Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes

Bartl, Martin; Kötzing, Martin; Schuster, Stefan; Li, Pu; Kaleta, Christoph

doi:10.1038/ncomms3243

Cited by 27 publications

(35 citation statements)

References 47 publications

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“…The work of Klipp et al [72] spurred the application of optimal control to characterize cellular dynamics related with metabolism [80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96]. Ewald et al [77] have recently reviewed many of these studies, illustrating how dynamic optimization is a powerful approach that can be used to decipher the activation and regulation of metabolism.…”

Section: Optimality In Cellular Systemsmentioning

confidence: 99%

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Using optimal control to understand complex metabolic pathways

Tsiantis

Banga

2020

Preprint

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Background: We revisit the idea of explaining and predicting dynamics in biochemical pathways from first-principles. A promising approach is to exploit optimality principles that can be justified from an evolutionary perspective. In the context of the cell, several previous studies have explained the dynamics of simple metabolic pathways exploiting optimality principles in combination with dynamic models, i.e. using an optimal control framework. For example, 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.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 based on multicriteria optimal control which has been designed with scalability and efficiency in mind, extending several recent methods. This framework includes mechanisms to avoid common pitfalls, such as local optima, unstable solutions or excessive computation time. 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 show how the multicriteria approach allows us to consider general cost/benefit trade-offs that have been likely favored by evolution. 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 networks.

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Section: Optimality In Cellular Systemsmentioning

confidence: 99%

“…[91]), the genomic organization of metabolic pathways (e.g. [85]), the development of effective treatments against pathogens (e.g. [93,97]), and other manifold applications in metabolic engineering and synthetic biology.…”

Section: Optimality In Cellular Systemsmentioning

confidence: 99%

Using optimal control to understand complex metabolic pathways

Tsiantis

Banga

2020

Preprint

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“…Second, discrepancies could be due to constraints or optimality principles that are not yet modeled or understood (such as proteome constraints added in moving from M- to ME-Models). Third, the environmental history of the organism may have coupled seemingly unrelated biological processes [59], or be optimized for fluctuating rather than static environments [60–63]. Finally, it is likely that transcriptional regulation is ‘moderately efficient’ rather than perfectly optimal.…”

Section: Defining and Understanding Regulatory Needsmentioning

confidence: 99%

Computing the functional proteome: recent progress and future prospects for genome-scale models

O’Brien

Palsson

2015

Current Opinion in Biotechnology

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Constraint-based models enable the computation of feasible, optimal, and realized biological phenotypes from reaction network reconstructions and constraints on their operation. To date, stoichiometric reconstructions have largely focused on metabolism, resulting in genome-scale metabolic models (M-Models). Recent expansions in network content to encompass proteome synthesis have resulted in models of metabolism and protein expression (ME-Models). ME-Models advance the predictions possible with constraint-based models from network flux states to the spatially resolved molecular composition of a cell. Specifically, ME-Models enable the prediction of transcriptome and proteome allocation and limitations, and basal expression states and regulatory needs. Continued expansion in reconstruction content and constraints will result in an increasingly refined representation of cellular composition and behavior.

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“…Optimality principles are often used to study and explain biological processes [ 36 , 37 ]. Originating in engineering, dynamic optimization has been used successfully to find optimal regimes in several biological systems including infection processes [ 38 – 42 ], protein assembly [ 43 ], metabolic pathways [ 44 ] and to optimize medical applications such as the treatment of cancer [ 45 ] or diabetes [ 46 ]. In the theoretical description and modelling of host–parasite interactions, Evolutionary Game Theory has turned out to be a very useful tool [ 47 – 51 ].…”

Section: Introductionmentioning

confidence: 99%

Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization

Dühring

Ewald

Germerodt

et al. 2017

J. R. Soc. Interface.

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The release of fungal cells following macrophage phagocytosis, called non-lytic expulsion, is reported for several fungal pathogens. On one hand, non-lytic expulsion may benefit the fungus in escaping the microbicidal environment of the phagosome. On the other hand, the macrophage could profit in terms of avoiding its own lysis and being able to undergo proliferation. To analyse the causes of non-lytic expulsion and the relevance of macrophage proliferation in the macrophage–Candida albicans interaction, we employ Evolutionary Game Theory and dynamic optimization in a sequential manner. We establish a game-theoretical model describing the different strategies of the two players after phagocytosis. Depending on the parameter values, we find four different Nash equilibria and determine the influence of the systems state of the host upon the game. As our Nash equilibria are a direct consequence of the model parameterization, we can depict several biological scenarios. A parameter region, where the host response is robust against the fungal infection, is determined. We further apply dynamic optimization to analyse whether macrophage mitosis is relevant in the host–pathogen interaction of macrophages and C. albicans. For this, we study the population dynamics of the macrophage–C. albicans interactions and the corresponding optimal controls for the macrophages, indicating the best macrophage strategy of switching from proliferation to attacking fungal cells.

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Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes

Cited by 27 publications

References 47 publications

Using optimal control to understand complex metabolic pathways

Using optimal control to understand complex metabolic pathways

Computing the functional proteome: recent progress and future prospects for genome-scale models

Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization

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