GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem

Abstract Motivation Non-linear ordinary differential equation (ODE) models that contain numerous parameters are suitable for inferring an emulated gene regulatory network (eGRN). However, the number of experimental measurements is usually far smaller than the number of parameters of the eGRN model that leads to an underdetermined problem. There is no unique solution to the inference problem for an eGRN using insufficient measurements. Results This work proposes an evolutionary modelling algorithm (EMA) that is based on evolutionary intelligence to cope with the underdetermined problem. EMA uses an intelligent genetic algorithm to solve the large-scale parameter optimization problem. An EMA-based method, GREMA, infers a novel type of gene regulatory network with confidence levels for every inferred regulation. The higher the confidence level is, the more accurate the inferred regulation is. GREMA gradually determines the regulations of an eGRN with confidence levels in descending order using either an S-system or a Hill function-based ODE model. The experimental results showed that the regulations with high-confidence levels are more accurate and robust than regulations with low-confidence levels. Evolutionary intelligence enhanced the mean accuracy of GREMA by 19.2% when using the S-system model with benchmark datasets. An increase in the number of experimental measurements may increase the mean confidence level of the inferred regulations. GREMA performed well compared with existing methods that have been previously applied to the same S-system, DREAM4 challenge and SOS DNA repair benchmark datasets. Availability and implementation All of the datasets that were used and the GREMA-based tool are freely available at https://nctuiclab.github.io/GREMA. Supplementary information Supplementary data are available at Bioinformatics online.

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Joint eQTL mapping and Inference of Gene Regulatory Network Improves Power of Detecting both cis- and trans-eQTLs

Bioinformatics ◽

10.1093/bioinformatics/btab609 ◽

2021 ◽

Author(s):

Xin Zhou ◽

Xiaodong Cai

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Complex Traits ◽

Multiple Testing ◽

Critical Role ◽

Supplementary Information ◽

Effective Sample Size ◽

Eqtl Mapping ◽

Gene Regulatory ◽

Cis And Trans

Abstract Motivation Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: 1) relatively small size of samples available, and 2) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. Results In this paper, we employ the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further employed to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. Availability R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. Supplementary information Supplementary data are available at Bioinformatics online.

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Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction

Bioinformatics ◽

10.1093/bioinformatics/btab647 ◽

2021 ◽

Author(s):

Gianvito Pio ◽

Paolo Mignone ◽

Giuseppe Magazzù ◽

Guido Zampieri ◽

Michelangelo Ceci ◽

...

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Transfer Learning ◽

Regulatory Network ◽

Drug Targets ◽

Human Gene ◽

Supplementary Information ◽

Model Organisms ◽

Metabolic Modelling ◽

Gene Regulatory

Abstract Motivation Gene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organisation across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge. Moreover, except for a few recent attempts, most of the existing approaches only consider the information of the organism under analysis, without exploiting the information of related model organisms. Results We propose a novel method for the reconstruction of the human gene regulatory network, based on a transfer learning strategy that synergically exploits information from human and mouse, conveyed by gene-related metabolic features generated in-silico from gene expression data. Specifically, we learn a predictive model from metabolic activity inferred via tissue-specific metabolic modelling of artificial gene knockouts. Our experiments show that the combination of our transfer learning approach with the constructed metabolic features provides a significant advantage in terms of reconstruction accuracy, as well as additional clues on the contribution of each constructed metabolic feature. Availability The system, the datasets and all the results obtained in this study are available at: https://doi.org/10.6084/m9.figshare.c.5237687 Supplementary information Supplementary data are available at Bioinformatics online.

