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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

Summary of IDC as recovered by  A more detailed graphic with annotations can be found at: http://bioinformatics.nyu.edu/Projects/GOALIE/malaria/index.shtml
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Fig1: Summary of IDC as recovered by A more detailed graphic with annotations can be found at: http://bioinformatics.nyu.edu/Projects/GOALIE/malaria/index.shtml

Mentions: This study presents our results of the analysis of the IDC of P. falciparum as previously described by Bozdech et al. (2003). P. falciparum is a strain of the human malaria parasite that was recently sequenced. This new information allows one the opportunity to gain further insight into the role of P. falciparum’s approximately 5,400 genes, the majority of whose functions remain unknown. It has been shown that a large percentage of the genome is active during the IDC and that the regulation pattern is such that as one set of genes is deactivated, another is being turned on, causing what the authors of Bozdech et al. (2003) refer to as a continuous cascade of activity, in which transcriptional regulation is controlled in a tightly timed choreography. The malaria parasite was chosen for this study due to the simplicity of its regulation pattern, making it a good candidate for determining whether we are able to replicate known results. Yet, traditional approaches to understand the structure of the temporal relations among these key processes have been difficult, and required tedious manual intervention. In this paper, we demonstrate GOALIE’s ability to automatically reconstruct the main features of the system, including the cascade of gene expression, as well as the stages of the IDC and their associated processes. Figure 1 depicts the IDC stages as found by We find that in most cases, genes remain in the same clusters throughout the time course, further supporting the results of Bozdech et al. (2003) (Table 1).Fig. 1


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)

Summary of IDC as recovered by  A more detailed graphic with annotations can be found at: http://bioinformatics.nyu.edu/Projects/GOALIE/malaria/index.shtml
© Copyright Policy
Related In: Results  -  Collection

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

Fig1: Summary of IDC as recovered by A more detailed graphic with annotations can be found at: http://bioinformatics.nyu.edu/Projects/GOALIE/malaria/index.shtml
Mentions: This study presents our results of the analysis of the IDC of P. falciparum as previously described by Bozdech et al. (2003). P. falciparum is a strain of the human malaria parasite that was recently sequenced. This new information allows one the opportunity to gain further insight into the role of P. falciparum’s approximately 5,400 genes, the majority of whose functions remain unknown. It has been shown that a large percentage of the genome is active during the IDC and that the regulation pattern is such that as one set of genes is deactivated, another is being turned on, causing what the authors of Bozdech et al. (2003) refer to as a continuous cascade of activity, in which transcriptional regulation is controlled in a tightly timed choreography. The malaria parasite was chosen for this study due to the simplicity of its regulation pattern, making it a good candidate for determining whether we are able to replicate known results. Yet, traditional approaches to understand the structure of the temporal relations among these key processes have been difficult, and required tedious manual intervention. In this paper, we demonstrate GOALIE’s ability to automatically reconstruct the main features of the system, including the cascade of gene expression, as well as the stages of the IDC and their associated processes. Figure 1 depicts the IDC stages as found by We find that in most cases, genes remain in the same clusters throughout the time course, further supporting the results of Bozdech et al. (2003) (Table 1).Fig. 1

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