A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism

Oveis Jamialahmadi, Sameereh Hashemi-Najafabadi, Ehsan Motamedian, Stefano Romeo and Fatemeh Bagheri

PLOS Computational Biology, 2019, vol. 15, issue 4, 1-29

Abstract: Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.Author summary: Several attempts have been made to develop computational approaches to integrate high-throughput omics data with generic models of human metabolism. However, no comprehensive and quantitative platform is available to examine the performance of these methods both functionally and structurally. Here, we collected numerous datasets to benchmark some of the context-specific methods used to study the cancer metabolism in order to provide a platform for future algorithm selection, comparison, or algorithm design. Utilizing the performance comparison results, we took a benchmark-driven approach to develop a context-specific reconstruction algorithm based on the advantageous features of algorithms studied here. The promising performance of our method may provide the opportunity for feature algorithm design studies on cancer metabolism.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006936

DOI: 10.1371/journal.pcbi.1006936

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