Predicting Phenotype from Genotype Through Reconstruction and Integrative Modeling of Metabolic and Regulatory Networks

2014 ◽  
pp. 307-325 ◽  
Author(s):  
Sriram Chandrasekaran
Keyword(s):  
Regulatory Networks ◽  
Integrative Modeling

scholarly journals Bayesian Integrative Modeling of Genome-Scale Metabolic and Regulatory Networks

Informatics ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 1 ◽  
Author(s):  
Hanen Mhamdi ◽  
Jérémie Bourdon ◽  
Abdelhalim Larhlimi ◽  
Mourad Elloumi
Keyword(s):  
Cancer Cells ◽  
Drug Targets ◽  
Regulatory Networks ◽  
Computational Models ◽  
Cancer Metabolism ◽  
Human Cancer ◽  
Genetic Regulation ◽  
Kinetic Properties ◽  
Drug Induced ◽  
Integrative Modeling

The integration of high-throughput data to build predictive computational models of cellular metabolism is a major challenge of systems biology. These models are needed to predict cellular responses to genetic and environmental perturbations. Typically, this response involves both metabolic regulations related to the kinetic properties of enzymes and a genetic regulation affecting their concentrations. Thus, the integration of the transcriptional regulatory information is required to improve the accuracy and predictive ability of metabolic models. Integrative modeling is of primary importance to guide the search for various applications such as discovering novel potential drug targets to develop efficient therapeutic strategies for various diseases. In this paper, we propose an integrative predictive model based on techniques combining semantic web, probabilistic modeling, and constraint-based modeling methods. We applied our approach to human cancer metabolism to predict in silico the growth response of specific cancer cells under approved drug effects. Our method has proven successful in predicting the biomass rates of human liver cancer cells under drug-induced transcriptional perturbations.

scholarly journals Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere

2013 ◽  
Vol 163 (4) ◽  
pp. 1487-1503 ◽  
Author(s):  
K. Hill ◽  
S. Porco ◽  
G. Lobet ◽  
S. Zappala ◽  
S. Mooney ◽  
...  
Keyword(s):  
Systems Biology ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Root Systems ◽  
Integrative Modeling ◽  
Gene Regulatory

Homology, Genes, and Evolutionary Innovation

2014 ◽  
Author(s):  
Günter P. Wagner
Keyword(s):  
Evolutionary Biology ◽  
Regulatory Networks ◽  
Biological Diversity ◽  
Cell Types ◽  
Origin And Evolution ◽  
Major Transitions ◽  
Form And Function ◽  
Historical Continuity ◽  
Evolutionary Innovation ◽  
Character Identity

Homology—a similar trait shared by different species and derived from common ancestry, such as a seal's fin and a bird's wing—is one of the most fundamental yet challenging concepts in evolutionary biology. This book provides the first mechanistically based theory of what homology is and how it arises in evolution. The book argues that homology, or character identity, can be explained through the historical continuity of character identity networks—that is, the gene regulatory networks that enable differential gene expression. It shows how character identity is independent of the form and function of the character itself because the same network can activate different effector genes and thus control the development of different shapes, sizes, and qualities of the character. Demonstrating how this theoretical model can provide a foundation for understanding the evolutionary origin of novel characters, the book applies it to the origin and evolution of specific systems, such as cell types; skin, hair, and feathers; limbs and digits; and flowers. The first major synthesis of homology to be published in decades, this book reveals how a mechanistically based theory can serve as a unifying concept for any branch of science concerned with the structure and development of organisms, and how it can help explain major transitions in evolution and broad patterns of biological diversity.

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