A Pareto approach to resolve the conflict between information gain and experimental costs: Multiple-criteria design of carbon labeling experiments.

Katharina Nöh,Sebastian Niedenführ,Wolfgang Wiechert,Martin Beyß,Vassily Hatzimanikatis

doi:10.1371/journal.pcbi.1006533

Katharina Nöh, Sebastian Niedenführ + Show 3 more

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https://doi.org/10.1371/journal.pcbi.1006533

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Abstract

Science revolves around the best way of conducting an experiment to obtain insightful results. Experiments with maximal information content can be found by computational experimental design (ED) strategies that identify optimal conditions under which to perform the experiment. Several criteria have been proposed to measure the information content, each emphasizing different aspects of the design goal, i.e., reduction of uncertainty. Where experiments are complex or expensive, second sight is at the budget governing the achievable amount of information. In this context, the design objectives cost and information gain are often incommensurable, though dependent. By casting the ED task into a multiple-criteria optimization problem, a set of trade-off designs is derived that approximates the Pareto-frontier which is instrumental for exploring preferable designs. In this work, we present a computational methodology for multiple-criteria ED of information-rich experiments that accounts for virtually any set of design criteria. The methodology is implemented for the case of 13C metabolic flux analysis (MFA), which is arguably the most expensive type among the ‘omics’ technologies, featuring dozens of design parameters (tracer composition, analytical platform, measurement selection etc.). Supported by an innovative visualization scheme, we demonstrate with two realistic showcases that the use of multiple criteria reveals deep insights into the conflicting interplay between information carriers and cost factors that are not amendable to single-objective ED. For instance, tandem mass spectrometry turns out as best-in-class with respect to information gain, while it delivers this information quality cheaper than the other, routinely applied analytical technologies. Therewith, our Pareto approach to ED offers the investigator great flexibilities in the conception phase of a study to balance costs and benefits.

Highlights

The successful design of tailor-made cell factories in the biotechnological and pharmaceutical industries needs firm understanding of the cellular functions and their underlying molecular mechanisms [1,2,3]
Trading-off information gain and costs in the design of carbon labeling experiments be showstoppers. In this situation the question arises: How to get the most out of the experiment for your invest in terms of time and money? We approach this question by formulating the design task as a multiple-criteria optimization problem
Its solution produces a set of Pareto-optimal design proposals that feature the trade-off between information gain, as measured by different metrics, and the costs

Summary

Introduction

The successful design of tailor-made cell factories in the biotechnological and pharmaceutical industries needs firm understanding of the cellular functions and their underlying molecular mechanisms [1,2,3]. For quantify the information gain, several optimality criteria (or precision scores) have been suggested, all approximating the average statistical confidence of parameter estimates [12,13]. The information criterion to be used for the ED is decided ad hoc, since the most “suited” one is not known in advance. Favoring a single criterion in the planning phase, may well lead to improvements in that criterion at the expense of a decline of others, taking the risk to under-explore the design space and, eventually, deriving misleading design decisions [14]. To remedy this limitation, several information criteria could be simultaneously taken into account

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