Professor James Bown

Professor of Systems Biology

School School of Design and Informatics

Department Division of Computing and Maths

Contact info

+44 (0)1382 30 8471


Innovation and Professional Development; School of Arts, Media and Computer Games: Taught to the MProf in Computer Games Development this module requires students to develop an innovative solution to an identifiable problem that sits beyond their current expertise. The student cohort is a mix of disciplines, including technology, art, design and sound production. Students must work in the face of an uncertain programme of research and development. Students are required to reflect on their development and how it has contributed to their professional practice.

Research Methods; Graduate School: Research Methods is a University-wide module delivered to all our post-graduate taught students. The module develops literature review skills, proposal writing and statistics at Masters level. The module draws on experts from across Abertay to provide insight into the nature of research at Masters level and to guide the production of a research proposal. For statistics teaching, the approach used is problem-led and the module instils in students how to select the right statistics in a given context.

Advanced Systems Biology in Biomedicine; School of Science, Engineering and Technology: Taught to Honours students on the BSc (Hons) Biomedical Science and BSc (Hons) Applied Biomedical Science, this module provides an integrative perspective on the role of systems biology and underpinning experimental systems in understanding and managing diseases, with a particular focus on cancer. Systems biology offers both a conceptual and a technical framework for integrating diverse, multi-scale, multi-stream data in bioscience generally.

My research explores the role of systems biology in understanding cancer. Cancer is a disease characterised by functional dysregulations within and surrounding affected cells, tissues and organs. These dysregulations confer cancerous cells with the ability to, among other features, proliferate at an increased rate, evade death and spread through the body. Cell behaviour is governed by a complex network of signalling regulatory pathways that ultimately dictate the development, maintenance and progression of the cancer as well as its histological and anatomical presentation and organisation. Anti-cancer drugs typically target key elements of this pathway in order to control these dysregulations, by either restoring normal functioning or blocking aberrant behaviour.

A major challenge in cancer therapy is drug resistance, which has at least two root causes. First, evolutionary mutations in cells can mean that the signalling network changes its function and the drugs lose efficacy. Second, the inherent heterogeneity of cells within a tumour combined with the specificity of the anti-cancer drugs means that a given therapy is likely to only target a sub-population of the cells in a tumour. Clearly to address this, we need to understand cellular signalling, tumour morphology and how each drives the other.

My research in cancer systems biology seeks to link cellular signalling and tumour morphology, and this requires a mix of complex systems modelling and agent-based modelling (EPSRC funded CoSMoS project), theoretical ecology in plant and fungal ecosystems, emergent behaviour in multi-agent robotic communities (EPSRC funded Artificial Cultures project) and interactive and dynamic visualisation of complex data. These areas of expertise combine to provide a unique approach to studying cancer based on the following research strands:

Bottom-up – Modelling the single cell. We have developed models of the cellular signalling pathways and their mechanistic responses to drug action. This allows us to explore the effects of cellular mutations on anti-cancer drug resistance (e.g. pdf), to unravel the regulatory mechanisms governing complex signalling dynamics (e.g. pdf) and to design combination therapies (e.g. pdf). Coupled to an innovative technology for dynamic and interactive visualisation of cell signalling dynamics we have developed an in silico drug discovery platform.

Top-down – Modelling the tumour. Tumour morphology may be studied in two dimensions, through imaging to show cross-sectional morphology or stained 2D-sections to revealing inner structures, or three dimensions to study whole tumour (surface) morphology. We have applied statistics developed in soil science to the study of 3D tumours. We have studied quantitatively the morphological response of tumours to therapeutic intervention (pdf).

Linking scales – Multi-cell modelling. In emerging work, we are developing a computational framework where individual cells, with potentially unique intra-cellular signalling networks, are able to interact in physical space. This framework will enable us to study heterogeneous populations of cells and how they interact to form emergent (tissue) structures.


