Abstract
In cellular scales, precise regulation of protein expression is challenging due to time delays and stochastic noises impairing the practical implementation of the existing controllers. To cope with such regulation issues in gene regulatory networks (GRNs), this paper designs a novel decentralized proportional–integral controller with an implementable structure that achieves robust perfect adaptation (RPA) in the presence of stochastic noises and time delays. In this regard, we first define a new augmented state space that incorporates the integral dynamics of the controller. Next, we exploit an appropriate Lyapunov–Krasovskii functional that enables us to quantify sufficient conditions for noise-to-state stability of the time-delayed GRNs in the form of linear matrix inequalities (LMIs). Then, we provide the proportional and integral gains of the decentralized controller using a modification of the variables along with a special structure of the derived LMIs. Finally, the proposed decentralized controller’s applicability and effectiveness are investigated using a synthetic network (known as Repressilator), and a GRN derived from a practical-based simulator. Due to its simple configuration, this feedback control strategy does not only provide RPA in cellular homeostasis but it can be universally implemented in any cellular environment.
Similar content being viewed by others
References
Khammash M, Di Bernardo M, Di Bernardo D (2019) Cybergenetics: theory and methods for genetic control system. In: 2019 IEEE 58th conference on decision and control (CDC). IEEE, pp 916–926
Chen S, Harrigan P, Heineike B, Stewart-Ornstein J, El-Samad H (2013) Building robust functionality in synthetic circuits using engineered feedback regulation. Curr Opin Biotechnol 24(4):790–796
Del Vecchio D, Dy AJ, Qian Y (2016) Control theory meets synthetic biology. J R Soc Interface 13(120):20160380
Milias-Argeitis A, Summers S, Stewart-Ornstein J, Zuleta I, Pincus D, El-Samad H, Khammash M, Lygeros J (2011) In silico feedback for in vivo regulation of a gene expression circuit. Nat Biotechnol 29(12):1114–1116
Milias-Argeitis A, Rullan M, Aoki SK, Buchmann P, Khammash M (2016) Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat Commun 7(1):1–11
Uhlendorf J, Miermont A, Delaveau T, Charvin G, Fages F, Bottani S, Batt G, Hersen P (2012) Long-term model predictive control of gene expression at the population and single-cell levels. Proc Natl Acad Sci 109(35):14271–14276
Menolascina F, Fiore G, Orabona E, De Stefano L, Ferry M, Hasty J, di Bernardo M, di Bernardo D (2014) In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS Comput Biol 10(5):e1003625
Perrino G, Wilson C, Santorelli M, di Bernardo D (2019) Quantitative characterization of \(\alpha \)-synuclein aggregation in living cells through automated microfluidics feedback control. Cell Rep 27(3):916–927
Saravanan S, Syed Ali M, Rajchakit G, Hammachukiattikul B, Priya B, Thakur GK (2021) Finite-time stability analysis of switched genetic regulatory networks with time-varying delays via Wirtinger’s integral inequality. Complexity 2021:1–21
Li L, Yang Y (2015) On sampled-data control for stabilization of genetic regulatory networks with leakage delays. Neurocomputing 149:1225–1231
Lu L, Xing Z, He B (2016) Non-uniform sampled-data control for stochastic passivity and passification of Markov jump genetic regulatory networks with time-varying delays. Neurocomputing 171:434–443
Yu T, Zhao T, Liu J, Zeng Q (2020) Dynamic output feedback control of discrete-time switched GRNS with time-varying delays. J Frankl Inst 357(2):1043–1069