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Optimal design of gene knockout experiments for gene regulatory network inference

Bioinformatics ◽

10.1093/bioinformatics/btv672 ◽

2015 ◽

Vol 32 (6) ◽

pp. 875-883 ◽

Cited By ~ 18

Author(s):

S. M. Minhaz Ud-Dean ◽

Rudiyanto Gunawan

Keyword(s):

Gene Regulatory Network ◽

Biological Networks ◽

Regulatory Network ◽

Network Inference ◽

Gene Knockout ◽

Directed Graphs ◽

Gene Interaction ◽

Supplementary Information ◽

Gene Regulatory Network Inference ◽

Gene Regulatory

Abstract Motivation: We addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference. Results: We developed REDuction of UnCertain Edges (REDUCE) algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene interactions that could not be verified by prior data. The optimal experiment corresponds to the maximum number of uncertain interactions that could be verified by the resulting data. For this purpose, we introduced the concept of edge separatoid which gave a list of nodes (genes) that upon their removal would allow the verification of a particular gene interaction. Finally, we proposed a procedure that iterates over performing KO experiments, ensemble update and optimal DOE. The case studies including the inference of Escherichia coli GRN and DREAM 4 100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. In comparison to systematic KOs, REDUCE could provide much higher information return per gene KO experiment and consequently more accurate GRN estimates. Conclusions: REDUCE represents an enabling tool for tackling the underdetermined GRN inference. Along with advances in gene deletion and automation technology, the iterative procedure brings an efficient and fully automated GRN inference closer to reality. Availability and implementation: MATLAB and Python scripts of REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning

Bioinformatics ◽

10.1093/bioinformatics/btz420 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5199-5206 ◽

Cited By ~ 2

Author(s):

Fredrik Wrede ◽

Andreas Hellander

Keyword(s):

Supervised Learning ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Network Models ◽

Feature Space ◽

Supplementary Information ◽

Human In The Loop ◽

Computational Exploration ◽

Gene Regulatory ◽

Simulation Results

Abstract Motivation Discrete stochastic models of gene regulatory network models are indispensable tools for biological inquiry since they allow the modeler to predict how molecular interactions give rise to nonlinear system output. Model exploration with the objective of generating qualitative hypotheses about the workings of a pathway is usually the first step in the modeling process. It involves simulating the gene network model under a very large range of conditions, due to the large uncertainty in interactions and kinetic parameters. This makes model exploration highly computational demanding. Furthermore, with no prior information about the model behavior, labor-intensive manual inspection of very large amounts of simulation results becomes necessary. This limits systematic computational exploration to simplistic models. Results We have developed an interactive, smart workflow for model exploration based on semi-supervised learning and human-in-the-loop labeling of data. The workflow lets a modeler rapidly discover ranges of interesting behaviors predicted by the model. Utilizing that similar simulation output is in proximity of each other in a feature space, the modeler can focus on informing the system about what behaviors are more interesting than others by labeling, rather than analyzing simulation results with custom scripts and workflows. This results in a large reduction in time-consuming manual work by the modeler early in a modeling project, which can substantially reduce the time needed to go from an initial model to testable predictions and downstream analysis. Availability and implementation A python-package is available at https://github.com/Wrede/mio.git. Supplementary information Supplementary data are available at Bioinformatics online.

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Reverse engineering of gene regulatory network using restricted gene expression programming

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500219 ◽

2016 ◽

Vol 14 (05) ◽

pp. 1650021 ◽

Cited By ~ 4

Author(s):

Bin Yang ◽

Sanrong Liu ◽

Wei Zhang

Keyword(s):

Gene Expression ◽

Evolutionary Algorithm ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Gene Expression Programming ◽

Cuckoo Search ◽

Area Of Interest ◽

Benchmark Datasets ◽

Gene Regulatory

Inference of gene regulatory networks has been becoming a major area of interest in the field of systems biology over the past decade. In this paper, we present a novel representation of S-system model, named restricted gene expression programming (RGEP), to infer gene regulatory network. A new hybrid evolutionary algorithm based on structure-based evolutionary algorithm and cuckoo search (CS) is proposed to optimize the architecture and corresponding parameters of model, respectively. Two synthetic benchmark datasets and one real biological dataset from SOS DNA repair network in E. coli are used to test the validity of our method. Experimental results demonstrate that our proposed method performs better than previously proposed popular methods.

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