Prof Guy-Bart STAN's research webpage

Who am I?

My name is Guy-Bart STAN. I am a permanent academic member of staff in the Department of Bioengineering and the head of the Control Engineering Synthetic Biology group at Imperial College London (U.K.).

I am a Royal Academy of Engineering Chair in Emerging Technologies, Co-Director of the Imperial College Centre for Synthetic Biology, and Deputy Director of the EPSRC-funded Centre for Doctoral Training in BioDesign Engineering. I have been holding a EPSRC Engineering Fellowship for Growth in Synthetic Biology for the period January 2015 - February 2020.

I am also the Lead PI on the UKRI-funded Engineering Biology Transition Award, "Artificial Intelligence for Engineering Biology" Consortium (AI-4-EB). The vision for AI-4-EB is to leverage and combine key enabling technologies in Artificial Intelligence (AI) and Engineering Biology (EB) to pioneer a new era of world-leading advances that accelerate learning and design of Engineered Biological Systems. Through the AI-4-EB Consortium, we are building a network of inter-connected and inter-disciplinary researchers to both develop and apply next-generation AI technologies to the design and optimisation of biological systems across scales. Overall, AI-4-EB provides the necessary step-change for the analysis of large and heterogeneous biological data sets, and for AI-based design and optimisation of biological systems with sufficient predictive power to accelerate Engineering Biology.

Complementary to the AI-4-EB Consortium described above, I have also been leading (with Prof Baldwin) a strategic initiative at Imperial College (Imperial-X Life) to develop new AI/machine learning methods and pipelines that are adapted to the challenges of data-based design and optimisation of engineered living cells.

I joined Imperial College in December 2009 as a Lecturer and got promoted to Reader in August 2014, and to full Professor in June 2019. From January 2006 until December 2009, I worked in the Control Group of the University of Cambridge (U.K.) as a Research Associate with support from EPSRC (EP/E02761X/1) for the period January 2007 - January 2010 and support from a European Commission FP6 Marie-Curie Intra-European Fellowship (EU FP6 IEF 025509 GASO) for the period January 2006 - January 2007. From January 2006 until December 2009, I was the weekly seminar organiser for the Cambridge University Control Group. From June to December 2005, I worked as Senior DSP Engineer at Philips Applied Technologies (now Philips Research). I received my electrical engineering degree (with a speciality in electronics) in June 2000 and my Ph.D. degree (in Applied Sciences with a focus on Analysis and Control of Nonlinear Dynamical Systems) in March 2005, both from the University of Liège, Belgium. During my PhD, I mainly worked in the Nonlinear Systems and Control group at the Systems and Modeling research unit of the University of Liège and was supported by a PhD Research Fellowship from the F.N.R.S. (the Belgian National Fund for Scientific Research).

My webpage in the Department of Bioengineering of Imperial College London (U.K.).

For a quick overview of what the Control Engineering Synthetic Biology group is and examples of projects we are working on please have a look at this short introductory brochure.

This 5 min video of a talk I gave at the World Economic Forum Summer Meeting 2015 is also a good introduction to some of the things were are interested in the Control Engineering Synthetic Biology group:

For a longer conversation on the theme of Engineering Biology, please see this video recording of our Critical Conversation on Engineering Biology with Dr Hayaatun Sillem CBE, CEO of the Royal Academy of Engineering.

2024

• GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning, Z. Li, V. Subasri, G.-B. Stan, Y. Zhao, B. Wang, In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver Convention Centre, Vancouver, Canada, 10-15 December, 2024.
• Absorb & Escape: Overcoming Single Model Limitations in Generating Heterogeneous Genomic Sequences, Z. Li, Y. Ni, G. Xia, W. Beardall, A. Das, G.-B. Stan, Y. Zhao, In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver Convention Centre, Vancouver, Canada, 10-15 December, 2024.
• A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits, K. Sechkar, H. Steel, G. Perrino, G.-B. Stan, Nature Communications, Volume 15 (2024), n°1981, doi:10.1038/s41467-024-46410-9.
• DiscDiff: Latent Diffusion Model for DNA Sequence Generation, Z. Li, Y. Ni, W. Beardall, G. Xia, A. Das, G.-B. Stan, Y. Zhao, In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Messe Wien Exhibition Congress Center, Vienna, Austria, 21-27 July, 2024.
• Microfluidic platform for microbial spore germination studies in multiple growth conditions, L. S. Bernier, A. Estoppey, S. Bindschedler, G.-B. Stan, P. Junier, C. E. Stanley, BMC Methods, Volume 1 (2024), n°12, doi:10.1186/s44330-024-00012-5.

2023

• Parsley: a web app for parsing data from plate readers, E. Csibra, G.-B. Stan, OUP Bioinformatics, Volume 39 (2023), n°12, doi:10.1093/bioinformatics/btad733.
• Latent Diffusion Model for DNA Sequence Generation, Z. Li, Y. Ni, T. Huygelen, A. Das, G. Xia, G.-B. Stan, Y. Zhao, In Proceedings of the NeurIPS 2023 AI for Science Workshop (NeurIPS AI4Science), New Orleans Ernest N. Morial Convention Center, USA, 16 December, 2023.
• Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer, Z. Li, A. Das, W. A. V. Beardall, Y. Zhao, G.-B. Stan, In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), Workshop on Computational Biology, Hawaii Convention Centre, USA, 23-29 July, 2023. Best Paper Award at the ICML Workshop on Computational Biology 2023.
• Host-aware RNA-based control of synthetic microbial consortia, A. Boo, H. Mehta, R. Ledesma-Amaro, G.-B. Stan, bioRxiv, (2023).
• Overcoming the speed limit of four‐way DNA branch migration with bulges in toeholds, F. Smith, A. Sengar, G.-B. Stan, T. Ouldridge, M. Stevens, J. Goertz, W. Bae, bioRxiv, (2023).
• Modelling genetic stability in engineered cell populations, D. Ingram, G.-B. Stan, Nature Communications, Volume 14 (2023), n°3471, doi:10.1038/s41467-023-38850-6.
• MIRELLA: a mathematical model explains the effect of MIcroRNA-mediated synthetic genes REgulation on intracellular resource aLLocAtion, F. Cella, G. Perrino, F. Tedeschi, G. Viero, C. Bosia, G.-B. Stan, Velia Siciliano, Nucleic Acids Research, (2023), doi:10.1093/nar/gkad151.
• Exploiting resource constraints for controlling biomolecular circuits, A. Patel, G.-B. Stan, In Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023), Marina Bay Sands, Singapore, 13-15 December, 2023.

