2024

Rational strain design with minimal phenotype perturbation

B. Narayanan; D. R. Weilandt; M. Masid; L. Miskovic; V. Hatzimanikatis

Nature Communications. 2024-01-24. Vol. 15, num. 1, p. 723. DOI : 10.1038/s41467-024-44831-0.

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2023

Metabolic interaction models recapitulate leaf microbiota ecology

M. Schafer; A. R. Pacheco; R. Kunzler; M. Bortfeld-Miller; C. M. Field et al.

Science. 2023-07-07. Vol. 381, num. 6653, p. eadf5121. DOI : 10.1126/science.adf5121.

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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.

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Dynamics of CLIMP-63 S-acylation control ER morphology

P. A. Sandoz; R. A. Denhardt-Eriksson; L. Abrami; L. A. Abriata; G. Spreemann et al.

Nature Communications. 2023-01-17. Vol. 14, num. 1. DOI : 10.1038/s41467-023-35921-6.

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

S. Choudhury; B. Narayanan; M. Moret; V. Hatzimanikatis; L. Miskovic

2023. DOI : 10.1101/2023.02.21.529387.

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2022

Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression

M. Sciacovelli; A. Dugourd; L. V. Jimenez; M. Yang; E. Nikitopoulou et al.

Nature Communications. 2022-12-20. Vol. 13, num. 1, p. 7830. DOI : 10.1038/s41467-022-35036-4.

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Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models

D. R. Weilandt; P. Salvy; M. Masid; G. Fengos; R. Denhardt-Erikson et al.

Bioinformatics. 2022-12-10. DOI : 10.1093/bioinformatics/btac787.

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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.

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Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

S. Choudhury; M. Moret; P. Salvy; D. Weilandt; V. Hatzimanikatis et al.

Nature Machine Intelligence. 2022-08-01. Vol. 4, num. 8, p. 710-719. DOI : 10.1038/s42256-022-00519-y.

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ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds

A. Sveshnikova; H. MohammadiPeyhani; V. Hatzimanikatis

Metabolic Engineering. 2022-07-01. Vol. 72, p. 259-274. DOI : 10.1016/j.ymben.2022.03.013.

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Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx

H. MohammadiPeyhani; J. Hafner; A. Sveshnikova; V. Viterbo; V. Hatzimanikatis

Nature Communications. 2022-03-23. Vol. 13, num. 1, p. 1560. DOI : 10.1038/s41467-022-29238-z.

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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.

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An inverse method for mechanical characterization of heterogeneous diseased arteries using intravascular imaging

B. Narayanan; M. L. Olender; D. Marlevi; E. R. Edelman; F. R. Nezami

Scientific Reports. 2021-11-18. Vol. 11, num. 1, p. 22540. DOI : 10.1038/s41598-021-01874-3.

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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.

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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.

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NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

H. Mohammadi Peyhani; A. Chiappino-Pepe; K. Haddadi; J. M. Hafner; N. Hadadi et al.

eLife. 2021-08-03. Vol. 10, p. e65543. DOI : 10.7554/eLife.65543.

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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.

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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.

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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.

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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.

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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.

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Constraint-based metabolic control analysis for rational strain engineering

S. Tsouka; M. Ataman; T. E. Hameri; L. Miskovic; V. Hatzimanikatis

Metabolic Engineering. 2021-04-22. Vol. 66, p. 191-203. DOI : 10.1016/j.ymben.2021.03.003.

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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.

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A computational workflow for the expansion of heterologous biosynthetic pathways to natural product derivatives

J. Hafner; J. Payne; H. MohammadiPeyhani; V. Hatzimanikatis; C. Smolke

Nature Communications. 2021-03-19. Vol. 12, num. 1, p. 1760. DOI : 10.1038/s41467-021-22022-5.

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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.

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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.

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2020

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

N. Hadadi; V. Pandey; A. Chiappino-Pepe; M. Morales; H. Gallart-Ayala et al.

Npj Systems Biology And Applications. 2020-12-01. Vol. 6, num. 1, p. 1. DOI : 10.1038/s41540-019-0121-4.

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Real-time optimization of load sharing for gas compressors in the presence of uncertainty

P. Milosavljevic; A. G. Marchetti; A. Cortinovis; T. Faulwasser; M. Mercangoez et al.

Applied Energy. 2020-08-15. Vol. 272, p. 114883. DOI : 10.1016/j.apenergy.2020.114883.

