Publications 

Current Submissions/Preprints:

1. Elena D'Ambrosio, Zhou Fang, Ankit Gupta and Mustafa Khammash. Filtered finite state projection method for the analysis and estimation of stochastic biochemical reaction networks, 2022. Available on bioArxiv here.

2. Maurice Filo, Ankit Gupta and Mustafa Khammash. Anti-Windup Protection Circuits for Biomolecular Integral Controllers. 2023. 

3. Yuji Hirono, Ankit Gupta and Mustafa Khammash. Complete characterization of robust perfect adaptation in biochemical reaction networks. 2023. 

Published Journal Articles:

1. Zhou Fang, Ankit Gupta and Mustafa Khammash. Advanced methods for gene network identification and noise decomposition from single-cell data. Nature Communications(accepted) 2024.

2. Ankit Gupta and Mustafa Khammash. The Internal Model Principle for biomolecular control theory. IEEE Open Journal of Control Systems. 2023. 

3. Ankit Gupta and Mustafa Khammash. Universal structural requirements for maximal robust perfect adaptation in biomolecular networks. Proceedings of the National Academy of Sciences, 2022 vol. 119, no. 43, p. e2207802119.

4. Zhou Fang, Ankit Gupta and Mustafa Khammash. Convergence of regularized particle filters for stochastic reaction networks. SIAM Journal on Numerical Analysis, 2023, Vol. 61, No. 2.

5. Ankit Gupta and Mustafa Khammash. Frequency spectra and the color of cellular noise. Nature Communications 2022, Vol.13, No. 4305.

6. Zhou Fang, Ankit Gupta and Mustafa Khammash. Stochastic filtering for multiscale stochastic reaction networks based on hybrid approximations. Journal of Computational Physics, 2022, Vol. 467, No. 111441.

7. Jérémy Andréoletti, Antoine Zwaans, Rachel Warnock, Gabriel Aguirre-Fernández, Joëlle Barido-Sottani, Ankit Gupta, Tanja Stadler and Marc Manceau. The Occurrence Birth-Death Process for combined-evidence analysis in macroevolution and epidemiology. Systematic Biology, 2022.

8. Ankit Gupta, Christoph Schwab and Mustafa Khammash. DeepCME: A deep learning framework for computing solution statistics of the chemical master equation. PLOS Computational Biology, 2021, Vol. 17(12).

9. Marc Manceau, Ankit Gupta, Timothy Vaughan and Tanja Stadler. The ancestral population size conditioned on the reconstructed phylogenetic tree with occurrence data. Journal of Theoretical Biology, 2021, Vol. 509.

10. Daniele Cappelletti, Ankit Gupta and Mustafa Khammash. A hidden integral structure endows Absolute Concentration Robust systems with resilience to dynamical concentration disturbances. Journal of Royal Society Interface 2020, 17:20200437.

11. Ankit Gupta, Marc Manceau, Timothy Vaughan, Mustafa Khammash and Tanja Stadler. The probability distribution of the reconstructed phylogenetic tree with occurrence data. Journal of Theoretical Biology, Vol. 488, 2020. Post-​print (PDF, 525 KB) made available under the CC-​BY-NC-ND 4.0 licence. 

12. Stephanie Aoki*, Gabriele Lillacci*, Ankit Gupta*, Armin Baumschlager, David Schweingruber and Mustafa Khammash. A universal  biomolecular integral feedback controller for robust perfect adaptation. Nature, 2019  (*co-first authors).

13. Patrik Dürrenberger, Ankit Gupta and Mustafa Khammash. A finite state projection method for steady-state sensitivity analysis of stochastic reaction networks. The Journal of Chemical Physics, 2019, Vol. 150(13).  

14. Ankit Gupta and Mustafa Khammash. Sensitivity analysis for multiscale stochastic reaction networks using hybrid approximations. Bulletin of Mathematical Biology. Special Issue: Gillespie and His Algorithms, 2018.

15. Corentin Briat*, Ankit Gupta* and Mustafa Khammash. Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks. Journal of the Royal Society Interface, 2018, Vol. 15 (143) (*co-first authors).

16. Ankit Gupta and Mustafa Khammash. Computational identification of irreducible state-spaces for stochastic reaction networks. SIAM Journal on Applied Dynamical Systems, 2018, Vol. 17 (2).

17. Ankit Gupta, Muruhan Rathinam and Mustafa Khammash. Estimation of parameter sensitivities for stochastic reaction networks using tau-leap simulations. SIAM Journal on Numerical Analysis, 2018, Vol. 56 (2).

18.  Ankit Gupta, Jan Mikelson and Mustafa Khammash. A finite state projection algorithm for the stationary solution of the chemical master equation. The Journal of Chemical Physics, 2017, Vol. 147(15).

