Physicochemical models of signaling pathways are characterized by high levels of structural and parametric uncertainty, reflecting both incomplete knowledge about signal transduction and the intrinsic variability of cellular processes. As a result, these models try to predict the dynamics of systems with tens or even hundreds of free parameters. At this level of uncertainty, model analysis should emphasize statistics of systems-level properties, rather than the detailed structure of solutions or boundaries separating different dynamic regimes. Based on the combination of random parameter search and continuation algorithms, we developed a methodology for the statistical analysis of mechanistic signaling models. In applying it to the well-studied MAPK cascade model, we discovered a large region of oscillations and explained their emergence from single-stage bistability. The surprising abundance of strongly nonlinear (oscillatory and bistable) input/output maps revealed by our analysis may be one of the reasons why the MAPK cascade in vivo is embedded in more complex regulatory structures. We argue that this type of analysis should accompany nonlinear multiparameter studies of stationary as well as transient features in network dynamics.

Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by datadependent acquisition (DDA) experiments are required prior to DIA analysis, which is timeconsuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.

An on-plate specific enrichment method is presented for the direct analysis of peptides phosphorylation. An array of sintered TiO 2 nanoparticle spots was prepared on a stainless steel plate to provide porous substrate with a very large specific surface and durable functions. These spots were used to selectively capture phosphorylated peptides from peptide mixtures, and the immobilized phosphopeptides could then be analyzed directly by MALDI MS after washing away the nonphosphorylated peptides. beta-Casein and protein mixtures were employed as model samples to investigate the selection efficiency. In this strategy, the steps of phosphopeptide capture, purification, and subsequent mass spectrometry analysis are all successfully accomplished on a single target plate, which greatly reduces sample loss and simplifies analytical procedures. The low detection limit, small sample size, and rapid selective entrapment show that this on-plate strategy is promising for online enrichment of phosphopeptides, which is essential for the analysis of minute amount of samples in high-throughput proteome research.