Intelligent Framework with Controlled Behavior for Gene Regulatory Networks Reconstruction

Gene Regulatory Networks (GRNs) are the pioneering methodology for finding new gene interactions getting insights of the biological processes using time series gene expression data. It remains a challenge to study the temporal nature of gene expression data that mimic complex non-linear dynamics of the network. In this paper, an intelligent framework of recurrent neural network (RNN) and swarm intelligence (SI) based Particle Swarm Optimization (PSO) with controlled behaviour has been proposed for the reconstruction of GRN from time-series gene expression data. A novel PSO algorithm enhanced by human cognition influenced by the ideology of Bhagavad Gita is employed for improved learning of RNN. RNN guided by the proposed algorithm simulates the nonlinear and dynamic gene interactions to a greater extent. The proposed method shows superior performance over traditional SI algorithms in searching biologically plausible candidate networks. The strength of the method is verified by analyzing the small artificial network and real data of Escherichia coli with improved accuracy.

A model explaining mRNA level fluctuations based on activity demands and RNA age

Cellular RNA levels typically fluctuate and are influenced by different transcription rates and RNA degradation rates. However, the understanding of the fundamental relationships between RNA abundance, environmental stimuli, RNA activities, and RNA age distributions is incomplete. Furthermore, the rates of RNA degradation and transcription are difficult to measure in transcriptomic experiments in living organisms, especially in studies involving humans. A model based on activity demands and RNA age was developed to explore the mechanisms of RNA level fluctuations. Using single-cell time-series gene expression experimental data, we assessed the transcription rates, RNA degradation rates, RNA life spans, RNA demand, accumulated transcription levels, and accumulated RNA degradation levels. This model could also predict RNA levels under simulation backgrounds, such as stimuli that induce regular oscillations in RNA abundance, stable RNA levels over time that result from long-term shortage of total RNA activity or from uncontrollable transcription, and relationships between RNA/protein levels and metabolic rates. This information contributes to existing knowledge.

Improvement of Free Fatty Acid Secretory Productivity in Aspergillus oryzae by Comprehensive Analysis on Time-Series Gene Expression

Aspergillus oryzae is a filamentous fungus that has historically been utilized in the fermentation of food products. In recent times, it has also been introduced as a component in the industrial biosynthesis of consumable compounds, including free fatty acids (FFAs), which are valuable and versatile products that can be utilized as feedstocks in the production of other commodities, such as pharmaceuticals and dietary supplements. To improve the FFA secretory productivity of A. oryzae in the presence of Triton X-100, we analyzed the gene expression of a wild-type control strain and a disruptant strain of an acyl-CoA synthetase gene, faaA, in a time-series experiment. We employed a comprehensive analysis strategy using the baySeq, DESeq2, and edgeR algorithms to clarify the vital pathways for FFA secretory productivity and select genes for gene modification. We found that the transport and metabolism of inorganic ions are crucial in the initial stages of FFA production and revealed 16 candidate genes to be modified in conjunction with the faaA disruption. These genes were verified through the construction of overexpression strains, and showed that the manipulation of reactions closer to the FFA biosynthesis step led to a higher increase in FFA secretory productivity. This resulted in the most successful overexpression strains to have an FFA secretory productivity more than two folds higher than that of the original faaA disruptant. Our study provides guidance for further gene modification for FFA biosynthesis in A. oryzae and for enhancing the productivity of other metabolites in other microorganisms through metabolic engineering.

Mapping global shifts in Saccharomyces cerevisiae gene expression across asynchronous time trajectories with diffusion maps

Grapes grown in a particular geographic region often produce wines with consistent characteristics, suggesting there are site-specific factors driving recurrent fermentation outcomes. However, our understanding of the relationship between site-specific factors, microbial metabolism, and wine fermentation outcomes are not well understood. Here, we used differences in Saccharomyces cerevisiae gene expression as a biosensor for differences among Pinot noir fermentations from 15 vineyard sites. We profiled time series gene expression patterns of primary fermentations, but fermentations proceeded at different rates, making analyzes of these data with conventional differential expression tools difficult. This led us to develop a novel approach that combines diffusion mapping with continuous differential expression analysis. Using this method, we identified vineyard specific deviations in gene expression, including changes in gene expression correlated with the activity of the non-Saccharomyces yeast Hanseniaspora uvarum, as well as with initial nitrogen concentrations in grape musts. These results highlight novel relationships between site-specific variables and Saccharomyces cerevisiae gene expression that are linked to repeated wine fermentation outcomes. In addition, we demonstrate that our analysis approach can extract biologically relevant gene expression patterns in other contexts (e.g., hypoxic response of Saccharomyces cerevisiae), indicating that this approach offers a general method for investigating asynchronous time series gene expression data.ImportanceWhile it is generally accepted that foods, in particular wine, possess sensory characteristics associated with or derived from their place of origin, we lack knowledge of the biotic and abiotic factors central to this phenomenon. We have used Saccharomyces cerevisiae gene expression as a biosensor to capture differences in fermentations of Pinot noir grapes from 15 vineyards across two vintages. We find that gene expression by non-Saccharomyces yeasts and initial nitrogen content in the grape must correlates with differences in gene expression among fermentations from these vintages. These findings highlight important relationships between site-specific variables and gene expression that can be used to understand, or possibly modify, wine fermentation outcomes. Our work also provides a novel analysis method for investigating asynchronous gene expression data sets that is able to reveal both global shifts and subtle differences in gene expression due to varied cell – environment interactions.

