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The concentration of genes that control domestication traits in so-called “agroislands” complicates breeding. Therefore, information about where such islands are located in the genome and with what traits of domestication they are associated is of great importance for further research aimed at improving modern varieties. In the search for such information in the genomes of chickpea landraces, the BayPass program was used to assess the association of single nucleotide polymorphisms with population-specific covariates chosen as complexes of domestication syndrome traits in cultivars of the same breeding purpose. The regions of the genome with which these covariates are associated are located on chromosomes 1, 5, 6, 7, and 8. These regions are potential agroislands, as indirectly confirmed by earlier data.

The roles that noncoding RNAs play in various cells have been a focus of intense research in recent years. Circular RNAs (circRNAs) are noncoding RNAs that were initially thought to be junk by-products of splicing. Many circRNAs have been found to demonstrate important regulatory functions and, in particular, to act as miRNA sponges. The function of an miRNA sponge was compared with the function of a classic transcription factor (TF)-dependent regulatory loop in terms of efficiency, working rate, and noise characteristics. A circRNA with multiple binding sites for miRNA was found to act more efficiently than a TF and the respective loop worked faster, but only when the binding sites were not fully saturated with miRNA molecules. The noise characteristics of the circRNA loop were significantly worse with an increasing number of binding sites. A circRNA with one binding site was shown to be inefficient as an miRNA sponge. The circRNA-mediated regulation was assumed to provide a specific tool to the cell.

The collection of seeds and living plants at the N.I. Vavilov All Russian Institute of Plant Genetic Resources (VIR) contains historic chickpea landraces sampled from major historic centers of chickpea cultivation and secondary diversification. In this work, we analyze the results obtained from phenotype characterization of 407 historic chickpea landraces grown in the trial fields of Kuban experimental station in 2017. GWAS analysis identified three single nucleotide polymorphisms on chromosomes 2, 7 and 8 that were highly associated with the days to flowering phenotype trait. These single nucleotide polymorphisms and the regions of genome near to these single nucleotide polymorphisms were identified in previous studies. Estimation of the Genotype–Environment interaction for phenotypic traits coincident with plant seeds led to identification of the best genotypes in the environments. These genotypes have an alternative homozygote at loci Ca7:30930779 which shows similar phenotypic traits that were described during phenotype characterization of seeds grown in the trial fields of the Kuban experimental station in 2016 and with the period of flowering typical of phenotype characterization of the plants in 2017. These results can accelerate the search for the seed samples that are the most promising for cultivation of chickpea landraces.

Precise prediction of the timing of floral initiation helps breeders create new varieties that can achieve maximum efficiency under the influence of a changing climate. A previously constructed model was used to compare the impact of daily weather parameters on the flowering time of wild varieties of chickpeas that were collected in different geographic locations in Turkey. We found that plants from the high altitude areas, unlike plant samples from lower altitudes, can adapt to lower temperatures and longer days. Forecasts of changes in time to flowering in the studied wild chickpea varieties were made with the model and climate change predictions using MarkSim software to generate daily weather data for Ankara. The mean thresholds for the sowing flowering period for the 2020–2039, 2040–2059, and 2060–2080 time periods shifted for 21 combinations of the scenarios of plant growth and development and plant collecting sites, accounting for approximately half of the 40 cases, thereby suggesting a moderate effect of climate change on flowering time in the studied varieties.

—We have analyzed the gene expression dynamics in floral transition in the CDC Frontier chickpea cultivar. We provide a model, in several versions, to predict the expression dynamics of five flowering genes, taking the expression of their regulators as an input. The models were trained using the random forest method on the previously published expression data for ten flowering genes under the short- and long-day growing conditions. The resulting models correctly predict the dynamics of the average expression levels under long days. We show that the models for CDC Frontier mainly reproduce the regulatory interactions between the key genes described for the Arabidopsis thaliana model plant. Based on the analysis, we hypothesize that the short-day data and the long-day data contain qualitatively different information, which may be due to different regulatory modules that function in different conditions. For the regulators of the flower meristem identity genes AP1 and LFY, our models predict FTa3 as the main activator and TFL1c as the main repressor under long days.

—The seed bank of the Vavilov All-Russia Institute of Plant Genetic Resources (VIR) contains a wide range of landraces that were collected almost 100 years ago, in which natural selection might leave signatures through crop diversification. In this study, we analyzed 407 landraces sampled at centers of the origin of the chickpea and at sites of secondary diversity. We hypothesize that a fraction of single nucleotide polymorphisms might have exhibited strong selection to a range of environmental conditions that chickpea experienced during domestication and subsequent geographic distribution. Using the BayPass package we identified 13 polymorphisms; these assort by environmental conditions and are strong candidates for local adaptation.

—Biodiversity-based approaches are at the forefront of creating new varieties in soybean production. In this paper, we analyzed the natural variability in the population of cultivated soybean varieties used in breeding in the Russian Federation using genome-wide association studies. Genome regions that control the protein and oil contents in seeds, as well as productivity, have been identified, many of which have not been described previously. These results open the way to the design of new markers for marker selection of this crop.

