For a huge chunk of my working life I have built computer models which were used to assess operational activities in different industries. The combination of mathematics and software can provide enormous power to help understand and assess complex processes. My doctorate put these skills to good use in that I used software implementations of evolutionary processes to build a system that evolved mathematical models which could validate the correctness, or otherwise, of large data sets. In plain English I used genetic algorithms to discover mathematical models which could pick out incorrect data values in large volumes of data. Think of a system that could take the largest Excel spreadsheets and automatically flag those rows of data which were most likely to be in error – all without knowing what the spreadsheet data represented or who had put it together or why.
Of course cancer is the ultimate in evolutionary systems – if you wanted to design a system to illustrate the evolution at work you’d come up with something pretty much like it. When we look at cancer and see that some treatments have fantastic initial responses, with tumours shrinking away to almost nothing, followed by a rebound in which the cancer comes back more aggressive and resistant to the treatment then we’re seeing evolution at work.
Given my background in computer modelling and my current work in oncology it should be no surprise that I’ve worked on a software model of tumour growth. I’ve called it NEATG – for Non-physiological Evolutionary Algorithm for Tumour Growth. It’s a computational model – it’s about algorithms rather than about trying to recreate in software the vast complexities and details of cells, proteins, signals and pathways. Although it’s a simple model by design, it does illustrate some interesting behaviour that brings to mind the behaviour of real tumour growth.
For example, the NEATG system can model the growth of a tumour mass (in two dimensions), it can model the rise of genetically different sub-populations of cancer cells, and it can model different interventions such as chemotherapy or nutrient deprivation. What is more it displays emergent behaviour – such as a more aggressive growth pattern following the cessation of treatment. This is behaviour that emerges naturally from the interactions between cells and tissues, not behaviour that has been explicitly programmed into the system as a set of predefined rules.
For now NEATG is a tool that can be used to explore different algorithmic scenarios – you can play try out different thought experiments to see what happens. It’s good for thinking about some of the most fundamental aspects of cancer without getting bogged down in the molecular biology. For example, while most people think of cancer as primarily a disease of disordered genes – a view known as the ‘somatic mutation theory’ of cancer – there is an alternative theory called the ‘tissue organisation field theory’ of cancer. In this theory disordered genes are more of a by-product than a cause of cancer, and it places more emphasis at the disordered tissue environment. Simplistically we can ask: is it the delinquent cell or the bad neighbourhood that causes cancer? This is a good question to explore using a suitable software model – and I hope that NEATG can be applied to this.
While it’s still early days for this piece of work, I have written a paper on it which is available as a preprint (i.e. prior to peer review) at PeerJ. If you’re interested please take a look.
Of course cancer is the ultimate in evolutionary systems – if you wanted to design a system to illustrate the evolution at work you’d come up with something pretty much like it. When we look at cancer and see that some treatments have fantastic initial responses, with tumours shrinking away to almost nothing, followed by a rebound in which the cancer comes back more aggressive and resistant to the treatment then we’re seeing evolution at work.
Given my background in computer modelling and my current work in oncology it should be no surprise that I’ve worked on a software model of tumour growth. I’ve called it NEATG – for Non-physiological Evolutionary Algorithm for Tumour Growth. It’s a computational model – it’s about algorithms rather than about trying to recreate in software the vast complexities and details of cells, proteins, signals and pathways. Although it’s a simple model by design, it does illustrate some interesting behaviour that brings to mind the behaviour of real tumour growth.
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| Tumour growth in NEATG |
For example, the NEATG system can model the growth of a tumour mass (in two dimensions), it can model the rise of genetically different sub-populations of cancer cells, and it can model different interventions such as chemotherapy or nutrient deprivation. What is more it displays emergent behaviour – such as a more aggressive growth pattern following the cessation of treatment. This is behaviour that emerges naturally from the interactions between cells and tissues, not behaviour that has been explicitly programmed into the system as a set of predefined rules.
For now NEATG is a tool that can be used to explore different algorithmic scenarios – you can play try out different thought experiments to see what happens. It’s good for thinking about some of the most fundamental aspects of cancer without getting bogged down in the molecular biology. For example, while most people think of cancer as primarily a disease of disordered genes – a view known as the ‘somatic mutation theory’ of cancer – there is an alternative theory called the ‘tissue organisation field theory’ of cancer. In this theory disordered genes are more of a by-product than a cause of cancer, and it places more emphasis at the disordered tissue environment. Simplistically we can ask: is it the delinquent cell or the bad neighbourhood that causes cancer? This is a good question to explore using a suitable software model – and I hope that NEATG can be applied to this.
While it’s still early days for this piece of work, I have written a paper on it which is available as a preprint (i.e. prior to peer review) at PeerJ. If you’re interested please take a look.
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