2022

• Absolute protein quantification using fluorescence measurements with FPCountR, E. Csibra, G.-B. Stan, Nature Communications, Volume 13 (2022), n°6600, doi:10.1038/s41467-022-34232-6.
• Robust counterselection and advanced λRed recombineering enable markerless chromosomal integration of large heterologous constructs, D. Bubnov, T. Yuzbashev, A. Khozov, O. Melkina, T. Vybormaya, G.-B. Stan, S. Sineoky, Nucleic Acids Research, Volume 50 (2022), n°15, pp. 8947–8960, doi:10.1093/nar/gkac649.
• Deep Learning Concepts and Applications for Synthetic Biology, W. Beardall, G.-B. Stan, M. Dunlop, GEN Biotechnology, Volume 1 (2022), n°4, pp. 360-371, doi:10.1089/genbio.2022.0017.
• Resource-aware whole-cell model of division of labour in microbial consortium for complex-substrate degradation, E. Atkinson, Z. Tuza, G. Perrino, G.-B. Stan, R. Ledesma-Amaro, Microbial Cell Factories, Volume 21 (2022), n°115, doi:10.1186/s12934-022-01842-0.
• Principles, Tools and Applications of Synthetic Consortia towards Microbiome Engineering, E. Atkinson, A. Boo, H. Peng, G.-B. Stan, R. Ledesma-Amaro, Principles in Microbiome Engineering, Wiley Online Library (2022), doi:10.1002/9783527825462.ch7.
• A linear programming-based strategy to save pipette tips in automated DNA assembly, K. Sechkar, Z. Tuza, G.-B. Stan, OUP Synthetic Biology, Volume 7 (2022), n°1, doi:10.1093/synbio/ysac004.
• Spores-on-a-Chip: New Frontiers for Spore Research, L. Bernier, P. Junier, G.-B. Stan, C. Stanley, Trends in Microbiology, Volume 30 (2022), n°6, pp. 515-518, doi:10.1016/j.tim.2022.03.003.
• Building an RNA-based Toggle Switch using Inhibitory RNA Aptamers, A. Climent-Catala, T. Ouldridge, G.-B. Stan, W. Bae, ACS Synthetic Biology, Volume 11 (2022), n°2, pp. 562-569, doi:10.1021/acssynbio.1c00580.
• A modular RNA interference system for multiplexed gene regulation, A. Dwijayanti, M. Storch, G.-B. Stan, G. Baldwin, Nucleic Acids Research, Volume 50 (2022), n°3, pp. 1783–1793, doi:10.1093/nar/gkab1301.
• Robust set-point regulation of gene expression using resource competition couplings in mammalian cells, G. Perrino, G.-B. Stan, Proceedings of the 2022 American Control Conference (ACC 2022), Atlanta Marriott Marquis, Atlanta, Georgia, USA, June 6-10, 2022.

2021

• Homeostasis, I. Slutsky, G. Schratt, G.-B. Stan, S. Nelson, F. J. Bruggeman, Cell Systems, Volume 12 (2021), n°12, pp. 1124-1126, doi:10.1016/j.cels.2021.11.002.
• Quorum Sensing in Synthetic Biology: A Review, A. Boo, R. Ledesma-Amaro, G.-B. Stan, Current Opinion in Systems Biology, (2021), doi:10.1016/j.coisb.2021.100378.
• ncRNAtools: An R toolkit for non-coding RNA, L. S. Vidal, R. Ayala, G.-B. Stan, R. Ledesma-Amaro, BioConductor, (2021), doi:10.18129/B9.bioc.ncRNAtools.
• Synthetic Biology Open Language Visual (SBOL Visual) Version 2.3, H. Baig, P. Fontanarossa, V. Kulkarni, J. McLaughlin, et al., Journal of Integrative Bioinformatics, (2021), doi:10.1515/jib-2020-0045.
• Control engineering and synthetic biology: Working in synergy for the analysis and control of microbial systems, G. Perrino, A. Hadjimitsis, R. Ledesma-Amaro, G.-B. Stan, Current Opinion in Microbiology, Volume 62 (2021), pp. 68-75, doi:10.1016/j.mib.2021.05.004.
• Quasi-robust control of biochemical reaction networks via stochastic morphing, T. Plesa, G.-B. Stan, T. Ouldridge, W. Bae, Journal of the Royal Society Interface, Volume 18 (2021), n°177, pp. 20200985, doi:10.1098/rsif.2020.0985.
• Handhold-Mediated Strand Displacement: A Nucleic Acid Based Mechanism for Generating Far-from-Equilibrium Assemblies through Templated Reactions, J. Cabello-Garcia, W. Bae, G.-B. Stan, T. Ouldridge, ACS Nano, Volume 15 (2021), n°2, pp. 3272–3283, doi:10.1021/acsnano.0c10068.
• In situ generation of RNA complexes for synthetic molecular strand displacement circuits in autonomous systems, W. Bae, G.-B. Stan, T. Ouldridge, Nano Letters, Volume 21 (2021), n°1, pp. 265–271, doi:10.1021/acs.nanolett.0c03629
• A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs, P. Sarvari, D. Ingram, G.-B. Stan, MDPI Biology, Special Issue on Computational Methods in Synthetic Biology, Volume 10 (2021), n°37, doi:10.3390/biology10010037.
• rfaRm: an R client-side interface to facilitate the analysis of the Rfam database of RNA families: Automated identification and annotation of non-coding RNA, L. S. Vidal, R. Ayala, G.-B. Stan, R. Ledesma-Amaro, PLoS ONE (2021), doi:10.1371/journal.pone.0245280.
• Approximations of countably-infinite linear programs over bounded measure spaces, J. Kuntz, P. Thomas, G.-B. Stan, M. Barahona, SIAM Journal on Optimization, Volume 31 (2021), n°1, pp. 604–625, doi:10.1137/19M1268847.
• Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations, J. Kuntz, P. Thomas, G.-B. Stan, M. Barahona, SIAM Review, Volume 63 (2021), n°1, pp. 3-64, doi:10.1137/19M1289625.

