scholarly journals Using optimal control to understand complex metabolic pathways

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Nikolaos Tsiantis ◽  
Julio R. Banga
Keyword(s):  
Optimal Control ◽  
Metabolic Pathways ◽  
Regulatory Networks ◽  
Complex Dynamics ◽  
Cost Benefit ◽  
Optimality Criteria ◽  
Enzyme Activation ◽  
Computational Framework ◽  
Trade Offs ◽  
Optimality Principles

Abstract 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.

scholarly journals Using optimal control to understand complex metabolic pathways

2020 ◽  
Author(s):  
Nikolaos Tsiantis ◽  
Julio R. Banga
Keyword(s):  
Optimal Control ◽  
Metabolic Pathways ◽  
Dynamic Models ◽  
Computation Time ◽  
Enzyme Activation ◽  
Computational Framework ◽  
Local Optima ◽  
Biochemical Pathways ◽  
Trade Offs ◽  
Optimality Principles

AbstractBackgroundWe 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.ResultsHere 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.ConclusionsWe 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.

Deciphering the regulation of metabolism with dynamic optimization: an overview of recent advances

2017 ◽  
Vol 45 (4) ◽  
pp. 1035-1043 ◽  
Author(s):  
Jan Ewald ◽  
Martin Bartl ◽  
Christoph Kaleta
Keyword(s):  
Dynamic Optimization ◽  
Metabolic Pathways ◽  
Regulatory Networks ◽  
Optimization Problems ◽  
Treatment Strategies ◽  
Whole Cell ◽  
Recent Advances ◽  
Regulatory Strategies ◽  
Regulation Of Metabolism ◽  
Optimality Principles

Understanding optimality principles shaping the evolution of regulatory networks controlling metabolism is crucial for deriving a holistic picture of how metabolism is integrated into key cellular processes such as growth, adaptation and pathogenicity. While in the past the focus of research in pathway regulation was mainly based on stationary states, more recently dynamic optimization has proved to be an ideal tool to decipher regulatory strategies for metabolic pathways in response to environmental cues. In this short review, we summarize recent advances in the elucidation of optimal regulatory strategies and identification of optimal control points in metabolic pathways. We discuss biological implications of the discovered optimality principles on genome organization and provide examples how the derived knowledge can be used to identify new treatment strategies against pathogens. Furthermore, we briefly discuss the variety of approaches for solving dynamic optimization problems and emphasize whole-cell resource allocation models as an important emerging area of research that will allow us to study the regulation of metabolism on the whole-cell level.

A CAD Package for High-Speed Cam Design Based on Direct Multiple Shooting Optimal Control Techniques

2004 ◽  
Author(s):  
Sebastian Mennicke ◽  
Richard W. Longman ◽  
Meng-Sang Chew ◽  
Hans Georg Bock
Keyword(s):  
Optimal Control ◽  
High Speed ◽  
Optimality Criteria ◽  
Iterative Refinement ◽  
Valve Train ◽  
Design Stage ◽  
Multiple Shooting ◽  
Cam Design ◽  
Trade Offs ◽  
Highly Nonlinear

High-speed automotive valve train design requires realistic models of the valve train. However, this frequently results in highly nonlinear systems with discontinuities and constraints. Optimality criteria and trade-offs for the designs are frequently performed through a process of simulation and iterative refinement. This paper presents CamOE, a cam design optimization package based on direct multiple shooting optimal control theory, incorporating structured sequential quadratic programming. The code allows the designer to incorporate the constraints of importance and to consider and synthesize appropriate optimality criteria. This allows him or her to synthesize the cam profile at the design stage without resorting to a tedious trial-and-error design process. This paper presents CamOE as a software environment that permits rapid feedback to the designer through the process of numerical experiments in specifying criteria and constraints on the automotive valve train.

scholarly journals Fundamental resource trade-offs for encoded distributed optimization

2020 ◽  
Author(s):  
A Salman Avestimehr ◽  
Seyed Mohammadreza Mousavi Kalan ◽  
Mahdi Soltanolkotabi
Keyword(s):  
Computational Time ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Computational Framework ◽  
Data Set ◽  
Trade Offs ◽  
Major Bottleneck ◽  
Computing Environments ◽  
Analyze Data

Abstract Dealing with the shear size and complexity of today’s massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern distributed computing environments is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. A recent computational framework, called encoded optimization, creates redundancy in the data to mitigate the effect of stragglers. In this paper, we develop novel mathematical understanding for this framework demonstrating its effectiveness in much broader settings than was previously understood. We also analyze the convergence behavior of iterative encoded optimization algorithms, allowing us to characterize fundamental trade-offs between convergence rate, size of data set, accuracy, computational load (or data redundancy) and straggler toleration in this framework.

