Vassily Hatzimanikatis

Dr. Vassily Hatzimanikatis is currently Associate Professor of Chemical Engineering and Bioengineering at Ecole Polytechnique Federale de Lausanne (EPFL), in Lausanne, Switzerland. Vassily received a PhD and an MS in Chemical Engineering from the California Institute of Technology, and his Diploma in Chemical Engineering from the University of Patras, in Greece. After the completion of his doctoral studies, he held a research group leader position at the Swiss Federal Institute of Technology in Zurich (ETHZ), Switzerland. Prior to joining EPFL, Vassily worked for three years in DuPont, Cargill, and Cargill Dow, and he has been assistant professor at Northwestern University, at Illinois, USA.

Vassilys research interests are in the areas of computational systems biology, biotechnology, and complexity. He is associate editor of the journals Biotechnology & Bioengineering, Metabolic Engineering and Integrative Biology, and he serves on the editorial advisory board of the journals Bioprocess and Biosystems Engineering, Journal of Chemical Technology and Biotechnology, and Industrial Biotechnology. He has written over 70 technical publications and he is co-inventor in three patents and patent applications.

Vassily is a fellow of the American Institute for Medical and Biological Engineering (2010), he was a DuPont Young Professor (2001-2004), and he has also received the Jay Bailey Young Investigator Award in Metabolic Engineering (2000), and the ACS Elmar Gaden Award (2011).

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Metabolism

Metabolism (məˈtæbəlɪzəm, from μεταβολή metabolē, "change") is the set of life-sustaining chemical reactions in organisms. The three main functions of metabolism are: the conversion of the energy

Metabolic network

A metabolic network is the complete set of metabolic and physical processes that determine the physiological and biochemical properties of a cell. As such, these networks comprise the chemical react

Metabolic engineering

Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the cell's production of a certain substance. These processes are chemical networks that

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

Large omics datasets are nowadays routinely generated to provide insights into cellular processes. Nevertheless, making sense of omics data and determining intracellular metabolic states remains challenging. Kinetic models of metabolism are crucial for integrating and consolidating omics data because they explicitly link metabolite concentrations, metabolic fluxes, and enzyme levels. However, the difficulties in determining kinetic parameters that govern cellular physiology prevent the broader adoption of these models by the research community. We present RENAISSANCE (REconstruction of dyNAmIc models through Stratified Sampling using Artificial Neural networks and Concepts of Evolution strategies), a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. We showcase RENAISSANCE’s capabilities through three applications: generation of kinetic models of E. coli metabolism, characterization of intracellular metabolic states, and assimilation and reconciliation of experimental kinetic data. We provide the open-access code to facilitate experimentalists and modelers applying this framework to diverse metabolic systems and integrating a broad range of available data. We anticipate that the proposed framework will be invaluable for researchers who seek to analyze metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnological studies.

2023

Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations

Vassily Hatzimanikatis, Asli Sahin, Daniel Robert Weilandt

One of the main challenges hampering the development of kinetic models is the lack of kinetic parameters for many enzymatic reactions. Here, the authors introduce a framework to explore the catalytically optimal operating conditions of any complex enzyme mechanism from an evolutionary perspective. Deciphering the metabolic functions of organisms requires understanding the dynamic responses of living cells upon genetic and environmental perturbations, which in turn can be inferred from enzymatic activity. In this work, we investigate the optimal modes of operation for enzymes in terms of the evolutionary pressure driving them toward increased catalytic efficiency. We develop a framework using a mixed-integer formulation to assess the distribution of thermodynamic forces and enzyme states, providing detailed insights into the enzymatic mode of operation. We use this framework to explore Michaelis-Menten and random-ordered multi-substrate mechanisms. We show that optimal enzyme utilization is achieved by unique or alternative operating modes dependent on reactant concentrations. We find that in a bimolecular enzyme reaction, the random mechanism is optimal over any other ordered mechanism under physiological conditions. Our framework can investigate the optimal catalytic properties of complex enzyme mechanisms. It can further guide the directed evolution of enzymes and fill in the knowledge gaps in enzyme kinetics.

NATURE PORTFOLIO2023

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" -Part 2

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details. The dataset contains the following files: 1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv). 2. scripts.zip - scripts to generate figure 2-5 on toy data This work was supported by funding from the Swiss National Science Foundation grant 315230_163423, the European Union's Horizon 2020 research and innovation programme under grant agreement 814408, Swedish Research Council Vetenskapsradet grant 2016-06160, and the Ecole Polytechnique Fédérale de Lausanne (EPFL).

EPFL Infoscience2023

ChE-201: Introduction to chemical engineering

Introduction to Chemical Engineering is an introductory course that provides a basic overview of the chemical engineering field. It addresses the formulation and solution of material and energy balances by using the physical/chemical properties of materials.

ChE-320: Bioreactor modeling and simulation

The course of Bioreactor modeling and simulation focuses on the principles of algorithmic design and analysis of biochemical reactors. The application of these designed reactors would be in the production line of the of pharmaceutical, biotech and chemical industries.