Abstract
Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction based on generic genome scale models like ReconX or HMR has the potential to become a diagnostic and treatment tool tailored to the analysis of specific individuals. The respective computational algorithms require a high level of predictive power, robustness and sensitivity. Although multiple context specific reconstruction algorithms were published in the last 10 years, only a fraction of them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding. This review describes and analyses common validation methods used for testing model building algorithms. Two major methods can be distinguished: consistency testing and comparison based testing. The first is concerned with robustness against noise, e.g., missing data due to the impossibility to distinguish between the signal and the background of non-specific binding of probes in a microarray experiment, and whether distinct sets of input expressed genes corresponding to i.e., different tissues yield distinct models. The latter covers methods comparing sets of functionalities, comparison with existing networks or additional databases. We test those methods on several available algorithms and deduce properties of these algorithms that can be compared with future developments. The set of tests performed, can therefore serve as a benchmarking procedure for future algorithms.
Highlights
Metabolic network reconstructions become ever more complicated and complete with reconstructions like Recon2 (Thiele et al, 2013) or HMR (Mardinoglu et al, 2014) containing more than 7000 reactions
There are several approaches for validation which can essentially be split into two different categories: Consistency testing and Comparison based testing
Comparison based testing is commonly employed to show the advantages of the presented algorithm compared over previous algorithms or to show the quality of the reconstructed network based on additional, formerly unknown, data. While the former has been employed for the validation of some algorithms (Wang et al, 2012; Vlassis et al, 2014; Robaina Estévez and Nikoloski, 2015), and becomes more important with an increasing number of available methods, it has recently been used to compare multiple methods systematically (Machado and Herrgård, 2014; Robaina Estévez and Nikoloski, 2014)
Summary
Metabolic network reconstructions become ever more complicated and complete with reconstructions like Recon (Thiele et al, 2013) or HMR (Mardinoglu et al, 2014) containing more than 7000 reactions While these reconstructions are a great tool for the analysis of the potential capabilities of an organism, one challenge faced by many researchers is that different cell types in multicellular organisms exhibit diverse functionality and the global generic network is too flexible. It is possible that small differences in the input dataset can lead to vastly different networks, or even very diverse datasets yield the same models The latter is true if e.g., a biomass function is set as objective function, since it will lead to the inclusion of a multitude of reactions, which might not be necessary if a specific tissue is supplied with some metabolites by other tissues.
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