Aging is a complex process affected by genetic, environmental and stochastic factors. How these factors interact is not fully understood. Our long term goal is to define the relationships between genetic pathways, environmental inputs and stochastic factors that contribute to aging, and build models that predict the aging outcomes of individuals based on their genetic markup, past experiences, and readouts from biomarkers.
The aims of this proposed project are to determine how transcriptional noises in environmental responses translate to heterogeneity in lifespan and aging in C. elegans, and to define environmental and genetic factors that contribute to these sources of noise. To achieve these aims, we will develop new automated microscopy systems capable of collecting the data necessary for our analysis. The central hypothesis is that noises in environment-related transcriptional responses can lead to heterogeneity in aging, and through feedback and feed-forward processes, these noises are either buffered or amplified, resulting in variation in the aging process. First we will analyze transcriptional noises in pathways affecting lifespan, dissecting it into components that can be attributed to various sources;we will also determine how each source of noise is affected by food and temperature. Because the genes tested communicate with each other in the nervous system, we will next determine how the communication process affects noise in this signaling network. Finally, we will determine whether noises in gene activity lead to variability in lifespan and other age-related declines. This proposal is innovative for several reasons. First, it combines molecular genetics and engineering to solve a fundamental problem in aging. Second, it provides a new framework to analyze transcriptional noise and feedback mechanisms in multicellular animals in vivo, to uncover how these processes ultimately affect aging in different environments. Lastly, it creates new automated platforms for high-throughput quantitative imaging to accelerate research. Our approach exploits the key advantages of the C. elegans model by integrating analysis of gene expression, signaling, behavior and physiology in the intact animal. This work is significant because it will provide new insights into how transcriptional noise arises in intercellular signaling pathways that affect lifespan, and uncover relationships between genetic pathways, environmental inputs, transcriptional noise and aging.

Public Health Relevance

Aging is an important and active area of research linking genes and environments to the degeneration of functions. It has direct applications to many human diseases such as neurodegeneration and muscle function declination.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZAG1-ZIJ-5 (A1))
Program Officer
Guo, Max
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Georgia Institute of Technology
Engineering (All Types)
Schools of Engineering
United States
Zip Code
Patel, Dhaval S; Diana, Giovanni; Entchev, Eugeni V et al. (2017) Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans. J Vis Exp :
Hwang, Hyundoo; Barnes, Dawn E; Matsunaga, Yohei et al. (2016) Muscle contraction phenotypic analysis enabled by optogenetics reveals functional relationships of sarcomere components in Caenorhabditis elegans. Sci Rep 6:19900
Entchev, Eugeni V; Patel, Dhaval S; Zhan, Mei et al. (2015) A gene-expression-based neural code for food abundance that modulates lifespan. Elife 4:e06259
Aubry, Guillaume; Zhan, Mei; Lu, Hang (2015) Hydrogel-droplet microfluidic platform for high-resolution imaging and sorting of early larval Caenorhabditis elegans. Lab Chip 15:1424-31
Zhan, Mei; Crane, Matthew M; Entchev, Eugeni V et al. (2015) Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans. PLoS Comput Biol 11:e1004194
Hwang, Hyundoo; Krajniak, Jan; Matsunaga, Yohei et al. (2014) On-demand optical immobilization of Caenorhabditis elegans for high-resolution imaging and microinjection. Lab Chip 14:3498-501
Lee, Hyewon; Kim, Shin Ae; Coakley, Sean et al. (2014) A multi-channel device for high-density target-selective stimulation and long-term monitoring of cells and subcellular features in C. elegans. Lab Chip 14:4513-4522
Fernandes de Abreu, Diana Andrea; Caballero, Antonio; Fardel, Pascal et al. (2014) An insulin-to-insulin regulatory network orchestrates phenotypic specificity in development and physiology. PLoS Genet 10:e1004225
Williams, Daniel C; Bejjani, Rachid El; Ramirez, Paula Mugno et al. (2013) Rapid and permanent neuronal inactivation in vivo via subcellular generation of reactive oxygen with the use of KillerRed. Cell Rep 5:553-63
Lee, Hyewon; Crane, Matthew M; Zhang, Yun et al. (2013) Quantitative screening of genes regulating tryptophan hydroxylase transcription in Caenorhabditis elegans using microfluidics and an adaptive algorithm. Integr Biol (Camb) 5:372-80

Showing the most recent 10 out of 20 publications