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× 2024 2023 Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations A. Sahin ; D. R. Weilandt ; V. Hatzimanikatis
Nature Communications . 2023-05-05. Vol. 14 , num. 1 . DOI : 10.1038/s41467-023-38159-4. 2022 A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions E. Vayena ; A. Chiappino-Pepe ; H. MohammadiPeyhani ; Y. Francioli ; N. Hadadi et al.
Proceedings Of The National Academy Of Sciences Of The United States Of America . 2022-11-15. Vol. 119 , num. 46 , p. e2211197119. DOI : 10.1073/pnas.2211197119. 2021 Editorial Overview: Mathematical modeling: It’s a matter of scale S. D. Finley ; V. Hatzimanikatis
Current Opinion In Systems Biology . 2021-12-01. Vol. 28 , p. 100360. DOI : 10.1016/j.coisb.2021.100360. NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs J. Hafner ; V. Hatzimanikatis
Bioinformatics . 2021-10-15. Vol. 37 , num. 20 , p. 3560-3568. DOI : 10.1093/bioinformatics/btab368. A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics O. Oftadeh ; P. Salvy ; M. Masid ; M. Curvat ; L. Miskovic et al.
Nature Communications . 2021-08-09. Vol. 12 , num. 1 , p. 4790. DOI : 10.1038/s41467-021-25158-6. The influence of the crowding assumptions in biofilm simulations L. Angeles-Martinez ; V. Hatzimanikatis
Plos Computational Biology . 2021-07-01. Vol. 17 , num. 7 , p. e1009158. DOI : 10.1371/journal.pcbi.1009158. Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities L. Angeles-Martinez ; V. Hatzimanikatis
Plos Computational Biology . 2021-07-01. Vol. 17 , num. 7 , p. e1009140. DOI : 10.1371/journal.pcbi.1009140. Development of Selective FXIa Inhibitors Based on Cyclic Peptides and Their Application for Safe Anticoagulation V. Carle ; Y. Wu ; R. Mukherjee ; X-D. Kong ; C. Rogg et al.
Journal Of Medicinal Chemistry . 2021-05-27. Vol. 64 , num. 10 , p. 6802-6813. DOI : 10.1021/acs.jmedchem.1c00056. Quantitative modeling of human metabolism: A call for a community effort M. Masid Barcon ; V. Hatzimanikatis
Current Opinion in Systems Biology . 2021-04-27. Vol. 26 , p. 109-115. DOI : 10.1016/j.coisb.2021.04.008. The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli T. Hameri ; G. Fengos ; V. Hatzimanikatis
Bmc Bioinformatics . 2021-03-20. Vol. 22 , num. 1 , p. 134. DOI : 10.1186/s12859-021-04066-y. Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism P. Salvy ; V. Hatzimanikatis
Proceedings Of The National Academy Of Sciences Of The United States Of America . 2021-02-23. Vol. 118 , num. 8 , p. e2013836118. DOI : 10.1073/pnas.2013836118. Offset-Free Economic MPC Based on Modifier Adaptation: Investigation of Several Gradient-Estimation Techniques M. Vaccari ; D. Bonvin ; F. Pelagagge ; G. Pannocchia
Processes . 2021-05-01. Vol. 9 , num. 5 , p. 901. DOI : 10.3390/pr9050901. The solubility parameters of carbon dioxide and ionic liquids: Are they an enigma? C. Panayiotou ; V. Hatzimanikatis
Fluid Phase Equilibria . 2021-01-01. Vol. 527 , p. 112828. DOI : 10.1016/j.fluid.2020.112828. 2020 Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN M. Masid ; M. Ataman ; V. Hatzimanikatis
Nature Communications . 2020-06-04. Vol. 11 , num. 1 , p. 2821. DOI : 10.1038/s41467-020-16549-2. redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast S. Tsouka ; V. Hatzimanikatis
Fems Yeast Research . 2020-03-01. Vol. 20 , num. 2 , p. foaa006. DOI : 10.1093/femsyr/foaa006. Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies M. Tokic ; V. Hatzimanikatis ; L. Miskovic
Biotechnology for Biofuels . 2020-02-28. Vol. 13 , num. 33 , p. 1-19. DOI : 10.1186/s13068-020-1665-7. The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models P. Salvy ; V. Hatzimanikatis
Nature Communications . 2020-01-13. Vol. 11 , p. 30. DOI : 10.1038/s41467-019-13818-7. Revisiting the concept of extents for chemical reaction systems using an enthalpy balance N. Ha Hoang ; D. Rodrigues ; D. Bonvin
Computers & Chemical Engineering . 2020-05-08. Vol. 136 , p. 106652. DOI : 10.1016/j.compchemeng.2019.106652. Visible light plays a significant role during bacterial inactivation by the photo-fenton process, even at sub-critical light intensities R. Mosteo ; A. Varon Lopez ; D. Muzard ; N. Benitez ; S. Giannakis et al.