2020

• Characterization, modelling and mitigation of gene expression burden in mammalian cells, T. Frei, F. Cella, F. Tedeschi, J. Gutierrez G.-B. Stan, M. Khammash, V. Siciliano, Nature Communications, Volume 11 (2020), 4641, doi:10.1038/s41467-020-18392-x.
• Synthetic Biology Open Language Visual (SBOL Visual) Version 2.2, H. Baig, P. Fontanarossa, V. Kulkarni, J. McLaughlin, et al., Journal of Integrative Bioinformatics, Volume 17 (2020), n°2-3, doi:10.1515/jib-2020-0014.
• rfaRm: An R interface to the Rfam database, L. S. Vidal, R. Ayala, G.-B. Stan, R. Ledesma-Amaro, BioConductor, (2020), doi:10.18129/B9.bioc.rfaRm.
• SBOL Visual 2 Ontology, G. Misirli, T. E. Gorochowski, G.-B. Stan, A. Wipat, C. Myers, ACS Synthetic Biology, Volume 9 (2020), n°4, pp. 972-977, doi:10.1021/acssynbio.0c00046.
• Solving Optimal Control Problems for Monotone Systems Using the Koopman Operator, A. Sootla, G.-B. Stan, D. Ernst, The Koopman Operator in Systems and Control, part of Lecture Notes in Control and Information Sciences, Eds.: Alexandre Mauroy, Igor Mezic, Yoshihiko Susuki, Springer, 2020, pp 283-312, doi:10.1007/978-3-030-35713-9_11.

2019

• Bounding the stationary distributions of the chemical master equation via mathematical programming, J. Kuntz, P. Thomas, G.-B. Stan, M. Barahona, Journal of Chemical Physics, Volume 151 (2019), n°3, doi:10.1063/1.5100670.
• Riboswitch identification using Ligase-Assisted Selection for the Enrichment of Responsive Ribozymes (LigASERR), M. C. Haines, M. Storch, D. A. Oyarzun, G.-B. Stan, G. Baldwin, OUP Synthetic Biology, Volume 4 (2019), n°1, doi:10.1093/synbio/ysz019.
• Host-Aware Synthetic Biology, A. Boo, T. Ellis, G.-B. Stan, Current Opinion in Systems Biology, Volume 14 (2019), pp. 66-72, doi:10.1016/j.coisb.2019.03.001.
• The exit time finite state projection scheme: bounding exit distributions and occupation measures of continuous-time Markov chains, J. Kuntz, P. Thomas, G.-B. Stan, M. Barahona, SIAM Journal of Scientific Computing, Volume 41 (2019), n°2, pp. A748–A769, doi:10.1137/18M1168261.
• Whole-cell biosensor with tuneable limit of detection enables low-cost agglutination assays for medical diagnostic applications, N. Kylilis, P. Riangrungroj, H.-E. Lai, V. Salema, L.A. Fernandez, G.-B. Stan, P. Freemont, K. Polizzi, ACS Sensors , Volume 4 (2019), n°2, pp. 370-378, doi:10.1021/acssensors.8b01163.
• An Automatic Sparse Model Estimation Method Guided by Constraints that Encode System Properties, Z. Tuza, G.-B. Stan, Proceedings of the 18th European Control Conference (ECC 2019), Naples, Italy, July 25-28, 2019.
• A systematic framework for biomolecular system identification, Z. Tuza, L. Bandiera, D. Gomez-Cabeza, G.-B. Stan, F. Menolascina, Proceedings of the 58th IEEE Conference on Decision and Control (CDC 2019), invited tutorial session on "BioControl", Nice, France, 11-13 December, 2019.

2018

• Tools for engineering coordinated system behaviour in synthetic microbial consortia, N. Kylilis, Z. A. Tuza, G.-B. Stan, K. Polizzi, Nature Communications, Volume 9 (2018), 2677, doi:10.1038/s41467-018-0504.
• Computational Re-Design of Synthetic Genetic Oscillators for Independent Amplitude and Frequency Modulation, M. Tomazou, M. Barahona, K. Polizzi, G.-B. Stan, Cell Systems, Volume 6 (2018), n°4, pp. 508-520, doi:10.1016/j.cels.2018.03.013.
• Burden-driven feedback control of gene expression, F. Ceroni, A. Boo, S. Furini, T. E. Gorochowski, O. Borkowski, Y. N. Ladak, A. R. Awan, C. Gilbert, G.-B. Stan, T. Ellis, Nature Methods, Volume 15 (2018), pp. 387-393, doi:10.1038/nmeth.4635. The paper made the cover of the May 2018 issue of Nature Methods.
• Cell-free prediction of protein expression costs for growing cells, O. Borkowski, C. Bricio, M. Murgiano, B. Rothschild-Mancinelli, G.-B. Stan, T. Ellis, Nature Communications, Volume 9 (2018), doi:10.1038/s41467-018-03970-x.
• Portable gene expression guaranteed, M. Tomazou, G.-B. Stan, Nature Biotechnology News and Views, Volume 36 (2018), n°4, pp. 313-314, doi:10.1038/nbt.4119
• Investigating the consequences of asymmetric endoplasmic reticulum inheritance in Saccharomyces cerevisiae under stress using a combination of single cell measurements and mathematical modelling, F. Jonas, K. Royle, R. Aw, G.-B. Stan, K. Polizzi, Systems and Synthetic Biology, Volume 3 (2018), n°1, pp. 64-75, doi:10.1016/j.synbio.2018.01.001.
• Synthetic Biology Open Language Visual (SBOL Visual) Version 2.0, R. S. Cox, C. Madsen, J. McLaughlin, T. Nguyen, N. Roehner, A. Wipat, B. Bartley, J. Beal, S. Bhatia, M. Bissell, K. Clancy, T. Gorochowski, R. Grunberg, A. Luna, C. Myers, N. Le Novere, M. Pocock, H. Sauro, J. T. Sexton, G.-B. Stan, J. J. Tabor, C. Voigt, Z. Zundel, K. Polizzi, Journal of Integrative Bioinformatics, Volume 15 (2018), n°1, doi:10.1515/jib-2017-0074.
• Characterization of Biologically Relevant Network Structures form Time-series Data, Z. Tuza, G.-B. Stan, Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018), Miami Beach, FL, USA, 17-19 December, 2018.

