scholarly journals An explorable public transcriptomics compendium for eukaryotic microalgae

2018 ◽  
Author(s):  
Justin Ashworth ◽  
Peter J. Ralph
Keyword(s):  
Systems Biology ◽  
Research Community ◽  
Polar Regions ◽  
Transcriptome Data ◽  
Molecular Systems ◽  
Photosynthetic Productivity ◽  
Dynamic Traits ◽  
Integrated Information

AbstractEukaryotic microalgae dominate primary photosynthetic productivity in fluctuating nutrient-rich environments, including coastal, estuarine and polar regions, where competition and complexity are presumably adaptive and dynamic traits. Numerous genomes and transcriptomes of these species have been carefully sequenced, providing an unprecedented view into the vast genetic repertoires and the diverse transcriptional programs operating inside these organisms. Here we collected, re-mapped, quantified and clustered publicly available transcriptome data for ten different eukaryotic microalgae in order to develop new insights into their molecular systems biology, as well as to provide a large new resource of integrated information to facilitate the efforts of others to further compare and contextualize the results of individual and new experiments within and between species. This is summarized herein and provided for public use by the eukaryotic microalgae research community.

Molecular Systems Biology and Control

2005 ◽  
Vol 11 (4-5) ◽  
pp. 396-435 ◽  
Author(s):  
Eduardo D. Sontag
Keyword(s):  
Systems Biology ◽  
Molecular Systems ◽  
And Control

scholarly journals Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 76 ◽  
Author(s):  
Farhana R. Pinu ◽  
David J. Beale ◽  
Amy M. Paten ◽  
Konstantinos Kouremenos ◽  
Sanjay Swarup ◽  
...  
Keyword(s):  
New Zealand ◽  
Systems Biology ◽  
Life Science ◽  
Biological Systems ◽  
Research Community ◽  
Data Sets ◽  
Omics Data ◽  
Omics Integration ◽  
Traditional Approaches ◽  
Multiple Interactions

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

Molecular systems biology

2007 ◽  
Vol 129 (2) ◽  
pp. 171-172
Author(s):  
N. Sewald ◽  
A. Pühler
Keyword(s):  
Systems Biology ◽  
Molecular Systems

scholarly journals Welcome to Molecular Systems Biology

EMBO Reports ◽  
2005 ◽  
Vol 6 (4) ◽  
pp. 291-291
Author(s):  
Frank Gannon
Keyword(s):  
Systems Biology ◽  
Molecular Systems

Advanced methods in molecular systems biology

2010 ◽  
Vol 149 (3) ◽  
pp. 95-97
Author(s):  
Andreas Dress ◽  
Eduardo Mendoza ◽  
Eberhard O. Voit
Keyword(s):  
Systems Biology ◽  
Molecular Systems

scholarly journals Computational modelling of atherosclerosis: developing a community resource

2018 ◽  
Author(s):  
Andrew Parton ◽  
Victoria McGilligan ◽  
Maurice O’Kane ◽  
Steven Watterson
Keyword(s):  
Cardiovascular Disease ◽  
Systems Biology ◽  
Computational Model ◽  
Network Model ◽  
Research Community ◽  
Markup Language ◽  
Standard Format ◽  
Drug Intervention ◽  
Future Studies ◽  
Plaque Growth

AbstractRationaleAtherosclerosis is a dynamical process that emerges from the interplay between lipid metabolism, inflammation and innate immunity. The arterial location of atherosclerosis makes it logistically and ethically difficult to study in vivo. To improve our understanding of the disease, we must find alternative ways to investigate its progression. There is currently no computational model of atherosclerosis openly available to the research community for use in future studies and for refinement and development.ObjectiveHere we develop the first predictive computational model to be made openly available and demonstrate its use for therapeutic hypothesis generation.Methods and ResultsWe compiled a dataset of relevant interactions from the literature along with available parameters. These were used to build a network model describing atherosclerotic plaque development. A visual map of the network model was produced using the Systems Biology Graphical Notation (SBGN) and a dynamic mathematical description of the network model that enables us to simulate plaque growth was developed and is made available using the Systems Biology Markup Language (SBML). We used this model to investigate whether multi-drug therapeutic interventions could be identified that stimulate plaque regression. The model produced comprised 20 cell types and 41 proteins with 89 species in total. The visual map is available for reuse and refinement using the SBGN Markup Language standard format and the mathematical model is available using the SBML standard format. We used a genetic algorithm to identify a multi-drug intervention hypothesis comprising five drugs that comprehensively reverse plaque growth within the model.ConclusionsWe have produced the first predictive mathematical and computational model of atherosclerosis that can be reused and refined by the cardiovascular research community. We demonstrated its potential as a tool for future studies of cardiovascular disease by using it to identify multi-drug intervention hypotheses.Subject CodesAtherosclerosis, Computational Biology, Lipids and Cholesterol, Cell Signaling/Signal Transduction, Cardiovascular Disease

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