Lab Members

Assistant Professor

Volkan Yazar

Assistant Professor in the Department of Molecular Biology and Genetics at Bilkent University.

Research Keywords:

• Systems Biology, Transcriptomics, Epigenetics, Neurodegeneration (particularly PD and ALS)

Ongoing Research Projects:

• Transcriptome dynamics of suppressive and adjuvant oligodeoxynucleotide (ODN)-based immune modulation using single cell-guided conventional differential expression, co-expression network, and longitudinal analyses: this project involves temporal characterization of cellular and molecular targets of immunomodulatory CpG (adjuvant) and A151 (suppressive) ODNs in murine system using a large set of expression arrays from our time series study with multi-group design.
  

• Investigation of the underlying molecular mechanisms of Parkinson’s disease (PD) and Amyotrophic Lateral Sclerosis (ALS) with a focus on the mechanistic underpinnings of the immune dysfunction observed in both neurodegenerative disorders using data mining and systems biology approaches: in this project, we leverage data sets deposited in public repositories as well as disease-specific databases to discover novel candidate molecules and master regulators associated with disease initiation and progression using a range of multi-modal, multi-omics data integration, and network-level analysis methods.
  

• Investigation of regeneration-associated gene (RAG) network activation by the master regulator ATF3 in SOD1 mutant familial ALS (fALS): based on our recent discovery that ATF3 arm of the RAG network gets activated at initial stages of SOD1 mutant fALS, this project will provide novel insights into early set of events that occur in fALS patients with various SOD1 mutations. Our approach involves the use of multi-omics data from SOD1 mutation carriers for longitudinal analysis of induced pluripotent stem cells differentiating into motor neurons.
  

Overall, considering intrinsic characteristics of raw data, we develop customized analysis workflows for individual data sets after rounds of data wrangling and use statistical learning and systems biology approaches to extract valuable biological information from high dimensional sequencing and array data at the epigenome, transcriptome, and proteome levels.