A proteome-integrated, carbon source dependent genetic regulatory network in Saccharomyces cerevisiae

Integrated regulatory networks can be powerful tools to examine and test properties of cellular systems, such as modelling environmental effects on the molecular bioeconomy, where protein levels are altered in response to changes in growth conditions.

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Effect of Leakage Delay on Stability of Neutral-Type Genetic Regulatory Networks

Abstract and Applied Analysis ◽

10.1155/2015/826020 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8

Author(s):

Li Li ◽

Yongqing Yang ◽

Chuanzhi Bai

Keyword(s):

Regulatory Network ◽

Regulatory Networks ◽

Neutral Type ◽

Sufficient Conditions ◽

Functional Method ◽

Genetic Regulatory Networks ◽

Genetic Regulatory Network ◽

Leakage Delay ◽

Leakage Delays ◽

The Stability

The stability of neutral-type genetic regulatory networks with leakage delays is considered. Firstly, we describe the model of genetic regulatory network with neutral delays and leakage delays. Then some sufficient conditions are derived to ensure the asymptotic stability of the genetic regulatory network by the Lyapunov functional method. Further, the effect of leakage delay on stability is discussed. Finally, a numerical example is given to show the effectiveness of the results.

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State Observer Design for Delayed Genetic Regulatory Networks

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/761562 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 6

Author(s):

Li-Ping Tian ◽

Zhi-Jun Wang ◽

Amin Mohammadbagheri ◽

Fang-Xiang Wu

Keyword(s):

Regulatory Network ◽

Regulatory Networks ◽

Estimation Error ◽

State Observer ◽

Genetic Regulatory Networks ◽

Genetic Regulatory Network ◽

Observer Design ◽

Linear Matrix ◽

Globally Asymptotically Stable ◽

Internal States

Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins). The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured. Therefore it becomes an important issue to estimate the unmeasured states via the available measurements. In this study, we design a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality (LMI) approach, a criterion is established to guarantee that the dynamic of estimation error is globally asymptotically stable. A gene repressillatory network is employed to illustrate the effectiveness of our design approach.

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ABF1 is a phosphoprotein and plays a role in carbon source control of COX6 transcription in Saccharomyces cerevisiae

Molecular and Cellular Biology ◽

10.1128/mcb.12.9.4197-4208.1992 ◽

1992 ◽

Vol 12 (9) ◽

pp. 4197-4208

Author(s):

S Silve ◽

P R Rhode ◽

B Coll ◽

J Campbell ◽

R O Poyton

Keyword(s):

Saccharomyces Cerevisiae ◽

Carbon Source ◽

Glucose Repression ◽

Carbon Sources ◽

Growth Conditions ◽

Source Control ◽

Specific Target Gene ◽

Phosphorylation Pattern ◽

Nonfermentable Carbon Source ◽

In Cells

Previously, we have shown that the Saccharomyces cerevisiae DNA-binding protein ABF1 exists in at least two different electrophoretic forms (K. S. Sweder, P. R. Rhode, and J. L. Campbell, J. Biol. Chem. 263: 17270-17277, 1988). In this report, we show that these forms represent different states of phosphorylation of ABF1 and that at least four different phosphorylation states can be resolved electrophoretically. The ratios of these states to one another differ according to growth conditions and carbon source. Phosphorylation of ABF1 is therefore a regulated process. In nitrogen-starved cells or in cells grown on nonfermentable carbon sources (e.g., lactate), phosphorylated forms predominate, while in cells grown on fermentable carbon sources (e.g., glucose), dephosphorylated forms are enriched. The phosphorylation pattern is affected by mutations in the SNF1-SSN6 pathway, which is involved in glucose repression-depression. Whereas a functional SNF1 gene, which encodes a protein kinase, is not required for the phosphorylation of ABF1, a functional SSN6 gene is required for itsd ephosphorylation. The phosphorylation patterns that we have observed correlate with the regulation of a specific target gene, COX6, which encodes subunit VI of cytochrome c oxidase. Transcription of COX6 is repressed by growth in medium containing a fermentable carbon source and is derepressed by growth in medium containing a nonfermentable carbon source. COX6 repression-derepression is under the control of the SNF1-SSN6 pathway. This carbon source regulation is exerted through domain 1, a region of the upstream activation sequence UAS6 that binds ABF1 (J. D. Trawick, N. Kraut, F. Simon, and R. O. Poyton, Mol. Cell Biol. 12:2302-2314, 1992). We show that the greater the phosphorylation of ABF1, the greater the transcription of COX6. Furthermore, the ABF1-containing protein-DNA complexes formed at domain 1 differ according to the phosphorylation state of ABF1 and the carbon source on which the cells were grown. From these findings, we propose that the phosphorylation of ABF1 is involved in glucose repression-derepression of COX6 transcription.

