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Systems biology via redescription and ontologies (I): finding phase changes with applications to malaria temporal data.

Kleinberg S, Casey K, Mishra B - Syst Synth Biol (2008)

Bottom Line: One set of tools that may prove useful are the formal principles of model building and checking, which could allow the biologist to frame these inherently temporal questions in a sufficiently rigorous framework.In response to these challenges, GOALIE (Gene ontology algorithmic logic and information extractor) was developed and has been successfully employed in the analysis of high throughput biological data (e.g. time-course gene-expression microarray data and neural spike train recordings).The method has applications to a wide variety of temporal data, indeed any data for which there exist ontological descriptions.

View Article: PubMed Central - PubMed

Affiliation: Courant Institute of Mathematical Sciences, New York University, 715 Broadway 10th floor, New York, NY, 10012, USA, samantha@cs.nyu.edu.

ABSTRACT
Biological systems are complex and often composed of many subtly interacting components. Furthermore, such systems evolve through time and, as the underlying biology executes its genetic program, the relationships between components change and undergo dynamic reorganization. Characterizing these relationships precisely is a challenging task, but one that must be undertaken if we are to understand these systems in sufficient detail. One set of tools that may prove useful are the formal principles of model building and checking, which could allow the biologist to frame these inherently temporal questions in a sufficiently rigorous framework. In response to these challenges, GOALIE (Gene ontology algorithmic logic and information extractor) was developed and has been successfully employed in the analysis of high throughput biological data (e.g. time-course gene-expression microarray data and neural spike train recordings). The method has applications to a wide variety of temporal data, indeed any data for which there exist ontological descriptions. This paper describes the algorithms behind GOALIE and its use in the study of the Intraerythrocytic Developmental Cycle (IDC) of Plasmodium falciparum, the parasite responsible for a deadly form of chloroquine resistant malaria. We focus in particular on the problem of finding phase changes, times of reorganization of transcriptional control.

No MeSH data available.


Related in: MedlinePlus

GOALIE’s output of the HKM of P. falciparum IDC as a graph of clusters
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Fig2: GOALIE’s output of the HKM of P. falciparum IDC as a graph of clusters

Mentions: The main output display of is the cluster graph. This is the visual display of the HKM and all of its associated information. For the dataset studied here, there are 4–5 clusters per window, and five windows. By studying the cluster centroid graphs (mean profiles for the expression patterns of the genes in each cluster), we can visually verify the cascade of genes as described in Bozdech et al. (2003). In Fig. 2, the thickness of the red edges (cluster connections) denotes that many of the terms selected (those related to biosynthesis, glycolysis, translation, and transcription), traveled along the same paths through time (i.e. they were in the connections between the clusters connected by the edges). This inference is consistent with the earlier semi-manual data analysis presented in Bozdech et al. (2003).Fig. 2


Systems biology via redescription and ontologies (I): finding phase changes with applications to malaria temporal data.

Kleinberg S, Casey K, Mishra B - Syst Synth Biol (2008)

GOALIE’s output of the HKM of P. falciparum IDC as a graph of clusters
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2553327&req=5

Fig2: GOALIE’s output of the HKM of P. falciparum IDC as a graph of clusters
Mentions: The main output display of is the cluster graph. This is the visual display of the HKM and all of its associated information. For the dataset studied here, there are 4–5 clusters per window, and five windows. By studying the cluster centroid graphs (mean profiles for the expression patterns of the genes in each cluster), we can visually verify the cascade of genes as described in Bozdech et al. (2003). In Fig. 2, the thickness of the red edges (cluster connections) denotes that many of the terms selected (those related to biosynthesis, glycolysis, translation, and transcription), traveled along the same paths through time (i.e. they were in the connections between the clusters connected by the edges). This inference is consistent with the earlier semi-manual data analysis presented in Bozdech et al. (2003).Fig. 2

Bottom Line: One set of tools that may prove useful are the formal principles of model building and checking, which could allow the biologist to frame these inherently temporal questions in a sufficiently rigorous framework.In response to these challenges, GOALIE (Gene ontology algorithmic logic and information extractor) was developed and has been successfully employed in the analysis of high throughput biological data (e.g. time-course gene-expression microarray data and neural spike train recordings).The method has applications to a wide variety of temporal data, indeed any data for which there exist ontological descriptions.

View Article: PubMed Central - PubMed

Affiliation: Courant Institute of Mathematical Sciences, New York University, 715 Broadway 10th floor, New York, NY, 10012, USA, samantha@cs.nyu.edu.

ABSTRACT
Biological systems are complex and often composed of many subtly interacting components. Furthermore, such systems evolve through time and, as the underlying biology executes its genetic program, the relationships between components change and undergo dynamic reorganization. Characterizing these relationships precisely is a challenging task, but one that must be undertaken if we are to understand these systems in sufficient detail. One set of tools that may prove useful are the formal principles of model building and checking, which could allow the biologist to frame these inherently temporal questions in a sufficiently rigorous framework. In response to these challenges, GOALIE (Gene ontology algorithmic logic and information extractor) was developed and has been successfully employed in the analysis of high throughput biological data (e.g. time-course gene-expression microarray data and neural spike train recordings). The method has applications to a wide variety of temporal data, indeed any data for which there exist ontological descriptions. This paper describes the algorithms behind GOALIE and its use in the study of the Intraerythrocytic Developmental Cycle (IDC) of Plasmodium falciparum, the parasite responsible for a deadly form of chloroquine resistant malaria. We focus in particular on the problem of finding phase changes, times of reorganization of transcriptional control.

No MeSH data available.


Related in: MedlinePlus