Computational Systems Biology
Deciphering trans-regulatory networks through computational and statistical models
Welcome to the Computational Systems Biology lab at Children's Medical Research Institute (CMRI), the Westmead Research Hub. Our research group also affiliates with the School of Mathematics and Statistics and is part (known as Computational Trans-regulatory Biology) of the Charles Perkins Centre (CPC) at the University of Sydney.
Our research aims to understand the molecular trans-regulatory networks (TRNs), comprised of cell signalling, transcriptional, translational, and (epi)genomic regulations, in controlling cell identities and cell-fate decisions. We develop computational and statistical models to reconstruct each layer of the TRNs (e.g. signalling networks, transcriptional networks), and characterise their cross-talk and trans-regulation in various cellular systems and diseases. By integrating multi-omic data for generating biological hypotheses and predictions at the systems level, we aim to address the following research questions:
- How do different layers of TRNs regulate each other in controlling stem/progenitor cell fate?
- Can we accurately predict stem/progenitor cell differentiation trajectories based on their TRNs?
- How can we control stem/progenitor cells fate for applications such as regenerative medicine?
Post-doc positions funded by NHMRC grant are available for conducting research on the broad area of computational systems biology in stem cells and theirtherapeutic application. Interested candidates are encouraged to please contact Pengyi Yang, lab head, for more details (pengyi.yang [at] sydney.edu.au)
PhD Scholarships are available for international candidates. Summer Research Scholarships are available for third year undergraduates. For more details regarding scholarships, please contact Pengyi Yang (Contact).
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).
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.
Very excited to share my first first author paper in @NAR_Open! We reconstructed key transcriptional networks underpinning the transition of stem cells between naive and formative pluripotency. 1/https://t.co/5UHqZ1VHIi— Hani Jieun Kim (@HaniKim127) December 19, 2019
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
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Our paper is highlighted on Nature Review Genetics! https://t.co/yqbnaYLjFN— Yang lab (@PengyiYang82) October 24, 2017