Systematic Models for Biological Systems Engineering Training Network

Informazioni relative al progetto

SyMBioSys

ID dell’accordo di sovvenzione: 675585

DOI

10.3030/675585

Progetto chiuso

Data della firma CE 11 Agosto 2015

Data di avvio 1 Settembre 2015

Data di completamento 31 Agosto 2019

Finanziato da

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Costo totale

€ 3 805 423,20

Contributo UE

€ 3 805 423,20

Coordinato da

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
United Kingdom

Periodic Reporting for period 2 - SyMBioSys (Systematic Models for Biological Systems Engineering Training Network)

Periodo di rendicontazione: 2017-09-01 al 2019-08-31

Sintesi del contesto e degli obiettivi generali del progetto

Mathematical, computational models are central in biomedical and biological systems engineering; models enable (i) the mechanistic justification of experimental results via current knowledge and (ii) the generation of 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.
1. Developing new algorithms and methods for reverse engineering and identifying dynamic models of biosystems and bioprocesses. The progress made towards this objective has resulted in a pipeline to build kinetic models for human metabolism using a thermodynamic-based curation of the genome-scale model (GEM) Recon 2.

2. Developing new model- based optimization algorithms for exploiting dynamic models of biological systems (e.g. predicting behaviour in biological networks, identifying design principles and selecting optimal treatment intervention). The progress made towards this objective has resulted in the reformulation of PHONEMeS as an Integer Linear Program (ILP) that is orders of magnitude more efficient than the original one, enabling larger and more sophisticated analysis

3. 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. The progress made towards this objective includes the stretch move algorithm, which was implemented within gPROMS, the software developed by PSE. Results were presented during the poster sessions of the Advanced Process Modelling Forum 2017 and Research Day UK 2017, organized by Process Systems Enterprise Ltd. and Imperial College London respectively.

4. Applying the new algorithms and software tools to biomedical and biological test cases. The progress made towards this objective in the context of biomarker discovery (using CKD as case study) has been identified as “promising” as one review entitled “Mechanism-Based Biomarker Discovery”, which has already been published in Drug Discovery Today (doi: 10.1016/j.drudis.2017.04.013).

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

Overall, SymBioSys has progressed well and according to the original plan with minor modifications (discussed in detail below). In WP1, research conducted can have a considerable impact on the analysis and modelling of cellular metabolism and growth regulation, as the methodologies under development go well beyond the state of the art. In WP2, identification strategies have been developed for large-scale logic-based dynamic models used for cell signalling networks along with the development of model building and reduction methods for control and optimization of bioprocesses. In WP3, research conducted is expected to have a considerable impact on the dynamic analysis cellular pathways, allowing a better understanding of these complex bio-systems and their regulation. Further, it will also allow us to solve practical problems related to metabolic engineering and to the detection of drug targets. In WP4 significant progress has been made on the development of software tools and implementation of new algorithms for model-based analysis and optimization, and using state-of-the-art software engineering practices to ensure usability and inter-operability in biological systems engineering research. In WP5, the tools developed in the previous WPs were applied in two real-life applications: a) towards the development of an in-silico leukemia model and b) towards the development of a platform to integrate data and models for categorizing patient subpopulations in Chronic Kidney Disease. In WP6, all training activities were organised and implemented according to the original plan and the contract with no major modifications.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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.

SymBioSys Overview