Dissecting Switches: Biophysical Models of Gene Regulation
Posted August 24, 2007
A May 17, 2007, meeting of the Academy's Computational Biology & Bioinformatics Discussion Group featured three speakers working to model the molecular and structural underpinnings of important aspects of gene regulation. Tamar Schlick of New York University described "mesoscale" models that provide a detailed view of nucleosome packing and the underlying structural patterns of folded chromatin fibers. In particular, she and her coworkers have clarified the role of the tails on the core histone proteins in promoting nucleosome organization. Alexandre Morozov of the Rockefeller University used statistical mechanics to predict which transcription factor-binding sites are most likely to be blocked by nucleosomes, and how the binding sites compete and cooperate to regulate gene expression. Ye Ding of the Wadsworth Center focused on posttranscriptional regulation by microRNA, suggesting that the ability of a miRNA to recognize a potential target messenger RNA depends on how much of the target site is "accessible," or unpaired, in its natural state. He described a model in which recognition requires nucleation at four contiguous sites, followed by hybridization; the model uniquely accounts for observed exceptions to the "seed rule" for identifying miRNA targets.
Use the tabs above to find a meeting report and multimedia from this event.
miRBASE repository of microRNA information.
Chromatin Modeling and Simulation: A Tale of Histone Tails
Arya G, Schlick T. 2007. Efficient global biopolymer sampling with end-transfer configurational bias Monte Carlo. J. Chem. Phys. 126: 044107.
Arya G, Schlick T. 2006. Role of histone tails in chromatin folding revealed by a mesoscopic oligonucleosome model. Proc. Natl. Acad. Sci. USA 103: 16236-16241. Full Text
Arya G, Zhang Q, Schlick T. 2006. Flexible histone tails in a new mesoscopic oligonucleosome model. Biophys. J. 91: 133-150. Full Text
Beard DA, Schlick T. 2001. Computational modeling predicts the structure and dynamics of chromatin fiber. Structure 9: 105-114. Full Text
Beard DA, Schlick T. 2001. Modeling salt-mediated electrostatics of macromolecules: the discrete surface charge optimization algorithm and its application to the nucleosome. Biopolymers 58: 106-115.
Strahs D, Schlick T. 2000. A-Tract bending: insights into experimental structures by computational models. J. Mol. Biol. 301: 643-663.
Sun J, Zhang Q, Schlick T. 2005. Electrostatic mechanism of nucleosomal array folding revealed by computer simulation. Proc. Natl. Acad. Sci USA 102: 8180-8185. Full Text
Biophysical Models of Chromatin Structure and Its Effect on Gene Regulation
Ioshikhes IP, Albert I, Zanton SJ, Pugh BF. 2006. Nucleosome positions predicted through comparative genomics. Nat. Genet. 38: 1210-1215.
Miller JA, Widom J. 2003. Collaborative competition mechanism for gene activation in vivo. Mol. Cell Biol. 23: 1623-1632. Full Text
Olson WK, Gorin AA, Lu XJ, et al. 1998. DNA sequence-dependent deformability deduced from protein-DNA crystal complexes. Proc. Natl. Acad. Sci. USA 95:11163-11168. Full Text
Segal E, Fondufe-Mittendorf Y, Chen L, et al. 2006. A genomic code for nucleosome positioning. Nature 442: 772-778.
Yuan GC, Liu YJ, Dion MF, et al. 2005. Genome-scale identification of nucleosome positions in S. cerevisiae. Science 309: 626-630.
A Target Structure Based Hybridization Model for Accurate Prediction of microRNA-Target Interactions
Brennecke J, Stark A, Russell RB, Cohen SM. 2005. Principles of microRNA-target recognition. PLoS Biol. 3 :e85. Full Text
Didiano D, Hobert O. Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions. Nat. Struct. Mol. Biol. 13: 849-851.
Ding Y, Chan CY, Lawrence CE. 2004. Sfold web server for statistical folding and rational design of nucleic acids. Nucleic Acids Res. 32: W135-W141. Full Text
Ding Y, Lawrence CE. 2003. A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res. 31: 7280-7301. Full Text
Lewis BP, Burge CB, Bartel DP. 2005. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120: 15-20. Full Text
Long D, Lee R, Williams P, et al. 2007. Potent effect of target structure on microRNA function. Nat. Struct. Mol. Biol. 14: 287-294.
Miranda KC, Huynh T, Tay Y, et al. 2006. A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126: 1203-1217.
Vella MC, Choi EY, Lin SY, et al. 2004. The C. elegans microRNA let-7 binds to imperfect let-7 complementary sites from the lin-41 3′UTR. Genes Dev. 18: 132-137.
Tamar Schlick, PhD
Tamar Schlick is professor of chemistry, mathematics, and computer sciences in the Department of Chemistry and the Courant Institute of Mathematical Sciences. She was the director of the new multidepartmental Computational Biology Doctoral Program at the Graduate School of Arts and Sciences and director of program development for the Department of Chemistry at NYU from 2003 to 2006. Schlick was an associate investigator of the Howard Hughes Medical Institute from 1994 to 2003. Schlick has received many honors, including being named 2005 Businesswoman of the Year, an American Physical Society Fellow, and an American Association for the Advancement of Science Fellow.
Tamar Schlick obtained her MS and PhD in applied mathematics at the Courant Institute of Mathematical Sciences at New York University.
Alexandre Morozov, PhD
Alexandre Morozov is a postdoctoral fellow in the laboratory of Eric Siggia at the Center for Studies in Physics and Biology, Rockefeller University. In the Fall 2007 semester, Alexandre Morozov will be joining the Rutgers BioMaPS Institute faculty with a joint appointment in the Physics Department. Morozov received his PhD in computational and theoretical biophysics under the direction of David Baker at the University of Washington.
Morozov's previous research efforts focused on developing methods and algorithms for predicting protein structures from amino acid sequences, predicting kinetics of protein folding, analyzing mechanisms of molecular recognition, and predicting binding affinities and specificities of protein–protein interactions. The goal of Morozov's current research is to predict on a whole-genome scale protein expression levels, including the variation that is influenced by cell type, environmental signals, developmental stage, and disease state. In effect, he seeks to improve our current understanding of the "transcriptional and post-transcriptional regulatory code" that links the DNA sequence with gene expression levels.
Ye Ding, PhD
Ye Ding is a research director at the Wadsworth Center in the Department of Developmental Genetics and Bioinformatics. He received his PhD from Carnegie Mellon University in 1990.
Ding's RNA bioinformatics program has the long term objectives of developing efficient and improved algorithms for RNA higher order structure prediction; developing computational tools for RNA structure-based applications; making the tools available to the scientific community through software and a web server; and applying these tools to molecular biology problems of high scientific significance through collaborations with molecular biologists.
Don Monroe is a science writer based in Murray Hill, New Jersey. After getting a PhD in physics from MIT, he spent more than fifteen years doing research in physics and electronics technology at Bell Labs. He writes on biology, physics, and technology.