Statistical biological physics and computational biology
Biological systems are complex systems. What are the design principles of those complex systems? How and why do they function the way they do? How can such extremely level of complexity emerge out of evolution? These are the questions that fascinate me. My research aims to finding clues to the answer of these questions, using complementary approaches of theoretical modeling and bioinformatic data mining.
Eukaryotic DNA is packaged into a highly organized chromatin structure with nucleosomes as the fundamental units. Chromatin not only enables the physical compaction of the genome but also provides an additional layer of regulation to the genes encoded in the DNA. Components of the nucleosome, the DNA sequence and the histone proteins, can be chemically modified. Specific arrangements of these modifications can make the DNA in the chromatin structure more or less accessible to the all-important DNA-binding proteins or can recruit target enzymes, ultimately resulting in the activation or repression of genes. The collection of the chromatin modifications, including post-translational modifications on histone tails and DNA methylation, is called the epigenome. Importantly, epigenome potentially encodes a large amount of heritable information, which is in addition to the information carried by the genome. As Watson said, "You can inherit something beyond the DNA sequence. That is where the real excitement in genetics is now".
How do cells establish and remember their identities? How and to what extent epigenetic marks regulate gene expression program? Do epigenetic marks cross talk? How does the epigenomic landscape change in developmental processes or disease states? How does the epigenome and genome interact in the evolutionary process? In collaboration with Dr. Keji Zhao's group at NIH, we are looking into these questions using the high-throughput data generated by ChIP-Seq.
2. Quantitative study of
evolution
Evolution is
the guiding principle for the entire biological world. My research interests
focus on mathematical models of evolution, including those motivated by laboratory
evolutionary experiments, such as directed in vitro evolution of
biomolecules and long term laboratory evolutionary experiments with
microorganisms. Those types of experiments
provide perfect systems for quantitative study and testing of theoretical
predictions.
Characterization
of evolutionary dynamics:
Evolution is a dynamical process that involves mutation,
recombination, selection and reproduction of a finite number of individuals. To
quantitatively understand the impact of each evolutionary factor,
we have been applying theoretical methods of non-equilibrium statistical
physics to investigate explicit evolutionary models with simple dynamical
fitness landscapes. One example is a model motivated by in vitro
directed molecular evolution of DNA sequences selected by competitive binding
to transcription factor proteins. In this system, the genotype, phenotype and
selection are linked firmly by a well-characterized thermodynamic model at the
molecular level. In addition to mean-field
studies on infinite populations, we find that fluctuation effects (genetic
drift) make a qualitative difference in the dynamical behaviors in many
evolutionary systems. We are working on developing theoretical approaches to
characterize this.
Evolution of
simple genetic circuits: Gene regulatory systems can be extremely
complex, as have been shown by a number of genes well characterized
experimentally. From the studies of simple evolutionary model, we have learned
some lessons on how hard it is for a complex system to evolve. For example, an organism adapts to a new environment by evolving a
new module of binding sites to existing transcription factors. The
potential module is functional only when two-thirds of its binding sites are
functional (i.e., matches well to the consensus sequences of the respective transcription
factors). Then, the time it takes to evolve such a module scales exponentially
with the number of binding sites needed in the module.
Therefore, evolution can put severe constraints on the organization and
complexity of the gene regulatory system. We are using simple gene regulatory
circuits as model systems to explore various ways complexity emerges out of
evolution.
Quantitative data of molecular processes are becoming available due to rapid advances in experimental techniques. This enables our description of biological systems beyond the cartoon picture. We are working on modeling systems of interests to understand how they work.
Soft condensed matter systems include polymer systems, liquid crystal systems, colloidal systems, granular systems and biological systems. Due to the interplay of extended objects, disorder and interaction, these systems exhibit very rich and interesting behaviors. My interests in this general area center on ordering and transport properties of such systems.
The
amorphous solidification transition is an equilibrium continuous phase
transition from a liquid state of matter to an amorphous solid state. This
transition occurs when a sufficiently large density of permanent random
constraints (e.g. chemical crosslinks) is introduced
to connect the constituents (e.g. linear, flexible macromolecules). An example
is the vulcanization transition in natural rubber. Systems
such as these feature the intriguing interplay of quenched disorder (produced
by the random crosslinks) and thermal fluctuations
(i.e. the Brownian motion of the
constituents). We developed a Landau theory with which we were able to
characterize not only the mean-field and but also critical behavior of the
transition using renormalization group approach..
My group works closely with Dr. Zeng's
group. Together we form the theoretical biophysics group in the department.
Students who are interested in joining my group are encouraged to talk with me.
Current:
Chongzhi Zang, graduate student, 2006-present
Wenjing
Yang, graduate student, 2007-present
Alumni:
Mimmie Huang, undergraduate student in CS, 2005 summer HHMI intern
Shane Bretz, REU undergraduate student, 2008 summer
Bionetworks etc: Chen Zeng (GWU)
Evolution: Drs. Terry Hwa, Herbert Levine (UCSD); Dr. Wei-Shau Hu
(NCI, NIH); Dr. Guangyu Wang(GWU)
Epigenomics: Dr. Keji Zhao (NHLBI, NIH)
Condensed Matter: Dr. Howard Wang (Binghamton Univ.)