title: Modelling in surgical oncology--part III: massive data sets and complex systems.
abstract: Human tumours are complex and unstable biological systems. New intellectual and mathematical approaches together with massive computing power are transforming our capacity to model and investigate such complexity. Computers also allow massive data sets to be collated and analysed. Such sets include the medical and epidemiological records of entire populations; the entire genetic code of the human being and of other species, including parasites and disease vectors; and the genotype of each and every individual. Massive data sets take us into new dimensions of complexity for which simple linear mathematics are insufficient. The analysis of the grades of complexity which determine protein and cell construction, cell to cell interactions within tissues and organs, the morphogenesis of entire organisms and population interactions with disease vectors require the sophisticated mathematical tools of non-linear analysis, neural networks, chaos and complexity theory. The capacity for closer representations of reality through powerful computational models also allows us to look afresh at the generalizations of conventional statistics. Within this computational cauldron, we may also find help in the better understanding of oncogenesis and cancer therapy. This paper, the third in our series on modelling in tumour biology, considers the breadth of opportunity and challenge at the interface between cell biology and biomathematics.
authors.affiliation: Royal South Hants Cancer Centre, Southampton University Hospitals, UK.
authors.forename: D A
authors.initials: DA
authors.lastname: Rew
mesh_terms: D019295:Computational Biology; D003198:Computer Simulation; D013502:General Surgery; D005826:Genetics, Medical; D006801:Humans; D008432:Mathematical Computing; D008495:Medical Oncology; D008954:Models, Biological; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism