ON EXPLORING EFFECTS OF MOLECULAR NOISE IN A SIMPLE VIRAL INFECTION MODEL

2010 ◽  
Vol 03 (01) ◽  
pp. 1-19 ◽  
Author(s):  
ZHI XIE ◽  
DON KULASIRI
Keyword(s):  
Viral Infection ◽  
Regulatory Networks ◽  
Virus Assembly ◽  
Intrinsic Noise ◽  
Genetic Regulatory Networks ◽  
Infection Model ◽  
Extrinsic Noise ◽  
Living Organisms ◽  
High Level ◽  
And Function

Intrinsic and extrinsic noises are all believed to be important in the development and function of many living organisms. In this study, we investigate the sources of the intrinsic noise and the influence of the extrinsic noise on an intracellular viral infection system. The contribution of the intrinsic noise from each reaction is measured by means of a special form of stochastic differential equations (SDEs), chemical Langevin equation. The intrinsic noise of the system is a linear sum of the noise in each of the reactions. The intrinsic noise mainly arises from the degradation of mRNA and the transcription processes. We then study the effects of extrinsic noise by the means of a general form of SDE. It is found that the noise of the viral components grows logarithmically with the increasing noise intensities. The system is most susceptible to the noise in the virus assembly process. A high level of noise in this process can even inhibit the growth of the viruses. This study also demonstrates the utility of SDEs in analyzing genetic regulatory networks perturbed by either inherent or parametric stochasticity.

scholarly journals A High-Level Petri Net Framework for Genetic Regulatory Networks

2007 ◽  
Vol 4 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Richard Banks ◽  
L. Jason Steggles
Keyword(s):  
Regulatory Networks ◽  
Formal Analysis ◽  
Boolean Model ◽  
Genetic Regulatory Networks ◽  
Cellular Systems ◽  
Logic Minimization ◽  
Formal Framework ◽  
The Individual ◽  
High Level ◽  
Compare And Contrast

Summary To understand the function of genetic regulatory networks in the development of cellular systems, we must not only realise the individual network entities, but also the manner by which they interact. Multi-valued networks are a promising qualitative approach for modelling such genetic regulatory networks, however, at present they have limited formal analysis techniques and tools. We present a flexible formal framework for modelling and analysing multi-valued genetic regulatory networks using high-level Petri nets and logic minimization techniques. We demonstrate our approach with a detailed case study in which part of the genetic regulatory network responsible for the carbon starvation stress response in Escherichia coli is modelled and analysed. We then compare and contrast this multivalued model to a corresponding Boolean model and consider their formal relationship.

scholarly journals Genetic Requirements for Klebsiella pneumoniae-Induced Liver Abscess in an Oral Infection Model

2009 ◽  
Vol 77 (7) ◽  
pp. 2657-2671 ◽  
Author(s):  
Ya-Chun Tu ◽  
Min-Chi Lu ◽  
Ming-Ko Chiang ◽  
Shu-Ping Huang ◽  
Hwei-Ling Peng ◽  
...  
Keyword(s):  
Klebsiella Pneumoniae ◽  
Liver Abscess ◽  
Regulatory Networks ◽  
Oral Infection ◽  
Genetic Regulatory Networks ◽  
Class I ◽  
Infection Model ◽  
Wild Type ◽  
Virulence Attenuation ◽  
Mutant Strains

ABSTRACT Klebsiella pneumoniae is the predominant pathogen of primary liver abscess. However, our knowledge regarding the molecular basis of how K. pneumoniae causes primary infection in the liver is limited. We established an oral infection model that recapitulated the characteristics of liver abscess and conducted a genetic screen to identify the K. pneumoniae genes required for the development of liver abscess in mice. Twenty-eight mutants with attenuated growth in liver or spleen samples out of 2,880 signature-tagged mutants that produced the wild-type capsule were identified, and genetic loci which were disrupted in these mutants were identified to encode products with roles in cellular metabolism, adhesion, transportation, gene regulation, and unknown functions. We further evaluated the virulence attenuation of these mutants in independent infection experiments and categorized them accordingly into three classes. In particular, the class I and II mutant strains exhibited significantly reduced virulence in mice, and most of these strains were not detected in extraintestinal tissues at 48 h after oral inoculation. Interestingly, the mutated loci of about one-third of the class I and II mutant strains encode proteins with regulatory functions, and the transcript abundances of many other genes identified in the same screen were markedly changed in these regulatory mutant strains, suggesting a requirement for genetic regulatory networks for translocation of K. pneumoniae across the intestinal barrier. Furthermore, our finding that preimmunization with certain class I mutant strains protected mice against challenge with the wild-type strain implied a potential application for these strains in prophylaxis against K. pneumoniae infections.

scholarly journals Insect Transcription Factors: A Landscape of Their Structures and Biological Functions in Drosophila and beyond

2018 ◽  
Vol 19 (11) ◽  
pp. 3691 ◽  
Author(s):  
Zhaojiang Guo ◽  
Jianying Qin ◽  
Xiaomao Zhou ◽  
Youjun Zhang
Keyword(s):  
Transcriptional Regulation ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Fruit Fly ◽  
Genetic Regulatory Networks ◽  
Biological Functions ◽  
Physiological Processes ◽  
Regulation Mechanisms ◽  
Living Organisms ◽  
Regulatory Functions

