Cellular signal transduction is a complex process involving protein-protein interactions (PPIs) that transmit information. For example, signals from the plasma membrane may be transduced to transcription factors to regulate gene expression. To obtain a global view of cellular signaling and to predict potential signal modulators, we searched for protein interaction partners of more than 450 signaling-related proteins by means of automated yeast two-hybrid interaction mating. The resulting PPI network connected 1126 proteins through 2626 PPIs. After expansion of this interaction map with publicly available PPI data, we generated a directed network resembling the signal transduction flow between proteins with a naïve Bayesian classifier. We exploited information on the shortest PPI paths from membrane receptors to transcription factors to predict input and output relationships between interacting proteins. Integration of directed PPI with time-resolved protein phosphorylation data revealed network structures that dynamically conveyed information from the activated epidermal growth factor and extracellular signal-regulated kinase (EGF/ERK) signaling cascade to directly associated proteins and more distant proteins in the network. From the model network, we predicted 18 previously unknown modulators of EGF/ERK signaling, which we validated in mammalian cell-based assays. This generic experimental and computational approach provides a framework for elucidating causal connections between signaling proteins and facilitates the identification of proteins that modulate the flow of information in signaling networks.

BackgroundThe cellular proteome and metabolome are underlying dynamic regulation allowing rapid adaptation to changes in the environment. System-wide analysis of these dynamics will provide novel insights into mechanisms of stress adaptation for higher photosynthetic organisms. We applied pulsed-SILAC labeling to a photosynthetic organism for the first time and we established a method to study proteome dynamics in the green alga Chlamydomonas reinhardtii, an emerging model system for plant biology. In addition, we combined the analysis of protein synthesis with metabolic profiling to study the dynamic changes of metabolism and proteome turnover under salt stress conditions.ResultsTo study de novo protein synthesis an arginine auxotroph Chlamydomonas strain was cultivated in presence of stable isotope-labeled arginine for 24 hours. From the time course experiment in 3 salt concentrations we could identify more than 2500 proteins and their H/L ratio in at least one experimental condition; for 998 protiens at least 3 ratio counts were detected in the 24 h time point (0 mM NaCl). After fractionation we could identify 3115 proteins and for 1765 of them we determined their de novo synthesis rate. Consistently with previous findings we showed that RuBisCO is among the most prominent proteins in the cell; and similar abundance and turnover for the small and large RuBisCO subunit could be calculated. The D1 protein was identified among proteins with a high synthesis rates. A global median half-life of 45 h was calculated for Chlamydomonas proteins under the chosen conditions.ConclusionTo investigate the temporal co-regulation of the proteome and metabolome, we applied salt stress to Chlamydomonas and studied the time dependent regulation of protein expression and changes in the metabolome. The main metabolic response to salt stress was observed within the amino acid metabolism. In particular, proline was up-regulated manifold and according to that an increased carbon flow within the proline biosynthetic pathway could be measured. In parallel the analysis of abundance and de novo synthesis of the corresponding enzymes revealed that metabolic rearrangements precede adjustments of protein abundance.

In this paper some of the general concepts underpinning the computer modelling of metabolic systems are introduced. The difference between kinetic and structural modelling is emphasized, and the more important techniques from both, along with the physiological implications, are described. These approaches are then illustrated by descriptions of other work, in which they have been applied to models of the Calvin cycle, sucrose metabolism in sugar cane, and starch metabolism in potatoes.

Cellular systems are networks of interacting components that change with time in response to external and internal events. Studying the dynamic behavior of these networks is the basis for an understanding of cellular functions and disease mechanisms. Quantitative time-series data leading to meaningful models can improve our knowledge of human physiology in health and disease, and aid the search for earlier diagnoses, better therapies and a healthier life. The advent of systems biology is about to take the leap into clinical research and medical applications. This review emphasizes the importance of a dynamic view and understanding of cell function. We discuss the potential for computer-aided mathematical modeling of biological systems in medical research with examples from some of the major therapeutic areas: cancer, cardiovascular, diabetic and neurodegenerative medicine.