ABSTRACT
We developed a method of entirely parallel differential evolution for identification of unknown parameters of mathematical models by minimization of the objective function that describes the discrepancy of the model solution and the experimental data. The method is implemented in the free and open source software available on the Internet. The method demonstrated a good performance comparable to the top three methods from CEC-2014 and was successfully applied to several biological problems.
Subject(s)
Biological Evolution , Models, Theoretical , Systems Biology , Internet , SoftwareABSTRACT
The hepatitis C virus (HCV) belongs to Flaviviridae family and causes hazardous liver diseases leading frequently to cirrhosis and hepatocellular carcinoma. HCV is able to rapidly acquire drug resistance and for this reason there is currently no effective anti-HCV therapy in spite of appearance of new potential drugs. Mathematical models are relevant to predict the efficacy of potential drugs against virus or host targets. One of the promising targets for development of new drugs is the viral NS3 protease. Here we developed a stochastic model of the subgenomic HCV replicon replication in Huh-7 cells and in the presence of the NS3 protease inhibitors. Along with consideration of the stochastic nature of the subgenomic HCV replicon replication the model takes into account the existence and generation of main NS3 protease drug resistant mutants, namely BILN-2061 (A156T, D168V, R155Q), VX-950 (A156S, A156T, T54A) and SCH-503034 (A156T, A156S, T54A). The model reproduces well the viral RNA kinetics in the cell from the moment of the subgenomic HCV replicon transfection to steady state, as well as the viral RNA suppression kinetics in the presence of NS3 protease inhibitors BILN-2061, VX-950 and SCH-503034. We showed that the resistant mutants should be taken into account for the correct description of biphasic kinetics of the viral RNA suppression. The mutants selected in the presence of different inhibitor concentrations have maximal replication capacity in the given inhibitor concentration range. Our model can be used to interpret the results of the new anti-HCV drug testing in replicon systems, as well as to predict the efficacy of new potential drugs and optimize the regimen of their use.
Subject(s)
Drug Resistance, Viral/genetics , Hepacivirus/genetics , Hepatitis C/genetics , Models, Theoretical , Genome, Viral , Hepacivirus/drug effects , Hepatitis C/virology , Humans , Mutation , Protease Inhibitors/chemistry , Protease Inhibitors/therapeutic use , RNA/chemistry , RNA/genetics , Replicon/drug effects , Replicon/genetics , Viral Nonstructural Proteins/antagonists & inhibitors , Virus Replication/geneticsABSTRACT
In this review we summarize original methods for the extraction quantitative information from the confocal images of gene expression patterns. These methods include image segmentation, extraction of quantitative numerical data on gene expression, removal of background signal and spatial registration. Finally it is possible to construct a spatiotemporal atlas of gene expression form individual images obtained at each developmental stage. Initially all methods were developed to extract quantitative numerical information form confocal images of segmentation gene expression in Drosophila melanogaster. Application of these methods to Drosophila images makes it possible to reveal new mechanisms of formation of segmentation gene expression domains, as well as to construct the quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.
Subject(s)
Gene Expression Profiling/methods , Gene Expression , Microscopy, Confocal , Animals , Drosophila Proteins/genetics , Drosophila melanogaster/embryology , Drosophila melanogaster/genetics , Drosophila melanogaster/ultrastructure , Embryo, Nonmammalian/ultrastructure , Gene Expression Regulation, Developmental , Genes, Insect , Image EnhancementABSTRACT
A model of the expression of gap genes during the early development of Drosophila embryo has been studied. The parameter values for the model have been obtained by fitting the solutions to experimental patterns under an additional assumption of the asymptotic stability of solutions at large times. The patterns at the beginning of gastrulation in these solutions are very close to the actual attractor in the model. It is shown that these solutions have a better stability, or robustness, to perturbations of concentrations and parameter values in the model.
Subject(s)
Drosophila melanogaster/genetics , Genes, Insect , Models, Genetic , Animals , Drosophila melanogaster/embryology , Embryo, NonmammalianABSTRACT
In this review, we summarize original methods for the extraction of quantitative information from confocal images of gene-expression patterns. These methods include image segmentation, the extraction of quantitative numerical data on gene expression, and the removal of background signal and spatial registration. Finally, it is possible to construct a spatiotemporal atlas of gene expression from individual images recorded at each developmental stage. Initially all methods were developed to extract quantitative numerical information from confocal images of segmentation gene expression in Drosophila melanogaster. The application of these methods to Drosophila images makes it possible to reveal new mechanisms in the formation of segmentation gene expression domains, as well as to construct a quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.
