State estimation for gene networks with intrinsic and extrinsic noise: A case study on E.coli arabinose uptake dynamics

Carta, Alfonso; Cinquemani, Eugenio

doi:10.23919/ecc.2013.6669467

Cited by 3 publications

(5 citation statements)

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“…The variation of parameters and initial states between cells also affects control performance. Carta et al and Maruthi et al introduced an algorithm to deal with intrinsic and extrinsic noise of known form [ 32 , 33 ]. They assumed an intrinsic noise that could be expressed in the form of a chemical Langevin approximation and developed a model-based control method of single-cell dynamics of yeast response to osmotic stress.…”

Section: Discussionmentioning

confidence: 99%

Model-based control of the temporal patterns of intracellular signaling <i>in silico</i>

et al. 2017

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The functions of intracellular signal transduction systems are determined by the temporal behavior of intracellular molecules and their interactions. Of the many dynamical properties of the system, the relationship between the dynamics of upstream molecules and downstream molecules is particularly important. A useful tool in understanding this relationship is a methodology to control the dynamics of intracellular molecules with an extracellular stimulus. However, this is a difficult task because the relationship between the levels of upstream molecules and those of downstream molecules is often not only stochastic, but also time-inhomogeneous, nonlinear, and not one-to-one. In this paper, we present an easy-to-implement model-based control method that makes the target downstream molecule to trace a desired time course by changing the concentration of a controllable upstream molecule. Our method uses predictions from Monte Carlo simulations of the model to decide the strength of the stimulus, while using a particle-based approach to make inferences regarding unobservable states. We applied our method to in silico control problems of insulin-dependent AKT pathway model and EGF-dependent Akt pathway model with system noise. We show that our method can robustly control the dynamics of the intracellular molecules against unknown system noise of various strengths, even in the absence of complete knowledge of the true model of the target system.

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Section: Discussionmentioning

confidence: 99%

Model-based control of the temporal patterns of intracellular signaling <i>in silico</i>

et al. 2017

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“…We start from the continuous-time stochastic Markov model of the CME, which is expressed in terms of discrete-valued state variables x. One possible approach for estimating state x from measurements y k is particle filtering [2]. In particle filtering, N hypothetical evolutions of the system state are randomly simulated up to the next measurement.…”

Section: Partial Information Case: Estimation Of System Statesmentioning

confidence: 99%

“…N > 1000) simulations of the system, which makes it poorly suited for online applications. In [2], we have proposed an alternative approach, Unscented Kalman Filtering (UKF) [16], based on a continuous-valued approximation of the CME model known as the Chemical Langevin Equation (CLE) [7]. In the current context, this approach is partly inappropriate, since the promoter state variables are inherently discrete (they take values 0 or 1 only).…”

Section: Partial Information Case: Estimation Of System Statesmentioning

confidence: 99%

“…, M , the W j are independent Wiener processes and v c •j is the subvector of v j corresponding to x c . The relevance of the Langevin approximation to mRNA and protein dynamics has been discussed in [7] and, for filtering applications, it has been assessed on a different but relevant system in [2]. Note that, while x d remains discrete-valued, x c may now take continuous values.…”

Section: Partial Information Case: Estimation Of System Statesmentioning

confidence: 99%

“…Empirical evaluation (not reported here) has led us to select N = 50, a value above which no significant improvement of filtering performance has been observed. The implementation of the UKF procedure is analogous to that of [2] and is omitted for brevity. We just note that, at every step k and for every particle i, UKF requires the numerical solution of 2n c + 1 ODEs over the time span [t k−1 , t k ), with n c = 2 being the number of continuous states.…”

Section: Partial Information Case: Estimation Of System Statesmentioning

confidence: 99%

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Towards Real-Time Control of Gene Expression at the Single Cell Level: A Stochastic Control Approach

Maruthi

Tkachev

Carta

et al. 2014

Computational Methods in Systems Biology

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International audienceRecent 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

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Stochastic Models for Studying the Role of Cellular Noise and Heterogeneity

Hortsch

Kremling

2021

Systems Medicine

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State estimation for gene networks with intrinsic and extrinsic noise: A case study on E.coli arabinose uptake dynamics

Cited by 3 publications

References 20 publications

Model-based control of the temporal patterns of intracellular signaling <i>in silico</i>

Model-based control of the temporal patterns of intracellular signaling <i>in silico</i>

Towards Real-Time Control of Gene Expression at the Single Cell Level: A Stochastic Control Approach

Stochastic Models for Studying the Role of Cellular Noise and Heterogeneity

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