Concept: Systems biology
Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.
The aim is to characterize subgroups or phenotypes of rheumatoid arthritis (RA) patients using a systems biology approach. The discovery of subtypes of rheumatoid arthritis patients is an essential research area for the improvement of response to therapy and the development of personalized medicine strategies.
Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction.
BACKGROUND: A standard graphical notation is essential to facilitate exchange of network representations of biologicalprocesses. Towards this end, the Systems Biology Graphical Notation (SBGN) has been proposed, and it isalready supported by a number of tools. However, support for SBGN in Cytoscape, one of the most widelyused platforms in biology to visualise and analyse networks, is limited, and in particular it is not possible toimport SBGN diagrams. RESULTS: We have developed CySBGN, a Cytoscape plug-in that extends the use of Cytoscape visualisation and analysisfeatures to SBGN maps. CySBGN adds support for Cytoscape users to visualize any of the threecomplementary SBGN languages: Process Description, Entity Relationship, and Activity Flow. Theinteroperability with other tools (CySBML plug-in and Systems Biology Format Converter) was alsoestablished allowing an automated generation of SBGN diagrams based on previously imported SBMLmodels. The plug-in was tested using a suite of 53 different test cases that covers almost all possible entities,shapes, and connections. A rendering comparison with other tools that support SBGN was performed. Toillustrate the interoperability with other Cytoscape functionalities, we present two analysis examples, shortestpath calculation, and motif identification in a metabolic network. CONCLUSIONS: CySBGN imports, modifies and analyzes SBGN diagrams in Cytoscape, and thus allows the application of thelarge palette of tools and plug-ins in this platform to networks and pathways in SBGN format.
Novel techniques for high-throughput steady-state metabolomic profiling yield information about changes of nearly thousands of metabolites. Such metabolomic profiles, when analyzed together with transcriptional profiles, can reveal novel insights about underlying biological processes. While a number of conceptual approaches have been developed for data integration, easily accessible tools for integrated analysis of mammalian steady-state metabolomic and transcriptional data are lacking. Here we present GAM (‘genes and metabolites’): a web-service for integrated network analysis of transcriptional and steady-state metabolomic data focused on identification of the most changing metabolic subnetworks between two conditions of interest. In the web-service, we have pre-assembled metabolic networks for humans, mice, Arabidopsis and yeast and adapted exact solvers for an optimal subgraph search to work in the context of these metabolic networks. The output is the most regulated metabolic subnetwork of size controlled by false discovery rate parameters. The subnetworks are then visualized online and also can be downloaded in Cytoscape format for subsequent processing. The web-service is available at:https://artyomovlab.wustl.edu/shiny/gam/.
Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem.
Spaceflight affects numerous organ systems in the body, leading to metabolic dysfunction that may have long-term consequences. Microgravity-induced alterations in liver metabolism, particularly with respect to lipids, remain largely unexplored. Here we utilize a novel systems biology approach, combining metabolomics and transcriptomics with advanced Raman microscopy, to investigate altered hepatic lipid metabolism in mice following short duration spaceflight. Mice flown aboard Space Transportation System -135, the last Shuttle mission, lose weight but redistribute lipids, particularly to the liver. Intriguingly, spaceflight mice lose retinol from lipid droplets. Both mRNA and metabolite changes suggest the retinol loss is linked to activation of PPARα-mediated pathways and potentially to hepatic stellate cell activation, both of which may be coincident with increased bile acids and early signs of liver injury. Although the 13-day flight duration is too short for frank fibrosis to develop, the retinol loss plus changes in markers of extracellular matrix remodeling raise the concern that longer duration exposure to the space environment may result in progressive liver damage, increasing the risk for nonalcoholic fatty liver disease.
- The European respiratory journal : official journal of the European Society for Clinical Respiratory Physiology
- Published almost 7 years ago
Inflammatory lung diseases are highly complex in respect of pathogenesis and relationships between inflammation, clinical disease and response to treatment. Sophisticated large-scale analytical methods to quantify gene expression (transcriptomics), proteins (proteomics), lipids (lipidomics) and metabolites (metabolomics) in the lungs, blood and urine are now available to identify biomarkers that define disease in terms of combined clinical, physiological and patho-biological abnormalities. The aspiration is that these approaches will improve diagnosis, i.e., define pathological phenotypes, and facilitate the monitoring of disease and therapy and, also, unravel underlying molecular pathways. Biomarker studies can either select pre-defined biomarker(s) measured by specific methods or apply an “unbiased” approach involving detection platforms that are indiscriminate in focus. This article reviews the technologies presently available to study biomarkers of lung disease within the ‘omics field. The contributions of the individual 'omics analytical platforms to the field of respiratory diseases are summarised, with the goal of providing background on their respective abilities to contribute to systems medicine-based studies of lung disease.
Peptide-based proteomic data sets are ever increasing in size and complexity. These data sets provide computational challenges when attempting to quickly analyze spectra and obtain correct protein identifications. Database search and de novo algorithms must consider high-resolution MS/MS spectra and alternative fragmentation methods. Protein inference is a tricky problem when analyzing large data sets of degenerate peptide identifications. Combining multiple algorithms for improved peptide identification puts significant strain on computational systems when investigating large data sets. This review highlights some of the recent developments in peptide and protein identification algorithms for analyzing shotgun mass spectrometry data when encountering the aforementioned hurdles. Also explored are the roles that analytical pipelines, public spectral libraries, and cloud computing play in the evolution of peptide-based proteomics.
Omics approaches have proven their value to provide a broad monitoring of biological systems. However, as no single analytical technique is sufficient to reveal the full biochemical content of complex biological matrices or biofluids, the fusion of information from several data sources has become a decisive issue. Omics studies generate an increasing amount of massive data obtained from different analytical devices. These data are usually high dimensional and extracting knowledge from these multiple blocks is challenging. Appropriate tools are therefore needed to handle these datasets suitably. For that purpose, a generic methodology is proposed by combining the strengths of established data analysis strategies, i.e. multiple kernel learning and OPLS-DA to offer an efficient tool for the fusion of Omics data obtained from multiple sources. Three real case studies are proposed to assess the potential of the method. A first example illustrates the fusion of mass spectrometry-based metabolomic data acquired in both negative and positive electrospray ionisation modes, from leaf samples of the model plant Arabidopsis thaliana. A second dataset involves the classification of wine grape varieties based on polyphenolic extracts analysed by two-dimensional heteronuclear magnetic resonance spectroscopy. A third case study underlines the ability of the method to combine heterogeneous data from systems biology with the analysis of publicly available data related to NCI-60 cancer cell lines from different tissue origins, which include metabolomics, transcriptomics and proteomics. The fusion of Omics data from different sources is expected to provide a more complete view of biological systems. The proposed method was demonstrated as a relevant and widely applicable alternative to handle efficiently the inherent characteristics of multiple Omics data, such as very large numbers of noisy collinear variables.