Title

A Computational Metabolic Model Of The Ng108-15 Cell For High Content Drug Screening With Electrophysiological Readout

Keywords

Cellular metabolism; Glycolysis; High-content screening; Hodgkin-Huxley model; NG108-15

Abstract

Computational Systems Modeling could play a significant role in improving and speeding up of the drug development process. By the incorporation of cellular modeling into a High Information Content Drug Screening platform the information content of the pharmacological test could be significantly increased through a deeper understanding of cellular pathways and signaling mechanisms. Unfortunately, many of the cellular signaling pathways in the cells are yet to be explored. Moreover, which is an even larger problem, their integration into a functional signaling network at the whole cell level is almost unknown or untested. Thus, there is an urgent need to develop a data-driven functional whole-cell model which enables the correlation of biochemical and physiological experimental results at the whole cell level with partial information available for the metabolic and signal transduction pathways of the cell. We have built a wholecell model of NG108-15 cells and validated some of the underlying cellular metabolic and signal transduction networks with a series of detailed experiments in order to predict cellular responses to a wide variety of extracellular stimuli. This validated assay system will be an important tool for the identification of cellular changes and activation of signal transduction pathways based on changes of electrophysiological properties and responses of the cell and would have a high impact on drug screening and toxicity evaluation at the cell-system level.

Publication Date

11-26-2012

Publication Title

2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012

Number of Pages

530-532

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2382936.2383011

Socpus ID

84869477463 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84869477463

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