2017

• The Interplay Between Feedback and Buffering in Homeostasis, E. Hancock, J. Ang, A. Papachristodoulou, G.-B. Stan, Cell Systems, Volume 5 (2017), n°5, pp. 498-508, doi:10.1016/j.cels.2017.09.013.
• Identification of Nonlinear State-Space Systems from Heterogeneous Datasets, W. Pan, Y. Yuan, L. Ljung, J. Goncalves, G.-B. Stan, IEEE Transactions on Network Control Systems, Volume 5 (2017), n°2, pp. 737-747, doi:10.1109/TCNS.2017.2758966.
• Constructing Synthetic Biology Workflows in the Cloud, G. Misirli, C. Madsen, I. Sainz de Murieta, M. Bultelle, K. Flanagan, M. Pocock, J. Hallinan, J. A. Mclaughlin, J. Clark-Casey, M. Lyne, G. Micklem, G.-B. Stan, R. Kitney, A. Wipat, IET Engineering Biology, Volume 1 (2017), n°1, pp. 61-65, doi:10.1049/enb.2017.0001.
• Intracellular delivery of biologic therapeutics by bacterial secretion systems, B. Walker, G.-B. Stan, K. Polizzi, Expert Reviews in Molecular Medicine, Volume 19 (2017), e6, doi:10.1017/erm.2017.7.

2016

• Bounding stationary averages of polynomial diffusions via semidefinite programming, J. Kuntz, M. Ottobre, G.-B. Stan, M. Barahona, SIAM Journal on Scientific Computing, Volume 38 (2016), n°6, pp. A3891–A3920, doi:10.1137/16M107801X.
• Overloaded and stressed: whole-cell considerations for bacterial synthetic biology, O. Borkowski, F. Ceroni, G.-B. Stan, T. Ellis, Current Opinion in Microbiology, Volume 33 (2016), pp. 123-130, doi:10.1016/j.mib.2016.07.009.
• A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems, W. Pan, Y. Yuan, J. Gonçalves, G.-B. Stan, IEEE Transacations on Automatic Control, Volume 61 (2016), n°1, pp. 182-187, doi:10.1109/TAC.2015.2426291.
• Shaping Pulses to Control Bistable Systems: Analysis, Computation and Counterexamples, A. Sootla, D. Oyarzun, D. Angeli, G.-B. Stan, Automatica, Volume 63 (2016), pp. 254-264, doi:10.1016/j.automatica.2015.10.037.
• Biomolecular implementation of nonlinear system theoretic operators, M. Foo, R. Sawlekar, J. Kim, D. Bates, G.-B. Stan, V. Kulkarni, Proceedings of the 15th European Control Conference (ECC 2016), Aalborg, Denmark, 29 June - 1 July, 2016.
• Online Model Selection for Synthetic Gene Networks, W. Pan, F. Menolascina, G.-B. Stan, Proceedings of the 55th IEEE Conference on Decision and Control (CDC 2016), Las Vegas, NV, USA, 12-14 December, 2016.

2015

• SBOL Visual: A Graphical Language for Genetic Designs, J. Y. Quinn, R. S. Cox III, A. Adler, J. Beal, S. Bhatia, Y. Cai, J. Chen, K. Clancy, M. Galdzicki, N. J. Hilson, N. Le Novere, A. J. Maheshwari, J. A. McLaughlin, C. J. Myers, U. P, M. Pocock, C. Rodriguez, L. Soldatova, G.-B. Stan, N. Swainston, A. Wipat, H. M. Sauro, PLoS Biology, 2015, doi:10.1371/journal.pbio.1002310.
• A minimal realization technique for the dynamical structure function of a class of LTI systems, Y. Yuan, A. Rai, E. Yeung, G.-B. Stan, S. Warnick, J. Gonçalves, IEEE Transactions on Control of Network Systems, Volume 4 (2015), n°2, doi:10.1109/TCNS.2015.2498468.
• Simplified Mechanistic Models of Gene Regulation for Analysis and Design, E. Hancock, G.-B. Stan, J. Arpino, A. Papachristodoulou, Journal of the Royal Society Interface, Volume 12 (2015), n°108, doi:10.1098/rsif.2015.0312. A preprint is available here, while the supplementary information is available here.
• Quantifying cellular capacity identifies gene expression designs with reduced burden, F. Ceroni, R. Algar, G.-B. Stan, T. Ellis, Nature Methods, Volume 12 (2015), n°5, pp. 415–418, doi:10.1038/nmeth.3339. Preprint available here. The paper made the cover of the May 2015 issue of Nature Methods.
• Noise propagation in synthetic gene circuits for metabolic control, D. Oyarzun, J.-B. Lugagne, G.-B. Stan, ACS Synthetic Biology, Volume 4 (2015), n°2, pp. 116-125, doi:10.1021/sb400126a.
• GeneGuard: a Modular Plasmid System Designed for Biosafety, O. Wright, M. Dalmans, G.-B. Stan, T. Ellis, ACS Synthetic Biology, Volume 4 (2015), n°3, pp. 307-3016, doi:10.1021/sb500234s.
• Online fault diagnosis for nonlinear power systems, W. Pan, Y. Yuan, H. Sandberg, J. Gonçalves, G.-B. Stan, Automatica, Volume 55 (2015), pp. 27-36, doi:10.1016/j.automatica.2015.02.032.
• A forward-design approach to increase the production of poly-3-hydroxybutyrate in genetically engineered Escherichia coli, R. Kelwick, M. Kopniczky, I. Bower, W. Chi, M. H. W. Chin, S. Fan, J. Pilcher, J. Strutt, A. J. Webb, K. Jensen, G.-B. Stan, R. Kitney, P. Freemont, PLoS ONE, Volume 10, (2015), n°2, doi:10.1371/journal.pone.0117202.
• The Moveable Feast of Predictive Reward Discounting in Humans, B. Schoenhense, L. Dickens, B. Caldas, G.-B. Stan, A. Faisal, Proceedings of the 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RDLM 2015), The University of Alberta, Edmonton, Alberta, Canada, 7-10 June, 2015.
• Shaping Pulses to Control Bistable Biological Systems, A. Sootla, D. Oyarzun, D. Angeli, G.-B. Stan, Proceedings of the 2015 American Control Conference (ACC 2015), Palmer House Hilton, Chicago, IL, 1-3 July, 2015.
• Identifying Biochemical Reaction Networks From Heterogeneous Datasets, W. Pan, Y. Yuan, L. Ljung, J. Gonçalves, G.-B. Stan, Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015), Osaka International Convention Center, Osaka, Japan, 15-18 December, 2015.

