scholarly journals Identifying Functional Mechanisms of Gene and Protein Regulatory Networks in Response to a Broader Range of Environmental Stresses

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Cheng-Wei Li ◽  
Bor-Sen Chen
Keyword(s):  
Regulatory Networks ◽  
Dynamic Models ◽  
Environmental Stresses ◽  
Protein Networks ◽  
Stochastic Dynamic ◽  
Core Network ◽  
Protective Mechanisms ◽  
Living Organisms ◽  
Final Survival ◽  
Functional Mechanisms

Cellular responses to sudden environmental stresses or physiological changes provide living organisms with the opportunity for final survival and further development. Therefore, it is an important topic to understand protective mechanisms against environmental stresses from the viewpoint of gene and protein networks. We propose two coupled nonlinear stochastic dynamic models to reconstruct stress-activated gene and protein regulatory networks via microarray data in response to environmental stresses. According to the reconstructed gene/protein networks, some possible mutual interactions, feedforward and feedback loops are found for accelerating response and filtering noises in these signaling pathways. A bow-tie core network is also identified to coordinate mutual interactions and feedforward loops, feedback inhibitions, feedback activations, and cross talks to cope efficiently with a broader range of environmental stresses with limited proteins and pathways.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional

scholarly journals Stochastic dynamic models and Chebyshev splines

2014 ◽  
Vol 42 (4) ◽  
pp. 610-634 ◽  
Author(s):  
Ruzong Fan ◽  
Bin Zhu ◽  
Yuedong Wang
Keyword(s):  
Dynamic Models ◽  
Stochastic Dynamic ◽  
Chebyshev Splines

scholarly journals A method for estimating Hill function-based dynamic models of gene regulatory networks

2018 ◽  
Vol 5 (2) ◽  
pp. 171226 ◽  
Author(s):  
Faizan Ehsan Elahi ◽  
Ammar Hasan
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Optimization Problem ◽  
Dynamic Models ◽  
Initial Conditions ◽  
Computational Cost ◽  
Optimization Approach ◽  
Continuous Models ◽  
Gene Regulatory ◽  
The Cost

Gene regulatory networks (GRNs) are quite large and complex. To better understand and analyse GRNs, mathematical models are being employed. Different types of models, such as logical, continuous and stochastic models, can be used to describe GRNs. In this paper, we present a new approach to identify continuous models, because they are more suitable for large number of genes and quantitative analysis. One of the most promising techniques for identifying continuous models of GRNs is based on Hill functions and the generalized profiling method (GPM). The advantage of this approach is low computational cost and insensitivity to initial conditions. In the GPM, a constrained nonlinear optimization problem has to be solved that is usually underdetermined. In this paper, we propose a new optimization approach in which we reformulate the optimization problem such that constraints are embedded implicitly in the cost function. Moreover, we propose to split the unknown parameter in two sets based on the structure of Hill functions. These two sets are estimated separately to resolve the issue of the underdetermined problem. As a case study, we apply the proposed technique on the SOS response in Escherichia coli and compare the results with the existing literature.

Dynamic pricing for online hotel demand: The case of resort hotels in Majorca

2019 ◽  
Vol 26 (2) ◽  
pp. 268-283 ◽  
Author(s):  
Aldric Vives ◽  
Marta Jacob
Keyword(s):  
Dynamic Pricing ◽  
Dynamic Models ◽  
Customer Behavior ◽  
Optimal Pricing ◽  
Stochastic Dynamic ◽  
Elastic Demand ◽  
Demand Functions ◽  
Elasticity Of Demand ◽  
Price Optimization ◽  
Resort Hotels

Online customer behavior in terms of price elasticity of demand and the effect of time along the booking horizon are key requirements for the price optimization process that allows hotels to maximize their revenues. In this vein, this study adapts the online transient hotel demand functions to deterministic and stochastic dynamic models—two extended optimal pricing methods existing in the literature—in order to determine the prices that maximize the revenues of two resort hotels located in Majorca. The main findings indicate that (1) seasonality, the number of rooms available, the hotel location, and the tourist profile affect dynamic pricing (DP); (2) the booking horizon limitation leads to larger revenue decreases under elastic demand; (3) higher levels in demand elasticities generally produce lower levels of prices; and (4) the distribution of elasticities across the booking horizon and the natural variability of demand have an impact on DP. Implication for industry revenue managers is that they have to consider the booking horizon duration together with the demand price sensitivity in order to maximize the hotel revenues.

scholarly journals MucA-Mediated Coordination of Type III Secretion and Alginate Synthesis in Pseudomonas aeruginosa

2004 ◽  
Vol 186 (22) ◽  
pp. 7575-7585 ◽  
Author(s):  
Weihui Wu ◽  
Hassan Badrane ◽  
Shiwani Arora ◽  
Henry V. Baker ◽  
Shouguang Jin
Keyword(s):  
Gene Expression ◽  
Pseudomonas Aeruginosa ◽  
Type Iii Secretion ◽  
Regulatory Networks ◽  
Protective Role ◽  
Environmental Stresses ◽  
Host Cells ◽  
Calcium Signal ◽  
Insertion Mutant ◽  
Type Iii

ABSTRACT The type III secretion system (T3SS) of Pseudomonas aeruginosa is an important virulence factor. The T3SS of P. aeruginosa can be induced by a low calcium signal or upon direct contact with the host cells. The exact pathway of signal sensing and T3SS activation is not clear. By screening a transposon insertion mutant library of the PAK strain, mutation in the mucA gene was found to cause repression of T3SS expression under both type III-inducing and -noninducing conditions. Mutation in the mucA gene is known to cause alginate overproduction, resulting in a mucoid phenotype. Alginate production responds to various environmental stresses and plays a protective role for P. aeruginosa. Comparison of global gene expression of mucA mutant and wild-type PAK under T3SS-inducing conditions confirmed the down regulation of T3SS genes and up regulation of genes involved in alginate biosynthesis. Further analysis indicated that the repression of T3SS in the mucA mutant was AlgU and AlgR dependent, as double mutants mucA/algU and mucA/algR showed normal type III expression. An algR::Gm mutant showed a higher level of type III expression, while overexpression of the algR gene inhibited type III gene expression; thus, it seems that the AlgR-regulated product inhibits the expression of the T3SS genes. It is likely that P. aeruginosa has evolved tight regulatory networks to turn off the energy-expensive T3SS when striving for survival under environmental stresses.

scholarly journals Structure-based control of complex networks with nonlinear dynamics

2017 ◽  
Vol 114 (28) ◽  
pp. 7234-7239 ◽  
Author(s):  
Jorge Gomez Tejeda Zañudo ◽  
Gang Yang ◽  
Réka Albert
Keyword(s):  
Nonlinear Dynamics ◽  
Regulatory Networks ◽  
Dynamic Models ◽  
Broad Class ◽  
Specific Model ◽  
Underlying Network ◽  
Functional Forms ◽  
Structural Controllability ◽  
Control Of Networks

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework’s applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

Sign in / Sign up

Export Citation Format

Share Document