Predictive and Accelerated Metabolic Engineering Network (PAcMEN)

Marie Curie-Innovative Training Networks

 

Duration: 2016-2020

Coordinator: Technical University of Denmark

Lighting up yeast cell factories by transcription factor-based biosensors - Vasil D'ambrosio, Michael K. Jensen

Engineering microbial fatty acid metabolism for biofuels and biochemicals - Eko Roy Marella, Carina Holkenbrink, Varena Siewers, Irina Borodina

pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis - Pierre Salvy, Georgios Fengos, Meric Ataman, Thomas Pathier, Keng C Soh, Vassily Hatzimanikatis

Modular 5′-UTR hexamers for context-independent tuning of protein expression in eukaryotes - Søren D Petersen, Jie Zhang, Jae S Lee, Tadas Jakočiūnas, Lise M Grav, Helene F Kildegaard, Jay D Keasling, Michael K Jensen

Advances in synthetic biology of oleaginous yeast Yarrowia lipolytica for producing non-native chemicals - Farshad Darvishi, Mehdi Ariana, Eko Roy Marella, Irina Borodina

Saccharomyces cerevisiae displays a stable transcription start site landscape in multiple conditions - Christoph S Boerlin, Nevena Cvetesic, Petter Holland, David Bergenholm, Verena Siewers, Boris Lenhard, Jens Nielsen

Genome editing in Kluyveromyces and Ogataea yeasts using a broad-host-range Cas9/gRNA co-expression plasmid - Hannes Juergens, Javier A Varela, Arthur R Gorter de Vries, Thomas Perli, Veronica J M Gast, Nikola Y Gyurchev, Arun S Rajkumar, Robert Mans, Jack T Pronk, Jonh P Morrissey, Jean-Marc G Daran.

Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites - Noushin Hadadi, Homa Mohamadi Peyhani, Ljubisa Miskovic, Marianne Seijo, Vassily Hatzimanikatis

​​Machine Learning Applied to Predicting Microorganism Growth Temperatures and Enzyme Catalytic OptimaGang Li, Kersten S. Rabe, Jens Nielsen and Martin K. M. Engqvist

ETFL: A formulation for flux balance models accounting for expression, thermodynamics, and resource allocation constraintsPierre Salvy,  Vassily Hatzimanikatis

​​A bioinformatic pipeline to analyze ChIP-exo datasets - Christoph S Börlin, David Bergenholm, Petter Holland, Jens Nielsen

The pan-genome of Saccharomyces cerevisiae - Gang Li, Boyang Ji, Jens Nielsen

​​A single-host fermentation process for the production of flavor lactones from non-hydroxylated fatty acidsEko RoyMarella, JonathanDahlin, Marie Inger Dam, Jolanda ter Horst, Hanne Bjerre Christensen, Suresh Sudarsan, Guokun Wang, Carina Holkenbrink, Irina Borodina

​​Multi-Omics Analysis of Fatty Alcohol Production in Engineered Yeasts Saccharomyces cerevisiae and Yarrowia lipolyticaJonathan Dahlin, Carina Holkenbrink, Eko Roy Marella, Guokun Wang, Ulf Liebal, Christian Lieven, Dieter Weber, Douglas McCloskey, Birgitta E. Ebert, Markus J. Herrgård, Lars Mathias Blank and Irina Borodina

​​Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges - Yu Chen, Gang Li, Jens Nielsen

​​A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism - Hongzhong Lu, Feiran Li, Benjamín J. Sánchez, Zhengming Zhu, Gang Li, Iván Domenzain, Simonas Marcišauskas, Petre Mihail Anton, Dimitra Lappa, Christian Lieven, Moritz Emanuel Beber, Nikolaus Sonnenschein, Eduard J. Kerkhoven & Jens Nielsen

The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models - Pierre Salvy & Vassily Hatzimanikatis

​​High-Resolution Scanning of Optimal Biosensor Reporter Promoters in Yeast - Francesca Ambri, Vasil D’Ambrosio, Roberto Di Blasi, Jerome Maury, Simo Abdessamad Baallal Jacobsen, Douglas McCloskey, Michael K. Jensen, Jay. D Keasling

​Vitamin requirements and biosynthesis in Saccharomyces cerevisiae - Thomas Perli, Anna K. Wronska, Raul A. Ortiz-Merino, Jack T. Pronk, Jean-Marc Daran

​​Adaptive laboratory evolution and reverse engineering of single-vitamin prototrophies in Saccharomyces cerevisiaeThomas Perli, Dewi P.I. Moonen, Marcel van den Broek, Jack T. Pronk, Jean-Marc Daran

Exploiting the Diversity of Saccharomycotina Yeasts To Engineer Biotin-Independent Growth of Saccharomyces cerevisiaeAnna K. Wronska, Meinske P. Haak, Ellen Geraats, Eva Bruins Slot, Marcel van den Broek, Jack T. Pronk, Jean-Marc Daran

Updated ATLAS of Biochemistry With New Metabolites and Improved Enzyme Prediction Power - Jasmin Hafner, Homa Mohammadi Peyhani, Anastasia Sveshnikova, Alan Scheidegger, Vassily Hatzimanikatis   

The transcription factor Leu3 shows differential binding behavior in response to changing leucine availabilityChristoph S. Börlin, Jens Nielsen and Verena Siewers

Display of functional nucleic acid polymerase on Escherichia coli surface and its application in directed polymerase evolution - Mu‐En Chung, Kati Goroncy, Alisa Kolesnikova, David Schönauer, Ulrich Schwaneberg

The Transcriptome and Flux Profiling of Crabtree‐Negative Hydroxy Acid‐Producing Strains of Saccharomyces cerevisiae Reveals Changes in the Central Carbon MetabolismMathew M. Jessop‐Fabre, Jonathan Dahlin, Mathias B. Biron, Vratislav Stovicek, Birgitta E. Ebert, Lars M. Blank, Itay Budin, Jay D. Keasling, Irina Borodina
Directed evolution of VanR biosensor specificity in yeast - Vasil D'Ambrosio, Subrata Pramanik, Kati Goroncy, Tadas Jakočiūnas, David Schönauer, Mehdi D. Davari, Ulrich Schwaneberg, Jay D.Keasling, Michael K. Jensen 
​​Regulatory control circuits for stabilizing long-term anabolic product formation in yeast Vasil D'Ambrosio, Eleonora Dore, Roberto Di Blasi, Marcel van den Broek, Suresh Sudarsan, Jolanda ter Horst, Francesca Ambri, Morten O.A. Sommer, Peter Rugbjerg, Jay D.Keasling, Robert Mans, Michael K. Jensen

​​Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism – Jie Zhang, Søren D. Petersen, Tijana Radivojevic, Andrés Ramirez, Andrés Pérez-Manríquez, Eduardo Abeliuk, Benjamín J. Sánchez, Zak Costello, Yu Chen, Michael J. Fero, Hector Garcia Martin, Jens Nielsen, Jay D. Keasling & Michael K. Jensen







H2020-MSCA-ITN

Research training

Publications

PAcMEN is a European training network, which offers excellent training in biotech research and innovation for 16 talented young scientists. PhD students will carry out cutting-edge research in metabolic engineering, modeling, systems and synthetic biology. In collaboration with industrial partners, they will create novel solutions for sustainable production of fuels and chemicals. The graduates will be prepared through research, business, and entrepreneurship training to launch their careers in industry or academia. 


Research Projects