Transcription factors (TFs) play essential roles in the transcriptional regulation of functional genes, and are involved in diverse physiological processes in living organisms. The fruit fly Drosophila melanogaster, a simple and easily manipulated organismal model, has been extensively applied to study the biological functions of TFs and their related transcriptional regulation mechanisms. It is noteworthy that with the development of genetic tools such as CRISPR/Cas9 and the next-generation genome sequencing techniques in recent years, identification and dissection the complex genetic regulatory networks of TFs have also made great progress in other insects beyond Drosophila. However, unfortunately, there is no comprehensive review that systematically summarizes the structures and biological functions of TFs in both model and non-model insects. Here, we spend extensive effort in collecting vast related studies, and attempt to provide an impartial overview of the progress of the structure and biological functions of current documented TFs in insects, as well as the classical and emerging research methods for studying their regulatory functions. Consequently, considering the importance of versatile TFs in orchestrating diverse insect physiological processes, this review will assist a growing number of entomologists to interrogate this understudied field, and to propel the progress of their contributions to pest control and even human health.

scholarly journals Network design principle for robust oscillatory behaviors with respect to biological noise

2021 ◽  
Author(s):  
Lingxia Qiao ◽  
Zhi-Bo Zhang ◽  
Wei Zhao ◽  
Ping Wei ◽  
Lei Zhang
Keyword(s):  
Chemical Reactions ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Intrinsic Noise ◽  
Biological Noise ◽  
Extrinsic Noise ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory ◽  
Activator Inhibitor ◽  
Oscillatory Behaviors

Oscillatory behaviors, which are ubiquitous in transcriptional regulatory networks, are often subject to inevitable biological noise. Thus a natural question is how transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of biological noise. Here, we search all two- and three-node transcriptional regulatory network topologies for those robustly capable of accurate oscillation against the parameter variability (extrinsic noise) or stochasticity of chemical reactions (intrinsic noise). We find that, no matter what source of the noise is applied, the topologies containing the repressilator with positive auto-regulation show higher robustness of accurate oscillation than those containing the activator-inhibitor oscillator, and additional positive auto-regulation enhances the robustness against noise. Nevertheless, the attenuation of different sources of noise is governed by distinct mechanisms: the parameter variability is buffered by the long period, while the stochasticity of chemical reactions is filtered by the high amplitude. Furthermore, we analyze the noise of a synthetic human nuclear factor κB (NF-κB) signaling network by varying three different topologies, and verify that the addition of a repressilator to the activator-inhibitor oscillator, which leads to the emergence of high-robustness motif—the repressilator with positive auto-regulation, improves the oscillation accuracy in comparison to the topology with only an activator-inhibitor oscillator. These design principles may be applicable to other oscillatory circuits.

Noises-induced toggle switch and stability in a gene regulation network

2014 ◽  
Vol 28 (31) ◽  
pp. 1450223 ◽  
Author(s):  
Chunhua Zeng ◽  
Tao Yang ◽  
Qinglin Han ◽  
Chun Zhang ◽  
Dong Tian ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Noise Intensity ◽  
Cross Correlation ◽  
First Passage Time ◽  
Intrinsic Noise ◽  
Passage Time ◽  
Toggle Switch ◽  
Stationary Probability Distribution ◽  
Extrinsic Noise ◽  
Gene Regulation Network

It is well-known that noises are inevitable in gene regulatory networks due to the low-copy numbers of molecules and environmental fluctuations. In this paper, we investigate the stationary probability distribution (SPD) between both low (OFF state) and high (ON state) protein levels and mean first passage time (MFPT) in an abstract model of the Myc/E2F/miR-17-92 network presented by Aguda et al., PNAS 105, 19678 (2008), where the gene expression is assumed to be disturbed simultaneously by intrinsic and extrinsic noises that were correlated. Our results show that (i) the OFF state is enhanced by the extrinsic noise (D), while the ON state is enhanced by the intrinsic noise (Q) or cross-correlation between two noises (λ); (ii) for the cases of negative or no cross-correlation (λ⩽0.0), the increase of the noise intensity (D or Q) leads to a decline of the MFPT and enhances the probability of toggle switch to the OFF state; (iii) but for the case of positive cross-correlation (λ>0.0), the MFPT as a function of the noise intensity (D or Q) exhibits a maximum, this maximum for MFPT identifies the characteristic of noise enhanced stability of the ON state and (iv) the cross-correlation between two noises can enhance stability of the ON state.

scholarly journals Computational Approaches for Modeling Intrinsic Noise and Delays in Genetic Regulatory Networks

2010 ◽  
pp. 169-197 ◽  
Author(s):  
Manuel Barrio ◽  
Kevin Burrage ◽  
Pamela Burrage ◽  
André Leier ◽  
Tatiana Márquez Lago
Keyword(s):  
Regulatory Networks ◽  
Computational Simulation ◽  
Network Motif ◽  
Regulatory System ◽  
Intrinsic Noise ◽  
Genetic Regulatory Networks ◽  
Biological Data ◽  
Protein Levels ◽  
Temporal Models ◽  
Oscillatory Gene Expression

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).

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