Pandiselvi S, Raja R, Cao J, Rajchakit G (2019) Stabilization of switched stochastic genetic regulatory networks with leakage and impulsive effects. Neural Process Lett 49(2):593–610
Pandiselvi S, Raja R, Cao J, Li X, Rajchakit G (2019) Impulsive discrete-time GRNS with probabilistic time delays, distributed and leakage delays: an asymptotic stability issue. IMA J Math Control Inf 36(1):79–100
Pandiselvi S, Raja R, Zhu Q, Rajchakit G (2018) A state estimation \({H_{\infty }}\) issue for discrete-time stochastic impulsive genetic regulatory networks in the presence of leakage, multiple delays and Markovian jumping parameters. J Frankl Inst 355(5):2735–2761
Pandiselvi S, Ramachandran R, Cao J, Rajchakit G, Seadawy AR, Alsaedi A (2018) An advanced delay-dependent approach of impulsive genetic regulatory networks besides the distributed delays, parameter uncertainties and time-varying delays. Nonlinear Anal Modell Control 23(6):803–829
Pandiselvi S, Raja R, Cao J, Rajchakit G, Ahmad B (2018) Approximation of state variables for discrete-time stochastic genetic regulatory networks with leakage, distributed, and probabilistic measurement delays: a robust stability problem. Adv Differ Equ 2018(1):1–27
Pan W, Wang Z, Gao H, Li Y, Du M (2010) Robust \({H_{\infty }}\) feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise. Int J Robust Nonlinear Control 20(18):2093–2107
He Y, Zeng J, Wu M, Zhang C-K (2012) Robust stabilization and \({H_{\infty }}\) controllers design for stochastic genetic regulatory networks with time-varying delays and structured uncertainties. Math Biosci 236(1):53–63
Mathiyalagan K, Sakthivel R (2012) Robust stabilization and \({H_{\infty }}\) control for discrete-time stochastic genetic regulatory networks with time delays. Can J Phys 90(10):939–953
Jiao H, Zhang L, Shen Q, Zhu J, Shi P (2018) Robust gene circuit control design for time-delayed genetic regulatory networks without sum regulatory logic. IEEE/ACM Trans Comput Biol Bioinf 15(6):2086–2093
Shafikhani I, Karimi HS, Mohammadian M, Ramezani A, Momeni HR (2021) A recursive delay estimation algorithm for linear multivariable systems with time-varying delays. arXiv preprint arXiv:2109.02767
Foo M, Kim J, Bates DG (2018) Modelling and control of gene regulatory networks for perturbation mitigation. IEEE/ACM Trans Comput Biol Bioinf 16(2):583–595
Imani M, Braga-Neto UM (2018) Control of gene regulatory networks using Bayesian inverse reinforcement learning. IEEE/ACM Trans Comput Biol Bioinf 16(4):1250–1261
Wan X, Wang Z, Han Q-L, Wu M (2019) A recursive approach to quantized \({H_{\infty }}\) state estimation for genetic regulatory networks under stochastic communication protocols. IEEE Trans Neural Netw Learn Syst 30(9):2840–2852
Song X, Wang M, Song S, Ahn CK (2019) Sampled-data state estimation of reaction diffusion genetic regulatory networks via space-dividing approaches. IEEE/ACM Trans Comput Biol Bioinform 18(2):718–730
Dunlop MJ, Keasling JD, Mukhopadhyay A (2010) A model for improving microbial biofuel production using a synthetic feedback loop. Syst Synth Biol 4(2):95–104
Stapleton JA, Endo K, Fujita Y, Hayashi K, Takinoue M, Saito H, Inoue T (2012) Feedback control of protein expression in mammalian cells by tunable synthetic translational inhibition. ACS Synth Biol 1(3):83–88
Åström KJ, Hägglund T (1995) PID controllers: theory, design, and tuning. In: Instrument society of America Research Triangle Park, NC, vol 2
