Ljubisa Miskovic
EPFL SB ISIC LCSB
CH J2 492 (Bâtiment CH)
Station 6
1015 Lausanne
+41 21 693 98 92
+41 21 693 28 35
Office: CH J2 492
EPFL › SB › ISIC › LCSB
Website: https://lcsb.epfl.ch
EPFL SB ISIC LCSB
CH J2 492 (Bâtiment CH)
Station 6
1015 Lausanne
EPFL SB ISIC LCSB
CH J2 492 (Bâtiment CH)
Station 6
1015 Lausanne
+41 21 693 28 35
EPFL › SB › SB-DEC › CF-SB
Website: https://sb.epfl.ch/conseil
Expertise
Systems Biology, Machine Learning, Dynamical Systems, Metabolic Engineering, Synthetic Biology, Parameter Estimation, Data-driven control
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.
Main publicatons
Kinetic-model-guided engineering of multiple<i> S.</i><i> cerevisiae</i> strains improves<i> p</i>-coumaric acid production
METABOLIC ENGINEERING. 2025. DOI : 10.1016/j.ymben.2025.06.008.The Dawn of High-throughput and Genome-scale Kinetic Modeling: Recent Advances and Future Directions
ACS SYNTHETIC BIOLOGY. 2025. DOI : 10.1021/acssynbio.4c00868.Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states
Nature Catalysis. 2024. DOI : 10.1038/s41929-024-01220-6.Rational strain design with minimal phenotype perturbation
Nature Communications. 2024. DOI : 10.1038/s41467-024-44831-0.Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks
Nature Machine Intelligence. 2022. 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. DOI : 10.1038/s41467-021-25158-6.Constraint-based metabolic control analysis for rational strain engineering
Metabolic Engineering. 2021. 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. DOI : 10.1186/s13068-020-1665-7.Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties
PLoS Computational Biology. 2019. DOI : 10.1371/journal.pcbi.1007242.Control Theory Concepts for Modeling Uncertainty in Enzyme Kinetics of Biochemical Networks
Industrial & Engineering Chemistry Research. 2019. 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 Of The United States Of America (PNAS). 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. 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.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.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.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.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.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.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.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
Past EPFL PhD Students as codirector
Stefano Andreozzi, Milenko Tokic, Subham Choudhury
Courses
Chemical process control
ME-323
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.
Numerical methods
ChE-312
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.