Water Research . 2020-05-01. Vol. 174 , p. 115636. DOI : 10.1016/j.watres.2020.115636. A note on efficient computation of privileged directions in modifier adaptation M. Singhal ; A. G. Marchetti ; T. Faulwasser ; D. Bonvin
Computers & Chemical Engineering . 2020-01-04. Vol. 132 , p. 106524. DOI : 10.1016/j.compchemeng.2019.106524. 2019 Statistical inference in ensemble modeling of cellular metabolism T. E. Hameri ; M-O. Boldi ; V. Hatzimanikatis
PLoS Computational Biology . 2019-12-09. Vol. 15 , num. 12 , p. e1007536. DOI : 10.1371/journal.pcbi.1007536. Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties L. Miskovic ; J. Béal ; M. Moret ; V. Hatzimanikatis
PLOS Computational Biology . 2019-08-20. Vol. 15 , num. 8 , p. e1007242. DOI : 10.1371/journal.pcbi.1007242. Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism D. R. Weilandt ; V. Hatzimanikatis
Biophysical Journal . 2019-07-23. Vol. 117 , num. 2 , p. 355-368. DOI : 10.1016/j.bpj.2019.06.017. 110th Anniversary: From Solubility Parameters to Predictive Equation-of-State Modeling C. Panayiotou ; I. Zuburtikudis ; V. Hatzimanikatis
Industrial & Engineering Chemistry Research . 2019-07-17. Vol. 58 , num. 28 , p. 12787-12800. DOI : 10.1021/acs.iecr.9b02908. Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH) B. Borer ; M. Ataman ; V. Hatzimanikatis ; D. Or
Plos Computational Biology . 2019-06-01. Vol. 15 , num. 6 , p. e1007127. DOI : 10.1371/journal.pcbi.1007127. Robust control of systems with sector nonlinearities via convex optimization: A data-driven approach A. Nicoletti ; A. Karimi
International Journal Of Robust And Nonlinear Control . 2019-03-25. Vol. 29 , num. 5 , p. 1361-1376. DOI : 10.1002/rnc.4439. A data-driven approach to model-reference control with applications to particle accelerator power converters A. Nicoletti ; M. Martino ; A. Karimi
Control Engineering Practice . 2019-02-01. Vol. 83 , p. 11-20. DOI : 10.1016/j.conengprac.2018.10.007. Accelerated and adaptive modifier-adaptation schemes for the real-time optimization of uncertain systems R. Schneider ; P. Milosavljevic ; D. Bonvin
Journal Of Process Control . 2019-11-01. Vol. 83 , p. 129-135. DOI : 10.1016/j.jprocont.2018.07.001. 110th Anniversary: A Feature-Based Analysis of Static Real-Time Optimization Schemes B. Srinivasan ; D. Bonvin
Industrial & Engineering Chemistry Research . 2019-08-07. Vol. 58 , num. 31 , p. 14227-14238. DOI : 10.1021/acs.iecr.9b02327. Dynamic Optimization of Reaction Systems via Exact Parsimonious Input Parameterization D. Rodrigues ; D. Bonvin
Industrial & Engineering Chemistry Research . 2019-07-03. Vol. 58 , num. 26 , p. 11199-11212. DOI : 10.1021/acs.iecr.8b05512. Discovery and validation of temporal patterns involved in human brain ketometabolism in cerebral microdialysis fluids of traumatic brain injury patients M. Eiden ; N. Christinat ; A. Chakrabarti ; S. Sonnay ; J-P. Miroz et al.
