The integration of mathematical modelling with experiments is one of the central elements in biological systems engineering and has led to models with improved predictive capabilities. One way to achieve such integration is through reverse-engineering approaches, where dynamical models of biological networks are inferred and fit to quantitative data. Current work shows how reverse engineering of simple
networks and numerical (in silico) simulations can be used to explain observed phenomena and, more importantly, to suggest new hypotheses and future experimental work. However, for more complex biological contexts, the integration of reverse engineering with identifiability analysis, optimal experimental design and uncertainty quantification still faces very significant challenges.
SyMBioSys will overcome these issues by developing a) “useable” dynamic models, b) novel algorithms and methodologies for model development and data/model reduction, and c) integrated software tools through i) integrating experimentation with modelling, ii) collaborating between disciplines, iii) interfacing industry and academia, iv) metricising model performance and v) implementing relevant applications in order to achieve a full systematic model-building cycle than can be implemented by non-modelling domain-experts.
SyMBioSys has already produced results that go beyond the current state of the art. Specifically, SyMBioSys will:
1) Develop proper kinetic models for complex biological systems, since the existing kinetic models in biology cannot yet deal with the true complexity of biological systems and have thus very limited predictive power;
2) Developing new algorithms for analysis and refinement of the kinetic models developed, including model inference and calibration, identifiability analysis, optimal experimental design and model reduction, and discrimination;
3) Develop new algorithms for exploitation of the new kinetic models, including dynamic optimization (to predict the dynamic behavior in biological networks) and mixed-integer optimal control (to identify design principles or optimal ways of intervention);
4) Develop high-level and user-friendly software tools, implementing the new algorithms above, to be used by a large community of biologists/experimentalists. Existing software developed by several of the industrial partners will be extended with the new methods to be developed by the academic partners; active user comments will feed back into the model and algorithm development to verify performance. The ultimate goal is to create computational workflows and optimization software;
5) Develop real-life applications of the kinetic models, methods and software developed to provide experimentally-verified solutions to real-life biotechnological and biomedical problems.
SyMBioSys is expected to have socio-economic impact. Specifically, the development of software tools by the SME partners will contribute to the economy of Europe plc and the research generated by the academic beneficiaries will have societal implications through, for instance, the identification of novel biomarkers for disease.
