Computational Systems Biology
Deciphering trans-regulatory networks through computational and statistical models
Pengyi Yang, Ph.D., heads the Computational Systems Biology group at Children's Medical Research Institute (CMRI), at the Westmead Research Hub, and the Computational Trans-Regulatory Biology group at Charles Perkins Centre (CPC), the University of Sydney. He is an ARC DECRA and USyd Robinson Fellow, and holds a Senior Lectureship at the School of Mathematics and Statistics, the University of Sydney.
Molecular trans-regulatory networks (TRNs) comprised of cell signalling, transcriptional, translational, and (epi)genomic regulations are central to health and disease. We develop computational and statistical models to reconstruct cell signalling, epigenomic/transcriptional, and proteomic networks, and characterise their cross-talk and trans-regulations in various cellular processes and systems. By integrating heterogeneous trans-omic data with the goal of generating testable hypotheses and predictions, we aim to tackle the following research questions:
- How do different layers of regulations talk to each other in controlling stem cell fate?
- Can we accurately predict stem cell differentiation trajectories based on their TRNs?
- The mechanisms of stem/progenitor cells in establishing identities and making cell fate decisions
Our lab is multi-disciplinary and combines computer science, engineering, mathematics and statistics, and molecular biology for understanding and harnessing stem/progenitor cells for development and therapeutics.
We are setting up a new laboratory at Children's Medical Research Institute (CMRI) at the Westmead Research Hub. PhD and Honours candidates are welcome to join us and work on either 'wet' (laboratory) or 'dry' (computational) projects (or both).
Our group also holds 'dry' space in both Charles Perkins Centre (CPC) and School of Mathematics and Statistics (Carslaw Building), the University of Sydney. Candidates whose background are in computer science, engineering, and/or mathematics and statistics are welcome to join us and select their preferred research location(s).
PhD Scholarships are available for international candidates. Summer Research Scholarships are available for third year undergraduates. For more details regarding scholarships, please contact Dr. Pengyi Yang (pengyi.yang [at] sydney.edu.au)
Our lab are experienced in developing machine learning algorithms and statistical models for analysing the following types of data:
- Mass spectrometry (MS)-based:
- Redox proteomics
- Next-generation sequencing (NGS)-based:
- Bulk RNA-seq and microarray
- Single-cell RNA-seq
- ChIP-seq and RIP-seq of DNA/RNA binding proteins
- RNA Polymerase II, Histones and DNaseI
- Hi-C and ChIA-PET
- and their cross-talk for comprehensive understanding of TRNs in controlling cell identities and cell-fate decisions.
Multi-omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency: Cell Systems https://t.co/CvQHyZpk10— Pengyi Yang (@PengyiYang82) May 8, 2019
scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets https://t.co/JhjAERWbQ5— Pengyi Yang (@PengyiYang82) April 26, 2019
This arvo we had a look at clustering & classification algorithms to make sense of the big data we #EMBLAPhD students are faced with nowadays! Thank you Dr. Kitty Lo, Dr. Pengyi Yang & Dr. Dario Strbenac from @Sydney_Uni Check out their in house R-Package: https://t.co/BMZXovaQFS pic.twitter.com/RiWf4DA0k1— EMBL Australia (@EMBLAustralia) July 10, 2018
See all news.
Our paper is highlighted on Nature Review Genetics! https://t.co/yqbnaYLjFN— Yang lab (@PengyiYang82) October 24, 2017