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Stability Analysis of Delayed Genetic Regulatory Networks via a Relaxed Double Integral Inequality

Mathematical Problems in Engineering ◽

10.1155/2017/4157256 ◽

2017 ◽

Vol 2017 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Fu-Dong Li ◽

Qi Zhu ◽

Hao-Tian Xu ◽

Lin Jiang

Keyword(s):

Stability Analysis ◽

Integral Inequality ◽

Regulatory Network ◽

Regulatory Networks ◽

Genetic Regulatory Networks ◽

Genetic Regulatory Network ◽

Double Integral ◽

Integral Term ◽

Double Inequality ◽

The Stability

Time delay arising in a genetic regulatory network may cause the instability. This paper is concerned with the stability analysis of genetic regulatory networks with interval time-varying delays. Firstly, a relaxed double integral inequality, named as Wirtinger-type double integral inequality (WTDII), is established to estimate the double integral term appearing in the derivative of Lyapunov-Krasovskii functional with a triple integral term. And it is proved theoretically that the proposed WTDII is tighter than the widely used Jensen-based double inequality and the recently developed Wiringter-based double inequality. Then, by applying the WTDII to the stability analysis of a delayed genetic regulatory network, together with the usage of useful information of regulatory functions, several delay-range- and delay-rate-dependent (or delay-rate-independent) criteria are derived in terms of linear matrix inequalities. Finally, an example is carried out to verify the effectiveness of the proposed method and also to show the advantages of the established stability criteria through the comparison with some literature.

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The Defect in Transcription-Coupled Repair Displayed by a Saccharomyces cerevisiae rad26 Mutant Is Dependent on Carbon Source and Is Not Associated With a Lack of Transcription

Genetics ◽

10.1093/genetics/158.3.989 ◽

2001 ◽

Vol 158 (3) ◽

pp. 989-997

Author(s):

Miriam Bucheli ◽

Lori Lommel ◽

Kevin Sweder

Keyword(s):

Saccharomyces Cerevisiae ◽

Dna Damage ◽

Carbon Source ◽

Excision Repair ◽

Carbon Sources ◽

Repair Process ◽

Growth Conditions ◽

Nucleotide Excision ◽

Evolutionarily Conserved ◽

Transcription Activators

Abstract Nucleotide excision repair (NER) is an evolutionarily conserved pathway that removes DNA damage induced by ultraviolet irradiation and various chemical agents that cause bulky adducts. Two subpathways within NER remove damage from the genome overall or the transcribed strands of transcribing genes (TCR). TCR is a faster repair process than overall genomic repair and has been thought to require the RAD26 gene in Saccharomyces cerevisiae. Rad26 is a member of the SWI/SNF family of proteins that either disrupt chromatin or facilitate interactions between the RNA Pol II and transcription activators. SWI/SNF proteins are required for the expression or repression of a diverse set of genes, many of which are differentially transcribed in response to particular carbon sources. The remodeling of chromatin by Rad26 could affect transcription and/or TCR following formation of DNA damage and other stress-inducing conditions. We speculate that another factor(s) can substitute for Rad26 under particular growth conditions. We therefore measured the level of repair and transcription in two different carbon sources and found that the defect in the rad26 mutant for TCR was dependent on the type of carbon source. Furthermore, TCR did not correlate with transcription rate, suggesting that disruption of RAD26 leads to a specific defect in DNA repair and not transcription.

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Improvement of Phytase Activity by a New Saccharomyces cerevisiae Strain Using Statistical Optimization

Enzyme Research ◽

10.4061/2011/796394 ◽

2011 ◽

Vol 2011 ◽

pp. 1-6 ◽

Cited By ~ 11

Author(s):

Edi Franciele Ries ◽

Gabriela Alves Macedo

Keyword(s):

Saccharomyces Cerevisiae ◽

Response Surface Methodology ◽

Carbon Source ◽

Culture Medium ◽

Growth Conditions ◽

Statistical Optimization ◽

Phytase Activity ◽

Phytase Production ◽

Sodium Phytate ◽

Full Factorial

Using statistical optimization, we enhanced the activity of phytase by a new Saccharomyces cerevisiae strain cultured in mineral medium. Concentrations of carbon source and inducer of phytase production were optimized using a 22 full factorial CCD and response surface methodology (RSM). Urea was fixed as nitrogen source in culture medium (0.15%, w/v). The culture medium consisting of 2.5% sucrose and 0.5% sodium phytate optimally supported the maximum phytase activity. In addition, we found that culture of the yeast at 35°C with shaking at 150 rpm supports maximum phytase production. The validity of this model was verified by culturing the organisms in flasks on a shaker. Using the optimized media and growth conditions, we obtained a 10-fold improvement in the production of phytase by S. cerevisiae.

