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A dual-parameter identification approach for data-based predictive modeling of hybrid gene regulatory network-growth kinetics in Pseudomonas putida mt-2

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Abstract

Data integration to model-based description of biological systems incorporating gene dynamics improves the performance of microbial systems. Bioprocess performance, typically predicted using empirical Monod-type models, is essential for a sustainable bioeconomy. To replace empirical models, we updated a hybrid gene regulatory network-growth kinetic model, predicting aromatic pollutants degradation and biomass growth in Pseudomonas putida mt-2. We modeled a complex biological system including extensive information to understand the role of the regulatory elements in toluene biodegradation and biomass growth. The updated model exhibited extra complications such as the existence of oscillations and discontinuities. As parameter estimation of complex biological models remains a key challenge, we used the updated model to present a dual-parameter identification approach (the ‘dual approach’) combining two independent methodologies. Approach I handled the complexity by incorporation of demonstrated biological knowledge in the model-development process and combination of global sensitivity analysis and optimisation. Approach II complemented Approach I handling multimodality, ill-conditioning and overfitting through regularisation estimation, global optimisation, and identifiability analysis. To systematically quantify the biological system, we used a vast amount of high-quality time-course data. The dual approach resulted in an accurately calibrated kinetic model (NRMSE: 0.17055) efficiently handling the additional model complexity. We tested model validation using three independent experimental data sets, achieving greater predictive power (NRMSE: 0.18776) than the individual approaches (NRMSE I: 0.25322, II: 0.25227) and increasing model robustness. These results demonstrated data-driven predictive modeling potentially leading to bioprocess’ model-based control and optimisation.

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 675585 (Marie Skłodowska-Curie ITN “SymBioSys”). JRB also acknowledges funding from the Spanish Ministry of Science, Innovation and Universities and the European Union FEDER under project grant SYNBIOCONTROL (DPI2017-82896-C2-2-R). Author JAP has been a Marie Skłodowska-Curie ESR at IIM-CSIC (Spain).

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Author notes

  1. Jake Alan Pitt

    Present address: College of Engineering, Mathematics & Physical Sciences, University of Exeter, Devon, UK

  2. Argyro Tsipa and Jake Alan Pitt have contributed equally to this work.

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Argyro Tsipa

  2. (Bio)Process Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208, Vigo, Spain

    Jake Alan Pitt & Julio R. Banga

  3. RWTH-Aachen University Hospital, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), 52074, Aachen, Germany

    Jake Alan Pitt

  4. Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Athanasios Mantalaris

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  1. Argyro Tsipa
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Correspondence to Julio R. Banga or Athanasios Mantalaris.

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Tsipa, A., Pitt, J.A., Banga, J.R. et al. A dual-parameter identification approach for data-based predictive modeling of hybrid gene regulatory network-growth kinetics in Pseudomonas putida mt-2. Bioprocess Biosyst Eng 43, 1671–1688 (2020). https://doi.org/10.1007/s00449-020-02360-2

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