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Background: Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation., Methods: We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer., Results: We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing., Conclusions: Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.

The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.

Mass spectrometry coupled to liquid chromatography (LC-MS and LC-MS/MS) is commonly used to analyze the protein content of biological samples in large scale studies, enabling quantitation and identification of proteins and peptides using a wide range of experimental protocols, algorithms, and statistical models to analyze the data. Currently it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists for peptide identification algorithms but data that represents a ground truth for the evaluation of LC-MS data is limited. Hence there have been attempts to simulate such data in a controlled fashion to evaluate and compare algorithms. We present MSSimulator, a simulation software for LC-MS and LC-MS/MS experiments. Starting from a list of proteins from a FASTA file, the simulation will perform in-silico digestion, retention time prediction, ionization filtering, and raw signal simulation (including MS/MS), while providing many options to change the properties of the resulting data like elution profile shape, resolution and sampling rate. Several protocols for SILAC, iTRAQ or MS(E) are available, in addition to the usual label-free approach, making MSSimulator the most comprehensive simulator for LC-MS and LC-MS/MS data.