Publications
Modeling Metabolism and Quantitative Systems Pharmacology models:
1. “History and future perspectives on the discipline of Quantitative Systems Pharmacology modeling and its applications.”
Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B,
Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. Front Physiol. 2021
Mar 25;12:637999. doi: 10.3389/fphys.2021.637999. PMID: 33841175; PMCID: PMC8027332.
2. “Metabolic graphs, LIFE method and the modeling of drug action on Mycobacterium tuberculosis.”
Sean T. McQuade, Nathaniel J. Merrill, and Benedetto Piccoli. arXiv preprint arXiv:2003.12400 (2020).
Advances in nonlinear biological systems: modeling and optimal control , 2020.
3. “Stability of metabolic networks via Linear-in-Flux-Expressions.”
NJ Merrill, Nathaniel; An, Zheming; ST McQuade, Sean; Garin, Federica; Azer, Karim; E Abrams, Ruth; Piccoli, Benedetto.
ISSN: 1556-181X; DOI: 10.3934/nhm.2019006 Networks Heterogeneous Media , 2019, Vol.14(1), p.101-
130 arXiv:1808.08263 (2018).
4. “Equilibria and control of metabolic networks with enhancers and inhibitors.”
Zheming An, N.J. Merrill, S.T. McQuade, B. Piccoli. “Mathematics in Engineering” 2019, Volume 1, Issue 3: 648-671.
doi: 10.3934/mine.2019.3.648
5. “Equilibria for large metabolic systems and the life approach.” McQuade, Sean T., 2018 Annual American
Control Conference (ACC). IEEE, 2018.
6. “Linear-In-Flux-Expresions methodology: Towards a robust mathematical framework for quantitative systems pharmacology simulators”
McQuade ST, Abrams RE, Barrett JS, Piccoli B, Azer K.
Linear-In-Flux-Expressions Methodology: Toward a Robust Mathematical Framework for Quantitative
Systems Pharmacology Simulators. Gene Regul Syst Bio. 2017 Jul 26;11:1177625017711414. doi:
10.1177/1177625017711414. PMID: 29581702; PMCID: PMC5862386.
7. “Equilibria for Large Metabolic Systems and the LIFE Approach”
ST McQuade et. al., Gene Regulation and Systems Biology 2017 vol. 11, pp. 1-15 (2017). ACC Proceedings.
1. “History and future perspectives on the discipline of Quantitative Systems Pharmacology modeling and its applications.”
Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B,
Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. Front Physiol. 2021
Mar 25;12:637999. doi: 10.3389/fphys.2021.637999. PMID: 33841175; PMCID: PMC8027332.
2. “Metabolic graphs, LIFE method and the modeling of drug action on Mycobacterium tuberculosis.”
Sean T. McQuade, Nathaniel J. Merrill, and Benedetto Piccoli. arXiv preprint arXiv:2003.12400 (2020).
Advances in nonlinear biological systems: modeling and optimal control , 2020.
3. “Stability of metabolic networks via Linear-in-Flux-Expressions.”
NJ Merrill, Nathaniel; An, Zheming; ST McQuade, Sean; Garin, Federica; Azer, Karim; E Abrams, Ruth; Piccoli, Benedetto.
ISSN: 1556-181X; DOI: 10.3934/nhm.2019006 Networks Heterogeneous Media , 2019, Vol.14(1), p.101-
130 arXiv:1808.08263 (2018).
4. “Equilibria and control of metabolic networks with enhancers and inhibitors.”
Zheming An, N.J. Merrill, S.T. McQuade, B. Piccoli. “Mathematics in Engineering” 2019, Volume 1, Issue 3: 648-671.
doi: 10.3934/mine.2019.3.648
5. “Equilibria for large metabolic systems and the life approach.” McQuade, Sean T., 2018 Annual American
Control Conference (ACC). IEEE, 2018.
6. “Linear-In-Flux-Expresions methodology: Towards a robust mathematical framework for quantitative systems pharmacology simulators”
McQuade ST, Abrams RE, Barrett JS, Piccoli B, Azer K.
