The University of Manchester has used data-driven methods extensively to elucidate structure and function in complex systems, leading, for example, to a complete description of the human metabolic network.


Focusing on advanced quantitative approaches to specific biotechnology challenges at the interface between medicine and biology and the physical sciences, engineering, mathematics and computation the MIB enjoys a unique pluralistic and open research culture that is supported by world-class infrastructure. The establishment of multi-skilled interdisciplinary teams with critical mass generates unique capabilities that cannot be realised through virtual associations between PIs or research units to develop regional, national and international partnerships in biotechnology research.

Bioanalytical Sciences Group

Most of their bioanalytical projects are at the interface between postgenomic biological systems, quantitative analytical chemistry and machine learning, with a special emphasis on evolutionary computing and systems biology. They have particular interests in chemical genomics, network biology and e-science. Other areas include pharmaceutical drug transporters, iron as a major cause of disease, and synthetic biotechnology.

Lead Researcher:

Professor Douglas Kell

His interests are focused on the development and application of novel analytical methods to the solution of complex biological problems. Such methods are both experimental and computational, with the majority of the group's activities being 'dry'. His recent research has involved implementing a non-deterministic universal Turing machine using DNA. The work demonstrated that the design works using both computational modelling and in vitro molecular biology experimentation.

He has been a pioneer in many areas of computational biology and experimental metabolomics, including the use of evolutionary, closed-loop methods for optimisation. He also contributed to the discovery of the first bacterial cytokine, currently on trial as part of a vaccine against tuberculosis.

Manchester Centre for Integrative Systems Biology (MCISB)

The Manchester Centre for Integrative Systems Biology (MCISB) at the University of Manchester is funded by the BBSRC and EPSRC to pioneer the development of new experimental and computational technologies in Systems Biology, and their exploitation. The MCISB is intended to provide a hub for cutting-edge systems biology research in the Manchester area, acting as a focal point for the creation of the necessary ideas and infrastructure, and for establishing new methods and routines. The MCISB is located within the Manchester Institute of Biotechnology.

Lead Researcher:

Professor Hans Westerhoff

Hans is the Director of the MCISB and holds the AstraZeneca Chair for Systems Biology at the University. His research interests include integrated experimental and computational systems biology of microorganisms; carbon, nitrogen and energy metabolism; metabolic maps; the systems biology of cancer, multifactorial disease and innate immunity; signal transduction; integral regulation of cell function; the silicon human; and personalized (n=1) medicine. More generally, Westerhoff studies how biological functions emerge in the complex interactions between the components of living systems. He has shown how this requires an integration of modelling, theory, and precise experimentation, and an engagement of diverse scientific disciplines from biochemistry and medicine to physics and philosophy.

Other Researchers within Manchester Institute of Biotechnology:

Professor Rainer Breitling

Rainer uses computational approaches to understand complex biological systems, ranging from microbes to man. He explors the application of bioinformatics and systems biology techniques to the engineering of “designer microbes”, and using metabolomics and transcriptomics in the diagnosis and debugging of the organisms created by synthetic biology approaches. Two main strategies dominates his work: on the one hand, he develops methods to interpret “molecular profiles”, e.g. the global patterns of gene expression or the abundance of small molecules (metabolites) in biological samples. On the other hand, he develops quantitative models to describe and predict the behaviour of cells, e.g. after drug treatment or genetic manipulation.


Other Researchers across the University:

Professor Magnus Rattray - Division of Informatics, Imaging & Data Science (Faculty of Biology, Medicine and Health)

Magnus is the Director of the Data Science Institute. He works on probabilistic modelling and Bayesian methodology with applications involving large-scale data from high-throughout molecular biology assays. His research group are developing and applying novel statistical methods for the analysis of these datasets.

His group works on how to learn models and make inferences given evidence from high-throughput biological datasets. The models that are developed range from mechanistic differential equation models of the cell to more abstract probabilistic latent variable models that can be used uncover interesting structure in high-dimensional data. They are particularly interested in hybrid models that combine aspects of mechanistic and probabilistic models.

They are applying their methods to infer gene regulatory networks from time-series mRNA expression and DNA-protein binding data, to uncover changes in the transcriptome from RNA-Seq datasets, and to develop novel inference algorithms for time-series data analysis and systems biology modelling.

Professor Andrew Brass

Andrew is a Professor of Bioinformatics in the School of Computer Science. He is a founding member of the bioinformatics group, where he has a wide range of projects in protein function prediction, gene expression analysis, intelligent integration, automated curation, and bioinformatics education. His research interests include Bioinformatics, Functional Genomics and Computational Biology.



Professor Korbinian Strimmer - School of Mathematics

Korbinian is a Professor in Statistics at the University of Manchester. He is interested in statistical and machine learning methodology for biomedical data science and how novel statistical and computational approaches and algorithms can aid in the analysis of large-scale complex high-dimensional data. This involves considerable challenges due to the high-dimensionality and complex structure of the data. The biomedical applications of his methods include, e.g., biomarker discovery, clinical diagnostics and systems biology.