Systems Biology

About

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.

Multiscale and multilevel modeling of biological systems

Biological systems are inherently multilevel, where intracellular behaviour relies on complex behaviours at the intracellular level, which itself can contain several levels (e.g. biochemical pathways, protein structures etc). We build multilevel models which then operate over multiple scales in time and space. Constructing such dynamic models requires the ability to model location which we achieve using Coloured Petri nets to enable a discrete compact mapping of space in 2D or 3D, which can be unfolded to yield very large and complex models. We then apply temporal model checking over the dynamic behaviours at different levels of organisation.

Quorum sensing driven biofilm formation

Single cell organisms are an example of such multilevel systems. We model the ways in which quorum sensing in bacteria is a driving force in the formation of biofilm, for example in the human gut. These models include the ability to describe spatial location, diffusion of biochemical substances, e.g. glucose or other carbon sources as well as Auto Inducers for bacterial communication, and chemotaxis towards carbon sources. They include bacterial behaviour (movement, division and death) as well as the human host cells and the aspects of the relationship between both organisms.

Planar cell polarity in Drosophila wing

We model intercellular communication at the tissue level in multicellular organisms, in this case the behaviour of planar cell polarity affecting hair formation in fly wing driven by intracellular signalling pathways. This also includes an element of short-distance communication between neighbouring cells, and differential behaviours in different locations within a cell. These models then enable the study of the effects of well-known gene mutations which impact on signalling pathways and thus how clones of mutated cells affect and potentially disrupt wing hair patterns.

Modelling and analysis of intra-cellular biochemical networks

In this area of Systems biology we construct dynamic models of intracellular biochemical networks using both continuous and stochastic representations. In general we use Petri nets as our descriptive formalism, which can map into continuous, stochastic and hybrid models. The systems range in size from small pathways, e.g. human signal transduction, to large networks, e.g. whole genome metabolic models (GEMs) of bacteria. These enable us to explore the consequences of mutations, drug application (signal transduction), and changes in carbon sources (GEMs). We use temporal logic model checking to explore the complex behaviours that these networks can exhibit.

Research papers

• Self, T., Gilbert, D., Heiner, M. (2018) 'Derivation of a biomass proxy for dynamic analysis of whole genome metabolic models'. Springer Lecture Notes in Bioinformatics (LNBI), 11095 (ISBN 978-3-319-99429-1).
• Xu, H., Curtis, TY., Powers, SJ., Raffan, S., Gao, R., Huang, J., Heiner, M., Gilbert, DR., Halford, NG. (2018). 'Genomic, Biochemical, and Modeling Analyses of Asparagine Synthetases from Wheat'. Frontiers in Plant Science, 8. ISSN: 1664-462X
• Colombo, R., Damiani, C., Gilbert, D., Heiner, M., Mauri, G., Pescini, D (2018) 'Emerging ensembles of kinetic parameters to identify experimentally observed phenotypes'. BMC Bioinformatics, 2018, 19 (251). ISSN: 1471-2105
• Viksna, J., Gilbert, D. (2016) 'Gene Duplication Models and Reconstruction of Gene Regulatory Network Evolution from Network Structure'.  Baltic Journal of Modern Computing, 4(4): pp. 876–895. ISSN: 2255-8950
• Rausanu S., Grosan C., Wu Z., Parvu O., Stoica R., Gilbert D. (2015) 'Computational models for inferring biochemical networks'. Neural Computing and Applications, Volume 26, Issue 2, pp 299–311. ISSN: 0941-0643
• Gilbert, D., Heiner, M., Ghanbar, L., Chodak, J. (2019) 'Spatial quorum sensing modelling using coloured hybrid Petri nets and simulative model checking'.  BMC Bioinformatics, 20 (Suppl 4):173, pp. 1-23. ISSN: 1471-2105
• Heiner, M., Gilbert, D. (2017) 'Coloured Petri nets for multi-level, multiscale, and multi-dimensional modelling of biological systems'.  Briefings in Bioinformatics. ISSN: 1467-5463
• Gilbert, D., Heiner, M., Jaraweera, Y., Rohr, C. (2010). 'Towards dynamic genome scale models'. Briefings in Bioinformatics. ISSN: 1467-5463
• Pârvu, O., Gilbert, D. (2016) 'A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking'. PLOS ONE, 11(5): pp. 1-43. ISSN: 1932-6203
• Pârvu 0., Gilbert D., Heiner M., Liu F., Saunders N., Shaw S. (2015) 'Spatial-Temporal Modelling and Analysis of Bacterial Colonies with Phase Variable Genes'. ACM Transactions on Modeling and Computer Simulation (TOMACS), Article No.: 13. ISSN: 1049-3301
• Pârvu, O., Gilbert, D. (2014) 'Automatic validation of computational models using pseudo-3D spatio-temporal model checking'. BMC Systems Biology, 2014, 8 (124). ISSN: 1752-0509
• Gao, Q., Gilbert, D., Heiner, M., Liu, F., Maccagnola, D., Tree, D. (2012) 'Multiscale Modeling and Analysis of Planar Cell Polarity in the Drosophila Wing'. IEEE/ACM Transactions on Computational Biology and Bioinformatics (Volume: 10). ISSN: 1545-5963