Control-theoretic methods for biological networks

Blanchini, Franco; EI-Samad, Hana; Giordano, Giulia; Sontag, Eduardo D.

doi:10.1109/cdc.2018.8618943

Cited by 13 publications

(9 citation statements)

References 98 publications

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“…Feedback is also a pillar in control theory, and in this context it is important to remember that the early concepts of feedback control were inspired by the study of regulation in biosystems [2]. Not surprisingly, a number of researchers have suggested that molecular systems biology can greatly benefit from the powerful methods of modern systems and control theory [1,[19][20][21][22][23][24]. Similar arguments have been made for synthetic biology [25][26][27][28][29].…”

Section: Dynamicsmentioning

confidence: 99%

“…Or, which are the most important constraints in terms of impact on the optimal cost? The terms f i are the right hand sides of the kinetics of the different reactions i = 1.. 22 . In this simplified model of the central carbon metabolism, several consecutive reactions have been merged for the sake of simplicity.…”

Section: Central Metabolism Of Saccharomyces Cerevisiae During Diauximentioning

confidence: 99%

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Using optimal control to understand complex metabolic pathways

Tsiantis

Banga

2020

BMC Bioinformatics

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Background Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria. Results Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments. Conclusions We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.

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Section: Dynamicsmentioning

confidence: 99%

Section: Central Metabolism Of Saccharomyces Cerevisiae During Diauximentioning

confidence: 99%

Using optimal control to understand complex metabolic pathways

Tsiantis

Banga

2020

BMC Bioinformatics

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“…Various process analytical technologies (PATs) and control methodologies have been proposed to guide biomanufacturing process decision making and variation control; see the review in Steinwandter et al (2019); Yoo et al (2021a). The classical feed-forward and feedback control strategies are often derived based on deterministic first-principle models (Hong et al, 2018;Kee et al, 2009;Blanchini et al, 2018). They typically overlook bioprocess stochastic uncertainty and model uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control

Zheng¹,

Xie²,

Wang³

et al. 2022

Preprint

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Driven by the key challenges of cell therapy manufacturing, including high complexity, high uncertainty, and very limited process observations, we propose a hybrid model-based reinforcement learning (RL) to efficiently guide process control. We first create a probabilistic knowledge graph (KG) hybrid model characterizing the risk-and science-based understanding of biomanufacturing process mechanisms and quantifying inherent stochasticity, e.g., batch-to-batch variation. It can capture the key features, including nonlinear reactions, nonstationary dynamics, and partially observed state. This hybrid model can leverage existing mechanistic models and facilitate learning from heterogeneous process data. A computational sampling approach is used to generate posterior samples quantifying model uncertainty. Then, we introduce hybrid model-based Bayesian RL, accounting for both inherent stochasticity and model uncertainty, to guide optimal, robust, and interpretable dynamic decision making. Cell therapy manufacturing examples are used to empirically demonstrate that the proposed framework can outperform the classical deterministic mechanistic model assisted process optimization.

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“…In the last few years control theory tools have been extensively used in biology, to both understand natural biological systems or design new ones to perform specific tasks (Blanchini et al 2018;Qian et al 2018). Within a cell, in fact, multiple regulatory mechanisms coexist (Alon 2007;Freeman 2014) and among these, transcriptional regulation involving genes and transcription factors plays a crucial role.…”

Section: Introductionmentioning

confidence: 99%

On convergence for hybrid models of gene regulatory networks under polytopic uncertainties: a Lyapunov approach

Pasquini

Angeli

2021

J. Math. Biol.

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Hybrid models of genetic regulatory networks allow for a simpler analysis with respect to fully detailed quantitative models, still maintaining the main dynamical features of interest. In this paper we consider a piecewise affine model of a genetic regulatory network, in which the parameters describing the production function are affected by polytopic uncertainties. In the first part of the paper, after recalling how the problem of finding a Lyapunov function is solved in the nominal case, we present the considered polytopic uncertain system and then, after describing how to deal with sliding mode solutions, we prove a result of existence of a parameter dependent Lyapunov function subject to the solution of a feasibility linear matrix inequalities problem. In the second part of the paper, based on the previously described Lyapunov function, we are able to determine a set of domains where the system is guaranteed to converge, with the exception of a zero measure set of times, independently from the uncertainty realization. Finally a three nodes network example shows the validity of the results.

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Control-theoretic methods for biological networks

Cited by 13 publications

References 98 publications

Using optimal control to understand complex metabolic pathways

Using optimal control to understand complex metabolic pathways

Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control

On convergence for hybrid models of gene regulatory networks under polytopic uncertainties: a Lyapunov approach

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