Briat C, Gupta A, Khammash M (2016) Antithetic integral feedback ensures robust perfect adaptation in noisy biomolecular networks. Cell Syst 2(1):15–26
Aoki SK, Lillacci G, Gupta A, Baumschlager A, Schweingruber D, Khammash M (2019) A universal biomolecular integral feedback controller for robust perfect adaptation. Nature 570(7762):533–537
Åström KJ, Murray RM (2021) Feedback systems: an introduction for scientists and engineers. Princeton University Press, Princeton
Briat C, Gupta A, Khammash M (2018) Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks. J R Soc Interface 15(143):20180079
Filo M, Kumar S, Khammash M (2022) A hierarchy of biomolecular proportional–integral-derivative feedback controllers for robust perfect adaptation and dynamic performance. Nat Commun 13(1):1–19
Frei T, Chang C-H, Filo M, Arampatzis A, Khammash M (2022) A genetic mammalian proportional–integral feedback control circuit for robust and precise gene regulation. Proc Natl Acad Sci 119(00):e2122132119
Xiao M, Zheng WX, Jiang G (2018) Bifurcation and oscillatory dynamics of delayed cyclic gene networks including small RNAS. IEEE Trans Cybern 49(3):883–896
Bakule L (2008) Decentralized control: an overview. Annu Rev Control 32(1):87–98
Siljak DD (2011) Decentralized control of complex systems. Courier Corporation
Šiljak DD, Zečević A (2005) Control of large-scale systems: beyond decentralized feedback. Annu Rev Control 29(2):169–179
Mukaidani H (2004) An LMI approach to decentralized guaranteed cost control for a class of uncertain nonlinear large-scale delay systems. J Math Anal Appl 300(1):17–29
Del Vecchio D, Abdallah H, Qian Y, Collins JJ (2017) A blueprint for a synthetic genetic feedback controller to reprogram cell fate. Cell Syst 4(1):109–120
Mohammadian M (2019) Decentralized controller design for stochastic gene regulatory networks. J Electr Comput Eng Innov (JECEI) 7(2):213–220
Zhang X, Zhang Z, Wang Y, Liu C (2020) Guaranteed cost control of genetic regulatory networks with multiple time-varying discrete delays and multiple constant distributed delays. IEEE Access 8:80175–80182
Swain PS, Elowitz MB, Siggia ED (2002) Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci 99(20):12795–12800
To T-L, Maheshri N (2010) Noise can induce bimodality in positive transcriptional feedback loops without bistability. Science 327(5969):1142–1145
Perez-Carrasco R, Guerrero P, Briscoe J, Page KM (2016) Intrinsic noise profoundly alters the dynamics and steady state of morphogen-controlled bistable genetic switches. PLoS Comput Biol 12(10):e1005154
Briat C, Khammash M (2020) In-silico proportional–integral moment control of stochastic gene expression. IEEE Trans Autom Control 66(7):3007–3019
Deng H, Krstic M, Williams RJ (2001) Stabilization of stochastic nonlinear systems driven by noise of unknown covariance. IEEE Trans Autom Control 46(8):1237–1253
Xu G, Bao H, Cao J (2020) Mean-square exponential input-to-state stability of stochastic gene regulatory networks with multiple time delays. Neural Process Lett 51(1):271–286
Li C, Chen L, Aihara K (2006) Stability of genetic networks with sum regulatory logic: Lur’e system and LMI approach. IEEE Trans Circuits Syst I Regul Pap 53(11):2451–2458
Xiao S, Wang X, Zhang X, Zhu J-W, Yang X (2021) State estimator design for genetic regulatory networks with leakage and discrete heterogeneous delays: a nonlinear model transformation approach. Neurocomputing 446:86–94
Mohammadian M, Momeni HR, Karimi HS, Shafikhani I, Tahmasebi M (2015) An LPV based robust peak-to-peak state estimation for genetic regulatory networks with time varying delay. Neurocomputing 160:261–273