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Impact of multi-micronutrient supplementation on lipidemia of children and adolescents

A. Chakrabarti; M. Eiden; D. Morin-Rivron; N. Christinat; J. P. Monteiro et al.

Clinical Nutrition. 2020-07-01. Vol. 39, num. 7, p. 2211-2219. DOI : 10.1016/j.clnu.2019.09.010.

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Updated ATLAS of Biochemistry with New Metabolites and Improved Enzyme Prediction Power

J. Hafner; H. MohammadiPeyhani; A. Sveshnikova; A. Scheidegger; V. Hatzimanikatis

Acs Synthetic Biology. 2020-06-19. Vol. 9, num. 6, p. 1479-1482. DOI : 10.1021/acssynbio.0c00052.

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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.

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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.

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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.

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MEMOTE for standardized genome-scale metabolic model testing

C. Lieven; M. E. Beber; B. G. Olivier; F. T. Bergmann; M. Ataman et al.

Nature Biotechnology. 2020-03-02. Vol. 38, p. 272–276. DOI : 10.1038/s41587-020-0446-y.

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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.

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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.

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Modifier Adaptation as a Feedback Control Scheme

A. G. Marchetti; T. d. A. Ferreira; S. Costello; D. Bonvin

Industrial & Engineering Chemistry Research. 2020-02-12. Vol. 59, num. 6, p. 2261-2274. DOI : 10.1021/acs.iecr.9b04501.

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Functional and Computational Genomics Reveal Unprecedented Flexibility in Stage-Specific Toxoplasma Metabolism

A. Krishnan; J. Kloehn; M. Lunghi; A. Chiappino-Pepe; B. S. Waldman et al.

Cell Host & Microbe. 2020-02-12. Vol. 27, num. 2, p. 290-+. DOI : 10.1016/j.chom.2020.01.002.

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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.

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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.

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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.

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Genome-Scale Identification of Essential Metabolic Processes for Targeting the Plasmodium Liver Stage

R. R. Stanway; E. Bushell; A. Chiappino-Pepe; M. Roques; T. Sanderson et al.

Cell. 2019-11-14. Vol. 179, num. 5, p. 1112-1128.e26. DOI : 10.1016/j.cell.2019.10.030.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Impact of iron reduction on the metabolism of Clostridium acetobutylicum

C. List; Z. Hosseini; K. Lederballe Meibom; V. Hatzimanikatis; R. Bernier‐Latmani

Environmental Microbiology. 2019. Vol. 21, num. 10, p. 3548-3563. DOI : 10.1111/1462-2920.14640.

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Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites

N. Hadadi; H. MohammadiPeyhani; L. Miskovic; M. Seijo; V. Hatzimanikatis

Proceedings of the National Academy of Sciences. 2019. p. 201818877. DOI : 10.1073/pnas.1818877116.

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Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

T. E. Hameri; G. Fengos; M. Ataman; L. Miskovic; V. Hatzimanikatis

Metabolic Engineering. 2019. Vol. 52, p. 29-41. DOI : 10.1016/j.ymben.2018.10.005.

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2018

Nanoparticle Conjugation of Human Papillomavirus 16 E7-long Peptides Enhances Therapeutic Vaccine Efficacy against Solid Tumors in Mice

G. Galliverti; M. Tichet; S. Domingos-Pereira; S. Hauert; D. Nardelli-Haefliger et al.

Cancer Immunology Research. 2018-11-01. Vol. 6, num. 11, p. 1301-1313. DOI : 10.1158/2326-6066.CIR-18-0166.

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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.

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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.

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2017

Single-molecule kinetic analysis of HP1-chromatin binding reveals a dynamic network of histone modification and DNA interactions

L. C. Bryan; D. R. Weilandt; A. L. Bachmann; S. Kilic; C. C. Lechner et al.

Nucleic Acids Research. 2017. Vol. 45, num. 18, p. 10504–10517. DOI : 10.1093/nar/gkx697.

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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.

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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.

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Exploring biochemical pathways for mono-ethylene glycol (MEG) synthesis from synthesis gas

M. A. Islam; N. Hadadi; M. Ataman; V. Hatzimanikatis; G. Stephanopoulos

Metabolic Engineering. 2017. Vol. 41, p. 173-181. DOI : 10.1016/j.ymben.2017.04.005.

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Mechanistic Modeling of Genetic Circuits for ArsR Arsenic Regulation

Y. Berset; D. Merulla; A. Joublin; V. Hatzimanikatis; J. R. Van Der Meer

Acs Synthetic Biology. 2017. Vol. 6, num. 5, p. 862-874. DOI : 10.1021/acs.synbio.6b00364.