—Soybean phenology is strongly influenced by temperature and day length, and phenological records clearly reflect the changes in climatic conditions. A model including three artificial neural networks was designed to predict the time intervals between sowing, emergence, flowering, and maturity as dependent on climatic factors. Ensemble regression models were constructed to predict the yield, seed protein, and oil content in soybean. Data on maturation were analyzed for early-maturing soybean accessions phenotyped at two experimental stations of Vavilov Institute of Plant Genetic Resources in the North-Caucasian and Northwestern regions of Russia. The model was implemented in Python using the Keras and TensorFlow packages.

Regression models for time to flowering had been developed for VIR chickpea landraces collected in Turkey and Ethiopia. Predicted flowering time coincides closely with experimental data. The difference between models is statistically significant. The impact of the temperature to the model for Ethiopia landraces is weaker than that for Turkey landraces, 48 and 60% respectively. The impact of precipitation on phenotype is more than 80%. The obtained results are in agreement with climate characteristics at collection sites.

—A method for certification of soybean varieties was developed based on single-nucleotide polymorphisms. A genetic algorithm was used to select an optimal set consisting of 28 single-nucleotide polymorphisms, which was then applied to certify 243 varieties. For all varieties, unique signatures of single-nucleotide polymorphisms were obtained, which differed from the signatures of any other variety for at least three positions.

Structural equation modeling (SEM) is a second-generation multivariate method to estimate the causal interactions in a set of variables and includes, as special cases, several statistical methods (regression analysis, path analysis, and confirmatory factor analysis). This review focuses on all of the main SEM models and various methods used to optimize the model parameters. Representative examples are discussed to illustrate SEM application in molecular biology, including modeling of biochemical processes, relationships between genetic markers and diseases, and interactions within gene networks.

This review deals with the recent studies expanding the idea of positional information in the early embryogenesis of Drosophila melanogaster. Previous studies showed that, in the course of segment determination in Drosophila, information created by gradients of products of maternal coordinate genes is not “read” statically, being interpreted by their zygotic target genes via regulatory interactions. This leads to spatial shifts in the expression of target genes relative to the original positions as well as to dynamic reduction in the zygotic expression variability. However, according to recent data, interpretation of positional information includes the interaction between not only zygotic target genes but also the maternal coordinate genes themselves. Different systems of maternal coordinate genes (maternal systems)—the posterior-anterior, terminal, and dorsoventral—can interact with each other. This is usually expressed in the regulation of zygotic target genes of one maternal system by other maternal systems. The concept of a “morphogenetic network” was introduced to define the interaction of maternal systems during determination of spatial gene expression in the early Drosophila embryo.

Various molecular configurations can be formed by combinations of free and occupied transcription-factor binding sites at an enhancer site that regulates the transcriptional activity of a target gene. Thermodynamic models of gene expression enable the calculation of the probability of transcriptional activation of a target gene using the probabilities that correspond to specific molecular configurations of an enhancer. A simple stochastic model for the formation of such configurations is presented in this study. The processes of the non-specific binding of transcription factors to DNA, the sliding of proteins along DNA, and energetically favorable binding to specific sites are taken into account in the model, which has been used to investigate the properties of molecular configurations of the regulatory site of the knirps gene, which is expressed during early embryonic development of Drosophila in a manner that is dependent on the activity of the Hunchback and Bicoid transcription factors. The assumptions that are included in the model allowed for discrimination between three successive dynamic stages that are characterized by different binding rates. Transcription-factor binding sites could be divided into three groups according to the degree of occupancy. The probability of occupation that was calculated for the sites in a quasi-equilibrium state was substantially different from the distribution that was predicted using the assumption of thermodynamic equilibrium that is employed in thermodynamic models of expression. These results can serve as the first step towards the refinement of expression models based on more detailed information on the regulatory regions of target genes.

Signaling networks play the central role in the regulation of processes in a single cell and in the entire body. A recent breakthrough in technologies for systems biology, which combine experimental and mathematical methods, permits scientists to model signaling pathways in an individual cell and in cell populations. This approach provides new information on mechanisms that regulate a variety of biological processes. Here we discuss the mathematical formalisms that are applied to signaling pathway modeling and relevant experimental methods.

We have developed a numerical method for the analysis of particle trajectories in living cells, where a type of movement is determined by Akaike's information criterion, while model parameters are identified by a weighted least squares method. The method is realized in computer software, written in the Java programming language, that enables us to automatically conduct the analysis of trajectories. The method is tested on synthetic trajectories with known parameters, and applied to the analysis of replication complexes in cells, infected with hepatitis C virus. Results of the analysis are in agreement with available data on the movement of biological objects along microtubules.

