Systematic models for biological systems engineering training network
Mathematical, computational models are central in biomedical and biological systems engineering; models enable (i) mechanistically justifying experimental results via current knowledge and (ii) generating new testable hypotheses or novel intervention methods. 

SyMBioSys is a joint academic/industrial training initiative supporting the convergence of engineering, biological and computational sciences. The consortium's mutual goal is developing a new generation of innovative and entrepreneurial early-stage researchers (ESRs) to develop and exploit cutting-edge dynamic (kinetic) mathematical models for biomedical and biotechnological applications. SyMBioSys integrates: (i) six academic beneficiaries with a strong record in biomedical and biological systems engineering research, these include four universities and two research centres; (ii) four industrial beneficiaries including key players in developing simulation software for process systems engineering, metabolic engineering and industrial biotechnology; (iii) three partner organisations from pharmaceutical, biotechnological and entrepreneurial sectors. SyMBioSys is committed to supporting the establishment of a Biological Systems Engineering research community by stimulating programme coordination via joint activities. 

The main objectives of this initiative are: 

* Developing new algorithms and methods for reverse engineering and identifying dynamic models of biosystems and bioprocesses

* Developing new model-based optimization algorithms for exploiting dynamic models of biological systems (e.g. predicting behavior in biological networks, identifying design principles and selecting optimal treatment intervention)
 
* Developing software tools, implementing the preceding novel algorithms, using state-of-the-art software engineering practices to ensure usability in biological systems engineering research and practice 

 * Applying the new algorithms and software tools to biomedical and biological test cases.