Inferring the Effective TOR-Dependent Network: A Computational Study in Yeast

Shahin Mohammadi, Shankar Subramaniam, and Ananth Grama


Calorie restriction (CR) is one of the most conserved non-genetic interventions that extends healthspan in evolutionarily distant species, ranging from yeast to mammals. The target of rapamycin (TOR) has been shown to play a key role in mediating healthspan extension in response to CR by integrating different signals that monitor nutrient-availability and orchestrating various components of cellular machinery in response. Both genetic and pharmacological interventions that inhibit the TOR pathway exhibit a similar phenotype, which is not further amplified by CR.


In this paper, we present the first comprehensive, computationally derived map of TOR downstream effectors, with the objective of discovering key lifespan mediators, their crosstalk, and high-level organization. We adopt a systematic approach for tracing information flow from the TOR complex and use it to identify relevant signaling elements. By constructing a high-level functional map of TOR downstream effectors, we show that our approach is not only capable of recapturing previously known pathways, but also suggests potential targets for future studies.

Information flow scores provide an aggregate ranking of relevance of proteins with respect to the TOR signaling pathway. These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways. We propose a novel statistical framework for integrating information flow scores, the set of differentially expressed genes in response to rapamycin treatment, and the transcriptional regulatory network. We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway. This network is hypothesized to mediate life-span extension in response to TOR inhibition.


Our approach, unlike experimental methods, is not limited to specific aspects of cellular response. Rather, it predicts transcriptional changes and post-translational modifications in response to TOR inhibition. The constructed effective response network greatly enhances understanding of the mechanisms underlying the aging process and helps in identifying new targets for further investigation of anti-aging regimes. It also allows us to identify potential network biomarkers for diagnosis and prognosis of age-related pathologies.

Keywords: Computational Aging, Information Flow Analysis, Yeast

Supplementary Materials
News & Announcements
Apr. 23, 2013
Final version of the manuscript, titled "Inferring the Effective TOR-Dependent Network: A Computational Study in Yeast," has been submitted to BMC Systems Biology
Nov. 12-15, 2012
Paper has been presented at the RECOMB SB/RG/DREAM 2012
Oct. 5th, 2012
Paper has been accepted for participation in the poster session at the RECOMB SB/RG/DREAM 2012
Sept. 7th, 2012
Preliminary version of the paper, titled "Systematic Identification of TOR Downstream Effectors Using Random-Walks on the Yeast Interactome," has been submitted to the RECOMB Conference on Regulatory and Systems Genomics