2014

• Model Reduction of Genetic-Metabolic Networks via Time Scale Separation, J. Kuntz, D. Oyarzun, G.-B. Stan, A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations, Eds.: V. Kulkarni, G.-B. Stan, K. Raman, Springer, July 2014, pp. 181-210, doi:10.1007/978-94-017-9041-3_7.
• Analysis of Synchronizing Biochemical Networks via Incremental Dissipativity, A. Hamadeh, J. Gonçalves, G.-B. Stan, A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular Systems, Eds.: V. Kulkarni, G.-B. Stan, K. Raman, Springer, July 2014, pp. 117-139, doi:10.1007/978-94-017-9047-5_5.
• The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology, M. Galdzicki, K. Clancy, E. Oberortner, M. Pocock, J. Quinn, C. Rodriguez, N. Roehner, M. Wilson, L. Adam, J. C. Anderson, B. Bartley, J. Beal, D. Chandran, J. Chen, D. Densmore, D. Endy, R. Gruenberg, J. Hallinan, N. Hillson, J. Johnson, A. Kuchinsky, M. Lux, G. Misirli, J. Peccoud, H. Plahar, E. Sirin, G.-B. Stan, A. Villalobos, A. Wipat, J. H. Gennari, C. Myers, H. Sauro, Nature Biotechnology, Volume 32 (2014), pp. 545–550, doi:10.1038/nbt.2891.
• Mathematical modeling of HIV dynamics after antiretroviral therapy initiation: A clinical research study, P. S. Rivadeneira, C. H. Moog, G.-B. Stan, C. Brunet, F. Raffi, V. Ferre, V. Costanza, M.-J. Mhawej, F. Biafore, D. A. Ouattara, D. Ernst, R. Fonteneau, X. Xia, AIDS Research and Human Retroviruses, Volume 30 (2014), n°9, pp. 831-834, doi:10.1089/AID.2013.0286.
• Mathematical modeling of HIV dynamics after antiretroviral therapy initiation: A Review, P. S. Rivadeneira, C. H. Moog, G.-B. Stan, C. Brunet, F. Raffi, V. Ferre, V. Costanza, M.-J. Mhawej, F. Biafore, D. A. Ouattara, D. Ernst, R. Fonteneau, X. Xia, BioResearch Open Access, Volume 3 (2014), n°5, pp. 233-241, doi:10.1089/biores.2014.0024.
• Modelling essential interactions between synthetic genes and their chassis cell, R. Algar, T. Ellis, G.-B. Stan, Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014), J.W. Marriott Hotel, Los Angeles, CA, USA, 15-17 December, 2014.
• Inference of Switched Biochemical Reaction Networks Using Sparse Bayesian Learning, W. Pan, Y. Yuan, A. Sootla, G.-B. Stan, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014), Nancy, France, 15-19 September, 2014.
• Distributed Reconstruction of Nonlinear Networks: An ADMM Approach, W. Pan, A. Sootla, G.-B. Stan, Proceedings of the 19th IFAC World Congress (IFAC 2014), Cape Town, South Africa, 24-29 August, 2014.
• Toggling a Genetic Switch Using Reinforcement Learning, A. Sootla, N. Strelkowa, D. Ernst, M. Barahona, G.-B. Stan, Proceedings of the 9th French Meeting on Planning, Decision Making and Learning (JFPDA 2014), Liege, Belgium, 12-13 May, 2014.

2013

• Observability and coarse-graining of consensus dynamics through the External Equitable Partition, N. O'Clery, Y. Yuan, G.-B. Stan, M. Barahona, Physical Review E, Volume 88 (2013), n°4, doi:10.1103/PhysRevE.88.042805.
• Tuning the Dials of Synthetic Biology: A Review, J. Arpino, E. Hancock, J. Anderson, M. Barahona, G.-B. Stan, A. Papachristodoulou, K. Polizzi, Microbiology, Special Issue on Synthetic Biology, Volume 159 (2013), pp. 1236-1253 doi:10.1099/mic.0.067975-0.
• Building-in Biosafety for Synthetic Biology, O. Wright, G.-B. Stan, T. Ellis, Microbiology, Special Issue on Synthetic Biology, Volume 159 (2013), pp. 1221-1235, doi:10.1099/mic.0.066308-0.
• Synthetic gene circuits for metabolic control: design trade-offs and constraints, D. Oyarzun, G.-B. Stan, Journal of the Royal Society Interface, Volume 10 (2013), n°78, doi:10.1098/rsif.2012.0671.
• Systematic Computation of Nonlinear Cellular and Molecular Dynamics with Low-Power CytoMimetic Circuits: A Simulation Study, K. Papadimitriou, G.-B. Stan, M. Drakakis, PLoS ONE, Volume 8 (2013), n°2, doi:10.1371/journal.pone.0053591.
• Synthetic Biology Open Language Visual (SBOL Visual), version 1.0.0, J. Quinn, J. Beal, S. Bhatia, P. Cai, J. Chen, K. Clancy, N. Hillson, M. Galdzicki, A. Maheshwari, U. P, M. Pocock, C. Rodriguez, G.-B. Stan, D. Endy, BBF RFC #93, 2013, doi:1721.1/78249
• Decentralised Minimal-time Consensus, Y. Yuan, G.-B. Stan, L. Shi, M. Barahona, J. Gonçalves, Automatica, Volume 49 (2013), n°5, pp. 1227-1235, doi:10.1016/j.automatica.2013.02.015
• Nonlinear Heat Transfer Processes in a Two-Phase Thermofluidic Oscillator, C. Markides, O. Adebayo, R. Solanki, G.-B. Stan, Applied Energy, Volume 104 (2013), pp. 958-977, doi:10.1016/j.apenergy.2012.11.056.
• Real-time Fault Diagnosis for Large-Scale Nonlinear Power Networks, W. Pan, Y. Yuan, H. Sandberg, J. Gonçalves, G.-B. Stan, Proceedings of the 52nd IEEE Conference on Decision and Control (CDC 2013), Palazzo dei Congressi, Florence, Italy, 10-13 December, 2013.
• On Periodic Reference Tracking Using Batch-Mode Reinforcement Learning with Application to Gene Regulatory Network Control, A. Sootla, N. Strelkowa, D. Ernst, M. Barahona, G.-B. Stan, Proceedings of the 52nd IEEE Conference on Decision and Control (CDC 2013), Palazzo dei Congressi, Florence, Italy, 10-13 December, 2013.
• Control of protein concentrations in heterogeneous cell populations, A. Vignoni, D. Oyarzun, J. Pico, G.-B. Stan, Proceedings of the 12th European Control Conference (ECC 2013), ETH Zurich, Switzerland, 17-19 July, 2013.
• Stochastic simulation of enzymatic reactions under transcriptional feedback regulation, J.-B. Lugagne, D. Oyarzun, G.-B. Stan, Proceedings of the 12th European Control Conference (ECC 2013), ETH Zurich, Switzerland, 17-19 July, 2013.

