Throughout my academic journey, I have been driven by deep curiosity and a desire to uncover new knowledge in the field of plant-environment interactions, with the ultimate goal of solving real-world problems. My work has revealed conserved and divergent gene regulatory network (GRN) features, shedding light on the evolution of GRNs in dynamic environments. I developed a novel data-collection pipeline that combines OMICs and mathematical modelling, recognized as a ground-breaking methodology in plant research. My approach has fostered collaborations between experimental and computational biologists, resulting in publications in leading journals. Currently, my laboratory's research is focusing on the evolutionary conservation and divergence of gene regulatory and signalling networks in dynamic environments, particularly in response to heat and drought stress. Our aim is to understand how biological networks can adapt and evolve in the face of genetic and environmental changes across all levels. To achieve this, we employ techniques ranging from big-data mining to predictive modelling, and from genomics to proteomics to construct and predict these networks. I am well-equipped to move forward with the overarching goal of bridging systems biology and predictive modelling in formulating a quantitative of biological networks in fundamental biology and in agriculture. In pursuing this path, I am committed to finding possible solutions for those real-world challenges within the field of plant science.

Our research: Plant-environment interaction in land plant evolution

During plant terrestrialization (moving from water to land environments), plants face many terrestrial stressors, such as drought, high salinity, and drastic temperature shifts. The main goal of our research is to understand how land plants adapt to those unfavoured environments at the molecular level during the course of evolution. We are also interested in applying the knowledge we obtained in such studies for designing stress-resilient crops for precision agriculture.

Our approaches: Experimental systems biology

Multi-omics: By using bulk RNA-seq, single-cell RNA-seq, ATAC-seq, and proteomics, we can accurately measure and quantify the genes and/ or protein changes at a system level. By harnessing the hidden information from this big data, it also allows us to infer hypotheses and answer new questions.

Multi-species: By combining the experimental data from diverse model plant species such as Arabidopsis thaliana (dicot and vascular plant), Oryza sativa (monocot and vascular plant), and Marchantia polymorpha (bryophyte and non-vascular plant), we can study the molecular evolution of distinct or conserved gene regulatory networks (GRNs) and signaling networks in the green lineage.

Multi-networks: Integrated network analysis is crucial for accurately predicting the regulatory circuit. Gene regulatory networks (GRNs) allow us to understand the dynamics and hierarchy of hundreds and thousands of regulators (e.g., Transcription factors, TF) and their targeted genes simultaneously. Likewise, signaling networks will provide us with a sophisticated understanding of kinase cascade dynamics upon environmental perturbations.