The wealth of morphological, histological, and molecular data from human cancers available to pathologists means that pathology is poised to become a truly quantitative systems science. By measuring morphological parameters such as tumor stage and grade, and by measuring molecular biomarkers such as hormone receptor status, pathologists have sometimes accurately predicted what will happen to a patient's tumor. While 'omic' technologies have seemingly improved prognostication and prediction, some molecular 'signatures' are not useful in clinical practice because of the failure to independently validate these approaches. Many associations between gene 'signatures' and clinical response are correlative rather than mechanistic, and such associations are poor predictors of how cellular biochemical networks will behave in perturbed, diseased cells. Using systems biology, the dynamics of reactions in cells and the behavior between cells can be integrated into models of cancer. The challenge is how to integrate multiple data from the clinic into tractable models using mathematical models and systems biology, and how to make the resultant model sufficiently robust to be of practical use. We discuss the difficulties in using mathematics to model cancer, and review some approaches that may be used to allow systems biology to be successfully applied in the clinic.

The cell cycle is implicated in diseases that are the leading cause of mortality and morbidity in the developed world. Until recently, the search for drug targets has focused on relatively small parts of the regulatory network under the assumption that key events can be controlled by targeting single pathways. This is valid provided the impact of couplings to the wider scale context of the network can be ignored. The resulting depth of study has revealed many new insights; however, these have been won at the expense of breadth and a proper understanding of the consequences of links between the different parts of the network. Since it is now becoming clear that these early assumptions may not hold and successful treatments are likely to employ drugs that simultaneously target a number of different sites in the regulatory network, it is timely to redress this imbalance. However, the substantial increase in complexity presents new challenges and necessitates parallel theoretical and experimental approaches. We review the current status of theoretical models for the cell cycle in light of these new challenges. Many of the existing approaches are not sufficiently comprehensive to simultaneously incorporate the required extent of couplings. Where more appropriate levels of complexity are incorporated, the models are difficult to link directly to currently available data. Further progress requires a better integration of experiment and theory. New kinds of data are required that are quantitative, have a higher temporal resolution and that allow simultaneous quantitative comparison of the concentration of larger numbers of different proteins. More comprehensive models are required and must accommodate not only substantial uncertainties in the structure and kinetic parameters of the networks, but also high levels of ignorance. The most recent results relating network complexity to robustness of the dynamics provide clues that suggest progress is possible.

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Ataxia-telangiectasia mutated (ATM) is known to play a central role in effecting the DNA damage response that protects somatic cells from potentially harmful mutations, and in this role it is a key anti-cancer agent. However, it also promotes repair of therapeutic damage (e.g. radiotherapy) and so frustrates the efficacy of some treatments. A better understanding of the mechanisms of ATM regulation is therefore important both in prevention and treatment of disease. While progress has been made in elucidating the key signal transduction pathways that mediate damage response in somatic cells, relatively little is known about whether these function similarly in pluripotent embryonic stem (ES) cells where ATM is also implicated in our understanding of adult stem cell ageing and in improvements in regenerative medicine. There is some evidence that different mechanisms may operate in ES cells and that our understanding of the mechanisms of ATM regulation is therefore incomplete. We investigated the behaviour of the damage response signalling pathway in mouse ES cells. We subjected the cells to the DNA-damaging agent doxorubicin, a drug that induces double-strand breaks, and measured ATM expression levels. We found that basal ATM gene expression was unaffected by doxorubicin treatment. However, following ATM kinase inhibition using a specific ATM inhibitor, we observed a significant increase in ATM and ataxia-telangiectasia and Rad3 related transcription. We demonstrate the use of a dynamical modelling approach to show that these results cannot be explained in terms of known mechanisms. Furthermore, we show that the modelling approach can be used to identify a novel feedback process that may underlie the anomalies in the data. The predictions of the model are consistent both with our in vitro experiments and with in vivo studies of ATM expression in somatic cells in mice, and we hypothesize that this feedback operates in both somatic and ES cells in vivo. The results point to a possible new target for ATM inhibition that overcomes the restorative potential of the proposed feedback.