Sliding Mode Reference Coordination of Constrained Feedback Systems

[EN] This paper addresses the problem of coordinating dynamical systems with possibly different dynamics (e.g., linear and nonlinear, different orders, constraints, etc.) to achieve some desired collective behavior under the constraints and capabilities of each system. To this end, we develop a new methodology based on reference conditioning techniques using geometric set invariance and sliding mode control: the sliding mode reference coordination (SMRCoord). The main idea is to coordinate the systems references. Starting from a general framework, we propose two approaches: a local one through direct interactions between the different systems by sharing and conditioning their own references and a global centralized one, where a central node makes decisions using information coming from the systems references. In particular, in this work we focus in implementation on multivariable systems like unmanned aerial vehicles (UAVs) and robustness to external perturbations. To show the applicability of the approach, the problem of coordinating UAVs with input constraints is addressed as a particular case of multivariable reference coordination with both global and local configuration.Research in this area is partially supported by Argentine government (ANPCyT PICT 2011-0888 and CONICET PIP 112-2011-00361), Spanish government (FEDER-CICYT DPI2011-28112-C04-01), and Universitat Politecnica de Valencia (Grant FPI/2009-21)Vignoni, A.; Garelli, F.; Picó, J. (2013). Sliding Mode Reference Coordination of Constrained Feedback Systems. Mathematical Problems in Engineering. 2013:1-11. https://doi.org/10.1155/2013/764348S1112013Information consensus in multivehicle cooperative control. (2007). IEEE Control Systems, 27(2), 71-82. doi:10.1109/mcs.2007.338264Cao, Y., Yu, W., Ren, W., & Chen, G. (2013). An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination. IEEE Transactions on Industrial Informatics, 9(1), 427-438. doi:10.1109/tii.2012.2219061Interconnected dynamic systems: An overview on distributed control. (2013). IEEE Control Systems, 33(1), 76-88. doi:10.1109/mcs.2012.2225929Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and Cooperation in Networked Multi-Agent Systems. Proceedings of the IEEE, 95(1), 215-233. doi:10.1109/jproc.2006.887293He, W., & Cao, J. (2011). Consensus control for high-order multi-agent systems. IET Control Theory & Applications, 5(1), 231. doi:10.1049/iet-cta.2009.0191Liu, L. (2012). Robust cooperative output regulation problem for non-linear multi-agent systems. IET Control Theory & Applications, 6(13), 2142-2148. doi:10.1049/iet-cta.2011.0575Pitarch, J. L., Sala, A., & Arino, C. V. (2014). Closed-Form Estimates of the Domain of Attraction for Nonlinear Systems via Fuzzy-Polynomial Models. IEEE Transactions on Cybernetics, 44(4), 526-538. doi:10.1109/tcyb.2013.2258910Nuñez, S., De Battista, H., Garelli, F., Vignoni, A., & Picó, J. (2013). Second-order sliding mode observer for multiple kinetic rates estimation in bioprocesses. Control Engineering Practice, 21(9), 1259-1265. doi:10.1016/j.conengprac.2013.03.003Wu, L., Su, X., & Shi, P. (2012). Sliding mode control with bounded gain performance of Markovian jump singular time-delay systems. Automatica, 48(8), 1929-1933. doi:10.1016/j.automatica.2012.05.064Cao, Y., Ren, W., & Meng, Z. (2010). Decentralized finite-time sliding mode estimators and their applications in decentralized finite-time formation tracking. Systems & Control Letters, 59(9), 522-529. doi:10.1016/j.sysconle.2010.06.002Cortés, J. (2006). Finite-time convergent gradient flows with applications to network consensus. Automatica, 42(11), 1993-2000. doi:10.1016/j.automatica.2006.06.015Rao, S., & Ghose, D. (2011). 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Reduction of population variability in protein expression: A control engineering approach

[Abstract] Controlling protein expression level is of interest in many applications. Yet, the stochastic nature of gene expression plays an important role and cannot be disregarded. We propose a gene synthetic circuit designed to control the mean gene expression in a population of cells and its variance. The circuit combines an intracellular negative feedback loop and quorum sensing based cell-to-cell communication system. Our in silico analysis using stochastic simulations reveals significant noise attenuation in gene expression through the interplay between quorum sensing and negative feedback, and explain their different roles for different noise sources. Preliminary in vivo results agree well with the computational results.Comisión Interministerial de Ciencia y Tecnología; DPI2014-55276-C5-

Sliding mode reference coordination of constrained feedback systems

This paper addresses the problem of coordinating dynamical systems with possibly different dynamics (e.g., linear and nonlinear, different orders, constraints, etc.) to achieve some desired collective behavior under the constraints and capabilities of each system. To this end, we develop a new methodology based on reference conditioning techniques using geometric set invariance and sliding mode control: the sliding mode reference coordination (SMRCoord). The main idea is to coordinate the systems references. Starting from a general framework, we propose two approaches: a local one through direct interactions between the different systems by sharing and conditioning their own references and a global centralized one, where a central node makes decisions using information coming from the systems references. In particular, in this work we focus in implementation on multivariable systems like unmanned aerial vehicles (UAVs) and robustness to external perturbations. To show the applicability of the approach, the problem of coordinating UAVs with input constraints is addressed as a particular case of multivariable reference coordination with both global and local configuration.Facultad de Ingenierí

Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise

[EN] Stochastic fluctuations in gene expression trigger both beneficial and harmful consequences for cell behavior. Therefore, achieving a desired mean protein expression level while minimizing noise is of interest in many applications, including robust protein production systems in industrial biotechnology. Here, we consider a synthetic gene circuit combining intracellular negative feed- back and cell-to-cell communication based on quorum sensing. Accounting for both intrinsic and extrinsic noise, stochastic simulations allow us to analyze the capability of the circuit to reduce noise strength as a function of its parameters. We obtain mean expression levels and noise strengths for all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in Escherichia coli. Our in silico experiments, validated by preliminary in vivo results, reveal significant noise attenuation in gene expression through the interplay between quorum sensing and negative feedback and highlight the differential role that they play in regard to intrinsic and extrinsic noise.his work was partially supported by the Spanish Government(CICYT DPI2014-55276-C5-1) and the European Union(FEDER). Y.B. thanks grant FPI/2013-3242 of UPV.Boada-Acosta, YF.; Vignoni, A.; Picó, J. (2017). Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise. ACS Synthetic Biology. 6(10):1903-1912. https://doi.org/10.1021/acssynbio.7b00087S1903191261

Multiobjective Identification of a Feedback Synthetic Gene Circuit

© 2020 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Kinetic (i.e., dynamic) semimechanistic models based on the first principles are particularly important in systems and synthetic biology since they can explain and predict the functional behavior that emerges from the time-varying concentrations in cellular components. However, gene circuit models are nonlinear higher order ones and have a large number of parameters. In addition, experimental measurements are often scarce, and enough signal excitability for identification cannot always be achieved. These characteristics render the identification problem ill-posed, so most gene circuit models present incomplete parameter identifiability. Thus, parameter identification of typical biological models still appears as an open problem, where ensemble modeling approaches and multiobjective optimization arise as natural options. We address the problem of identifying the stochastic model of a closed-loop synthetic genetic circuit designed to minimize the gene expression noise. The model results from the feedback interaction between two subsystems. Besides incomplete parameter identifiability, the closed-loop dynamics cannot be directly identified due to the lack of enough input signal excitability. We apply a two-stage approach. First, the open-loop averaged time-course experimental data are used to identify a reduced-order stochastic model of the system direct chain. Then, closed-loop steady-state stochastic distributions are used to identify the remaining parameters in the feedback configuration. In both cases, multiobjective optimization is used to address the parameter identifiability, providing sets of parameters valid for different state-space regions. The methodology gives good identification results, provides clear guidelines on the effect of the parameters under different scenarios, and it is particularly useful for easily combining time-course population averaged and steady-state single-cell distribution experimental data.This work was supported by the European Union and Spanish Government, MINECO/AEI/FEDER under Grant DPI2017-82896-C2-1-R. The work of Y. Boada was supported by the Universitat Politecnica de Valencia under Grant FPI/2013-3242.Boada-Acosta, YF.; Vignoni, A.; Picó, J. (2020). Multiobjective Identification of a Feedback Synthetic Gene Circuit. IEEE Transactions on Control Systems Technology. 28(1):208-223. https://doi.org/10.1109/TCST.2018.2885694S20822328

Specific growth rate estimation in (fed-)batch bioreactors using second-order sliding observers

[EN] This paper addresses the estimation of specific growth rate of microorganisms in bioreactors using sliding observers. In particular, a second-order sliding observer based on biomass concentration measurement is proposed. Differing from other proposals that only guarantee bounded errors, the proposed observer provides a smooth estimate that converges in finite time to the time-varying parameter. Stability is proved using a Lyapunov approach. The observer exhibits also robustness to process uncertainties since no model of the reaction is used for its design. In addition, the off-surface coordinate of the sliding observer is useful to determine the convergence time as well as to identify sensor faults and unexpected behaviors. Because of the structure of the output error injection, chattering phenomena of conventional sliding mode algorithms are substantially reduced. The features of the proposed observer are assessed by numerical and experimental data. (C) 2011 Elsevier Ltd. All rights reserved.This work was supported by the National University of La Plata (Project 11-1127), ANPCyT (PICT2007-00535) and CONICET (PIP112-200801-01052) of Argentina; the Technical University of Valencia (PAID-02-09 program and FPI-2009/21 grant), the CICYT (DPI2005-01180) and AECID (A/024186/09) of Spain: and by FEDER funds of the European Union.De Battista, H.; Picó, J.; Garelli, F.; Vignoni, A. (2011). Specific growth rate estimation in (fed-)batch bioreactors using second-order sliding observers. Journal of Process Control. 21(7):1049-1055. https://doi.org/10.1016/j.jprocont.2011.05.008S1049105521

Extended metabolic biosensor design for dynamic pathway regulation of cell factories