Mohammadian M, Momeni HR, Zahiri J, Karimi HS (2020) Switched adaptive observer for structure identification in gene regulatory networks. In: 2020 28th Iranian conference on electrical engineering (ICEE). IEEE, pp 1–5
Li J, Dong H, Liu H, Han F (2021) Sampled-data non-fragile state estimation for delayed genetic regulatory networks under stochastically switching sampling periods. Neurocomputing 463:168–176
Yao L, Zhang W, Xie X-J (2020) Stability analysis of random nonlinear systems with time-varying delay and its application. Automatica 117:108994
Boyd SP, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge
Zhang F, Zhang Q (2006) Eigenvalue inequalities for matrix product. IEEE Trans Autom Control 51(9):1506–1509
Sadeghzadeh A (2018) Gain-scheduled continuous-time control using polytope-bounded inexact scheduling parameters. Int J Robust Nonlinear Control 28(17):5557–5574
Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403(6767):339–342
Kaern M, Elston TC, Blake WJ, Collins JJ (2005) Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet 6(6):451–464
Becskei A, Serrano L (2000) Engineering stability in gene networks by autoregulation. Nature 405(6786):590–593
Schaffter T, Marbach D, Floreano D (2011) Genenetweaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27(16):2263–2270
Marbach D, Schaffter T, Mattiussi C, Floreano D (2009) Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J Comput Biol 16(2):229–239
Oksendal B (2013) Stochastic differential equations: an introduction with applications. Springer, New York
Fridman E (2014) Introduction to time-delay systems: analysis and control. Springer, New York
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix
Proof of Relationship (17)
Based on Ito’s formula [64], the infinitesimal generator of the proposed Lyapunov function equals to:
By defining \( {{\xi }^{T}}\left( t \right) =\left[ \begin{matrix} {{{\bar{x}}}^{T}}\left( t \right) &{} \quad {{{\bar{x}}}^{T}}\left( t-\sigma \left( t \right) \right) \\ \end{matrix} \right] \) and \({{\eta }^{T}}\left( t \right) =\left[ \begin{matrix} {{{\bar{y}}}^{T}}\left( t \right) &{} \quad {{{\bar{y}}}^{T}}\left( t-\tau \left( t \right) \right) \\ \end{matrix} \right] \), and using Ito’s isometry [64] and the Jensen inequality [65], the following inequalities can be obtained:
and
Now, we can write:
By considering \(\begin{matrix} {{{{\bar{y}}}}^{T}}\left( t-\tau \left( t \right) \right) &{} \quad {{{{\bar{f}}}}^{T}}\left( {{{{\bar{y}}}}^{T}}\left( t-\tau \left( t \right) \right) \right) \\ \end{matrix} ]^{T},\) we have:
where
By using Schur’s lemma, pre and post multiplying with
and defining \(U={{P}^{-1}}\), \(V={{Q}^{-1}}\), \(X={{{\bar{K}}}_{x}}{{P}^{-1}}\), \(Y={{{\bar{K}}}_{y}}{{Q}^{-1}}\), \(\Gamma ={{\Lambda }^{-1}}\), \({{R}_{1}}={{\varepsilon }_{{{R}_{1}}}}P\), \({{R}_{2}}={{\varepsilon }_{{{R}_{2}}}}Q\), \({{N}_{1}}={{P}^{-1}}{{{\tilde{N}}}_{1}}{{P}^{-1}}\), \({{N}_{2}}={{P}^{-1}}{{{\tilde{N}}}_{2}}{{P}^{-1}}\), \({{M}_{1}}={{Q}^{-1}}{{{\tilde{M}}}_{1}}{{Q}^{-1}}\), \({{M}_{2}}={{Q}^{-1}}{{{\tilde{M}}}_{2}}{{Q}^{-1}}\), \({{Z}_{1}}={{\varepsilon }_{{{Z}_{1}}}}P\) and \({{Z}_{2}}={{\varepsilon }_{{{Z}_{2}}}}Q\), the LMIs presented in (13) can be deduced.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mohammadian, M., Sufi Karimi, H. Decentralized PI Controller Design for Robust Perfect Adaptation in Noisy Time-Delayed Genetic Regulatory Networks. Neural Process Lett 55, 6815–6842 (2023). https://doi.org/10.1007/s11063-023-11162-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-023-11162-y