To better understand and subsequently treat cancer more effectively, a significant effort is underway to develop and use models of cancer pathophysiology in order to simulate cancer evolution and promote individualized, that is, patient-specific optimization of, disease treatment. The latter is leading to a central clinical question from the context of predictive oncology: Is it possible to select the best targeted therapy for a patient by computer simulation? (Johnson et al).

In this Cancer Informatics article, Author Dr David Johnson along with Co-Authors discuss the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios. His recently published article, Dealing with Diversity in Computational Cancer Modeling  reviews XML-based mark-up languages for biological modelling and discusses future efforts. In this featured author, Dr. Johnson answers questions about his background, his research and his manuscript.

How did you become interested in studying the diversity in computational cancer modeling?

Interoperability of models and data has become a hot topic in not just computational cancer modeling, but in scientific modeling and simulation as a whole. My colleagues and I decided to look at diversity within cancer modeling as there are a number of significant interoperability efforts to deal with diversity in computational biology but none within the cancer domain. There are established efforts in the realms of systems biology and cell-based modeling, but from our experiences cancer models span across multiple biological scales and have different stakeholder concerns to current efforts, and therefore deserve additional attention.

What was previously known about the need for interconnecting computational cancer models from different sources and scales?  How has your work in this area advanced understanding of it?

Previously there was little work dedicated to interconnecting cancer models, but the markup language approaches reviewed in the paper tackle, at the very least, the scale issue within their respective subdomains. For the most part these approaches are declarative and predominantly mathematical. We identified a need for a different approach for our work within the European Commission supported Transatlantic Tumor Model Repositories project (www.tumor-project.eu) in which we required diverse kinds of computational cancer models to interoperate, all of which were existing and from different sources. The aim of our commentary paper is to give an overview of the diversity issues in the clinical cancer domain, a brief overview of some of the state of the art and the ongoing efforts that we are involved in with the Transatlantic Tumor Model Repositories project.

More information about Dr David Johnson’s work is available at his website.  The paper Dealing with Diversity in Computational Cancer Modeling is freely available to download, comment on, and share.

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