2012

• Engineering and ethical perspectives in synthetic biology, J. Anderson, N. Strelkowa, G.-B. Stan, T. Douglas, J. Savulescu, M. Barahona, and A. Papachristodoulou, EMBO reports, Volume 13 (2012), n°7, pp. 584-590, doi:10.1038/embor.2012.81.
• Global State Synchronization in Networks of Cyclic Feedback Systems, A. O. Hamadeh, G.-B. Stan, R. Sepulchre, and J. M. Gonçalves, IEEE Transacations on Automatic Control, Volume 57 (2012), n°2, pp. 478-483, doi:10.1109/TAC.2011.2164015.
• Arterial Reservoir-Excess Pressure and Ventricular Work, K. H. Parker, J. Alastruey, G.-B. Stan, Medical & Biological Engineering & Computing Journal, Volume 50 (2012), n°4, pp. 419-424, doi:10.1007/s11517-012-0872-1, Supplementary material.
• Synthetic Biology Open Language (SBOL) Version 1.1.0, M. Galdzicki, M. L. Wilson, C. A. Rodriguez, M. R. Pocock, E. Oberortner, L. Adam, A. Adler, J. C. Anderson, J. Beal, C. Yizhi, D. Chandran, D. Densmore, O. A. Drory, D. Endy, J. H. Gennari, R. Grunberg, T. S. Ham, J. N. Hillson, J. D. Johnson, A. Kuchinsky, M. W. Lux, C. Madsen, G. Misirli, C. J. Myers, C. Olguin, J. Peccoud, H. Plahar, D. Platt, N. Roehner, E. Sirin, T. F. Smith, G.-B. Stan, A. Villalobos, A. Wipat, and H. M. Sauro, BBF RFC 87, 2012, doi:1721.1/73909
• Design constraints in an operon circuit for engineered control of metabolic networks, D. Oyarzun, G.-B. Stan, Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), Maui, Hawaii, USA, 10-13 December, 2012.
• Reconstruction of Arbitrary Biochemical Reaction Networks: A Compressive Sensing Approach, W. Pan, Y. Yuan, J. Gonçalves, G.-B. Stan, Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), Maui, Hawaii, USA, 10-13 December, 2012.
• Dynamical Structure Function Identifiability Conditions Enabling Signal Structure Reconstruction, J. Adebayo, T. Southwick, V. Chetty, E. Yeung, Y. Yuan, J. Gonçalves, J. Grose, J. Prince, G.-B. Stan, S. Warnick, Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), Maui, Hawaii, USA, 10-13 December, 2012.
• Design tradeoffs in a synthetic gene control circuit for metabolic networks, D. Oyarzun, G.-B. Stan, Proceedings of the 2012 American Control Conference (ACC 2012), Montreal, Canada, 27-29 June, 2012.

2011

• The circadian oscillator gene GIGANTEA mediates a long-term response of the Arabidopsis thaliana circadian clock to sucrose, N. Dalchau, S. J. Baek, H. M. Briggs, F. C. Robertson, A. N. Dodd, M. J. Gardner, M. A. Stancombe, M. J. Haydon, G.-B. Stan, J. M. Gonçalves, A. A. R. Webb, Proceedings of the National Academy of Sciences of the United States of America (PNAS), Volume 108 (2011), n°12, pp. 5104-5109, doi:10.1073/pnas.1015452108, Supplementary material.
• Essential information for synthetic DNA sequences, J. Peccoud, J. C. Anderson, D. Chandran, D. Densmore, M. Galdzicki, M. W. Lux, C. A. Rodriguez, G.-B. Stan, H. M. Sauro, Nature Biotechnology, Volume 22 (Jan 2011), n°1, p. 22, doi:10.1038/nbt.1753.
• Computational design approaches and tools for synthetic biology, J. MacDonald, C. Barnes, R. Kitney, P. Freemont, G.-B. Stan, Integrative Biology, Special Issue on Synthetic Biology, Volume 3 (2011), n°2, pp. 97-108, doi:10.1039/c0ib00077a.
• Fast consensus via predictive pinning control, H.-T. Zhang, M. Z. Q. Chen, G.-B. Stan, IEEE Transaction on Circuits and Systems I, Volume 58 (2011), n°9, pp. 2247-2258, doi:10.1109/TCSI.2011.2123450.
• Robust dynamical network structure reconstruction, Y. Yuan, G.-B. Stan, S. Warnick, J. M. Gonçalves, Automatica, Special Issue on Systems Biology, Volume 47 (2011), n°6, pp. 1230-1235, doi:10.1016/j.automatica.2011.03.008.
• Synthetic Biology Open Language (SBOL) Version 1.0.0, M. Galdzicki, M. L. Wilson, C. A. Rodriguez, L. Adam, A. Adler, J. C. Anderson, J. Beal, D. Chandran, D. Densmore, O. A. Drory, D. Endy, J. H. Gennari, R. Grunberg, T. S. Ham, A. Kuchinsky, M. W. Lux, C. Madsen, G. Misirli, C. J. Myers, J. Peccoud, H. Plahar, M. R. Pocock, N. Roehner, T. F. Smith, G.-B. Stan, A. Villalobos, A. Wipat, and H. M. Sauro, BBF RFC 84, 2011, doi:1721.1/66172
• Network of passive oscillators, V. Kulkarni, M. Riedel, G.-B. Stan, Proceedings of the 49th Annual Allerton Conference on Communication, Control, and Computing, University of Illinois at Urbana-Champaign, Allerton Retreat Center, Monticello, Illinois, USA, 28-30 September, 2011.
• Decentralised Minimal-time Consensus, Y. Yuan, G.-B. Stan, L. Shi, M. Barahona, J. Gonçalves, Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), Orlando, Florida, USA, 12-15 December, 2011.

