In Silico Systems Biology Approaches for the Identification of Antimicrobial Targets

2013 ◽  
pp. 13-30 ◽  
Author(s):  
Malabika Sarker ◽  
Carolyn Talcott ◽  
Amit K. Galande
Keyword(s):  
Systems Biology ◽  
In Silico

scholarly journals An integrated dataset for in silico drug discovery

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  
...  
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Drug Development ◽  
Data Integration ◽  
In Silico ◽  
Drug Repositioning ◽  
Integrated Systems ◽  
Integration Platform ◽  
Treatment Indications ◽  
New Treatment

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

scholarly journals In Silico Modeling of Shear-Stress-Induced Nitric Oxide Production in Endothelial Cells through Systems Biology

2013 ◽  
Vol 104 (10) ◽  
pp. 2295-2306 ◽  
Author(s):  
Andrew Koo ◽  
David Nordsletten ◽  
Renato Umeton ◽  
Beracah Yankama ◽  
Shiva Ayyadurai ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Endothelial Cells ◽  
Shear Stress ◽  
Systems Biology ◽  
In Silico ◽  
Nitric Oxide Production ◽  
Oxide Production ◽  
In Silico Modeling

scholarly journals Pseudomonas aeruginosa reverse diauxie is an optimized, resource utilization strategy

2020 ◽  
Author(s):  
S. Lee McGill ◽  
Yeni Yung ◽  
Kristopher A. Hunt ◽  
Michael A. Henson ◽  
Luke Hanley ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Systems Biology ◽  
In Silico ◽  
Carbon Sources ◽  
Opportunistic Pathogen ◽  
Metabolic Model ◽  
Wide Distribution ◽  
Overflow Metabolism ◽  
A Genome

AbstractPseudomonas aeruginosa is a globally-distributed bacterium often found in medical infections. The opportunistic pathogen uses a different, carbon catabolite repression (CCR) strategy than many, model microorganisms. It does not utilize a classic diauxie phenotype, nor does it follow common systems biology assumptions including preferential consumption of glucose with an ‘overflow’ metabolism. Despite these contradictions, P. aeruginosa is competitive in many, disparate environments underscoring knowledge gaps in microbial ecology and systems biology. Physiological, omics, and in silico analyses were used to quantify the P. aeruginosa CCR strategy known as ‘reverse diauxie’. An ecological basis of reverse diauxie was identified using a genome-scale, metabolic model interrogated with in vitro omics data. Reverse diauxie preference for lower energy, nonfermentable carbon sources, such as acetate or succinate over glucose, was predicted using a multidimensional strategy which minimized resource investment into central metabolism while completely oxidizing substrates. Application of a common, in silico optimization criterion, which maximizes growth rate, did not predict the reverse diauxie phenotypes. This study quantifies P. aeruginosa metabolic strategies foundational to its wide distribution and virulence.

scholarly journals Combining Chemoinformatics with Bioinformatics: In Silico Prediction of Bacterial Flavor-Forming Pathways by a Chemical Systems Biology Approach “Reverse Pathway Engineering”

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e84769 ◽  
Author(s):  
Mengjin Liu ◽  
Bruno Bienfait ◽  
Oliver Sacher ◽  
Johann Gasteiger ◽  
Roland J. Siezen ◽  
...  
Keyword(s):  
Systems Biology ◽  
In Silico ◽  
Pathway Engineering ◽  
In Silico Prediction ◽  
Chemical Systems

scholarly journals Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Cristina Segú-Vergés ◽  
Mireia Coma ◽  
Christoph Kessel ◽  
Serge Smeets ◽  
Dirk Foell ◽  
...  
Keyword(s):  
Innate Immunity ◽  
Systems Biology ◽  
Treatment Strategy ◽  
In Silico ◽  
Systemic Juvenile Idiopathic Arthritis ◽  
Treat To Target ◽  
Molecular Characteristics ◽  
Expression Data ◽  
Still’S Disease ◽  
Still's Disease

Abstract Background Systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still’s disease (AOSD) are manifestations of an autoinflammatory disorder with complex pathophysiology and significant morbidity, together also termed Still’s disease. The objective of the current study is to set in silico models based on systems biology and investigate the optimal treat-to-target strategy for Still’s disease as a proof-of-concept of the modeling approach. Methods Molecular characteristics of Still’s disease and data on biological inhibitors of interleukin (IL)-1 (anakinra, canakinumab), IL-6 (tocilizumab, sarilumab), and glucocorticoids as well as conventional disease-modifying anti-rheumatic drugs (DMARDs, methotrexate) were used to construct in silico mechanisms of action (MoA) models by means of Therapeutic Performance Mapping System (TPMS) technology. TPMS combines artificial neuronal networks, sampling-based methods, and artificial intelligence. Model outcomes were validated with published expression data from sJIA patients. Results Biologicals demonstrated more pathophysiology-directed efficiency than non-biological drugs. IL-1 blockade mainly acts on proteins implicated in the innate immune system, while IL-6 signaling blockade has a weaker effect on innate immunity and rather affects adaptive immune mechanisms. The MoA models showed that in the autoinflammatory/systemic phases of Still’s disease, in which the innate immunity plays a pivotal role, the IL-1β-neutralizing antibody canakinumab is more efficient than the IL-6 receptor-inhibiting antibody tocilizumab. MoA models reproduced 67% of the information obtained from expression data. Conclusions Systems biology-based modeling supported the preferred use of biologics as an immunomodulatory treatment strategy for Still’s disease. Our results reinforce the role for IL-1 blockade on innate immunity regulation, which is critical in systemic autoinflammatory diseases. This further encourages early use on Still’s disease IL-1 blockade to prevent the development of disease or drug-related complications. Further analysis at the clinical level will validate the findings and help determining the timeframe of the window of opportunity for canakinumab treatment.

scholarly journals Head to head evaluation of second generation ALK inhibitors brigatinib and alectinib as first-line treatment for ALK+ NSCLC using an in silico systems biology-based approach

Oncotarget ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 316-332
Author(s):  
Enric Carcereny ◽  
Alonso Fernández-Nistal ◽  
Araceli López ◽  
Carmen Montoto ◽  
Andrea Naves ◽  
...  
Keyword(s):  
Systems Biology ◽  
In Silico ◽  
Second Generation ◽  
Line Treatment ◽  
First Line ◽  
Alk Inhibitors ◽  
First Line Treatment
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