Optimality and identification of dynamic models in systems biology: an inverse optimal control framework

Tsiantis, Nikolaos; Balsa‐Canto, Eva; Banga, Julio R.

doi:10.1093/bioinformatics/bty438

Cited by 11 publications

(15 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, we proposed an alternative based on an inverse optimal control formulation [129] that aims to find the optimality criteria that, given a dynamic model, can explain a set of given dynamic (time series) measurements. In other words, inverse optimal control can be used to systematically infer optimality principles in complex pathways from measurements and a prior dynamic model.…”

Section: Cellular Trade-offs and Multicriteria Optimalitymentioning

confidence: 99%

Using optimal control to understand complex metabolic pathways

Tsiantis

Banga

2020

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Cellular Trade-offs and Multicriteria Optimalitymentioning

confidence: 99%

Using optimal control to understand complex metabolic pathways

Tsiantis

Banga

2020

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…This study-hypothesize-test approach has been the one followed by the vast majority of the works cited above. Recently, we proposed an alternative based on an inverse optimal control formulation [127] that aims to find the optimality criteria that, given a dynamic model, can explain a set of given dynamic (time series) measurements. In other words, inverse optimal control can be used to systematically infer optimality principles in complex pathways from measurements and a prior dynamic model.…”

Section: Cellular Trade-offs and Multicriteria Optimalitymentioning

confidence: 99%

Using optimal control to understand complex metabolic pathways

Tsiantis

Banga

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Typically, we know which states could in principle be measured and we can define a maximum set Z 0 of potential sensor nodes. If the resulting system with the maximum output set Z 0 is invertible, one can start the acquisition of time series data and feed them into one of the algorithms [9][10][11][12][13][14][15]. to infer the input.…”

Section: Sensor Node Placement For Invertibilitymentioning

confidence: 99%

“…Algorithms to estimate the inputs from the outputs of systems described by ordinary differential equations (ODEs) are an ongoing research topic, see e.g. [9][10][11][12][13][14][15]. However, no such algorithm can succeed, if the output doesn't provide sufficient information about the input.…”

Section: Introductionmentioning

confidence: 99%

Structural Invertibility and Optimal Sensor Node Placement for Error and Input Reconstruction in Dynamic Systems

et al. 2019

View full text Add to dashboard Cite

Despite recent progress in our understanding of complex dynamic networks, it remains challenging to devise sufficiently accurate models to observe, control or predict the state of real systems in biology, economics or other fields. A largely overlooked fact is that these systems are typically open and receive unknown inputs from their environment. A further fundamental obstacle are structural model errors caused by insufficient or inaccurate knowledge about the quantitative interactions in the real system.Here, we show that unknown inputs to open systems and model errors can be treated under the common framework of invertibility, which is a requirement for reconstructing these disturbances from output measurements. By exploiting the fact that invertibility can be decided from the influence graph of the system, we analyse the relationship between structural network properties and invertibility under different realistic scenarios. We show that sparsely connected scale free networks are the most difficult to invert. We introduce a new sensor node placement algorithm to select a minimum set of measurement positions in the network required for invertibility. This algorithm facilitates optimal experimental design for the reconstruction of inputs or model errors from output measurements. Our results have both fundamental and practical implications for nonlinear systems analysis, modelling and design.

show abstract

Optimality and identification of dynamic models in systems biology: an inverse optimal control framework

Cited by 11 publications

References 6 publications

Using optimal control to understand complex metabolic pathways

Using optimal control to understand complex metabolic pathways

Using optimal control to understand complex metabolic pathways

Structural Invertibility and Optimal Sensor Node Placement for Error and Input Reconstruction in Dynamic Systems

Contact Info

Product

Resources

About