has revolutionised oncology – and is one of our key areas of focus here...

Written by Dr Jim Millen In these unprecedented times the life sciences sector is rightly focused on finding a vaccine against COVID-19.  It is estimated that it will take 12 to 18 months before an effective vaccine can be developed and manufactured.  In the meantime, health providers are focusing on managing the immediate crisis by delaying treatment of other less-urgent and non-coronavirus cases. But what challenges...

Written by David Orrell In a 1904 paper on weather forecasting, the Norwegian physicist and meteorologist Vilhelm Bjerknes noted that the accuracy of a predictive model depends on two things: “1. A sufficiently accurate knowledge of the state of the atmosphere at the initial time. 2. A sufficiently accurate knowledge of the laws according to which one state of the atmosphere develops from another.” Or in...

Written by David Orrell As discussed in the last post, models have two functions – to describe, and to predict. In systems biology, and other areas of science, these are often conflated. We say a model is predictive, when really we mean it is descriptive. But prediction – from the Latin praedicere for “make known beforehand” – is not the same as just reproducing something that...

Written by David Orrell.    The Virtual Tumour (“VT”) is the main program we use at Physiomics to optimise oncology treatments and predict the effects of drug combinations. This post is the first in a series that will describe the aims, motivations, and some of the history of the project – and gives a peek at the mathematics inside. Today’s topic: why we built the model. The VT,...