Abstract
Recent works have demonstrated the experimental feasibility of real-time gene expression control based on deterministic controllers. By taking control of the level of intracellular proteins, one can probe single-cell dynamics with unprecedented flexibility. However, single-cell dynamics are stochastic in nature, and a control framework explicitly accounting for this variability is presently lacking. Here we devise a stochastic control framework, based on Model Predictive Control, which fills this gap. Based on a stochastic modelling of the gene response dynamics, our approach combines a full state-feedback receding-horizon controller with a real-time estimation method that compensates for unobserved state variables. Using previously developed models of osmostress-inducible gene expression in yeast, we show in silico that our stochastic control approach outperforms deterministic control design in the regulation of single cells. The present new contribution leads to envision the application of the proposed framework to wetlab experiments on yeast.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bemporad, A., Morari, M.: Control of systems integrating logic, dynamics, and constraints. Automatica 35(3), 407–427 (1999)
Carta, A., Cinquemani, E.: State estimation for gene networks with intrinsic and extrinsic noise: a case study on E.coli arabinose uptake dynamics. In: European Control Conference, ECC 2013, Zurich, Suisse, pp. 3658–3663 (2013)
Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proc. of the 23rd International Conference on Machine Learning, pp. 161–168. ACM (2006)
Ernst, D., Geurts, P., Wehenkel, L.: Tree-based batch mode reinforcement learning. Journal of Machine Learning Research, 503–556 (2005)
Espinoza, M., Suykens, J.A.K., De Moor, B.: Fixed-size least squares support vector machines: A large scale application in electrical load forecasting. Computational Management Science 3(2), 113–129 (2006)
Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics 22(4), 403–434 (1976)
Gillespie, D.T.: The chemical Langevin equation. Journal of Chemical Physics 113(1), 297–306 (2000)
Gonzalez, A.M., Uhlendorf, J., Cinquemani, E., Batt, G., Ferrari-Trecate, G.: Identification of biological models from single-cell data: A comparison between mixed-effects and moment-based inference. In: European Control Conference, ECC 2013, pp. 3652–3657 (2013)
Haesaert, S., Babuska, R., Abate, A.: Sampling-based approximations with quantitative performance for the probabilistic reach-avoid problem over general Markov processes. arXiv preprint, arXiv:1409.0553 (2014)
Kallenberg, O.: Foundations of modern probability. Probability and its Applications. Springer, New York (2002)
Menolascina, F., Fiore, G., Orabona, E., De Stefano, L., Ferry, M., Hasty, J., di Bernardo, M., di Bernardo, D.: In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS Computational Biology 10(5), e1003625 (2014)
Milias-Argeitis, A., Summers, S., Stewart-Ornstein, J., Zuleta, I., Pincus, D., El-Samad, H., Khammash, M., Lygeros, J.: In silico feedback for in vivo regulation of a gene expression circuit. Nature Biotechnology 29, 1114–1116 (2011)
Muzzey, D., Gómez-Uribe, C.A., Mettetal, J.T., van Oudenaarden, A.: A systems-level analysis of perfect adaptation in yeast osmoregulation. Cell 138(1), 160–171 (2009)
Olson, E.J., Hartsough, L.L., Landry, B.P., Shroff, R., Tabor, J.J.: Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals. Nature Methods 11, 449–455 (2014)
Pelckmans, K., Suykens, J.A.K., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De Moor, B., Vandewalle, J.: LS-SVMlab: a matlab/c toolbox for least squares support vector machines. Tutorial, Leuven, Belgium (2002)
Toettcher, J.E., Gong, D., Lim, W.A., Weiner, O.D.: Light-based feedback for controlling intracellular signaling dynamics. Nature Methods 8, 837–839 (2011)
Uhlendorf, J., Bottani, S., Fages, F., Hersen, P., Batt, G.: Towards real-time control of gene expression: controlling the HOG signaling cascade. In: 16th Pacific Symposium of Biocomputing, pp. 338–349 (2011)
Uhlendorf, J., Miermont, A., Delaveau, T., Charvin, G., Fages, F., Bottani, S., Batt, G., Hersen, P.: Long-term model predictive control of gene expression at the population and single-cell levels. PNAS 109(35), 14271–14276 (2012)
Wan, E.A., Van Der Merwe, R.: The unscented kalman filter for nonlinear estimation. In: Adaptive Systems for Signal Processing, Communications, and Control Symposium, AS-SPCC 2000, pp. 153–158. IEEE (2000)
Yang, X., Payne-Tobin Jost, A., Weiner, O.D., Tang, C.: A light-inducible organelle-targeting system for dynamically activating and inactivating signaling in budding yeast. Molecular Biology of the Cell 24(15), 2419–2430 (2013)
Zechner, C., Ruess, J., Krenn, P., Pelet, S., Peter, M., Lygeros, J., Koeppl, H.: Moment-based inference predicts bimodality in transient gene expression. PNAS 109(21), 8340–8345 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Maruthi, L.R.M. et al. (2014). Towards Real-Time Control of Gene Expression at the Single Cell Level: A Stochastic Control Approach. In: Mendes, P., Dada, J.O., Smallbone, K. (eds) Computational Methods in Systems Biology. CMSB 2014. Lecture Notes in Computer Science(), vol 8859. Springer, Cham. https://doi.org/10.1007/978-3-319-12982-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-12982-2_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12981-5
Online ISBN: 978-3-319-12982-2
eBook Packages: Computer ScienceComputer Science (R0)