Expression patterns of segmentation genes form under the influence of gradients of maternal transcription factors, which initiate spatially local expression in the segmentation gene cascade. Bcd acts as one of these activators. A model of the regulation in the gap-gene network was studied by varying the Bcd concentration. The topology that is known for the gap-gene network was found to be insufficient for explaining the experimental finding that the hb anterior expression domain shifts when the Bcd concentration changes in the embryo. Modeling that was performed to comply with this experimental finding yielded a new topology, which determined the proper shifts of the hb expression domain. The result indicates that interactions of hb, Kr, and gt act as key regulatory factors to ensure the correct behavior of the hb expression pattern upon changes in Bcd concentration. This study made it possible to specify the limits of the validity of phenomenological models of gene networks.

Gene networks frequently contain a motif with a feed-forward loop, in which a transcription factor regulates target-gene expression both directly and indirectly via another regulator that also controls the target gene. The results of mathematical modeling of the feed-forward loops with either transcription factor or miRNA as a repressor in the indirect pathway are described. The behavior of such loops in time has been studied. As has been shown, replacement of the second transcription factor with miRNA changes the dynamics of feed-forward loops, conferring new properties to a biological system that are critical for fine tuning of its functions.

ICCV 96029 is the earliest flowering chickpea cultivar. We analyzed the model of the gene network controlling floral transition and hypothesized that this chickpea cultivar is significantly different from other legumes in organization of mobile flowering signals related to the expression of the FT (FLOWERING LOCUS T) gene paralogs. Unlike pea and other legumes, that according to previously obtained data could demonstrate cross-regulation between FT-like genes and different nature of the associated flowering signals, the simulation results for chickpea suggest that the FT paralogs play additive roles in the promotion of flowering.

Image analysis plays an important role both in medical diagnostics and in biology. The main reasons that prevent the creation of objective and reliable methods of analysis of biomedical images are the high variability and heterogeneity of the biological material, distortion introduced by the experimental procedures, and the large size of the images. This paper presents preliminary results on creating a system called Ter-aPro, which combines a platform for image processing (ProStack) and a raster data storage system (rasdaman). This integrated system can be used in a cloud environment, providing access to the methods of visualization, analysis, and processing of a large amount of images through the Internet. Such an approach increases the speed and quality of image understanding and softens the limitations imposed by other systems. The system allows us to view uploaded images in the browser without having to install additional software on any device connected to the Internet, such as tablet computers and smartphones. This paper presents the preliminary results of processing biomedical images.

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 mode of their use.

We describe a method to solve multi-objective inverse problems under uncertainty. The method was tested on non-linear models of dynamic series and population dynamics, as well as on the spatiotemporal model of gene expression in terms of non-linear differential equations. We consider how to identify model parameters when experimental data contain additive noise and measurements are performed in discrete time points. We formulate the multi-objective problem of optimization under uncertainty. In addition to a criterion of least squares difference we applied a criterion which is based on the integral of trajectories of the system spatiotemporal dynamics, as well as a heuristic criterion CHAOS based on the decision tree method. The optimization problem is formulated using a fuzzy statement and is constrained by penalty functions based on the normalized membership functions of a fuzzy set of model solutions. This allows us to reconstruct the expression pattern of hairy gene in Drosophila even-skipped mutants that is in good agreement with experimental data. The reproducibility of obtained results is confirmed by solution of inverse problems using different global optimization methods with heuristic strategies.

New nonlinear regression models were developed to predict climatic factor dependences of the periods of time from sowing to seedling emergence and from seedling emergence to flowering in soybean. The developed regression models are more accurate than earlier models, but are more complex in structure.

Living organisms well adapt themselves to changes in the environment and are robust to potential damage such as mutations. The epigenetic mechanism whereby the suppression of phenotypic variation is achieved has been called canalization. This paper summarizes results of research that employed experimental and theoretical approaches to uncover the mechanisms of canalization of variation in expression of segmentation genes.

Analysis of the quantitative data obtained by processing the confocal images showed that the initial variability of the expression pattern of Drosophila zygotic segmentation genes was strongly reduced by the onset of gastrulation. The following variability components were studied: the range of gene expression intensity in different embryos, the time and succession of the formation of expression domain, types of formation, and domain positioning. At the level of zygotic genes, the positioning error proved to be dynamically filtered with time.

We perform elastic registration by an algorithm based on a pixelwise and regularized optimization criterion. We express the deformation field thanks to B-splines, which allows us to deal with a rich variety of deformations. The algorithm is able to handle soft landmark constraints, which is particularly useful when parts of the images contain very little information or when it is unevenly distributed. We solve the problem by minimizing a distance between the target image and the warped source. We regularize this minimization problem by divergence and curl. We apply the proposed algorithm to the registration of the confocal scanning microscopy images of Drosophila embryos.

The differential evolution entirely parallel method has been developed to enable the identification of unknown parameters of mathematical models by minimization of the deviation of the solution from experimental data. The method is implemented in a free open-source software that is downloadable from the Internet. The results of processing of test functions showed that the accuracy of the method is comparable to that of the three best algorithms from CEC-2014. The method has been successfully used in a number of real biological problems.