Linear-In-Flux-Expressions Methodology: Toward a Robust Mathematical Framework for Quantitative
Systems Pharmacology Simulators. Gene Regul Syst Bio. 2017 Jul 26;11:1177625017711414. doi:
10.1177/1177625017711414. PMID: 29581702; PMCID: PMC5862386.
7. “Equilibria for Large Metabolic Systems and the LIFE Approach”
ST McQuade et. al., Gene Regulation and Systems Biology 2017 vol. 11, pp. 1-15 (2017). ACC Proceedings.
Opinion Formation models:
1. “Social dynamics models with time-varying influence.”
Sean McQuade, Benedetto Piccoli, and Nastassia Pouradier Duteil.
Mathematical Models and Methods in Applied Sciences 29.04 (2019): 681-716.
ISSN: 0218-2025 , 1793-6314; DOI: 10.1142/S02182025194000
2. “Interaction Network, State Space, and Control in Social Dynamics”
A Aydoğdu et. al. (eds) Active Particles, Volume 1. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser,
Cham (2017)
3. “Opinion dynamics on a general compact Riemannian manifold.”
Aydo, Aylin, Sean McQuade, and
Nastassia Duteil. Networks and Heterogeneous Media 12.3 (2017): 489-523.
ISSN: 1556-181X; DOI: 10.3934/nhm.2017021 Networks Heterogeneous Media , 2017, Vol.12(3), p.489-523
4. “Interaction network, state space, and control in social dynamics.”
Aydoğdu, Aylin, et al.
Active particles. Vol. 1. Advances in theory, models, and applications , 2017 Birkhäuser, Cham, 2017. 99-140.
5. “Enhancing the Utility of Systems Pharmacology Modeling in Pharmaceutical R&D: Lessons from the development of a PCSK9 Inhibitor Model.”
Azer, Karim, et al.
journal OF Pharmacokinetics and Pharmacodynamics. Vol. 42. 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER/PLENUM PUBLISHERS, 2015.
1. “Social dynamics models with time-varying influence.”
Sean McQuade, Benedetto Piccoli, and Nastassia Pouradier Duteil.
Mathematical Models and Methods in Applied Sciences 29.04 (2019): 681-716.
ISSN: 0218-2025 , 1793-6314; DOI: 10.1142/S02182025194000
2. “Interaction Network, State Space, and Control in Social Dynamics”
A Aydoğdu et. al. (eds) Active Particles, Volume 1. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser,
Cham (2017)
3. “Opinion dynamics on a general compact Riemannian manifold.”
Aydo, Aylin, Sean McQuade, and
Nastassia Duteil. Networks and Heterogeneous Media 12.3 (2017): 489-523.
ISSN: 1556-181X; DOI: 10.3934/nhm.2017021 Networks Heterogeneous Media , 2017, Vol.12(3), p.489-523
4. “Interaction network, state space, and control in social dynamics.”
Aydoğdu, Aylin, et al.
Active particles. Vol. 1. Advances in theory, models, and applications , 2017 Birkhäuser, Cham, 2017. 99-140.
5. “Enhancing the Utility of Systems Pharmacology Modeling in Pharmaceutical R&D: Lessons from the development of a PCSK9 Inhibitor Model.”
Azer, Karim, et al.
journal OF Pharmacokinetics and Pharmacodynamics. Vol. 42. 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER/PLENUM PUBLISHERS, 2015.
SEIR models
1. ``Control of COVID-19 outbreak using an extended SEIR model."
Luo, Qi; Weightman, Ryan; McQuade, Sean T; Trélat, Emmanuel; Barbour, William; Work, Dan; Samaranayake, Samitha; Piccoli, Benedetto.
ISSN: 1556-1801 , 1556-181X; DOI: 10.3934/nhm.2022016
Networks and Heterogeneous Media , 2022, Vol.17(3), p.443-4
2. “Control of COVID-19 outbreak using an extended SEIR model.”
McQuade, Sean T., et al.
Mathematical Models and Methods in Applied Sciences (2021): 1-26.