2010

• Correct biological timing in Arabidopsis requires multiple light signaling pathways, N. Dalchau, K. E. Hubbard, C. T. Hotta, F. C. Robertson, H. M. Briggs, G.-B. Stan, J. M. Gonçalves, A. A. R. Webb, Proceedings of the National Academy of Sciences of the United States of America (PNAS), Volume 107 (2010), n° 29, pp. 13171-13176, doi:10.1073/pnas.1001429107, Supplementary material.
• Constructive Synchronization of Networked Feedback Systems, A. O. Hamadeh, G.-B. Stan, J. Gonçalves, Proceedings of the 49th IEEE Conference on Decision and Control (CDC 2010), Atlanta, Georgia, USA, 15-17 December, 2010.
• Robust Dynamical Network Reconstruction, Y. Yuan, G.-B. Stan, S. Warnick, J. Gonçalves, Proceedings of the 49th IEEE Conference on Decision and Control (CDC 2010), Atlanta, Georgia, USA, 15-17 December, 2010.
• Minimal-time output final value of unknown DT-LTI systems with application to the decentralised network consensus problem, Y. Yuan, G.-B. Stan, L. Shi, M. Barahona, J. Gonçalves, Proceedings of the 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010), University Congress Center, Budapest, Hungary, 5-9 July, 2010.
• Robust dynamical network structure reconstruction with application to systems biology, Y. Yuan, G.-B. Stan, S. Warnick, J. Gonçalves, Proceedings of the 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010), University Congress Center, Budapest, Hungary, 5-9 July, 2010.

2009

• Apoptosis characterizes immunological failure of HIV infected patients, M.-J. Mhawej, C. Brunet-François, R. Fonteneau, D. Ernst, V. Ferré, G.-B. Stan, F. Raffi, C. H. Moog, IFAC Control Engineering Practice, Volume 17 (2009), pp. 798-804, doi:10.1016/j.conengprac.2009.01.001
• Decentralized final value theorem for discrete-time LTI systems with application to minimal-time distributed consensus, Y. Yuan, G.-B. Stan, L. Shi, J. Gonçalves, Proceedings of the 48th IEEE Conference on Decision and Control (CDC 2009), Shangai, China, 16-18 December, 2009.
• Minimal dynamical structure realisations with application to network reconstruction from data, Y. Yuan, G.-B. Stan, Sean Warnick, J. Gonçalves, Proceedings of the 48th IEEE Conference on Decision and Control (CDC 2009), Shangai, China, 16-18 December, 2009.

2008

• Collective behavior coordination with predictive mechanisms, H.-T. Zhang, M. Z. Chen, G.-B. Stan, T. Zhou and J. M. Maciejowski, IEEE Circuits and Systems Magazine, Volume 8 (2008), n° 3, pp. 67-85 doi:10.1109/MCAS.2008.928446
• Ultrafast Consensus via Predictive Mechanisms, H.-T. Zhang, M. Z. Chen, T. Zhou, and G.-B. Stan, Europhysics Letters, Volume 83 (2008), n° 4, doi:10.1209/0295-5075/83/40003
• Modelling the influence of activation-induced apoptosis of CD4+ and CD8+ T-cells on the immune system response of a HIV infected patient, G.-B. Stan, F. Belmudes, R. Fonteneau, F. Zeggwagh, M.-A. Lefebvre, C. Michelet, and D. Ernst, IET Systems Biology, Volume 2 (2008), n° 2, pp. 94-102, doi:10.1049/iet-syb:20070029
• Dynamical structure analysis of sparsity and minimality heuristics for reconstruction of biochemical networks, R. Howes, L. Eccleston, J. Gonçalves, G.-B. Stan, S. Warnick, Proceedings of the 47th IEEE Conference on Decision and Control (CDC 2008), Cancun, Mexico, 9-11 December, 2008.
• Robust Synchronization in Networks of Cyclic Feedback Systems, A. O. Hamadeh, G.-B. Stan, J. Gonçalves, Proceedings of the 47th IEEE Conference on Decision and Control (CDC 2008), Cancun, Mexico, 9-11 December, 2008.
• Global Asymptotic Stability of the Limit Cycle in Piecewise Linear versions of the Goodwin Oscillator, A. Salinas-Varela, G.-B. Stan, J. Gonçalves, Proceedings of the 17th IFAC World Congress (IFAC 2008), Seoul, Korea, 6-11 July, 2008.

2007

• Analysis of interconnected oscillators by dissipativity theory, G.-B. Stan, R. Sepulchre, IEEE Transactions on Automatic Control, Volume 52 (2007), n° 2, pp. 256-270, doi:10.1109/TAC.2006.890471
• The cross-entropy method for power system combinatorial optimization problems, D. Ernst, M. Glavic, G.-B. Stan, S. Mannor, L. Wehenkel, Proceedings of the 7th IEEE Power Engineering Society (PowerTech 2007), Lausanne, Switzerland, 1-5 July, 2007.
• Output synchronization in networks of cyclic biochemical oscillators, G.-B. Stan, A. O. Hamadeh, J. Gonçalves, R. Sepulchre, Proceedings of the 26th American Control Conference (ACC 2007), Times Square, New York City, USA, July 11-13, 2007.

2006

• Clinical data based optimal STI strategies for HIV: a reinforcement learning approach, D. Ernst, G.-B. Stan, J. Gonçalves, L. Wehenkel, Proceedings of the 45th IEEE Conference on Decision and Control (CDC 2006), San Diego, CA, USA, December 13-15, 2006.
• Clinical data based optimal STI strategies for HIV: a reinforcement learning approach, D. Ernst, G.-B. Stan, J. Gonçalves, L. Wehenkel, Proceedings of the 15th annual machine learning conference of Belgium and The Netherlands (BENELEARN 2006), Ghent, Belgium, May 11-12, 2006.

