Vester's Sensitivity Model for Genetic Networks with Time-Discrete Dynamics

Date

2014-07-03

Author

Moreno, Liana Amaya
DEFTERLİ, ÖZLEM
Fuegenschuh, Armin
Weber, Gerhard Wilhelm

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

210
views

0
downloads

We propose a new method to explore the characteristics of genetic networks whose dynamics are described by a linear discrete dynamical model x(t+1) = Ax(t). The gene expression data x(t) is given for various time points and the matrix A of interactions among the genes is unknown. First we formulate and solve a parameter estimation problem by linear programming in order to obtain the entries of the matrix A. We then use ideas from Vester's Sensitivity Model, more precisely, the Impact Matrix, and the determination of the Systemic Roles, to understand the interactions among the genes and their role in the system. The method identifies prominent outliers, that is, the most active, reactive, buffering and critical genes in the network. Numerical examples for different datasets containing mRNA transcript levels during the cell cycle of budding yeast are presented.

Subject Keywords

Linear programming, Parameter estimation, Discrete Dynamical System

URI

https://hdl.handle.net/11511/53485

Conference Name

1st International Conference on Algorithms for Computational Biology (AlCoB)

Collections

Graduate School of Applied Mathematics, Conference / Seminar

Suggestions

OpenMETU
Core

Expectation propagation for state estimation with discrete-valued hidden random variables Sarıtaş, Elif; Orguner, Umut; Department of Electrical and Electronics Engineering (2023-2-21) In this thesis, the expectation propagation (EP) approach of Minka is considered for the estimation problems in dynamical systems with discrete hidden random variables where optimal posteriors are usually intractable. The concept of context adjustment is introduced to avoid/alleviate indefinite covariance problems encountered in standard EP implementations in a systematic way. Additionally, the moment projection (Mprojection) problem involving pseudo-Gaussian likelihoods as factors is solved to be used in t...
Minimum order linear system identification and parameter estimation with application Erdoğan, Onur Cem; Balkan, Raif Tuna; Platin, Bülent Emre; Department of Mechanical Engineering (2014) Design, control, and investigation of complex systems require a tool to understand and model system behavior. This tool is the system identification, which convert the system response to a mathematical formulation. During the identification phase, the utilized model is important to convey system behavior. In this study, a number of minimum order and non-parametric system identification algorithms are implemented for the identification of linear time invariant mechanical systems. For this purpose, impulse re...
Analysis Window Length Selection For Linear Signal Models Yazar, Alper; Candan, Çağatay (2015-05-19) A method is presented for the selection of analysis window length, or the number of input samples, for linear signal modeling without compromising the model assumptions. It is assumed that the signal of interest lies in a known linear space and noisy samples of the signal is provided. The goal is to use as many signal samples as possible to mitigate the effect of noise without violating the assumptions on the model. An application example is provided to illustrate the suggested method.
Evolution operator approach for solving linear ordinary differential equations and computation of EXP (A) Azzam, Abdelnasser; Ergenç, Tanıl; Department of Mathematics (1991) An Evolution Operator Approach which were developed to solve nonlinear O.D.E. systems, X=f(x), X(0)=£, is discussed for the linear systems X=AX where A is nxn symmetric matrix. In this approach, each component of the solution vector is represented as an action of evolution operator, exp(itL), on xj and then approximated by method of Moment using [N+1,N] Pade’ approximation. In applications, the most important part of this method is the computation of dynamical and spectral coefficients [6]. The recursive fo...
Multi-objective decision making using fuzzy discrete event systems: A mobile robot example Boutalis, Yiannis; Schmidt, Klaus Verner (2010-09-29) In this paper, we propose an approach for the multi-objective control of sampled data systems that can be modeled as fuzzy discrete event systems (FDES). In our work, the choice of a fuzzy system representation is justified by the assumption of a controller realization that depends on various potentially imprecise sensor measurements. Our approach consists of three basic steps that are performed in each sampling instant. First, the current fuzzy state of the system is determined by a sensor evaluation. Seco...

Citation Formats

L. A. Moreno, Ö. DEFTERLİ, A. Fuegenschuh, and G. W. Weber, “Vester’s Sensitivity Model for Genetic Networks with Time-Discrete Dynamics,” Tarragona, Spain, 2014, vol. 8542, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53485.