Offering mentoring/hosting to Postdoctoral Fellow that wants to write a MSCA-PF2026 proposal

Get in touch with me if interested in mechanisms of transcription regulation, chromatin modification, nascent RNAs and how their deregulation occurs in cancer or neuropathies. Both molecular and mathematical biologists profiles are welcome! We are in Greece 🇬🇷 at @IMBB_FORTH. Our lab works towards the characterization of molecular transactions ruling healthy/disease gene expression programs. We aim for better fundamental understanding of essential chromatin/cellular processes ruling RNA polymerase II transcription initiation and elongation at protein coding- and non-coding- (lncRNAs, eRNAs…), how genomic DNA sequences integrity is guaranteed in the face of environmental and genetic perturbations, and what might be the impact of defined gene regulatory networks (GRNs) on cells commitment in developmental or pathogenic (de)differentiation lineages. We apply 4D (time and space) -resolution biochemical and Next Generation Sequencing (NGS) protocols as well as bioinformatics analyses/simulations to decipher gene control mechanisms at the molecular/multi-molecular and cellular/multicellular levels. Using human cell lines, mouse models or available data, we resolve chromatin status (ChIP-seq, ATAC-seq), protein complexes (IP, RIP, ChIP-western), transcription machinery binding sites (ChIP-seq, Cut-and-Run), DNA topology (4C-seq, HiC), DNA integrity (damage/repaired DNA patterns), nascent/mature RNA levels and synthesis rate (EU-seq, mRNA-seq, quant-seq). We also rely on single cell (sc) (scRNA-seq and scATAC-seq) approaches to establish transcriptional and chromatin regulatory networks and show what is their effect on cellular differentiation trajectories. To hold a chance in deciphering the mechanisms ruling complex transcriptional networks and predict the contribution of the numerous factors involved, we also want to attempt to develop mathematical models (MM). we have set-up collaborations across fields of cancer biology, bioinformatics and mathematics and we aim to extract the best out of carefully curated existing transcriptomics and epigenomics datasets.Unlike most previously attempted unilateral approaches that suffer from mere "correlation detection" approaches to understand complex biological phenomena, we will use mathematics and computational biology to infer details of transcription/chromatin dynamics. For instance Machine learning (ML) based approach will guide us in determining the key components of our system at minimal cost. Overall, such data driven approach will enable us to bonify the ground-truth information provided by experimental data and which we expect to help the elucidation of the “principal regulatory code” governing physiological and cancer-specific gene expression. Overall, the variety of wet and dry techniques we routinely develop should be integrated to infer transcriptional and epigenetic principles. We aim to develop approaches that will fine-tune model parameters and enhance its ability to predict molecular and disease phenotypes or pinpoint when/how mechanisms of gene regulation might go eerie in disease.