scholarly journals Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Hong Qin
Keyword(s):  
Mortality Rate ◽  
Network Model ◽  
Gene Networks ◽  
Gene Network ◽  
Cellular Aging ◽  
System Level ◽  
Gene Interactions ◽  
Virtual Age ◽  
Average Lifespan ◽  
Natural Isolates

Abstract Background Cellular aging is best studied in the budding yeast Saccharomyces cerevisiae. As an example of a pleiotropic trait, yeast lifespan is influenced by hundreds of interconnected genes. However, no quantitative methods are currently available to infer system-level changes in gene networks during cellular aging. Results We propose a parsimonious mathematical model of cellular aging based on stochastic gene interaction networks. This network model is made of only non-aging components: the strength of gene interactions declines with a constant mortality rate. Death of a cell occurs in the model when an essential node loses all of its interactions with other nodes, and is equivalent to the deletion of an essential gene. Stochasticity of gene interactions is modeled using a binomial distribution. We show that the exponential increase of mortality rate over time can emerge from this gene network model during the early stages of aging.We developed a maximal likelihood approach to estimate three lifespan-influencing network parameters from experimental lifespans: t0, the initial virtual age of the network system; n, the average lifespan-influencing interactions per essential node; and R, the initial mortality rate. We applied this model to yeast mutants with known effects on replicative lifespans. We found that deletion of SIR2, FOB1, and HXK2 considerably altered the initial virtual age but not the average lifespan-influencing interactions per essential node, suggesting that these mutations mainly influence the reliability of gene interactions but not the overall configurations of gene networks.We applied this model to investigate replicative lifespans of yeast natural isolates. We estimated that the average number of lifespan-influencing interactions per essential node is 7.0 (6.1–8) and the average estimated initial virtual age is 45.4 (30.6–74) cell divisions in these isolates. We also found that t0 could potentially mediate the observed Strehler-Mildvan correlation in yeast natural isolates. Conclusions Our theoretical model provides a parsimonious interpretation of experimental lifespan data from the perspective of gene networks. We hope that our work will stimulate more interest in developing network models to study aging as a pleiotropic trait.

scholarly journals Gene Profiling of Aortic Valve Interstitial Cells under Elevated Pressure Conditions: Modulation of Inflammatory Gene Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
James N. Warnock ◽  
Bindu Nanduri ◽  
Carol A. Pregonero Gamez ◽  
Juliet Tang ◽  
Daniel Koback ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Network Model ◽  
Gene Networks ◽  
Gene Network ◽  
Molecular Mechanisms ◽  
Elevated Pressure ◽  
Interstitial Cells ◽  
Gene Profiling ◽  
Differentially Expressed ◽  
Valve Interstitial Cells

The study aimed to identify mechanosensitive pathways and gene networks that are stimulated by elevated cyclic pressure in aortic valve interstitial cells (VICs) and lead to detrimental tissue remodeling and/or pathogenesis. Porcine aortic valve leaflets were exposed to cyclic pressures of 80 or 120 mmHg, corresponding to diastolic transvalvular pressure in normal and hypertensive conditions, respectively. Linear, two-cycle amplification of total RNA, followed by microarray was performed for transcriptome analysis (with qRT-PCR validation). A combination of systems biology modeling and pathway analysis identified novel genes and molecular mechanisms underlying the biological response of VICs to elevated pressure. 56 gene transcripts related to inflammatory response mechanisms were differentially expressed. TNF-α, IL-1α, and IL-1βwere key cytokines identified from the gene network model. Also of interest was the discovery that pentraxin 3 (PTX3) was significantly upregulated under elevated pressure conditions (41-fold change). In conclusion, a gene network model showing differentially expressed inflammatory genes and their interactions in VICs exposed to elevated pressure has been developed. This system overview has detected key molecules that could be targeted for pharmacotherapy of aortic stenosis in hypertensive patients.

scholarly journals Population structure promotes the evolution of costly sex in artificial gene networks

