I have 20 year’s experience researching the coordination of cellular responses to xenobiotic challenge. My research has focussed on members of the super-family of nuclear receptors, which act as ligand-activated transcription factors, sensing their cellular surroundings and coordinating network responses to any disruption of homeostasis. In particular, I have championed the understanding of nuclear receptor interactions, including the pregnane-X receptor (PXR; the main nuclear receptor responsible for cellular response to xenobiotic challenge), the glucocorticoid receptor (GR), vitamin D receptor (VDR), peroxisome proliferator activated receptors (PPARα/β/γ) and Aryl hydrocarbon receptor (AhR).

I teach aspects of drug metabolism and toxicology to many different student groups, from undergraduate biochemists to continuing professional development courses. In addition to teaching the basic theory, I hope to pass on my enthusiasm and enjoyment of these subjects. Learning about how drugs are designed, and how we can ensure that they are safe when given to patients, is a subject that should teach everyone how far we have come in understanding and treating diseases in the last few years, but also how far we still have to go...

MuFINS - A MUlti-Formalism Interaction Network Simulator: Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. MuFINS is fully described in the accompanying paper (available here), including three use cases that demonstrate powerful options within the software: the integration of a signaling network with a GSMN through linear inhibitory and activation edges; the integration of a signaling network with a GSMN through the QSSPN approach; and, the generation of context-specific GSMNs using fast iMAT, a more computationally efficient version of iMAT. The software can be downloaded here, and includes help files and videos.

Review - The Systems Biology of Mood Disorders: One in twenty-five people suffer from a mood (or affective) disorder such as bipolar disorder. Current treatments are sub-optimal with poor patient response and uncertain modes-of-action. There is thus a need to better understand underlying mechanisms that determine mood, and how these go wrong in affective disorders. Systems biology approaches have yielded important biological discoveries for other complex diseases such as cancer, and their potential in affective disorders is reviewed in this manuscript.In common with many complex diseases much time and money has been spent on the generation of large-scale experimental datasets, but at present computational systems biology has only be applied to understanding affective disorders on a few occasions. These studies provide sufficient novel biological insight to motivate further use of computational biology in this field. Read the full article here.

Understanding how metabolism changes during breast cancer: A major roadblock in the effective treatment of cancers is their heterogeneity, whereby multiple molecular landscapes are classified as a single disease. We have explored the contribution of cellular metabolism to cancer heterogeneity, creating personalized metabolic landscapes for 2,000 breast tumours. Classification of these personalized landscapes reveals a novel poor prognosis cluster, reproducible between independent datasets. Active serotonin production is a major metabolic feature of the poor prognosis group, and we confirm this effect using in breast cancer cell lines. Not only does this data provide new insights into how metabolism alters during cancer, but also supports the reconsideration of concomitant serotonin-specific uptake inhibitors treatment during breast cancer chemotherapy. Read the full paper here.

Novel ways to kill breast cancer cells: Breast cancer is the commonest form of cancer in women, with a lifetime risk of 1:8. An area of increasing interest is that of metabolic reprogramming, where drug-induced alterations in tumour cell metabolism slows tumour growth and/or increases sensitivity to existing therapeutics. The farnesoid X-receptor (FXR) is a member of the nuclear receptor family of transcription factors, which act as central regulators of metabolic enzyme expression. In this paper we demonstrate how activation of FXR can induce cell death in breast cancer cell lines, and describe the molecular mechanisms that underlie this. This work motivates further examination of nuclear receptors as novel targets for anti-cancer chemotherapy. You can read the paper here.

Leoncikas V, Wu H, Ward LT, kierzek A, Plant NJ. (2016) 'Generation of 2,000 breast cancer metabolic landscapes reveals a poor prognosis group with active serotonin production'. Scientific Reports, 6 doi: 10.1038/srep19771 Alasmael N, Mohan R, Meira LB, Swales KE, Plant NJ. (2015) 'Activation of the Farnesoid X-receptor in breast cancer cell lines results in cytotoxicity but not increased migration potential'. CANCER LETTERS, 370 (2), pp. 250-259. doi: 10.1016/j.canlet.2015.10.031 Fisher CP, Plant NJ, Moore JB, Kierzek AM. (2013) 'QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells.'. Bioinformatics, England: 29 (24), pp. 3181-3190. doi: 10.1093/bioinformatics/btt552