2005

• Feedback Mechanisms for Global Oscillations in Lure Systems, R. Sepulchre, G.-B. Stan, Systems & Control Letters, Volume 54 (2005), n° 8 , pp. 809-818, doi:10.1016/j.sysconle.2004.12.004
• Global analysis and synthesis of oscillations: a dissipativity approach, G.-B. Stan, Ph.D. thesis, University of Liege, Belgium, March, 2005.

2004

• Global Analysis of Limit Cycles in Networks of Oscillators, G.-B. Stan, R. Sepulchre, Proceedings of the 6th IFAC International Symposium on Nonlinear Control Systems (NOLCOS 2004), Stuttgart, Germany, September 1-3, 2004, pp. 1433-1438.
• Dissipativity and Global Analysis of Limit Cycles in Networks of Oscillators, G.-B. Stan, R. Sepulchre, Proceedings of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2004), Leuven, Belgium, July 5-9, 2004.

2003

• Dissipativity characterization of a class of oscillators and networks of oscillators, G.-B. Stan, R. Sepulchre, Proceedings of the 42nd IEEE Conference on Decision and Control (CDC 2003), Maui, Hawaii, USA, December 9-12, 2003, pp. 4169-4173.

2002

• Comparison of Different Impulse Response Measurement Techniques, G.-B. Stan, J.-J. Embrechts, D. Archambeau, Journal of the Audio Engineering Society, Volume 50 (2002), n° 4, pp. 249-262. (see also this link ULG's ORBI publications database)

2001

• Determination of the Scattering Coefficient of Random Rough Diffusing Surfaces for Room Acoustics Applications, J.-J. Embrechts, D. Archambeau, G.-B. Stan, Acta-Acoustica Acoustica, Volume 87 (2001), n° 4 pp. 482-494.

Internal Reports

• Comparison of Algorithms for Biological Network Reconstruction from Data, Openwetware webpage of Nuri Purswani as part of her MSc project with me in 2010.
• Global Analysis of Limit Cycles in the Chua System, Internal report, Cambridge University, UK, April 2006, available upon request.
• Dissipativity and Global Analysis of Limit Cycles, Internal Report, Montefiore, Ulg, 2004, available upon request.

Abstract:

The main theme of this research concerns the global (as opposed to local) analysis and synthesis of stable limit cycle oscillations in dynamical systems. The global analysis of oscillations in systems and networks of interconnected systems is a longstanding problem. Dynamical systems that exhibit robust nonlinear oscillations are called oscillators. Oscillators are ubiquitous in physical, biological, biochemical, and electromechanical systems. Detailed models of oscillators abound in the literature, most frequently in the form of a set of nonlinear differential equations whose solutions robustly converge to a limit cycle oscillation. Local stability analysis is possible by means of Floquet theory but global stability analysis is usually restricted to simple (second order) models. For these simple models, global analysis is performed by using specific low dimensional tools (phase plane methods, Poincaré-Bendixson theorem, etc.) which do not generalise easily to complex (high dimensional) models. As a consequence, global analysis of complex models is quite difficult since there currently exists no general analysis method. This lack of general analysis methods typically forces complex models of oscillators to be studied only through numerical simulation methods. Although numerical simulations of these models may give a first insight into their behaviour, a more in-depth understanding is generally impeded by the complexity of the models and the challenge of rigorous global stability analysis. Moreover, even in the case of simple models, the low dimensional methods used for their analysis do not generalise to the analysis of a network of interconnected oscillators. These considerations show the need for developing general methods that allow the global analysis of oscillators, either isolated or in interconnection. This thesis constitutes the first step towards the development of such a unified oscillators theory. In this aim, this thesis considers an extension of the dissipativity theory introduced by Willems. Nowadays, dissipativity is considered as one of the most general nonlinear (global) stability analysis method for equilibrium points in dynamical systems and networks of interconnected dynamical systems. In this thesis, we show that dissipativity theory can be extended to allow (global) stability analysis of limit cycles in many Lure-type models of oscillators and networks of oscillators. These Lure-type models of oscillators have been named passive oscillators. As the main contributions of this research, we show the implications of this extended dissipativity theory for

• the global stability analysis of isolated passive oscillators
• the global stability analysis of networks of passive oscillators
• the global stability analysis of synchronised oscillations in networks of identical passive oscillators

Furthermore, based on these results, we also propose a limit cycle oscillations synthesis method based on the design of a nonlinear parametric proportional-integral controller aimed at the generation of limit cycle oscillations with large basins of attraction in stabilisable nonlinear systems.

Abstract:

In this thesis, we compare four of the most used impulse response measurement techniques: Maximum Length Sequence (MLS), Inverse Repeated Sequence (IRS), Time Stretched Pulses, and Logarithmic Sinesweep. These methods are generally used for the measurement of the impulse response of acoustical systems such as transducers, rooms, and binaural impulse responses. The choice of one of these methods depending on the measurement conditions is critical. Therefore an extensive comparison has been realised. This comparison has been done through the implementation and realisation of a complete, fast, reliable, and cheap measurement system. In particular, these different methods have been compared with respect to best achievable signal-to-noise ratio, ease of use, harmonic distortion rejection/measurement, and robustness to measurement conditions (temperature change, impulsive and white noise, etc.). It is shown that in the presence of non white noise, the MLS and IRS techniques are more appropriate. On the contrary, in quiet environments the Logarithmic Sinesweep method is the most accurate: it allows for a direct improvement of the signal-to-noise ratio of up to 30 dB over the other methods, which can be critical for audio virtual reality systems such as auralization systems. Indeed, capturing binaural room impulse responses for high-quality auralization purposes requires a signal-to-noise ratio of more than 90 dB which is unattainable with other measurement techniques due to inherent nonlinearities in the measurement system (especially the loudspeaker), but fairly easy to reach with logarithmic sinesweeps due to the possibility of completely rejecting (and measuring) harmonic distortions. As a consequence, the sinesweep method opens the way for the development of high-quality auralization and sound spatialisation systems, which constitute the basis for advanced audio virtual reality systems.