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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

Gantt chart view of selected GO terms. Each bar represents a window of time, with up-regulated terms labeled in red, down regulated terms in green and terms not enriching any cluster in the window labeled with black
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Fig3: Gantt chart view of selected GO terms. Each bar represents a window of time, with up-regulated terms labeled in red, down regulated terms in green and terms not enriching any cluster in the window labeled with black

Mentions: A second way one may interpret the results is by using Gantt Charts (Clark 1952), bar graphs for visualizing data with a temporal component. In these graphs are available for each ontology term within the dataset. They contain one bar per window, color coded to show the processes’ overall expression level in that window. This expression (i.e., up, down, normal, inactive—colored red, green, yellow and black respectively) is computed using the cluster centroids for each cluster in which the ontology term and its descendants appear. These charts facilitate summarization of the data, as users may choose to view the graphs for all terms or a selected subset of terms. Note that there is some information loss in this process, but the charts are intended to help make sense of the cluster graph. Allowing users to get an overall sense for how a process is regulated is helpful to that end. For example, in the case of the IDC (a chart depicting a small subset of its GO terms is shown in Fig. 3), we see that “DNA replication initiation” is up-regulated in windows 3 and 4. This is consistent with our identification of those windows as the Trophozoite and Schizont stages, as replication was identified as a process active during these stages in Bozdech et al. (2003).Fig. 3


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)

Gantt chart view of selected GO terms. Each bar represents a window of time, with up-regulated terms labeled in red, down regulated terms in green and terms not enriching any cluster in the window labeled with black
© Copyright Policy
Related In: Results  -  Collection

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

Fig3: Gantt chart view of selected GO terms. Each bar represents a window of time, with up-regulated terms labeled in red, down regulated terms in green and terms not enriching any cluster in the window labeled with black
Mentions: A second way one may interpret the results is by using Gantt Charts (Clark 1952), bar graphs for visualizing data with a temporal component. In these graphs are available for each ontology term within the dataset. They contain one bar per window, color coded to show the processes’ overall expression level in that window. This expression (i.e., up, down, normal, inactive—colored red, green, yellow and black respectively) is computed using the cluster centroids for each cluster in which the ontology term and its descendants appear. These charts facilitate summarization of the data, as users may choose to view the graphs for all terms or a selected subset of terms. Note that there is some information loss in this process, but the charts are intended to help make sense of the cluster graph. Allowing users to get an overall sense for how a process is regulated is helpful to that end. For example, in the case of the IDC (a chart depicting a small subset of its GO terms is shown in Fig. 3), we see that “DNA replication initiation” is up-regulated in windows 3 and 4. This is consistent with our identification of those windows as the Trophozoite and Schizont stages, as replication was identified as a process active during these stages in Bozdech et al. (2003).Fig. 3

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