From components to regulatory motifs in signalling networks

Abstract

The developments in biochemistry and molecular biology over the past 30 years have produced an impressive parts list of cellular components. It has become increasingly clear that we need to understand how components come together to form systems. One area where this approach has been growing is cell signalling research. Here, instead of focusing on individual or small groups of signalling proteins, researchers are now using a more holistic perspective. This approach attempts to view how many components are working together in concert to process information and to orchestrate cellular phenotypic changes. Additionally, the advancements in experimental techniques to measure and visualize many cellular components at once gradually grow in diversity and accuracy. The multivariate data, produced by experiments, introduce new and exciting challenges for computational biologists, who develop models of cellular systems made up of interacting cellular components. The integration of high-throughput experimental results and information from legacy literature is expected to produce computational models that would rapidly enhance our understanding of the detail workings of mammalian cells. Keywordscell signalling; systems biology; network analysis; biochemical networks; graph theoryThe processes of life require the continual transmission of information. From molecular components within cells to organisms to ecosystems, natural life exists because information is communicated, received and adjustments made in response to the information. The developments in biochemistry and molecular biology over the past 30 years have produced an impressive parts list of cellular components. Although much more needs to be learned about cellular components, it has become increasingly clear that we need to understand how components come together to form systems. One area where this approach has been growing is in cell signalling research. Here, instead of focusing on individual or small groups of signalling proteins, researchers are now altering their focus and turning towards investigating cells from a more holistic perspective [1]. This approach attempts to view how many components are working together in concert to process information and to orchestrate cellular phenotypic changes. The advancements in experimental techniques to measure and visualize many cellular components at once gradually grow in diversity and accuracy. The multivariate data, produced by experiments, introduce new and exciting challenges for computational biologists, who develop models of cellular systems made up of interacting cellular components. At the same time, computational biologists realize the richness of knowledge content embedded in the legacy literature in the fields of biochemistry, molecular biology and cell physiology. The biomedical research literature, from the past three to four decades, describes how individual cellular components interact and exchange information. NIH Public Access Author ManuscriptBrief Funct Genomic Proteomic. Author manuscri...

The GAPs, GEFs, GDIs and…now, GEMs: New kids on the heterotrimeric G protein signaling block

“…Systems biology approaches, particularly mathematical and computational modeling, have emerged as an important toolkit for studying these signaling pathways. 41,42 Because multiple feedforward/feedback cycles modulate GIV-dependent signaling and orchestrate it in separate time and space (Fig. 2), mathematical modeling, constrained by experimental data, is expected to be more reliable.…”

Network integration and graph analysis in mammalian molecular systems biology

“…Besides synaptic vesicles, the molecular organisation of macro-molecular complexes is still mostly unknown. A first step towards assembling this 3D puzzle is identifying protein–protein interactions, their localisation and the functional relationship among the components [5]. …”

“…Alternatively and complementarily, network integration and graph analysis highlighted in this review represent a practical alternative to network inference and dynamical simulations. Some of the challenges within this research domain are: ‘how to project lists of genes or proteins identified in multivariate experiments onto large-scale known intracellular interaction networks?’, ‘how to integrate different networks so they can be used as background knowledge to fill in missing gaps not captured experimentally?’ and ‘how to develop heuristics to overcome the NP-hardness of the graph search problem?’ Effective data integration with filtering, graph querying and visualisation tools are key components for success in this subfield of systems biology [5]. …”

The cognitive phenotype of Down syndrome: Insights from intracellular network analysis

“…94 This approach to constructing and validating models is also referred to as reverse engineering. Kurata and coworkers, 95 D'haeseleer and coworkers, 96 and others distinguish between forward engineering of biochemical networks and reverse engineering of biochemical networks.…”

“…Integration of literature data with high-throughput experiments is the most promising method toward high-quality computational models because it capitalizes on the greatest amount of experimental evidence. 94 …”