2018 ◽  
Keyword(s):  
Population Structure ◽  
Network Model ◽  
Gene Networks ◽  
Gene Network ◽  
Critical Role ◽  
Effective Rate ◽  
Structured Populations ◽  
Evolution Of Sex ◽  
Short Term ◽  
Asexual Populations

AbstractWe build on previous observations that Hill-Robertson interference generates an advantage of sex that, in structured populations, can be large enough to explain the evolutionary maintenance of costly sex. We employed a gene network model that explicitly incorporates interactions between genes. Mutations in the gene networks have variable effects that depend on the genetic background in which they appear. Consequently, our simulations include two costs of sex—recombination and migration loads—that were missing from previous studies of the evolution of costly sex. Our results suggest a critical role for population structure that lies in its ability to align the long- and short-term advantages of sex. We show that the addition of population structure favored the evolution of sex by disproportionately decreasing the equilibrium mean fitness of asexual populations, primarily by increasing the strength of Muller’s Ratchet. Population structure also increased the ability of the short-term advantage of sex to counter the primary limit to the evolution of sex in the gene network model—recombination load. On the other hand, highly structured populations experienced migration load in the form of Dobzhansky–Muller incompatibilities, decreasing the effective rate of migration between demes and, consequently, accelerating the accumulation of drift load in the sexual populations.

scholarly journals Analysis of functional importance of binding sites in the Drosophila gap gene network model

BMC Genomics ◽  
2015 ◽  
Vol 16 (Suppl 13) ◽  
pp. S7 ◽  
Author(s):  
Konstantin Kozlov ◽  
Vitaly V Gursky ◽  
Ivan V Kulakovskiy ◽  
Arina Dymova ◽  
Maria Samsonova
Keyword(s):  
Network Model ◽  
Binding Sites ◽  
Gene Network ◽  
Functional Importance ◽  
Gap Gene

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks

2017 ◽  
Vol 15 (02) ◽  
pp. 1650045 ◽  
Author(s):  
Olga V. Petrovskaya ◽  
Evgeny D. Petrovskiy ◽  
Inna N. Lavrik ◽  
Vladimir A. Ivanisenko
Keyword(s):  
Mathematical Modeling ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Modeling ◽  
Computer Experiments ◽  
Linear Control ◽  
Expression Of Genes ◽  
Gene Regulatory

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

scholarly journals On Robust State Estimation of Gene Networks

2010 ◽  
Vol 2 ◽  
pp. 117959721000200 ◽  
Author(s):  
Chia-Hua Chuang ◽  
Chun-Liang Lin
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Quantitative Analysis ◽  
State Estimation ◽  
Gene Networks ◽  
Gene Network ◽  
Biological Systems ◽  
Uncertain Process ◽  
Network Systems ◽  
Internal States

Gene networks in biological systems are not only nonlinear but also stochastic due to noise corruption. How to accurately estimate the internal states of the noisy gene networks is an attractive issue to researchers. However, the internal states of biological systems are mostly inaccessible by direct measurement. This paper intends to develop a robust extended Kalman filter for state and parameter estimation of a class of gene network systems with uncertain process noises. Quantitative analysis of the estimation performance is conducted and some representative examples are provided for demonstration.

A NEW METHOD OF DETERMINING THRESHOLD OF GENE NETWORK BASED ON PHENOTYPES

2011 ◽  
Vol 19 (04) ◽  
pp. 607-616
Author(s):  
YUANYUAN ZHANG ◽  
SHUDONG WANG ◽  
MEIXI YANG ◽  
DASHUN XU ◽  
DAZHI MENG
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Structural Parameters ◽  
Network Modeling ◽  
Genetic Diseases ◽  
Disease Stage ◽  
External Representation ◽  
Functional Relationships ◽  
Disease Phenotypes ◽  
Significant Difference

