Analysis of genetic variation and potential applications in genome-scale metabolic modeling

Abstract

Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.

A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

“…This reconstruction serves as a platform for analyzing many types of molecular and phenotypic data using a variety of algorithms (Lewis et al, 2012). Furthermore, since this reconstruction provides a mechanistic link between the genotype and phenotype of CHO cells (via enumeration of enzymes underlying metabolic pathways), it allows for the effective integration of orthogonal data types such as metabolomics, transcriptomics, genetic variants, and growth rates (Cardoso et al, 2015; Hyduke et al, 2013). We demonstrated this with our cell line specific models for the CHO-K1, -S, and -DG44 lines.…”

Stoichiometric Representation of Gene–Protein–Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction

“…A new generation of genome-scale models and simulation methods is on the rise [ 63 ]. This includes genome-scale models that account for gene expression and protein production [ 18 , 64 , 65 ], models that account for protein structure [ 66 ], and methods that predict the effect of genetic variation in protein function [ 67 ]. While such detailed models are not readily available for every organism, our method provides a suitable approach to leverage existing models to a new level.…”

Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network

“…The study of these secondary alterations might allow to understand the pathophysiology of these diseases, as well as to identify new biomarkers and therapeutic targets [15]. Nevertheless, to understand the effect of these secondary altered processes and to identify new biomarkers require to consider these diseases as a biological system, which are the product of the relation of multiple metabolic pathways, genes, proteins, and networks [16,17]. Impairment of metabolic normal states generate changes in concentrations of metabolites, consequence of disturbance of distribution of water in compartments or the high or low activity of enzymes that is altered by many mechanism [18,19].…”