Ebiomedicine . 2019-06-01. Vol. 44 , p. 607-617. DOI : 10.1016/j.ebiom.2019.05.054. Distributed modifier-adaptation schemes for the real-time optimisation of uncertain interconnected systems R. Schneider ; P. Milosavljevic ; D. Bonvin
International Journal Of Control . 2019-05-04. Vol. 92 , num. 5 , p. 1123-1136. DOI : 10.1080/00207179.2017.1383632. Education in Process Systems Engineering: Why it matters more than ever and how it can be structured I. T. Cameron ; S. Engell ; C. Georgakis ; N. Asprion ; D. Bonvin et al.
Computers & Chemical Engineering . 2019-07-12. Vol. 126 , p. 102-112. DOI : 10.1016/j.compchemeng.2019.03.036. Incremental Parameter Estimation under Rank-Deficient Measurement Conditions K. Villez ; J. Billeter ; D. Bonvin
Processes . 2019-02-01. Vol. 7 , num. 2 , p. 75. DOI : 10.3390/pr7020075. Investigating the deregulation of metabolic tasks via Minimum Network Enrichment Analysis (MiNEA) as applied to nonalcoholic fatty liver disease using mouse and human omics data V. Pandey ; V. Hatzimanikatis
PLOS Computational Biology . 2019-04-19. Vol. 15 , num. 4 , p. e1006760. DOI : 10.1371/journal.pcbi.1006760. Control Theory Concepts for Modeling Uncertainty in Enzyme Kinetics of Biochemical Networks L. Miskovic ; M. Tokic ; G. Savoglidis ; V. Hatzimanikatis
Industrial & Engineering Chemistry Research . 2019-05-10. Vol. 58 , num. 30 , p. 13544–13554. DOI : 10.1021/acs.iecr.9b00818. Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models V. Pandey ; N. Hadadi ; V. Hatzimanikatis
PLOS Computational Biology . 2019-05-13. Vol. 15 , num. 5 , p. e1007036. DOI : 10.1371/journal.pcbi.1007036. 2018 pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis P. Salvy ; G. Fengos ; M. Ataman ; T. Pathier ; K. C. Soh et al.
Bioinformatics . 2018-07-02. p. 1-3. DOI : 10.1093/bioinformatics/bty499. Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors M. Tokic ; N. Hadadi ; M. Ataman ; D. Neves ; B. E. Ebert et al.
ACS Synthetic Biology . 2018-07-18. Vol. 7 , num. 8 , p. 1858-1873. DOI : 10.1021/acssynbio.8b00049. 2017 redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models M. Ataman ; D. F. H. Gardiol ; G. Fengos ; V. Hatzimanikatis
Plos Computational Biology . 2017. Vol. 13 , num. 7 , p. e1005444. DOI : 10.1371/journal.pcbi.1005444. lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites M. Ataman ; V. Hatzimanikatis
Plos Computational Biology . 2017. Vol. 13 , num. 7 , p. e1005513. DOI : 10.1371/journal.pcbi.1005513. 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 L. Miskovic ; S. Alff-Tuomala ; K. C. Soh ; D. Barth ; L. Salusjärvi et al.
