The Structure, Function and Evolution of Biological Networks
The Walhout lab wishes to understand biological networks and how these networks adapt to different conditions. We use systems biology approaches to dissect these complex networks. These approaches combine high-quality and large-scale genetic and biochemical data sets and uses computational modeling to integrate the data such that the organizing principles and emergent properties of biological systems are unveiled.
To examine biological networks we mainly use the model organism round worm Caenorhabditis elegans. Worms are highly adaptable, easy to manipulate, and have many analogs in human genetics. Furthermore, there are many genetic tools and worm-specific techniques that are not available for studying higher eukaryotes. Overall, our research involves two broad areas of biology:
1: Gene Regulatory Networks
2: Regulation of Metabolic Networks read more…
Genome-scale metabolic network modeling.
Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.
Yilmas LS, Walhout AJM (2017) Metabolic network modeling with model organisms. Curr. Opin. Chem. Biol. 36, 32-39.
The December issues of Cold Spring Harbor Protocols features four protocols on how to prepare reagents for yeast one-hybrid analysis.
An important question when studying gene regulation is which transcription factors (TFs) interact with which cis-regulatory elements, such as promoters and enhancers. Addressing this issue in complex multicellular organisms is challenging as several hundreds of TFs and thousands of regulatory elements must be considered in the context of different tissues and physiological conditions. Yeast one-hybrid (Y1H) assays provide a powerful “gene-centered” method to identify the TFs that can bind a DNA sequence of interest. In this introduction, we describe the basic principles of the Y1H assay and its advantages and disadvantages and briefly discuss how it is complementary to “TF-centered” methods that identify protein-DNA interactions for a known protein of interest.
Fuxman Bass JI, Reece-Hoyes JS, Walhout AJ. (2016). Gene-Centered Yeast One-Hybrid Assays. Cold Spring Harb. Prot., 2016(12).
Generating Bait Strains for Yeast One-Hybrid Assays
Performing Yeast One-Hybrid Library Screens
Colony Lift Colorimetric Assay for β-Galactosidase Activity
Zymolyase-Treatment and Polymerase Chain Reaction Amplification from Genomic and Plasmid Templates from Yeast
TF–cofactor protein–protein interaction network from Reece‐Hoyes et al (2013) was used to predict activators and repressors. Blue, predicted repressors; red, predicted activators; yellow, cofactors; blue outline, co‐repressors; red outline, co‐activators.
Transcription factors (TFs) play a central role in controlling spatiotemporal gene expression and the response to environmental cues. A comprehensive understanding of gene regulation requires integrating physical protein–DNA interactions (PDIs) with TF regulatory activity, expression patterns, and phenotypic data. Although great progress has been made in mapping PDIs using chromatin immunoprecipitation, these studies have only characterized ~10% of TFs in any metazoan species. The nematode C. elegans has been widely used to study gene regulation due to its compact genome with short regulatory sequences. Here, we delineated the largest gene‐centered metazoan PDI network to date by examining interactions between 90% of C. elegans TFs and 15% of gene promoters. We used this network as a backbone to predict TF binding sites for 77 TFs, two‐thirds of which are novel, as well as integrate gene expression, protein–protein interaction, and phenotypic data to predict regulatory and biological functions for multiple genes and TFs.
Fuxman Bass, JI, Pons, C, Kozlowski, L, Reece‐Hoyes, JS, Shrestha, S, Holdorf, AD, Mori, A, Myers, CL, Walhout, AJM. (2016). A gene‐centered C. elegans protein–DNA interaction network provides a framework for functional predictions. Mol. Sys. Biol. 12: 884. doi: 10.15252/msb.20167131
Best wishes to Dr. Juan Fuxman Bass who has left the lab to start his own lab as an Assistant Professor in the Department of Biology at Boston University. He will be working on transcription factors that regulate the immune response. We will miss you Juan!
Dr. Fuxman Bass’s website: https://www.bu.edu/biology/people/profiles/juan-fuxman-bass/
The Walhout lab participated in the annual joint Cancer Center for Systems Biology (CCSB) and Program in Systems Biology (PSB) retreat in Gloucester, MA, on September 7-9. The retreat attendees heard talks from their colleagues in CCSB and PSB, as well as lectures form other invited speakers. Distinguished Professor John Roth of UC Davis opened the retreat with a historical perspective of his work on mutation selection in bacteria. University of Toronto Professor and Howard Hughes Medical Institute Senior International Research Scholar Charlie Boone presented the keynote lecture on genetic networks in yeast, and Tufts University Professor and Howard Hughes Medical Institute Professor David Walt discussed how basic science research lead to the co-founding of the company Illumina. Walhout lab post-doctoral fellows Jingyan Zhang and Huimin Na, and graduate student Aurian Garcia-Gonzalez all gave short talks about their exciting research on C. elegans networks.