Experimental field exclosure of birds and bats in agricultural systems — Methodological insights, potential improvements, and cost-benefit trade-offs

2019 ◽  
Vol 35 ◽  
pp. 1-12 ◽  
Author(s):  
Bea Maas ◽  
Sacha Heath ◽  
Ingo Grass ◽  
Camila Cassano ◽  
Alice Classen ◽  
...  
Keyword(s):  
Cost Benefit ◽  
Agricultural Systems ◽  
Experimental Field ◽  
Trade Offs

Sustainable Distribution Design: Contrasting Disposable, Recyclable, and Reusable Strategies for Packaging Materials Using a Total Cost Analysis With an Illustration of Milk Distribution

2010 ◽  
Author(s):  
Sri Satya Kanaka Nagendra Jayanty ◽  
William J. Sawaya ◽  
Michael D. Johnson
Keyword(s):  
Cost Benefit ◽  
Packaging Materials ◽  
Policy Makers ◽  
Specific Product ◽  
Cost Structures ◽  
Trade Offs ◽  
Technological Advances ◽  
The Cost ◽  
Total Cost Analysis ◽  
Plastic Containers

Engineers, policy makers, and managers have shown increasing interest in increasing the sustainability of products over their complete lifecycles and also from the ‘cradle to grave’ or from production to the disposal of each specific product. However, a significant amount of material is disposed of in landfills rather than being reused in some form. A sizeable proportion of the products being dumped in landfills consist of packaging materials for consumable products. Technological advances in plastics, packaging, cleaning, logistics, and new environmental awareness and understanding may have altered the cost structures surrounding the lifecycle use and disposal costs of many materials and products resulting in different cost-benefit trade-offs. An explicit and well-informed economic analysis of reusing certain containers might change current practices and results in significantly less waste disposal in landfills and in less consumption of resources for manufacturing packaging materials. This work presents a method for calculating the costs associated with a complete process of implementing a system to reuse plastic containers for food products. Specifically, the different relative costs of using a container and then either disposing of it in a landfill, recycling the material, or reconditioning the container for reuse and then reusing it are compared explicitly. Specific numbers and values are calculated for the case of plastic milk bottles to demonstrate the complicated interactions and the feasibility of such a strategy.

scholarly journals Ensuring Compliance From 35,000 Feet: Accountability and Trade-Offs in Aviation Safety Regulatory Networks

2016 ◽  
Vol 50 (10) ◽  
pp. 1478-1507 ◽  
Author(s):  
Russell W. Mills ◽  
Christopher J. Koliba ◽  
Dorit Rubinstein Reiss
Keyword(s):  
Regulatory Networks ◽  
Federal Aviation Administration ◽  
The United States ◽  
Network Governance ◽  
Regulatory Regime ◽  
Democratic Accountability ◽  
Public Administrators ◽  
Trade Offs ◽  
Process Oriented ◽  
Air Carriers

A puzzle that faces public administrators within regulatory networks is how to balance the need for public or democratic accountability with increasing demands from interest groups and elected officials to utilize the expertise of the private sector in developing process-oriented programs that ensure compliance. This article builds upon the network governance accountability framework developed by Koliba, Mills, and Zia to explore the dominant accountability frames and the accountability trade-offs that shape the process-oriented regulatory regime used by the Federal Aviation Administration (FAA) to oversee and regulate air carriers in the United States.

scholarly journals Computational modeling of anthocyanin pathway evolution

2019 ◽  
Author(s):  
Lucas C. Wheeler ◽  
Stacey D. Smith
Keyword(s):  
Metabolic Pathways ◽  
Flower Color ◽  
Complex Phenotype ◽  
Pathway Evolution ◽  
Anthocyanin Pathway ◽  
Trade Offs ◽  
Wide Range ◽  
Pathway Function ◽  
Simple Kinetic Model ◽  
New Mutations

AbstractAlteration of metabolic pathways is a key component of the evolution of new phenotypes. Flower color is a striking example of the importance of metabolic evolution in a complex phenotype, wherein shifts in the activity of the underlying pathway lead to a wide range of pigments. Although experimental work has identified common classes of mutations responsible for transitions among colors, we lack a unifying model that relates pathway function and activity to the evolution of distinct pigment phenotypes. One challenge in creating such a model is the branching structure of pigment pathways, which may lead to evolutionary trade-offs due to competition for shared substrates. In order to predict the effects of shifts in enzyme function and activity on pigment production, we created a simple kinetic model of a major plant pigmentaion pathway: the anthocyanin pathway. This model describes the production of the three classes of blue, purple and red anthocyanin pigments, and accordingly, includes multiple branches and substrate competition. We first studied the general behavior of this model using a realistic, functional set of parameters. We then stochastically evolved the pathway toward a defined optimum and and analyzed the patterns of fixed mutations. This approach allowed us to quantify the probability density of trajectories through pathway state space and identify the types and number of changes. Finally, we examine whether the observed trajectories and constraints help to explain experimental observations, i.e., the predominance of mutations which change color by altering the function of branching genes in the pathway. These analyses provide a theoretical framework which can be used to predict the consequences of new mutations in terms of both pigment phenotypes and pleiotropic effects.

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