My interests pertain to the nature, inheritance, and evolution of phenotypic variability in living systems. Specifically, how does phenotypic variation arise in an organism during ontogeny; how is the expression or release of this variation structured, constrained, memorized, and propagated by an organism’s genome regulatory architecture; and what is the effect of this variation on subsequent generations (that is, how does ontogenetic variation affect evolution)?

Research themes: development, epigenomics, evolution, systems biology, insect sociality

Contact

Daniel F. Simola

1041 BRB II/III University of Pennsylvania
421 Curie Boulevard
Philadelphia, PA 19104

campus map | area map

Postdoctoral research

• May 2010—present: Epigenetics of aging and behavior in ants, Lab of Shelley Berger, University of Pennsylvania
• Sep 2009—Apr 2010: Development of novel methods for SNP discovery using next-generation sequencing technology, Lab of Junhyong Kim, University of Pennsylvania

Education

• University of Pennsylvania
  PhD in Genomics and Computational Biology, 2009

  Dissertation: Evolution of Genome-Wide Gene Regulation in the Budding Yeast Cell-Division Cycle

• Dartmouth College
  BA Computer Science, with High Honors, 2003

• St. Joseph’s Preparatory School
  High school diploma, 1999

Teaching experience

• Sole teaching assistant for Principles of Computational Biology (GCB 536) in 2008 and 2009. I was responsible for generating homework assignments, grading exams, holding office hours, and leading review sessions.

Conference talks

• Heterochronic Evolution Reveals Modular Timing Changes in Budding Yeast Transcriptomes Evolution Conference, 2010, Portland, OR

• Evolution of Gene Expression in the Cell-division Cycle of Woodland Populations of Budding Yeast Penn Center for Bioinformatics and Genomics Annual Retreat, Nov 2008, Philadelphia, PA

• Evolution of the Cell-cycle in Natural Populations of Budding Yeast.
  Evolution Conference, 2006, SUNY StonyBrook

Conferences

• Evolution. Portland, OR, 2010.
• Evolution. SUNY Stony Brook, 2006.
• Yeast Genetics and Molecular Biology. Princeton University, 2006.

Graduate course work

• ESE674: Information theory
• GCB 531: Genomics
• BIOL597: Developmental neurobiology seminar
• BIOL446: Introductory statistics
• BIOM600: Cell biology
• BIOM555: Control of gene expression
• CAMB550: Genetics
• CIS520: Introductory machine learning
• CIS700: Machine learning for bioinformatics
• Seminar on sequence alignment (Sampath Kannan, Sridhar Hannenhalli)
• GCB537: Seminar in phylogeny (Junhyong Kim)
• Independent study: Modeling gene expression (Junhyong Kim)
• Independent study: Population genetics (Warren Ewens)

Graduate Rotations

Dendritic Hotspots of Translational Activity

PI: Jim Eberwine

The functional characterization of the neurological mechanism effecting synaptic plasticity is still largely unknown. Recent experiments have shown that vertebrate neurons both sequester mRNA at dendritic terminals and have the capacity to translate this mRNA locally. It is likely that local translation machinery is utilized in dendrites to organize and mediate responses to presynaptic signaling. Such a mechanism could contribute to the formation and/or maintenance of a synaptic structure which is fundamental to learning and memory. Further experiments have presented temporal and spatial data corresponding to local translation events. Analysis of the temporal data suggests that dendritic ribosomal machinery can be stimulated to translate mRNA both at an exponential rate and a linear rate, in contrast to strictly linear translation rate in the cell soma. Analysis of spatial data of hotspots suggests that they could take up permanent residence at specific locations within dendrites.

Prediction of Transcription Factor Gene Expression in Yeast

PI: Junhyong Kim

Our goal is to predict the gene expression levels of known transcription factors in Saccharomyces cerevisiae. I used regression to evaluate linear mathematical models, based on the yeast transcriptional regulatory network and expression data from the cell cycle. A Fourier approach was taken, expressing the temporal expression profiles of genes as periodic waves. Each model’s explanatory variables are taken from a basis subset of transcription factor genes, whose proteins were found to regulate every downstream transcription factor gene. Thus each model represents the expression of a target gene as a weighted linear combination of waves.

• Gene Expression Prediction write-up
• Presentation

Expression Analysis of Mouse Chromosome 5 and Presynaptic Transmssion Genes

PI: Maja Bucan

There are 350 known genes and over 150 putative genes that lie along a 77 megabase stretch of Mouse Chromosome 5. This region has been important in human disease modeling, as a balancer chromosome for this region is available, which facilitates mutagenesis. My goal was to create an expression map of this region, using microarrays developed in the lab, in conjunction with Novartis and other microarray data sources. Such an expression map will serve as a tool to search for functional correlations with disease genes, to validate putative genes, and to understand expression regulation in a continuous range of DNA.

• Rotation Presentation

Undergraduate Research

Dartmouth Senior Thesis: Discovery, Visualization and Analysis of Gene Regulatory Sequence Elements in Genomes

Advisor: Bob Gross

The advent of rapid DNA sequencing has produced an explosion in the amount of available sequence information, permitting us to ask many new questions about DNA. There is a pressing need to design algorithms that can provide answers to questions related to the control of gene expression, and thus to the structure, function, and behavior of organisms. Such algorithms must filter through massive amounts of informational noise to identify meaningful conserved regulatory DNA sequence elements.

We are approaching these questions with the notion that visualization is a key to exploring data relationships. Understanding the exact nature of these relationships can be very difficult by simply interpreting raw data. The ability to look at data in a graphical form allows us to apply our innate capacity to think visually to discern the subtle relationships that might not be recognizable otherwise. This thesis provides computational tools to visually identify and analyze candidate motifs in the DNA of a species.

This includes a parsing utility to store genomic data and an application to search for and visually identify motifs. Using these tools, novel and previously compiled gene sets were identified using the genome of the plant species Arabidopsis thaliana.

• Thesis manuscript and pdf
• Thesis Presentation