19. Ankit Gupta, Andreas Milias-Argeitis and Mustafa Khammash. Dynamic disorder in simple enzymatic reactions induces stochastic amplification of substrate. Journal of the Royal Society Interface, 2017, Vol. 14(132). 

20. Ankit Gupta*, Benjamin Hepp* and Mustafa Khammash. Noise Induces the Population-Level Entrainment of Incoherent, Uncoupled Intracellular Oscillators. Cell Systems, 2016, Vol. 3(6) (*co-first authors).

21. Corentin Briat*, Ankit Gupta* and Mustafa Khammash. Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks. Cell Systems, 2016, Vol. 2(1) (*co-first authors).

22. Benjamin Hepp, Ankit Gupta and Mustafa Khammash. Adaptive Hybrid Simulations for Multiscale Stochastic Reaction Networks. The Journal of Chemical Physics, 2015, Vol. 142(3).

23. Ankit Gupta and Mustafa Khammash. An efficient and unbiased method for sensitivity analysis of stochastic reaction networks. Journal of the Royal Society Interface, 2014, Vol. 11. 

24. Ankit Gupta and Mustafa Khammash. Sensitivity analysis for stochastic chemical reaction networks with multiple time-scales. Electronic Journal of Probability, 2014, Vol. 19(59).

25. Ankit Gupta*, Corentin Briat* and Mustafa Khammash. A scalable computational framework for establishing long-term behavior of stochastic reaction networks. PLOS Computational Biology, 2014, Vol. 10(6) (*co-first authors).

26. Ankit Gupta, Viet Chi Tran and J.A.J Metz. A new proof for the convergence of an individual based model to the Trait substitution sequence. Acta Applicandae Mathematicae, 2014, Vol. 131. 

27. Ankit Gupta and Mustafa Khammash. Unbiased estimation of parameter sensitivities for stochastic chemical reaction networks. SIAM : Journal on Scientific Computing, 2013 Vol. 35. 

28. Ankit Gupta. The Fleming-Viot limit of an interacting spatial population with fast density regulation. Electronic Journal of Probability, 2012 Vol.17.

29. Ankit Gupta. Stochastic model for cell polarity. Annals of Applied Probability, 2012 Vol.22.

30. Ankit Gupta and B. Sury. Decimal expansion of 1/p and subgroup sums, Integers, 2005 Vol 5(1), A19.

Conference Contributions:

1. Ankit Gupta and Mustafa Khammash. Padé SSA: A frequency domain method for estimating the dynamics of stochastic reaction networks. IEEE Conference on Decision and Control (CDC), Cancun, Mexico, 2022.

2. Ankit Gupta and Mustafa Khammash. Analytical computation of the power spectral density for unimolecular stochastic reaction networks. IFAC World Congress, Berlin, Germany, 2020.

3. Zhou Fang, Ankit Gupta and Mustafa Khammash. Stochastic Filters Based on Hybrid Approximations of Multiscale Stochastic Reaction Networks. IEEE Conference on Decision and Control (CDC), Jeju Island, South Korea, 2020.

4. Ankit Gupta, Mustafa Khammash and Guido Sanguinetti. Bayesian Parameter Estimation for Stochastic Reaction Networks from Steady-State Observation. 17th International Conference, Computational Methods in Systems Biology (CMSB), Trieste, Italy, 2019.

5. Ankit Gupta and Mustafa Khammash. An antithetic integral rein controller for bio-​molecular networks. IEEE Conference on Decision and Control (CDC), 2019.

6. Daniele Cappelletti, Ankit Gupta and Mustafa Khammash. A Linear Constrained Integral Feedback for a Class of Reaction Systems with Absolute Concentration Robustness. IEEE Conference on Decision and Control (CDC), 2019.

7. Ankit Gupta, Patrik Dürrenberger and Mustafa Khammash. Quantification of loading effects in interconnections of stochastic reaction networks. Proceedings of the 18th European Control Conference (ECC), 2019.

8. Ankit Gupta, Corentin Briat and Mustafa Khammash. Variance reduction in stochastic gene expression under integral feedback control. IEEE Conference on Decision and Control (CDC), 2018.

9. Ankit Gupta and Mustafa Khammash. Finding the steady-state solution of the chemical master equation. IEEE Conference on Control Technology and Applications, Hawaii, USA, 2017.

10. Ankit Gupta and Mustafa Khammash. Determining the long-term behavior of cell populations: A new procedure for detecting ergodicity in large stochastic reaction networks. 19th IFAC World Congress, Cape Town, South Africa, 2014.

11. Corentin Briat, Ankit Gupta, Iman Shames and Mustafa Khammash. Scalable tests for ergodicity analysis of large-scale interconnected stochastic reaction networks.  21st International Symposium on Mathematical Theory of Networks and Systems, Groningen, The Netherlands, 2014.

Ph.D. Thesis:

Stochastic models for cell polarity, University of Wisconsin, Madison, 2010