Novel Study of Model-Based Clustering Time Series Gene Expression in Different Tissues: Applications to Aging Process

Background: Aging is an organized biological process that is regulated by highly interconnected pathways between different cells and tissues in the living organism. Identification of similar genes between tissues in different ages may also help to discover the general mechanism of aging or to discover more effective therapeutic decisions. Objective: Objective: According to the wide application of model-based clustering techniques, the aim is to evaluate the performance of the Mixture of Multivariate Normal Distributions (MMNDs) as a valid method for clustering time series gene expression data with the Mixture of Matrix-Variate Normal Distributions (MMVNDs). Methods: In this study, the expression of aging data from NCBI’s Gene Expression Omnibus was elaborated to utilize proper data. A set of common genes which were differentially expressed between different tissues were selected and then clustered together through two methods. Finally, the biological significance of clusters was evaluated, using their ability to find genes in the cell using Enricher. Results: The MMVNDs is more efficient to find co-express genes. Six clusters of genes were observed using the MMVNDs. According to the functional analysis, most genes in clusters 1-6 are related to the B-cell receptors and IgG immunoglobulin complex, proliferating cell nuclear antigen complex, the metabolic pathways of iron, fat, and body mass control, the defense against bacteria, the cancer development incidence, and the chronic kidney failure, respectively. Conclusion: Results showed that most biological changes of aging between tissues are related to the specific components of immune cells. Also, the application of MMVNDs can increase the ability to find similar genes.

A neuro-evolution approach to infer a Boolean network from time-series gene expressions

Summary In systems biology, it is challenging to accurately infer a regulatory network from time-series gene expression data, and a variety of methods have been proposed. Most of them were computationally inefficient in inferring very large networks, though, because of the increasing number of candidate regulatory genes. Although a recent approach called GABNI (genetic algorithm-based Boolean network inference) was presented to resolve this problem using a genetic algorithm, there is room for performance improvement because it employed a limited representation model of regulatory functions. In this regard, we devised a novel genetic algorithm combined with a neural network for the Boolean network inference, where a neural network is used to represent the regulatory function instead of an incomplete Boolean truth table used in the GABNI. In addition, our new method extended the range of the time-step lag parameter value between the regulatory and the target genes for more flexible representation of the regulatory function. Extensive simulations with the gene expression datasets of the artificial and real networks were conducted to compare our method with five well-known existing methods including GABNI. Our proposed method significantly outperformed them in terms of both structural and dynamics accuracy. Conclusion Our method can be a promising tool to infer a large-scale Boolean regulatory network from time-series gene expression data. Availability and implementation The source code is freely available at https://github.com/kwon-uou/NNBNI. Supplementary information Supplementary data are available at Bioinformatics online.

Evaluation of classification and forecasting methods on time series gene expression data

Time series gene expression data is widely used to study different dynamic biological processes. Although gene expression datasets share many of the characteristics of time series data from other domains, most of the analyses in this field do not fully leverage the time-ordered nature of the data and focus on clustering the genes based on their expression values. Other domains, such as financial stock and weather prediction, utilize time series data for forecasting purposes. Moreover, many studies have been conducted to classify generic time series data based on trend, seasonality, and other patterns. Therefore, an assessment of these approaches on gene expression data would be of great interest to evaluate their adequacy in this domain. Here, we perform a comprehensive evaluation of different traditional unsupervised and supervised machine learning approaches as well as deep learning based techniques for time series gene expression classification and forecasting on five real datasets. In addition, we propose deep learning based methods for both classification and forecasting, and compare their performances with the state-of-the-art methods. We find that deep learning based methods generally outperform traditional approaches for time series classification. Experiments also suggest that supervised classification on gene expression is more effective than clustering when labels are available. In time series gene expression forecasting, we observe that an autoregressive statistical approach has the best performance for short term forecasting, whereas deep learning based methods are better suited for long term forecasting.