Bringing Genomes to Life: The Use of Genome-Scale In Silico Models

2008 ◽  
pp. 14-36
Author(s):  
Ines Thiele ◽  
Bernhard Ø. Palsson
Keyword(s):  
In Silico ◽  
In Silico Models ◽  
Genome Scale

scholarly journals Gene Expression Profiling and the Use of Genome-Scale In Silico Models of Escherichia coli for Analysis: Providing Context for Content

2009 ◽  
Vol 191 (11) ◽  
pp. 3437-3444 ◽  
Author(s):  
Nathan E. Lewis ◽  
Byung-Kwan Cho ◽  
Eric M. Knight ◽  
Bernhard O. Palsson
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
In Silico ◽  
In Silico Models ◽  
Genome Scale

Genome-scale microbial in silico models: the constraints-based approach

2003 ◽  
Vol 21 (4) ◽  
pp. 162-169 ◽  
Author(s):  
Nathan D Price ◽  
Jason A Papin ◽  
Christophe H Schilling ◽  
Bernhard O Palsson
Keyword(s):  
In Silico ◽  
In Silico Models ◽  
Genome Scale

Special Feature — Systems Biology

2006 ◽  
Vol 10 (03) ◽  
pp. 123-154
Keyword(s):  
Systems Biology ◽  
In Silico ◽  
In Silico Models ◽  
Genome Scale

Constraint-based Genome-scale In Silico Models for Systems Biology. Korean Systems Biology Project. Systems Biology's Promises and Challenges.

scholarly journals Combining species specific in vitro & in silico models to predict in vivo food effect in a preclinical stage – case study of Venetoclax

2021 ◽  
pp. 105840
Author(s):  
Laura J. Henze ◽  
Niklas J. Koehl ◽  
Joseph P. O'Shea ◽  
René Holm ◽  
Maria Vertzoni ◽  
...  
Keyword(s):  
In Silico ◽  
Food Effect ◽  
Preclinical Stage ◽  
Species Specific ◽  
In Silico Models

scholarly journals Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

2012 ◽  
Vol 78 (24) ◽  
pp. 8735-8742 ◽  
Author(s):  
Yilin Fang ◽  
Michael J. Wilkins ◽  
Steven B. Yabusaki ◽  
Mary S. Lipton ◽  
Philip E. Long
Keyword(s):  
Proteomic Analysis ◽  
In Silico ◽  
Field Experiments ◽  
Metabolic Model ◽  
Geobacter Metallireducens ◽  
Content Type ◽  
Proteomic Data ◽  
Subsurface Environment ◽  
A Genome ◽  
Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

Estimation of uncertainty in multi-level in silico models predicting biomarkers of drug-induced proarrhythmic risk

2021 ◽  
Vol 350 ◽  
pp. S64-S65
Author(s):  
K. Kopanska ◽  
J.C. Gómez-Tamayo ◽  
J. Llopis-Lorente ◽  
B.A. Trenor-Gomis ◽  
J. Sáiz ◽  
...  
Keyword(s):  
In Silico ◽  
Drug Induced ◽  
Proarrhythmic Risk ◽  
Multi Level ◽  
In Silico Models ◽  
Estimation Of Uncertainty

scholarly journals Patient-specific in silico models can quantify primary implant stability in elderly human bone

2017 ◽  
Author(s):  
Juri A. Steiner ◽  
Urs A.T. Hofmann ◽  
Patrik Christen ◽  
Jean M. Favre ◽  
Stephen J. Ferguson ◽  
...  
Keyword(s):  
In Silico ◽  
Human Bone ◽  
Patient Specific ◽  
Implant Stability ◽  
Primary Implant ◽  
In Silico Models
Sign in / Sign up

Export Citation Format

Share Document