STATE SPACE MODELING OF YEAST GENE EXPRESSION DYNAMICS

2007 ◽  
Vol 05 (01) ◽  
pp. 31-46 ◽  
Author(s):  
OLLI HAAVISTO ◽  
HEIKKI HYÖTYNIEMI ◽  
CHRISTOPHE ROOS
Keyword(s):  
Gene Expression ◽  
State Space ◽  
Dynamic Models ◽  
System Modeling ◽  
Dynamic State ◽  
Yeast Genome ◽  
High Dimensional ◽  
Dimensional System ◽  
Yeast Saccharomyces Cerevisiae ◽  
Space Modeling

Combined interaction of all the genes forms a central part of the functional system of a cell. Thus, especially the data-based modeling of the gene expression network is currently one of the main challenges in the field of systems biology. However, the problem is an extremely high-dimensional and complex one, so that normal identification methods are usually not applicable specially if aiming at dynamic models. We propose in this paper a subspace identification approach, which is well suited for high-dimensional system modeling and the presented modified version can also handle the underdetermined case with less data samples than variables (genes). The algorithm is applied to two public stress-response data sets collected from yeast Saccharomyces cerevisiae. The obtained dynamic state space model is tested by comparing the simulation results with the measured data. It is shown that the identified model can relatively well describe the dynamics of the general stress-related changes in the expression of the complete yeast genome. However, it seems inevitable that more precise modeling of the dynamics of the whole genome would require experiments especially designed for systemic modeling.

scholarly journals Bayesian Decoder Models with a Discriminative Observation Process

2020 ◽  
Author(s):  
Mohammad R. Rezaei ◽  
Alex E. Hadjinicolaou ◽  
Sydney S. Cash ◽  
Uri T. Eden ◽  
Ali Yousefi
Keyword(s):  
State Space ◽  
Neural Activity ◽  
Model Development ◽  
High Dimensional ◽  
Modeling Framework ◽  
Movement Trajectories ◽  
State Process ◽  
Observation Process ◽  
Space Modeling ◽  
Low Dimensional

AbstractThe Bayesian state-space neural encoder-decoder modeling framework is an established solution to reveal how changes in brain dynamics encode physiological covariates like movement or cognition. Although the framework is increasingly being applied to progress the field of neuroscience, its application to modeling high-dimensional neural data continues to be a challenge. Here, we propose a novel solution that avoids the complexity of encoder models that characterize high-dimensional data as a function of the underlying state processes. We build a discriminative model to estimate state processes as a function of current and previous observations of neural activity. We then develop the filter and parameter estimation solutions for this new class of state-space modeling framework called the “direct decoder” model. We apply the model to decode movement trajectories of a rat in a W-shaped maze from the ensemble spiking activity of place cells and achieve comparable performance to modern decoding solutions, without needing an encoding step in the model development. We further demonstrate how a dynamical auto-encoder can be built using the direct decoder model; here, the underlying state process links the high-dimensional neural activity to the behavioral readout. The dynamical auto-encoder can optimally estimate the low-dimensional dynamical manifold which represents the relationship between brain and behavior.

scholarly journals Noncoding RNA participation in gene expression regulation in yeast Saccharomyces cerevisiae

2011 ◽  
Vol 9 (1) ◽  
pp. 3-14
Author(s):  
Olga V Kochenova
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Gene Regulation ◽  
Antisense Rna ◽  
Noncoding Rna ◽  
Gene Expression Regulation ◽  
Regulation Of Gene Expression ◽  
Yeast Genome ◽  
Yeast Saccharomyces Cerevisiae ◽  
Main Components

Saccharomyces cerevisiae lacks the main components of RNAi-dependent gene silencing. Nevertheless, regulation of gene expression in S. cerevisiae could be accomplished via some other types of noncoding RNA, particularly via antisense RNA. Although, there is a high percent of untranslated RNA in yeast genome only few evidences of noncoding RNA gene regulation exist in yeast S. cerevisiae, some of them are reviewed in the present paper. 

scholarly journals A First Step towards Learning which uORFs Regulate Gene Expression

2006 ◽  
Vol 3 (2) ◽  
pp. 109-122 ◽  
Author(s):  
◽  
Christopher H. Bryant ◽  
Graham J.L. Kemp ◽  
Marija Cvijovic
Keyword(s):  
Gene Expression ◽  
Inductive Logic ◽  
Preliminary Evidence ◽  
Open Reading Frames ◽  
Yeast Saccharomyces Cerevisiae ◽  
Regulate Gene Expression ◽  
Integrative System ◽  
Upstream Open Reading Frames ◽  
Reading Frames ◽  
Regulate Gene

Summary We have taken a first step towards learning which upstream Open Reading Frames (uORFs) regulate gene expression (i.e., which uORFs are functional) in the yeast Saccharomyces cerevisiae. We do this by integrating data from several resources and combining a bioinformatics tool, ORF Finder, with a machine learning technique, inductive logic programming (ILP). Here, we report the challenge of using ILP as part of this integrative system, in order to automatically generate a model that identifies functional uORFs. Our method makes searching for novel functional uORFs more efficient than random sampling. An attempt has been made to predict novel functional uORFs using our method. Some preliminary evidence that our model may be biologically meaningful is presented.

Sign in / Sign up

Export Citation Format

Share Document