Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics

Linearized catalytic reaction equations (modelling, for example, the dynamics of genetic regulatory networks), under the constraint that expression levels, i.e. molecular concentrations of nucleic material, are positive, exhibit non-trivial dynamical properties, which depend on the average connectivity of the reaction network. In these systems, an inflation of the edge of chaos and multi-stability have been demonstrated to exist. The positivity constraint introduces a nonlinearity, which makes chaotic dynamics possible. Despite the simplicity of such minimally nonlinear systems , their basic properties allow us to understand the fundamental dynamical properties of complex biological reaction networks. We analyse the Lyapunov spectrum, determine the probability of finding stationary oscillating solutions, demonstrate the effect of the nonlinearity on the effective in- and out-degree of the active interaction network , and study how the frequency distributions of oscillatory modes of such a system depend on the average connectivity.

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A Simple Computational Cell: Coupling Boolean Gene and Protein Networks

Artificial Life ◽

10.1162/artl_a_00060 ◽

2012 ◽

Vol 18 (2) ◽

pp. 223-236 ◽

Cited By ~ 4

Author(s):

Larry Bull

Keyword(s):

Information Processing ◽

Protein Interaction ◽

Protein Interaction Network ◽

Regulatory Networks ◽

Interaction Network ◽

Boolean Model ◽

Genetic Regulatory Networks ◽

Simulated Evolution ◽

Cell Coupling ◽

Computational Cell

This article presents an abstract, tunable model containing two of the principal information-processing features of cells and explores its use with simulated evolution. The random Boolean model of genetic regulatory networks is extended to include a protein interaction network. The underlying behavior of the resulting two coupled dynamical networks is investigated before their evolvability is explored using a version of the NK model of fitness landscapes.

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Modeling Genetic Regulatory Networks Using First-Order Probabilistic Logic

10.21236/ada582376 ◽

2013 ◽

Author(s):

Nand Kishore ◽

Radhakrishnan Balu ◽

Shashi P. Karna

Keyword(s):

Regulatory Networks ◽

Genetic Regulatory Networks ◽

Probabilistic Logic ◽

First Order

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Genome-Wide Identification of differently expressed lncRNAs, mRNAs, and circRNAs in patients with osteoarthritis

Current Bioinformatics ◽

10.2174/1574893615999200706002907 ◽

2020 ◽

Vol 15 ◽

Author(s):

Yeqing Sun ◽

Lei Chen ◽

Yingqi Zhang ◽

Jincheng Zhang ◽

Shashi Ranjan Tiwari

Keyword(s):

Regulatory Networks ◽

Expression Profiles ◽

Interleukin 1 ◽

Interaction Network ◽

Circular Rna ◽

Negative Regulation ◽

Differentially Expressed ◽

Positive Regulation ◽

Proteinprotein Interaction ◽

Differently Expressed Genes

Background: Osteoarthritis (OA), one of the most important causes leading to joint disability, was considered as an untreatable disease. A series of genes were reported to regulate the pathogenesis of OA, including microRNAs, Long non-coding RNAs and Circular RNA. So far, the expression profiles and functions of lncRNAs, mRNAs, and circRNAs in OA are not fully understood. Objective: The present study aimed to identify differently expressed genes in OA. Methods: The present study conducted RNA-seq to identify differently expressed genes in OA. Ontology (GO) analysis was used to analysis the Molecular Function and Biological Process. KEGG pathway analysis was used to perform the differentially expressed lncRNAs in biological pathways. Results: Hierarchical clustering revealed a total of 943 mRNAs, 518 lncRNAs, and 300 circRNAs were dysregulated in OA compared to normal samples. Furthermore, we constructed differentially expressed mRNAs mediated proteinprotein interaction network, differentially expressed lncRNAs mediated trans regulatory networks, and competitive endogenous RNA (ceRNA) to reveal the interaction among these genes in OA. Bioinformatics analysis revealed these dysregulated genes were involved in regulating multiple biological processes, such as wound healing, negative regulation of ossification, sister chromatid cohesion, positive regulation of interleukin-1 alpha production, sodium ion transmembrane transport, positive regulation of cell migration, and negative regulation of inflammatory response. To the best of our knowledge, this study for the first time revealed the expression pattern of mRNAs, lncRNAs and circRNAs in OA. Conclusion: This study provided novel information to validate these differentially expressed RNAs may be as possible biomarkers and targets in OA.

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Reachable Set Estimation of Switched Genetic Regulatory Networks with Mixed Delays and Bounded Disturbances*

2020 59th IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc42340.2020.9304129 ◽

2020 ◽

Author(s):

Xian Zhang ◽

Lina Zhang ◽

Yu Xue ◽

Xin Wang ◽

Mouquan Shen ◽

...

Keyword(s):

Regulatory Networks ◽

Genetic Regulatory Networks ◽

Reachable Set ◽

Mixed Delays ◽

Set Estimation ◽

Bounded Disturbances ◽

Reachable Set Estimation

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Transcriptional landscape of cellular networks reveal interactions driving the dormancy mechanisms in cancer

Scientific Reports ◽

10.1038/s41598-021-94005-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Dilara Uzuner ◽

Yunus Akkoç ◽

Nesibe Peker ◽

Pınar Pir ◽

Devrim Gözüaçık ◽

...

Keyword(s):

Cancer Cells ◽

Regulatory Networks ◽

Metastatic Cancer ◽

Interaction Network ◽

Current Treatment ◽

Cellular Mechanisms ◽

Protein Protein Interaction ◽

Cancer Dormancy ◽

Protein Protein Interaction Networks ◽

First Time

AbstractPrimary cancer cells exert unique capacity to disseminate and nestle in distant organs. Once seeded in secondary sites, cancer cells may enter a dormant state, becoming resistant to current treatment approaches, and they remain silent until they reactivate and cause overt metastases. To illuminate the complex mechanisms of cancer dormancy, 10 transcriptomic datasets from the literature enabling 21 dormancy–cancer comparisons were mapped on protein–protein interaction networks and gene-regulatory networks to extract subnetworks that are enriched in significantly deregulated genes. The genes appearing in the subnetworks and significantly upregulated in dormancy with respect to proliferative state were scored and filtered across all comparisons, leading to a dormancy–interaction network for the first time in the literature, which includes 139 genes and 1974 interactions. The dormancy interaction network will contribute to the elucidation of cellular mechanisms orchestrating cancer dormancy, paving the way for improvements in the diagnosis and treatment of metastatic cancer.

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