Faculty Opinions recommendation of Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson's, Huntington's, Alzheimer's, prions, bactericides, chemical toxicology and others as examples.

Victor Norris
Lihe Chen ◽  
Hyun Jun Jung ◽  
Arnab Datta ◽  
Euijung Park ◽  
Brian G. Poll ◽  

Systems biology can be defined as the study of a biological process in which all of the relevant components are investigated together in parallel to discover the mechanism. Although the approach is not new, it has come to the forefront as a result of genome sequencing projects completed in the first few years of the current century. It has elements of large-scale data acquisition (chiefly next-generation sequencing–based methods and protein mass spectrometry) and large-scale data analysis (big data integration and Bayesian modeling). Here we discuss these methodologies and show how they can be applied to understand the downstream effects of GPCR signaling, specifically looking at how the neurohypophyseal peptide hormone vasopressin, working through the V2 receptor and PKA activation, regulates the water channel aquaporin-2. The emerging picture provides a detailed framework for understanding the molecular mechanisms involved in water balance disorders, pointing the way to improved treatment of both polyuric disorders and water-retention disorders causing dilutional hyponatremia. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

2020 ◽  
Vol 36 (8) ◽  
pp. 2620-2622 ◽  
Irina Balaur ◽  
Ludovic Roy ◽  
Alexander Mazein ◽  
S Gökberk Karaca ◽  
Ugur Dogrusoz ◽  

Abstract Motivation CellDesigner is a well-established biological map editor used in many large-scale scientific efforts. However, the interoperability between the Systems Biology Graphical Notation (SBGN) Markup Language (SBGN-ML) and the CellDesigner’s proprietary Systems Biology Markup Language (SBML) extension formats remains a challenge due to the proprietary extensions used in CellDesigner files. Results We introduce a library named cd2sbgnml and an associated web service for bidirectional conversion between CellDesigner’s proprietary SBML extension and SBGN-ML formats. We discuss the functionality of the cd2sbgnml converter, which was successfully used for the translation of comprehensive large-scale diagrams such as the RECON Human Metabolic network and the complete Atlas of Cancer Signalling Network, from the CellDesigner file format into SBGN-ML. Availability and implementation The cd2sbgnml conversion library and the web service were developed in Java, and distributed under the GNU Lesser General Public License v3.0. The sources along with a set of examples are available on GitHub (https://github.com/sbgn/cd2sbgnml and https://github.com/sbgn/cd2sbgnml-webservice, respectively). Supplementary information Supplementary data are available at Bioinformatics online.

2021 ◽  
Vol 15 ◽  
Hassan Dakik ◽  
Sarah Mantash ◽  
Ali Nehme ◽  
Firas Kobeissy ◽  
Masoud Zabet-Moghaddam ◽  

Advances in large-scale proteomics analysis have been very useful in understanding pathogenesis of diseases and elaborating therapeutic strategies. Proteomics has been employed to study Parkinson disease (PD); however, sparse studies reported proteome investigation after cell therapy approaches. In this study, we used liquid chromatography–tandem mass spectrometry and systems biology to identify differentially expressed proteins in a translational mouse model of PD after cell therapy. Proteins were extracted from five nigrostriatal-related brain regions of mice previously lesioned with 6-hydroxydopamine in the substantia nigra. Protein expression was compared in non-grafted brain to 1 and 7 days after intranigral grafting of E12.5 embryonic ventral mesencephalon (VM). We found a total of 277 deregulated proteins after transplantation, which are enriched for lipid metabolism, oxidative phosphorylation and PD, thus confirming that our animal model is similar to human PD and that the presence of grafted cells modulates the expression of these proteins. Notably, seven proteins (Acta1, Atp6v1e1, Eci3, Lypla2, Pip4k2a, Sccpdh, and Sh3gl2) were commonly down-regulated after engraftment in all studied brain regions. These proteins are known to be involved in the formation of lipids and recycling of dopamine (DA) vesicle at the synapse. Moreover, intranigral transplantation of VM cells decreased the expression of proteins related to oxidative stress, especially in the nigrostriatal pathway containing the DA grafted neurons. In the same regions, an up-regulation of several proteins including α-synuclein and tyrosine hydroxylase was observed, whereas expression of tetraspanin 7 was shut down. Overall, these results suggest that intranigral transplantation of VM tissue in an animal model of PD may induce a decrease of oxidative stress in the nigrostriatal pathway and a restoration of the machinery of neurotransmitters, particularly DA release to promote DA transmission through a decrease of D2 DA receptors endocytosis. Identification of new mechanistic elements involved in the nigrostriatal reconstruction process, using translational animal models and systems biology, is a promising approach to enhance the repair of this pathway in PD patients undergoing cell therapy.