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On Lewis acidity/basicity and hydrogen bonding in the equation-of-state approach

C. Panayiotou; S. Mastrogeorgopoulos; V. Hatzimanikatis

Journal Of Chemical Thermodynamics. 2017. Vol. 110, p. 3-15. DOI : 10.1016/j.jct.2017.02.003.

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Redefining solubility parameters: Bulk and surface properties from unified molecular descriptors

C. Panayiotou; S. Mastrogeorgopoulos; D. Aslanidou; M. Avgidou; V. Hatzimanikatis

Journal Of Chemical Thermodynamics. 2017. Vol. 111, p. 207-220. DOI : 10.1016/j.jct.2017.03.035.

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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.

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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.

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Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks

A. Chiappino Pepe; S. Tymoshenko; M. Ataman; D. Soldati-Favre; V. Hatzimanikatis

PLoS Computational Biology. 2017. Vol. 13, num. 3, p. e1005397. DOI : 10.1371/journal.pcbi.1005397.

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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.

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2016

Principles of Systems Biology, No. 11

G. Wayne; A. Graves; D. Hassabis; S. Saha; C. A. Weber et al.

Cell Systems. 2016. Vol. 3, num. 5, p. 406-410. DOI : 10.1016/j.cels.2016.11.010.

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Molecular thermodynamics of metabolism: hydration quantities and the equation-of-state approach

C. Panayiotou; S. Mastrogeorgopoulos; M. Ataman; N. Hadadi; V. Hatzimanikatis

Physical Chemistry Chemical Physics. 2016. Vol. 18, num. 47, p. 32570-32592. DOI : 10.1039/c6cp06281d.

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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.

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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.

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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.

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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.

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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.

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The SIB Swiss Institute of Bioinformatics’ resources: focus on curated databases

L. A. Bultet; J. Aguilar Rodriguez; C. H. Ahrens; E. L. Ahrne; N. Ai et al.

Nucleic Acids Research. 2016. Vol. 44, num. D1, p. D27-D37. DOI : 10.1093/nar/gkv1310.

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Model-Driven Understanding of Palmitoylation Dynamics: Regulated Acylation of the Endoplasmic Reticulum Chaperone Calnexin

T. Dallavilla; L. Abrami; P. A. Sandoz; G. Savoglidis; V. Hatzimanikatis et al.

PLOS Computational Biology. 2016. Vol. 12, num. 2, p. e1004774. DOI : 10.1371/journal.pcbi.1004774.

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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.

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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.

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2015

Do genome-scale models need exact solvers or clearer standards?

A. Ebrahim; E. Almaas; E. Bauer; A. Bordbar; A. P. Burgard et al.

Molecular Systems Biology. 2015. Vol. 11, num. 10, p. 831. DOI : 10.15252/msb.20156157.

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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.

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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.

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Solvation quantities from a COSMO-RS equation of state

C. Panayiotou; I. Tsivintzelis; D. Aslanidou; V. Hatzimanikatis

Journal Of Chemical Thermodynamics. 2015. Vol. 90, p. 294-309. DOI : 10.1016/j.jct.2015.07.011.

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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.

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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.

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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.

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Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis

S. Tymoshenko; R. D. Oppenheim; R. Agren; J. Nielsen; D. Soldati-Favre et al.

PLOS Computational Biology. 2015. Vol. 11, num. 5, p. e1004261. DOI : 10.1371/journal.pcbi.1004261.

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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.

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2014

Kinetic models in industrial biotechnology – Improving cell factory performance

J. Almquist; M. Cvijovic; V. Hatzimanikatis; J. Nielsen; M. Jirstrand

Metabolic Engineering. 2014. Vol. 24, p. 38-60. DOI : 10.1016/j.ymben.2014.03.007.

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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.

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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.

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A Novel Pulse-Chase SILAC Strategy Measures Changes in Protein Decay and Synthesis Rates Induced by Perturbation of Proteostasis with an Hsp90 Inhibitor

I. Fierro-Monti; J. Racle; C. Hernandez; P. Waridel; V. Hatzimanikatis et al.

Plos One. 2013. Vol. 8, num. 11, p. e80423. DOI : 10.1371/journal.pone.0080423.

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A Genome-Scale Integration and Analysis of Lactococcus lactis Translation Data

J. Racle; F. Picard; L. Girbal; M. Cocaign-Bousquet; V. Hatzimanikatis

Plos Computational Biology. 2013. Vol. 9, num. 10, p. e1003240. DOI : 10.1371/journal.pcbi.1003240.

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