3. McQuade, Sean T; Weightman, Ryan; Merrill, Nathaniel J; Yadav, Aayush; Trélat, Emmanuel; Allred, Sarah R; Piccoli, Benedetto.
ISSN: 0218-2025 , 1793-6314; DOI: 10.1142/S0218202521500512 Mathematical
Models and Methods in Applied Sciences , 2021, Vol.31(12), p.2399-2424
4. “Regional Health System Shortfalls with a Novel COVID-19 Model.”
Allred, Sarah R., et al. Rutgers-Camden research brief (2020).
1. ``Control of COVID-19 outbreak using an extended SEIR model."
Luo, Qi; Weightman, Ryan; McQuade, Sean T; Trélat, Emmanuel; Barbour, William; Work, Dan; Samaranayake, Samitha; Piccoli, Benedetto.
ISSN: 1556-1801 , 1556-181X; DOI: 10.3934/nhm.2022016
Networks and Heterogeneous Media , 2022, Vol.17(3), p.443-4
2. “Control of COVID-19 outbreak using an extended SEIR model.”
McQuade, Sean T., et al.
Mathematical Models and Methods in Applied Sciences (2021): 1-26.
3. McQuade, Sean T; Weightman, Ryan; Merrill, Nathaniel J; Yadav, Aayush; Trélat, Emmanuel; Allred, Sarah R; Piccoli, Benedetto.
ISSN: 0218-2025 , 1793-6314; DOI: 10.1142/S0218202521500512 Mathematical
Models and Methods in Applied Sciences , 2021, Vol.31(12), p.2399-2424
4. “Regional Health System Shortfalls with a Novel COVID-19 Model.”
Allred, Sarah R., et al. Rutgers-Camden research brief (2020).
Car following models for vehicular traffic
1. “Limitations and improvements of the intelligent driver model (idm).”
Albeaik, Saleh, et al.
SIAM Journal on Applied Dynamical Systems 21.3 (2022): 1862-1892.
https://doi.org/10.1137/21M140647
2. “A Rigorous Multi-Population Multi-lane Hybrid Traffic Model and Its Mean-Field Limit for Dissipation of Waves via Autonomous Vehicles.”
K Lee et. al., Kardous, Nicolas, et al. (2021).
https://doi.org/10.1137/21M1406477
3. “Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers.”
Lee, Jonathan W., et al. arXiv preprint arXiv:2104.11267 (2021).
https://doi.org/10.48550/arXiv.2104.11267
4. “Are commercially implemented adaptive cruise control systems string stable?” George Gunter, Derek Gloudemans Raphael E Stern, Sean McQuade, Rahul Bhadani, Matt Bunting, Maria Laura Delle Monache, Roman Lysecky, Benjamin Seibold, Jonathan Sprinkle, Benedetto Piccoli, Daniel B. Work.
arXiv preprint arXiv:1905.02108 (2019).
DOI: 10.1109/TITS.2020.3000682
1. “Limitations and improvements of the intelligent driver model (idm).”
Albeaik, Saleh, et al.
SIAM Journal on Applied Dynamical Systems 21.3 (2022): 1862-1892.
https://doi.org/10.1137/21M140647
2. “A Rigorous Multi-Population Multi-lane Hybrid Traffic Model and Its Mean-Field Limit for Dissipation of Waves via Autonomous Vehicles.”
K Lee et. al., Kardous, Nicolas, et al. (2021).
https://doi.org/10.1137/21M1406477
3. “Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers.”
Lee, Jonathan W., et al. arXiv preprint arXiv:2104.11267 (2021).
https://doi.org/10.48550/arXiv.2104.11267
4. “Are commercially implemented adaptive cruise control systems string stable?” George Gunter, Derek Gloudemans Raphael E Stern, Sean McQuade, Rahul Bhadani, Matt Bunting, Maria Laura Delle Monache, Roman Lysecky, Benjamin Seibold, Jonathan Sprinkle, Benedetto Piccoli, Daniel B. Work.
arXiv preprint arXiv:1905.02108 (2019).
DOI: 10.1109/TITS.2020.3000682