With the rapid growth of microarray data, it has become a hot topic to reveal complex behaviors and functions of life system by studying the relationships among genes. In the process of reverse network modeling, the relationships with less relevance are generally not considered by determining a threshold when the relationships among genes are mined. However, there are no effective methods to determine the threshold up to now. It is worthwhile to note that the phenotypes of genetic diseases are generally regarded as external representation of the functions of genes. Therefore, two types of phenotype networks are constructed from gene and disease views, respectively, and through comparing these two types of phenotype networks, the threshold of gene network corresponding to a certain disease can be determined when their similarity reaches to maximum. Because the gene network is determined based on the relationships among phenotypes and phenotypes are external representation of the functions of genes, it is considered that relationships in the gene network may show functional relationships among genes in biological system. In this work, the thresholds 0.47 and 0.48 of gene network are determined based on Parkinson disease phenotypes. Furthermore, the validity of these thresholds is verified by the specificity and susceptibility of phenotype networks. Also, through comparing the structural parameters of gene networks for normal and disease stage at different thresholds, significant difference between the two gene networks at threshold 0.47 or 0.48 is found. The significant difference of structural parameters further verifies the efficiency of this method.

scholarly journals Dynamical properties and path dependence in a gene-network model of cell differentiation

2020 ◽  
Author(s):  
Michele Braccini ◽  
Andrea Roli ◽  
Marco Villani ◽  
Roberto Serra
Keyword(s):  
Cell Differentiation ◽  
Network Model ◽  
Gene Network ◽  
Path Dependence ◽  
Control Mechanism ◽  
Boolean Networks ◽  
Biological Cells ◽  
Random Boolean Networks ◽  
Dynamical Properties

Abstract In this work, we explore the properties of a control mechanism exerted on random Boolean networks that takes inspiration from the methylation mechanisms in cell differentiation and consists in progressively freezing (i.e. clamping to 0) some nodes of the network. We study the main dynamical properties of this mechanism both theoretically and in simulation. In particular, we show that when applied to random Boolean networks, it makes it possible to attain dynamics and path dependence typical of biological cells undergoing differentiation.

EFFECTIVE MODELS FOR GENE NETWORKS AND THEIR APPLICATIONS

2012 ◽  
Vol 07 (01n02) ◽  
pp. 41-70 ◽  
Author(s):  
JASON SHULMAN ◽  
LARS SEEMANN ◽  
GREGG W. ROMAN ◽  
GEMUNU H. GUNARATNE
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Stationary Solutions ◽  
The State ◽  
Effective Model ◽  
Knockout Mutant ◽  
Double Knockout

Networks are used to abstract large, highly-coupled sets of objects. Their analyses have included network classification into a few broad classes and selection of small substructures that perform simple yet common tasks. One issue that has received little attention is how the state of a network can be moved according to a pre-specified set of guidelines. In this paper, we address this question in the context of gene networks. In general, neither the full membership of the gene network associated with a biological process nor the precise form of interactions between nodes is known. What is available, through microarrays or sequencing, are gene expression profiles of an organism or its viable mutants. Our approach relies only on these expression profiles, and not on the availability of an accurate model for the network. The first step is to select a small set of core- or master- nodes, such as transcription factors or microRNAs, that can be used to alter the levels of many of the remaining genes in the network. We ask how the state — or solution — of the gene network changes as the levels of these master nodes are altered externally. The object of our study is, not the network, but the surface of these solutions. We argue that it can be approximated using gene expression profiles of the organism and single manipulation of master node activity. This is done through an "effective model." The effective model as well as error estimates for its predictions can be derived from experimental data. The method is validated using synthetic gene networks that have stationary solutions and those that are periodically driven, e.g., circadian networks. An effective model for the oxygen-deprivation network of E.coli is constructed using previously published gene expression profiles, and used to predict the expression levels in a double knockout mutant. Less that 30% of the predictions lie outside the 5% confidence level. We propose the use of the effective model methodology to compute how Drosophila melanogaster in the normal state can be genetically altered into a pre-defined sleep deprived-like state.

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