2021 ◽  
Vol 3 (Supplement_2) ◽  
pp. ii1-ii1
Niven Narain ◽  
Michael Kiebish ◽  
Vivek Vishnudas ◽  
Vladimir Tolstikov ◽  
Gregory Miller ◽  

Abstract The past decade has been witness to an explosive proliferation of data analytics modalities, all seeking to unravel insight into large-scale data sets. Machine learning and AI methodologies now occupy a central role in analyses of data sets that range in nature from genomics, “omics”, clinical, real-world evidence, and demographic data. Despite advances in data analytics/machine learning, access to complex population level clinical and related datasets, translating information into actionable guidance in human health and disease remains a challenge. Interrogative Biology, a systems biology/AI platform generates an unbiased, data-informed network for identifying targets (disease drivers) and biomarkers for disease interception at the point of transition to dysregulation, preceding clinical phenotype. The data topology is enabled by a systematic acquisition and interrogation of longitudinal bio-samples of clinically annotated human matrices (e.g. blood, urine, saliva, tissues) subjected to comprehensive multi-omic (genomic, proteomics, lipidomics and metabolomics) profiling over time. The molecular profiles are integrated with clinical health information using Bayesian artificial intelligence analytics, bAIcis, to generate causal network maps of overall health. Differentials between “health” and “disease” network maps identifies drivers (targets and biomarkers) of disease and are rapidly validated in orthogonal wet-lab disease specific perturbed model systems. Target information imputed into the bAIcis framework can define therapeutic strategies including identification of existing drugs and bio-actives for corrective response. Using a combination of clinic based sampling and dried blood spot analysis for longitudinal dynamic monitoring of markers of health-disease status provides opportunity for proactive clinical management and intervention for corrective response in advance of major deterioration of health status. Taken together, the approach herein allows for health surveillance based on in-depth biological profiling of alterations in the patient narrative to guide treatment modalities and strategies in a longitudinal and dynamic manner to identify, track, intercept, and arrest human disease.

2015 ◽  
Marco Fondi ◽  
Pietro Liò

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organisation and ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi –omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.

2012 ◽  
Vol 303 (11) ◽  
pp. C1115-C1124 ◽  
Mark A. Knepper

Over the past 80 years, physiological research has moved progressively in a reductionist direction, providing mechanistic information on a smaller and smaller scale. This trend has culminated in the present focus on “molecular physiology,” which deals with the function of single molecules responsible for cellular function. There is a need to assemble the information from the molecular level into models that explain physiological function at cellular, tissue, organ, and whole organism levels. Such integration is the major focus of an approach called “systems biology.” The genome sequencing projects provide a basis for a new kind of systems biology called “data-rich” systems biology that is based on large-scale data acquisition methods including protein mass spectrometry, DNA microarrays, and deep sequencing of nucleic acids. These techniques allow investigators to measure thousands of variables simultaneously in response to an external stimulus. My laboratory is applying such an approach to the question: “How does the peptide hormone vasopressin regulate water permeability in the renal collecting duct?” We are using protein mass spectrometry to identify and quantify the phosphoproteome of collecting duct cells. The response to vasopressin, presented in the form of a network model, includes a general downregulation of proline-directed kinases (MAP kinases and cyclin-dependent kinases) and upregulation of basophilic kinases (ACG kinases and calmodulin-dependent kinases). Further progress depends on characterization and localization of candidate protein kinases in these families. The ultimate goal is to use multivariate statistical techniques and differential equations to obtain predictive models describing vasopressin signaling in the renal collecting duct.

2019 ◽  
Vol 1 (1) ◽  
pp. 166-184 ◽  
Svetlana Postnova

Sleep and circadian rhythms are regulated across multiple functional, spatial and temporal levels: from genes to networks of coupled neurons and glial cells, to large scale brain dynamics and behaviour. The dynamics at each of these levels are complex and the interaction between the levels is even more so, so research have mostly focused on interactions within the levels to understand the underlying mechanisms—the so-called reductionist approach. Mathematical models were developed to test theories of sleep regulation and guide new experiments at each of these levels and have become an integral part of the field. The advantage of modelling, however, is that it allows us to simulate and test the dynamics of complex biological systems and thus provides a tool to investigate the connections between the different levels and study the system as a whole. In this paper I review key models of sleep developed at different physiological levels and discuss the potential for an integrated systems biology approach for sleep regulation across these levels. I also highlight the necessity of building mechanistic connections between models of sleep and circadian rhythms across these levels.

2016 ◽  
Adam P Arkin ◽  
Rick L Stevens ◽  
Robert W Cottingham ◽  
Sergei Maslov ◽  
Christopher S Henry ◽  

AbstractThe U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology — predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

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