Proteomic and Computational Methods in Systems Modeling of Cellular Signaling

Abstract

Cellular signaling lies at the core of cellular behavior, and is central for the understanding of many pathologic conditions. To comprehend how signal transduction is orchestrated at the molecular level remains the ultimate challenge for cell biology. In the last years there has been a revolution in the development of high-throughput methodologies in proteomics and genomics, which have provided extensive knowledge about expression profiles and molecular interaction-networks. However, these methods have typically provided qualitative and static information. This is about to turn, and several high-throughput methods are now available that provide quantitative and temporal information. These data are well suited for analysis by computational methods and bioinformatics, which are becoming increasingly valuable tools to grasp the complexity of cellular networks. At present, several cellular pathways have been modeled in silico and the analysis provides new understanding of the underlying properties that contribute to their dynamic features. Here, we review methodologies that are used for in silico modeling as well as methods to obtain large-scale quantitative data, and discuss how they can be integrated to generate powerful and predictive models of cellular processes. We argue that the generation of such models provide powerful tools to understand how systems properties emerges in healthy and pathologic states, and to generate efficient strategies for pharmacological intervention.

Proteomics and Host–Pathogen Interactions

“…10,30,114 Moreover, it has been proved that the results of this type of integrated approach has a concrete impact on the discovery of the causes of infectious diseases, as well as on improving the diagnosis, vaccine development, and rational drug design. 115e117 Despite a theoretical aspect, 118 the pathogeno-proteomics concept brought new insights into important aspects of cell signaling 119 and molecular medicine. 120,121 As an example, proteomics and bioinformatics tools enable the formulation of relevant biological hypothesis on why part of the fungal population is killed while a significantly high percentage survives in C. albicansemacrophage interactions, 122 leading to addition of a specific database for studying C. albicansehost interactions.…”

Animal Models in Biomedical Research

“…There are numerous examples of this in the literature today including the widespread use of RNA/cDNA microarrays, high-throughput sequencing, metabolomics and high-throughput drug screening Carrico et al, 2013;O'Brien et al, 2012;Rodriguez and Gutierrez-de-Teran, 2013;Henson et al, 2012;Tian et al, 2012;Garcia-Reyero and Perkins, 2011;Koyuturk, 2010;Laird, 2010;Yen et al, 2009;Dalby, 2007;Kleppe et al, 2006;Zhang and Zhang, 2006;Hennig, 2004;Capecchi et al, 2004;Kim, 2002;Varfolomeev et al, 2002;Gerlai, 2002). The prediction was that the integration of computation into biology would transform or revolutionize the biological field.…”

Cellular and Immunological Regulation of Viral Myocarditis

“…In fact, viruses activate signal-transduction networks through multiple independent events, which include viral docking to receptors, viral protein synthesis, viral progeny release and virusinduced inflammatory responses (Tam, 2006). Thus, to properly understand how signaltransduction networks are disrupted by viruses, a global multivariate approach is required (Ideker et al, 2001;Kleppe et al, 2006). One of the major challenges of defining a signal-transduction network model in the context of a disease is to study a holistic picture of molecular structures, coming together to make complex and dynamic networks.…”