Browse
Submit Data
Databases
API
Help

Dataset Information

28 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Merging systems biology with pharmacodynamics.

ABSTRACT: The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology approaches to drive the drug discovery processes, predict rare adverse events, and catalyze the practice of personalized precision medicine. Here, we introduce the concept of enhanced pharmacodynamic (ePD) models, which synergistically combine the desirable features of systems biology and current PD models within the framework of ordinary or partial differential equations. ePD models that analyze regulatory networks involved in drug action can account for a drug's multiple targets and for the effects of genomic, epigenomic, and posttranslational changes on the drug efficacy. This new knowledge can drive drug discovery and shape precision medicine.

SUBMITTER: Iyengar R

PROVIDER: S-EPMC3405973 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Merging systems biology with pharmacodynamics.

Iyengar Ravi R Zhao Shan S Chung Seung-Wook SW Mager Donald E DE Gallo James M JM

Science translational medicine 20120301 126

The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology approaches to drive the drug discovery processes, predict rare adverse events, and catalyze the practice of personalized precision medicine. Here, we introduce the concept of enhanced pharmacodynamic (ePD) models, which synergistically combine the desirable features of systems biology and current PD models within the framework of ordin ...[more]

PMID: 22440734

Similar Datasets

Merging candidate gene approach with high throughput systems biology technology for the development of xylose utilising S. cerevisiae strains

Project description:Though highly efficient at fermenting hexose sugars, the ethanologenic yeast Saccharomyces cerevisiae has limited ability to ferment five-carbon sugars. As a significant portion of sugars found in cellulosic biomasses is the five carbon sugar xylose, S. cerevisiae must be engineered to metabolize pentose sugars. Here we combined classical candidate gene approach with systems biology to develop xylose-utilising S. cerevisiae strains. The introduction of an exogenous xylose isomerase (XYLA) and an additional copy of the endogenous xylulokinase gene (XKS1) results in the significant improvement of xylose consumption. Microarray studies reveal that the introduction of XYLA and XKS1 results in the dramatic transcriptional remodelling of the cell under both glucose and xylose conditions. To further investigate the cellular processes impacted by the introduction of XYLA and XKS1, using genome-wide chemical and synthetic lethal screens we identified greater than 40 deletion mutants that impact xylose utilization. We identified four genes, ALP1, ISC1, RPL20B and BUD21, that when individually deleted allow S. cerevisiae to utilize xylose as the sole carbon source. When these mutants are combined with XYLA and XKS1, it results in strains with significant improvement in xylose consumption. We have demonstrated that systems biology techniques combined with candidate gene approaches can successfully lead to novel genetic strategies for the improvement of xylose utilization

2012-07-15 | GSE27325 | GEO

Merging candidate gene approach with high throughput systems biology technology for the development of xylose utilising S. cerevisiae strains

2012-07-14 | E-GEOD-27325 | biostudies-arrayexpress

Bacterial Genotoxins: Merging the DNA Damage Response into Infection Biology.

Project description:Bacterial genotoxins are unique among bacterial toxins as their molecular target is DNA. The consequence of intoxication or infection is induction of DNA breaks that, if not properly repaired, results in irreversible cell cycle arrest (senescence) or death of the target cells. At present, only three bacterial genotoxins have been identified. Two are protein toxins: the cytolethal distending toxin (CDT) family produced by a number of Gram-negative bacteria and the typhoid toxin produced by Salmonella enterica serovar Typhi. The third member, colibactin, is a peptide-polyketide genotoxin, produced by strains belonging to the phylogenetic group B2 of Escherichia coli. This review will present the cellular effects of acute and chronic intoxication or infection with the genotoxins-producing bacteria. The carcinogenic properties and the role of these effectors in the context of the host-microbe interaction will be discussed. We will further highlight the open questions that remain to be solved regarding the biology of this unusual family of bacterial toxins.

| S-EPMC4598774 | biostudies-literature

Systems Biology of Gastrointestinal and Related Diseases

Project description:This is a longitudinal observational study on patients with gastrointestinal and related disease. The study will be conducted for at least 10 years, following each participant over time, as they either go through relapses and remissions, or progression of their disease.

| 2751363 | ecrin-mdr-crc

Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

Project description:Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

| S-EPMC3972654 | biostudies-literature

Systems biology of interstitial lung diseases

Project description:This SuperSeries is composed of the SubSeries listed below.

2011-04-19 | GSE21411 | GEO

Computational systems chemical biology.

Project description:There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

| S-EPMC3547368 | biostudies-literature

AutoAnalyze in Systems Biology.

Project description:AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools.

| S-EPMC6328952 | biostudies-literature

Integrative radiation systems biology.

Project description:Maximisation of the ratio of normal tissue preservation and tumour cell reduction is the main concept of radiotherapy alone or combined with chemo-, immuno- or biologically targeted therapy. The foremost parameter influencing this ratio is radiation sensitivity and its modulation towards a more efficient killing of tumour cells and a better preservation of normal tissue at the same time is the overall aim of modern therapy schemas. Nevertheless, this requires a deep understanding of the molecular mechanisms of radiation sensitivity in order to identify its key players as potential therapeutic targets. Moreover, the success of conventional approaches that tried to statistically associate altered radiation sensitivity with any molecular phenotype such as gene expression proofed to be somewhat limited since the number of clinically used targets is rather sparse. However, currently a paradigm shift is taking place from pure frequentistic association analysis to the rather holistic systems biology approach that seeks to mathematically model the system to be investigated and to allow the prediction of an altered phenotype as the function of one single or a signature of biomarkers. Integrative systems biology also considers the data from different molecular levels such as the genome, transcriptome or proteome in order to partially or fully comprehend the causal chain of molecular mechanisms. An example for the application of this concept currently carried out at the Clinical Cooperation Group "Personalized Radiotherapy in Head and Neck Cancer" of the Helmholtz-Zentrum München and the LMU Munich is described. This review article strives for providing a compact overview on the state of the art of systems biology, its actual challenges, potential applications, chances and limitations in radiation oncology research working towards improved personalised therapy concepts using this relatively new methodology.

| S-EPMC3901372 | biostudies-literature

Systems biology of SNPs.

Project description:Genome-scale networks can now be reconstructed based on high-throughput data sets. Mathematical analyses of these networks are used to compute their candidate functional or phenotypic states. Analysis of functional states of networks shows that the activity of biochemical reactions can be highly correlated in physiological states, forming so-called co-sets representing functional modules of the network. Thus, detrimental sequence defects in any one of the genes encoding members of a co-set can result in similar phenotypic consequences. Here we show that causal single nucleotide polymorphisms in genes encoding mitochondrial components can be classified and correlated using co-sets.

| S-EPMC1681509 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data