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

Maruthi, Lakshmeesh R. M.; Tkachev, Ilya; Carta, Alfonso; Cinquemani, Eugenio; Hersen, Pascal; Batt, Grégory; Abate, Alessandro

doi:10.1007/978-3-319-12982-2_12

Cited by 2 publications

(2 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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“…Motivating example As motivation, consider a population of yeasts under osmotic stress [22]. The stress level of the population can be studied through a protein which can be marked (by a chemical reagent).…”

Section: Introductionmentioning

confidence: 99%

Distribution-based objectives for Markov Decision Processes

Akshay

Genest

Vyas

2018

Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science

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We consider distribution-based objectives for Markov Decision Processes (MDP). This class of objectives gives rise to an interesting trade-off between full and partial information. As in full observation, the strategy in the MDP can depend on the state of the system, but similar to partial information, the strategy needs to account for all the states at the same time.In this paper, we focus on two safety problems that arise naturally in this context, namely, existential and universal safety. Given an MDP A and a closed and convex polytope H of probability distributions over the states of A, the existential safety problem asks whether there exists some distribution ∆ in H and a strategy of A, such that starting from ∆ and repeatedly applying this strategy keeps the distribution forever in H . The universal safety problem asks whether for all distributions in H , there exists such a strategy of A which keeps the distribution forever in H . We prove that both problems are decidable, with tight complexity bounds: we show that existential safety is PTIME-complete, while universal safety is co-NP-complete.Further, we compare these results with existential and universal safety problems for Rabin's probabilistic finite-state automata (PFA), the subclass of Partially Observable MDPs which have zero observation. Compared to MDPs, strategies of PFAs are not statedependent. In sharp contrast to the PTIME result, we show that existential safety for PFAs is undecidable, with H having closed and open boundaries. On the other hand, it turns out that the universal safety for PFAs is decidable in EXPTIME, with a co-NP lower bound. Finally, we show that an alternate representation of the input polytope allows us to improve the complexity of universal safety for MDPs and PFAs.

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

Cited by 2 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>

Distribution-based objectives for Markov Decision Processes

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