[EN] Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctua-tions. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implement-ing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feed-back controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.This work is partially supported by grant MINECO/AEI and EU DPI2017-82896-C2-1-R. P.C. acknowledges support from the Universitat Politecnica de Valencia Talento Programme.Boada-Acosta, YF.; Vignoni, A.; Picó, J.; Carbonell, P. (2020). Extended metabolic biosensor design for dynamic pathway regulation of cell factories. iScience. 23(7):1-25. https://doi.org/10.1016/j.isci.2020.101305S125237Agrawal, D. K., Dolan, E. M., Hernandez, N. E., Blacklock, K. M., Khare, S. D., & Sontag, E. D. (2020). Mathematical Models of Protease-Based Enzymatic Biosensors. ACS Synthetic Biology, 9(2), 198-208. doi:10.1021/acssynbio.9b00279Arnold, F. H. (2017). Directed Evolution: Bringing New Chemistry to Life. Angewandte Chemie International Edition, 57(16), 4143-4148. doi:10.1002/anie.201708408Boada, Y., Vignoni, A., & Picó, J. (2017). Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise. ACS Synthetic Biology, 6(10), 1903-1912. doi:10.1021/acssynbio.7b00087Boada, Y., Vignoni, A., & Picó, J. (2017). Multi-objective optimization for gene expression noise reduction in a synthetic gene circuit * *This work is partially supported by Spanish government and European Union (FEDER-CICYT DPI2014-55276-C5-1). Y.B. thanks grant FPI/2013-3242 of Universitat Politècnica de València, and also thanks the support from the Ayudas para movilidad dentro del Programa para la Formación de Personal Investigador (FPI) de la UPV para estancias 2016. A.V. thanks the Max Planck Society, the CSBD and the MPI-CBG. The authors are grateful to Prof. Dr. Ivo F. Sbalzarini for hosting Y.B in the MOSAIC Group for a research stay, also to Pietro Incadorna from the MOSAIC Group at CSBD for his help in the parallel algorithm implementation, and to Dr. Gilberto Reynoso-Meza from the PPGEPS at Pontifícia Universidade Católica do Paraná for his always helpful comments regarding the MOOD. IFAC-PapersOnLine, 50(1), 4472-4477. doi:10.1016/j.ifacol.2017.08.376Boada, Y., Vignoni, A., & Pico, J. (2020). Multiobjective Identification of a Feedback Synthetic Gene Circuit. IEEE Transactions on Control Systems Technology, 28(1), 208-223. doi:10.1109/tcst.2018.2885694Briat, C., Gupta, A., & Khammash, M. (2016). Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks. Cell Systems, 2(1), 15-26. doi:10.1016/j.cels.2016.01.004Briat, C., & Khammash, M. (2018). Perfect Adaptation and Optimal Equilibrium Productivity in a Simple Microbial Biofuel Metabolic Pathway Using Dynamic Integral Control. 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Sliding mode reference coordination of constrained feedback systems

This paper addresses the problem of coordinating dynamical systems with possibly different dynamics (e.g., linear and nonlinear, different orders, constraints, etc.) to achieve some desired collective behavior under the constraints and capabilities of each system. To this end, we develop a new methodology based on reference conditioning techniques using geometric set invariance and sliding mode control: the sliding mode reference coordination (SMRCoord). The main idea is to coordinate the systems references. Starting from a general framework, we propose two approaches: a local one through direct interactions between the different systems by sharing and conditioning their own references and a global centralized one, where a central node makes decisions using information coming from the systems references. In particular, in this work we focus in implementation on multivariable systems like unmanned aerial vehicles (UAVs) and robustness to external perturbations. To show the applicability of the approach, the problem of coordinating UAVs with input constraints is addressed as a particular case of multivariable reference coordination with both global and local configuration.Facultad de Ingenierí

Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity

[EN] Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.This research was partially supported by grants MINECO/AEI, EU DPI2017-82896-C21-R 662 and MICINN/AEI, EU PID2020-117271RB-C21. F.N.S.-N. thanks the UPV grant number PAID-01-2017.Santos-Navarro, FN.; Boada-Acosta, YF.; Vignoni, A.; Picó, J. (2021). Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity. Applied Sciences. 11(13):1-17. https://doi.org/10.3390/app11135859S117111

Contractivity of a genetic circuit with internal feedback and cell-to-cell communication

[EN] We consider a realistic model of the synthetic gene circuit combining cell-to-cell communication system via quorum sensing, and a synthetic repressible promoter implementing intracellular negative feedback control. The circuit has been shown to increase robustness with respect to both extrinsic and intrinsic noise elsewhere. As a first step towards an analytic analysis, in this paper we use contraction theory to perform a stability analysis. From it, we infer the components of the circuit most affecting the rate of contractivity, using biologically sensible values of the circuit parameters.This research was partially funded by grant FEDER-CICYT DPI2014-55276-C5-1-R. Yadira Boada thanks grant FPI/2013-3242 of the Universitat Politècnica de València.Picó-Marco, E.; Boada-Acosta, YF.; Picó, J.; Vignoni, A. (2016). Contractivity of a genetic circuit with internal feedback and cell-to-cell communication. IFAC-PapersOnLine. 49(26):213-218. https://doi.org/10.1016/j.ifacol.2016.12.128S213218492