From 2012 (selected)

Goltsov A, Tashkandi G, Langdon SP, Harrison DJ, Bown JL. Kinetic modelling of in vitro data of PI3K, mTOR1, PTEN enzymes and on-target inhibitors Rapamycin, BEZ235, and LY294002. European Journal of Pharmaceutical Sciences, 2017, 97, 170-181. pdf

Bown J, Shovman M, Robertson P, Boiko A, Goltsov A, Mullen P and Harrison DJ. A signalling toolkit to support rational design of combination therapies and biomarker discovery: SiViT. Oncotarget, 2016, 5. DOI: 10.18632/oncotarget.8747 pdf

Khalil, H, Langdon, S, Goltsov, A, Soininen, T, Harrison, D, Bown, J, & Deeni, Y. A novel mechanism of action of HER2 targeted immunotherapy is explained by inhibition of NRF2 function in ovarian cancer cells. Oncotarget, 2016, 5. pdf

Goltsov, A, Langdon SP, Goltsov G, Harrison DJ and Bown J. Customizing the therapeutic response of signalling networks to promote antitumor responses by drug combinations. Frontiers in Oncology. 2014, 4, 13. pdf

Khalil HS, Goltsov A, Langdon SP, Harrison DJ, Bown J and Deeni Y. Quantitative analysis of NRF2 pathway reveals key elements of the regulatory circuits underlying antioxidant response and proliferation of ovarian cancer cells. J Biotechnology, 11, 2014; DOI: 10.1016/j.jbiotec.2014.09.027. pdf

Goltsov A, Deeni Y, Khalil HS, Soininen T, Kyriakidis S, Hu H, Langdon SP, Harrison DJ, Bown J. Systems analysis of drug-induced receptor tyrosine kinase reprogramming following targeted mono-and combination anti-cancer therapy 2014 Cells 3(2): 563-591 pdf

Goltsov, A, Langdon, SP, Harrison, DJ and Bown, J. Dynamic Reprogramming of Signalling Networks – A New Challenge in Cancer Therapy. 2014. Enliven Bioinform 1(1). pdf

Goltsov A, Langdon SP, Goltsov G, Harrison DJ, Bown J. Customising the therapeutic response of signalling networks to promote antitumor responses by drug combinations, 2014. Frontiers in Oncology, 4(13). pdf

Idowu MA, Khalil HS, Bown JL, Zhelev N. Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition. 2013. Biodiscovery 2013; 9: 4; DOI: 10.7750/BioDiscovery.2013.9.4 pdf

Hu H, Goltsov A, Bown J, Sims AH, Langdon SP, Harrison DJ, Faratian D. Feedforward and feedback regulation of the MAPK and PI3K oscillatory circuit in breast cancer. 2013. Cellular Signalling 25(1), 26-32. pdf

Savage, A, Katz, E, Eberst, E, Falconer, RE, Houston, A, Harrison DJ and Bown, J. Characterising the tumour morphological response to therapeutic intervention, Disease Models and Mechanisms. 2013 6(1) 252-260. pdf

Bown, J., Andrews, P., Deeni, Y., Goltsov, A., Idowu, M., Polack, F.A.C., Sampson, A. T., Shovman, M. and Stepney, S. Engineering Simulations for Cancer Systems Biology 2012 13(12) 1560-1574. pdf

Goltsov A., Faratian D., Langdon S.P., Harrison D.J., Bown J. Features of the reversible sensitivity-resistance transition in ERK/PI3K/PTEN/AKT signalling network at HER2 inhibition. 2012. Cellular Signalling 24(2):493-504 pdf

Idowu, M. and Bown, J. Towards an Exact Reconstruction of a Time-Invariant Model from Time Series Data. Journal of Computer Science Systems Biology, 2011, 4:55-70. pdf

Milazzo, L., Bown, J. L., Eberst, A., Phillips, G., Crawford, J. W. 2011. Modelling of Healthcare Associated Infections: A study on the dynamics of pathogen transmission by using an individual-based approach. Computer Methods And Programs In Biomedicine. Vl 104. Is 2. 260-265. pdf

More Information


Visiting Fellow, Department of Computer Science, University of York, 2011-

External examiner, Media School, Bournemouth University, 2011-

QAA Scotland Enhancement Theme for Research-Teaching linkages. Institutional Contact, 2007

Meet the rest of the team

Dr Euan Dempster

Dr Euan Dempster

Division of Computing and Maths | Lecturer

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Mr Chris Acornley

Mr Chris Acornley

Division of Computing and Maths | Teaching Fellow

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