Biotechology for Biofuels . 2017. Vol. 10 , p. 166. DOI : 10.1186/s13068-017-0838-5. Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks A. Kiparissides ; V. Hatzimanikatis
Metabolic Engineering . 2017. Vol. 39 , p. 117-127. DOI : 10.1016/j.ymben.2016.11.006. Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites N. Hadadi ; J. Hafner ; K. C. Soh ; V. Hatzimanikatis
Biotechnology Journal . 2017. Vol. 12 , num. 1 , p. 1600464. DOI : 10.1002/biot.201600464. 2016 ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies N. Hadadi ; J. Hafner ; A. Shajkofci ; A. Zisaki ; V. Hatzimanikatis
Acs Synthetic Biology . 2016. Vol. 5 , num. 10 , p. 1155-1166. DOI : 10.1021/acssynbio.6b00054. Analysis of Translation Elongation Dynamics in the Context of an Escherichia coli Cell J. Pinto Vieira ; J. Racle ; V. Hatzimanikatis
Biophysical Journal . 2016. Vol. 110 , num. 9 , p. 2120-2131. DOI : 10.1016/j.bpj.2016.04.004. Sustainability assessment of succinic acid production technologies from biomass using metabolic engineering M. Morales ; M. Ataman ; S. Badr ; S. Linster ; I. Kourlimpinis et al.
Energy & Environmental Science . 2016. Vol. 9 , num. 9 , p. 2794-2805. DOI : 10.1039/c6ee00634e. Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models A. Isakova ; Y. Berset ; V. Hatzimanikatis ; B. Deplancke
Journal Of Biological Chemistry . 2016. Vol. 291 , num. 19 , p. 10293-10306. DOI : 10.1074/jbc.M115.691154. A method for analysis and design of metabolism using metabolomics data and kinetic models: Application on lipidomics using a novel kinetic model of sphingolipid metabolism G. Savoglidis ; A. X. D. S. Dos Santos ; I. Riezman ; P. Angelino ; H. Riezman et al.
Metabolic Engineering . 2016. Vol. 37 , p. 46-62. DOI : 10.1016/j.ymben.2016.04.002. Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models S. Andreozzi ; A. Charkrabarti ; K. C. Soh ; A. Burgard ; T-H. Yang et al.
Metabolic Engineering . 2016. Vol. 35 , p. 148-159. 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 S. Andreozzi ; L. Miskovic ; V. Hatzimanikatis
Metabolic Engineering . 2016. Vol. 33 , p. 158-168. DOI : 10.1016/j.ymben.2015.10.002. 2015 Heading in the right direction: thermodynamics-based network analysis and pathway engineering M. Ataman ; V. Hatzimanikatis
Current Opinion in Biotechnology . 2015. Vol. 36 , p. 176-182. DOI : 10.1016/j.copbio.2015.08.021. Integrative approaches for signalling and metabolic networks V. Hatzimanikatis ; J. Saez-Rodriguez
Integrative Biology . 2015. Vol. 7 , num. 8 , p. 844-845. DOI : 10.1039/c5ib90030a. Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes L. Miskovic ; M. Tokic ; G. Fengos ; V. Hatzimanikatis
Current Opinion in Biotechnology . 2015. Vol. 36 , p. 146-153. DOI : 10.1016/j.copbio.2015.08.019. Noise analysis of genome-scale protein synthesis using a discrete computational model of translation J. Racle ; A. J. Stefaniuk ; V. Hatzimanikatis
The Journal of Chemical Physics . 2015. Vol. 143 , num. 4 , p. 044109. DOI : 10.1063/1.4926536. Molecular thermodynamics of metabolism: quantum thermochemical calculations for key metabolites N. Hadadi ; M. Ataman ; V. Hatzimanikatis ; C. Panayiotou
Physical Chemistry Chemical Physics . 2015. Vol. 17 , num. 16 , p. 10438-10453. DOI : 10.1039/c4cp05825a. Antihypertensive drugs metabolism: an update to pharmacokinetic profiles and computational approaches A. Zisaki ; L. Miskovic ; V. Hatzimanikatis
Current Pharmaceutical Design . 2015. Vol. 21 , num. 6 , p. 806-822. 2014 A computational framework for integration of lipidomics data into metabolic pathways N. Hadadi ; K. Cher Soh ; M. Seijo ; A. Zisaki ; X. Guan et al.
Metabolic engineering . 2014. Vol. 23 , p. 1-8. DOI : 10.1016/j.ymben.2013.12.007. 2013 Metabolic modeling in biotechnology and medical research D. Mattanovich ; V. Hatzimanikatis
Biotechnology Journal . 2013. Vol. 8 , num. 9 , p. 962-963. DOI : 10.1002/biot.201300378.