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Bifurcation Analysis and Impulsive Control of Genetic Regulatory Networks with Multi Delays

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2019.p0743 ◽

2019 ◽

Vol 23 (4) ◽

pp. 743-748 ◽

Cited By ~ 1

Author(s):

Feng Liu ◽

Jie Ren ◽

Ting Dong ◽

Shiqi Zheng ◽

◽

...

Keyword(s):

Numerical Simulation ◽

Hopf Bifurcation ◽

Regulatory Network ◽

Regulatory Networks ◽

Control Method ◽

Impulsive Control ◽

Genetic Regulatory Networks ◽

Genetic Regulatory Network ◽

The Stability ◽

Theoretical Results

In this study, the stability and Hopf bifurcation of a genetic regulatory network with delays are addressed. Some bifurcations may cause network oscillation and induce instability. An impulsive control method is proposed to control the bifurcations. A numerical simulation was performed to demonstrate the effectiveness of the theoretical results.

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Numerical Methods for Genetic Regulatory Network Identification Based on a Variational Approach

Computational Methods in Applied Mathematics ◽

10.1515/cmam-2015-0019 ◽

2016 ◽

Vol 16 (1) ◽

pp. 77-103

Author(s):

Xiaobing Feng ◽

Miun Yoon

Keyword(s):

Differential Equation ◽

Variational Principle ◽

Numerical Methods ◽

Regulatory Network ◽

Regulatory Networks ◽

Numerical Solutions ◽

Genetic Regulatory Networks ◽

Genetic Regulatory Network ◽

Variational Framework ◽

Network Identification

AbstractThis paper studies differential equation-based mathematical models and their numerical solutions for genetic regulatory network identification. The primary objectives are to design, analyze, and test a general variational framework and numerical methods for seeking its approximate solutions for reverse engineering genetic regulatory networks from microarray datasets. In the proposed variational framework, no structure assumption on the genetic network is presumed, instead, the network is solely determined by the microarray profile of the network components and is identified through a well chosen variational principle which minimizes an energy functional. The variational principle serves not only as a selection criterion to pick up the right solution of the underlying differential equation model but also provides an effective mathematical characterization of the small-world property of genetic regulatory networks which has been observed in lab experiments. Five specific models within the variational framework and efficient numerical methods and algorithms for computing their solutions are proposed and analyzed. Model validations using both synthetic network datasets and subnetwork datasets of Saccharomyces cerevisiae (yeast) and E. coli are performed on all five proposed variational models and a performance comparison versus some existing genetic regulatory network identification methods is also provided.

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Problems for Structure Learning

Handbook of Research on Computational Methodologies in Gene Regulatory Networks ◽

10.4018/978-1-60566-685-3.ch013 ◽

2010 ◽

pp. 310-333 ◽

Cited By ~ 1

Author(s):

Frank Wimberly ◽

David Danks ◽

Clark Glymour ◽

Tianjiao Chu

Keyword(s):

Graphical Models ◽

Microarray Data ◽

Regulatory Network ◽

Conditional Independence ◽

Regulatory Networks ◽

Structure Learning ◽

Genetic Regulatory Networks ◽

Genetic Regulatory Network ◽

Complexity Of Algorithms ◽

Realistic Data

Machine learning methods to find graphical models of genetic regulatory networks from cDNA microarray data have become increasingly popular in recent years. We provide three reasons to question the reliability of such methods: (1) a major theoretical challenge to any method using conditional independence relations; (2) a simulation study using realistic data that confirms the importance of the theoretical challenge; and (3) an analysis of the computational complexity of algorithms that avoid this theoretical challenge. We have no proof that one cannot possibly learn the structure of a genetic regulatory network from microarray data alone, nor do we think that such a proof is likely. However, the combination of (i) fundamental challenges from theory, (ii) practical evidence that those challenges arise in realistic data, and (iii) the difficulty of avoiding those challenges leads us to conclude that it is unlikely that current microarray technology will ever be successfully applied to this structure learning problem.

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Control in Inhibitory Genetic Regulatory Network Models

Contemporary Mathematics ◽

10.37256/cm.152020538 ◽

2020 ◽

Author(s):

D. Ogorelova ◽

F. Sadyrbaev ◽

V. Sengileyev

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Network ◽

Regulatory Networks ◽

Network Models ◽

Genetic Regulatory Network ◽

Field Analysis ◽

Geometrical Approach ◽

Full Generality ◽

First Order ◽

Gene Regulatory

The system of two the first order ordinary differential equations arising in the gene regulatory networks theory is studied. The structure of attractors for this system is described for three important behavioral cases: activation, inhibition, mixed activation-inhibition. The geometrical approach combined with the vector field analysis allows treating the problem in full generality. A number of propositions are stated and the proof is geometrical, avoiding complex analytic. Although not all the possible cases are considered, the instructions are given what to do in any particular situation.

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