Towards precise reconstruction of gene regulatory networks by data integration

Liu, Zhiping

doi:10.1007/s40484-018-0139-4

Cited by 5 publications

(4 citation statements)

References 119 publications

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“…This makes the law of localization a very efficient method for finding perturbation responses. The growing availability of multiple high-throughput (knockdown/knockout) data and (cellular) perturbation screens that can be compared with predicted response patterns from the law of localization may promote better network reconstruction, − model discrimination, , and network engineering …”

Section: Introductionmentioning

confidence: 99%

Localization of Noise in Biochemical Networks

Fajiculay

Hsu

2023

ACS Omega

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Noise, or uncertainty in biochemical networks, has become an important aspect of many biological problems. Noise can arise and propagate from external factors and probabilistic chemical reactions occurring in small cellular compartments. For species survival, it is important to regulate such uncertainties in executing vital cell functions. Regulated noise can improve adaptability, whereas uncontrolled noise can cause diseases. Simulation can provide a detailed analysis of uncertainties, but parameters such as rate constants and initial conditions are usually unknown. A general understanding of noise dynamics from the perspective of network structure is highly desirable. In this study, we extended the previously developed law of localization for characterizing noise in terms of (co)variances and developed noise localization theory. With linear noise approximation, we can expand a biochemical network into an extended set of differential equations representing a fictitious network for pseudo-components consisting of variances and covariances, together with chemical species. Through localization analysis, perturbation responses at the steady state of pseudo-components can be summarized into a sensitivity matrix that only requires knowledge of network topology. Our work allows identification of buffering structures at the level of species, variances, and covariances and can provide insights into noise flow under non-steady-state conditions in the form of a pseudo-chemical reaction. We tested noise localization in various systems, and here we discuss its implications and potential applications. Results show that this theory is potentially applicable in discriminating models, scanning network topologies with interesting noise behavior, and designing and perturbing networks with the desired response.

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Section: Introductionmentioning

confidence: 99%

Localization of Noise in Biochemical Networks

Fajiculay

Hsu

2023

ACS Omega

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“…The use of gene expression datasets for the reconstruction of gene regulatory networks (GRN) and the simulation of the reconstructed models is one of the topical directions of current bioinformatics [ 1 , 2 , 3 , 4 ]. GRN in this case is a group of molecular elements interconnections that determines the functional possibilities of a biological organism.…”

Section: Introductionmentioning

confidence: 99%

Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

Babichev

Škvor

2020

Diagnostics

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In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients’ health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient’s health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients’ gene expression profiles—the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps—in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers—logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient’s health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient’s health.

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“…The knowledge-based models could rely on the prior information, e.g., reference regulatory networks documented in the databases, and then these reference networks are trimmed based on their consistencies with the gene expressions [ 11 – 13 ]. The prior knowledge is useful for the inference due to the noisy data in the -omics technology.…”

Section: Introductionmentioning

confidence: 99%

LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks

2020

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Background: Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information. Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data. However, all these approaches require an a priori known network structure. Results: Here, by introducing a new probabilistic logic for continuous data, we propose a novel logic-based approach (called the LogicNet) for the simultaneous reconstruction of the GRN structure and identification of the logics among the regulatory genes, from the continuous gene expression data. In contrast to the previous approaches, the LogicNet does not require an a priori known network structure to infer the logics. The proposed probabilistic logic is superior to the existing fuzzy logics and is more relevant to the biological contexts than the fuzzy logics. The performance of the LogicNet is superior to that of several Mutual Information-based and regression-based tools for reconstructing GRNs. Conclusions: The LogicNet reconstructs GRNs and logic functions without requiring prior knowledge of the network structure. Moreover, in another application, the LogicNet can be applied for logic function detection from the known regulatory genes-target interactions. We also conclude that computational modeling of the logical interactions among the regulatory genes significantly improves the GRN reconstruction accuracy.

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Towards precise reconstruction of gene regulatory networks by data integration

Cited by 5 publications

References 119 publications

Localization of Noise in Biochemical Networks

Localization of Noise in Biochemical Networks

Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks

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