Ljubisa Miskovic
EPFL SB ISIC LCSB
CH H4 595 (Bâtiment CH)
Station 6
1015 Lausanne
+41 21 693 98 92
+41 21 693 28 35
Office:
CH H4 595
EPFL
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SB
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ISIC
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LCSB
Web site: Web site: https://lcsb.epfl.ch
Web site: Web site: https://www.epfl.ch/schools/sb/scgc/
Web site: Web site: https://sb.epfl.ch/conseil
Fields of expertise
Systems Biology
Machine Learning
Dynamical Systems
Metabolic Engineering
Synthetic Biology
Parameter Estimation
Data-driven control
Machine Learning
Dynamical Systems
Metabolic Engineering
Synthetic Biology
Parameter Estimation
Data-driven control
Biography
Ljubisa Miskovic earned his Ph.D. degree in Automatic Control from the Swiss Federal Institute of Technology (EPFL) under the co-supervision of Dominique Bonvin and Alireza Karimi, in 2006. He pursued his postdoctoral studies at the Centre for Systems Engineering and Applied Mechanics, Universite Catholique de Louvain with Michel Gevers before moving to the laboratory of Vassily Hatzimanikatis at the EPFL. In 2010, he became a research scientist. His research interests include systems biology, metabolic engineering, synthetic biology, data-driven control design, system identification, stochastic processes and estimation theory.Publications
Infoscience publications
Main publicatons
Rational strain design with minimal phenotype perturbation
Nature Communications. 2024-01-24. DOI : 10.1038/s41467-024-44831-0.Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks
Nature Machine Intelligence. 2022-08-01. DOI : 10.1038/s42256-022-00519-y.A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics
Nature Communications. 2021-08-09. DOI : 10.1038/s41467-021-25158-6.Constraint-based metabolic control analysis for rational strain engineering
Metabolic Engineering. 2021-04-22. DOI : 10.1016/j.ymben.2021.03.003.Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies
Biotechnology for Biofuels. 2020-02-28. DOI : 10.1186/s13068-020-1665-7.Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties
PLOS Computational Biology. 2019-08-20. DOI : 10.1371/journal.pcbi.1007242.Control Theory Concepts for Modeling Uncertainty in Enzyme Kinetics of Biochemical Networks
Industrial & Engineering Chemistry Research. 2019-05-10. DOI : 10.1021/acs.iecr.9b00818.Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites
Proceedings of the National Academy of Sciences. 2019. DOI : 10.1073/pnas.1818877116.Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations
Metabolic Engineering. 2019. DOI : 10.1016/j.ymben.2018.10.005.Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors
ACS Synthetic Biology. 2018-07-18. DOI : 10.1021/acssynbio.8b00049.A Design-Build-Test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models
Biotechology for Biofuels. 2017. DOI : 10.1186/s13068-017-0838-5.Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models
Metabolic Engineering. 2016. DOI : 10.1016/j.ymben.2016.01.009.iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks
Metabolic Engineering. 2016. DOI : 10.1016/j.ymben.2015.10.002.Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes
Current Opinion in Biotechnology. 2015. DOI : 10.1016/j.copbio.2015.08.019.Antihypertensive drugs metabolism: an update to pharmacokinetic profiles and computational approaches
Current Pharmaceutical Design. 2015.Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiology constraints
Biotechnology Journal. 2013. DOI : 10.1002/biot.201300091.From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks
FEMS Yeast Research. 2011. DOI : 10.1111/j.1567-1364.2011.00771.x.Modeling of uncertainties in biochemical reactions
Biotechnology and Bioengineering. 2010. DOI : 10.1002/bit.22932.Production of biofuels and biochemicals: in need of an ORACLE
Trends in Biotechnology. 2010. DOI : 10.1016/j.tibtech.2010.05.003.Closed-loop identication of MIMO systems: a new look at identifiability and experiment design
European Journal of Control. 2010. DOI : 10.3166/EJC.16.228-239.Identification and the information matrix: how to get just sufficiently rich?
IEEE Transactions on Automatic Control. 2009. DOI : 10.1109/TAC.2009.2034199.Iterative minimization of H2 control performance criteria
Automatica. 2008. DOI : 10.1016/j.automatica.2008.03.014.Closed-loop Identification of Multivariable Systems: With or Without Excitation of All References?
Automatica. 2008. DOI : 10.1016/j.automatica.2007.11.016.Correlation-Based Tuning of Decoupling Multivariable Controllers
Automatica. 2007. DOI : 10.1016/j.automatica.2007.02.006.Identification of multi-input systems: variance analysis and input design issues
Automatica. 2006. DOI : 10.1016/j.automatica.2005.12.017.Iterative Correlation-Based Controller Tuning
International Journal of Adaptive Control and Signal Processing. 2004. DOI : 10.1002/acs.825.Control of an Active Suspension System as a Benchmark for Design and Optimization of Restricted Complexity Controllers
European Journal of Control. 2003. DOI : 10.3166/ejc.9.3-12.Correlation-Based Tuning of a Restricted-Complexity Controller for an Active Suspension System
European Journal of Control. 2003. DOI : 10.3166/ejc.9.77-83.Iterative Correlation-Based Controller Tuning: Application to a Magnetic Suspension System
Control Engineering Practice. 2003. DOI : 10.1016/S0967-0661(02)00191-0.Application of the minimum state error variance approach to nonlinear system control
International Journal of Systems Science. 2002. DOI : 10.1080/00207720210123706.Nonlinear system control using the MSEV approach
Control and Intelligent Systems. 2000.Teaching & PhD
PhD Students
Toumpe Ilias,Past EPFL PhD Students
Andreozzi Stefano , Choudhury Subham , Tokic Milenko ,Courses
Numerical methods
This course introduces students to modern computational and mathematical techniques for solving problems in chemistry and chemical engineering. The use of introduced numerical methods will be demonstrated using the python programming language.
Chemical process control
Provide the students with basic notions and tools for the modeling and analysis of dynamic systems. Show them how to design controllers and analyze the performance of controlled systems.