Research interests

With the wealth of data accumulating from completely sequenced genomes and other high-throughput experiments, we now have an unprecedented opportunity to systematically integrate different types of data to unveil novel biological insights. We are utilising computational techniques to analyse publicly available data, as well as new -omic data generated in-house, to improve our understanding on the mechanisms that control gene expression in response to different environmental cues.

As part of Mahidol University's initiative to promote and support computational and systems biology research, the group are part of Integrative Computational BioScience (ICBS) center, which bring together young scientists from different field, ranging from molecular biology, medicine, and engineering, to solve complex biological questions. 

Our group are currently employing Systems Biology to engage Regulation of Gene Expression in the following contexts:

1. Mechanism of diseases and Precision Medicine 

In collaboration with several groups at  Faculty of Medicine Ramathibodi hospital  and Faculty of Medicine Siriraj hospital, we are establishing a pipeline for analyses of large scale data for clinical interpretation and exploration of new disease mechanisms. We have recently obtained genome-scale data of several genetic diseases and cancers (including genomes, transcriptomes, exomes, and proteomes) to look into. 

We have recently established a single-cell omics facility at Mahidol University, to further characterise the mechanisms of several diseases and host immune systems, including cancers and viral infections, at single-cell resolution using  single-cell ATAC-seq and single-cell RNA-seq.

Read more: Suktitipat 2014, Jinawath 2016, Kitdumrongthum2018, Sukjoi2020, Schweitzer2020, Poonpanichakul2021,  Arora2022, Hepkema2023, Punyawatthananukool2024

2. Adaptation to climate change

We collaborate with multiple groups in Thailand and abroad to Systems Biology approaches (e.g. RNA-seq and ChIP-seq) and bioinformatics (e.g. machine learning) to contribute new insights into to effects and adaptation to abiotic stresses such as temperature and salinity, especially in the context of climate change. 

Read more: Mhauntong 2015, Jung 2016, Cortijo and Charoensawan 2017, Ezer2017, Gavrin2020, Tong2020, Jenkitkonchai2021, Sriden2022, Phosuwan2024