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

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.

SUBMITTER: Kleinberg S

PROVIDER: S-EPMC2553327 | biostudies-other | 2007 Dec

REPOSITORIES: biostudies-other

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Project description:Traumatic brain injury (TBI) represents a critical health problem of which diagnosis, management, and treatment remain challenging. TBI is a contributing factor in approximately one-third of all injury-related deaths in the United States. The Centers for Disease Control and Prevention estimate that 1.7 million people suffer a TBI in the United States annually. Efforts continue to focus on elucidating the complex molecular mechanisms underlying TBI pathophysiology and defining sensitive and specific biomarkers that can aid in improving patient management and care. Recently, the area of neuroproteomics-systems biology is proving to be a prominent tool in biomarker discovery for central nervous system injury and other neurological diseases. In this work, we employed the controlled cortical impact (CCI) model of experimental TBI in rat model to assess the temporal-global proteome changes after acute (1?day) and for the first time, subacute (7?days), post-injury time frame using the established cation-anion exchange chromatography-1D SDS gel electrophoresis LC-MS/MS platform for protein separation combined with discrete systems biology analyses to identify temporal biomarker changes related to this rat TBI model. Rather than focusing on any one individual molecular entity, we used in silico systems biology approach to understand the global dynamics that govern proteins that are differentially altered post-injury. In addition, gene ontology analysis of the proteomic data was conducted in order to categorize the proteins by molecular function, biological process, and cellular localization. Results show alterations in several proteins related to inflammatory responses and oxidative stress in both acute (1?day) and subacute (7?days) periods post-TBI. Moreover, results suggest a differential upregulation of neuroprotective proteins at 7?days post-CCI involved in cellular functions such as neurite growth, regeneration, and axonal guidance. Our study is among the first to assess temporal neuroproteome changes in the CCI model. Data presented here unveil potential neural biomarkers and therapeutic targets that could be used for diagnosis, for treatment and, most importantly, for temporal prognostic assessment following brain injury. Of interest, this work relies on in silico bioinformatics approach to draw its conclusion; further work is conducted for functional studies to validate and confirm the omics data obtained.

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

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