|Academic Profile |
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Asst Prof Marek Mutwil
Assistant Professor, School of Biological Sciences
|Marek received his Bachelor and Master degrees in biochemistry at the University of Copenhagen, Denmark, where he studied the biosynthesis of polysaccharides in tip-growing cells in the group of prof. William G.T. Willats. He then joined the group of prof. Staffan Persson at the Max Planck Institute of Molecular Plant Physiology (MPIMP), focusing on computational biology of plants. After a brief postdoc, he established a research group at MPIMP, where he used computational biology to study gene co-function networks, with the aim to understand how genes work together to express plant traits and to elucidate how plants evolve new pathways. His group at NTU was established in December 2017.|
|Dr. Marek Mutwil and his research group combine computational and experimental biology to obtain a genome-wide understanding of gene function in plants. Despite decades of intensive research, only ~15% of genes of the most popular model plant Arabidopsis thaliana have been functionally characterized. To remedy this paucity of functional information, which prevents us from tailoring plants to our needs, the group produces large-scale transcriptomic (in form of RNA sequencing) and protein-protein interaction (PPI) data with the aim to build gene co-function networks. These networks can reveal genes that operate together in the same metabolic pathway, protein complex or regulatory circuit which makes them powerful tools to understand how gene products work together in the cell. The group uses gene co-function networks to answer three questions:|
1) What are the functions of plant genes? To generate cutting edge gene function predictions we utilize our own and publicly available large-scale biological data, together with state-of-the-art ensemble prediction algorithms. We make these predictions publicly available with our popular online databases, such as GeneCAT and PlaNet.
2) How are biological networks evolving? Biological features (e.g. secondary metabolites or disease resistance) are encoded by polygenic gene modules, which often cannot be identified by genomics. To uncover them, we use a massively multiplexed protein-protein interaction assay which allows rapid elucidation of genome-wide PPI networks. By constructing and comparing networks of green algae, mosses, vascular-, seed- and flowering plants, we will gain a systems-level, kingdom-wide understanding of when these modules appear and how they change in plant evolution.
3) Which gene modules biosynthesize high-value compounds found in plants? Co-function networks based on RNA sequencing data are a proven tool to identify genes involved in biosynthesis of plant secondary metabolites. The group will focus on elucidating module-metabolite relationships with a special focus on plants of importance to Singapore and the bioprospecting of the rainforest. The functions of the elucidated modules, i.e. production of the secondary metabolites, will be tested by expression in heterologous systems, such as bacteria and/or yeast.
- Characterization, in vitro and in vivo validation of local tropical herbs for colon cancer, stomach cancer and liver cancer prevention and treatment
- Gene Co-function Networks As Tools To Understand Plant Evolution And Secondary Metabolism
- Towards revealing biosynthesis of anti-tumor metabolites in Hedyotis diffusa
- Rendón-Anaya M, Ibarra-Laclette E, Méndez-Bravo A, Lan T, Zheng C, Carretero-Paulet L, Perez-Torres CA, Chacón-López A, Hernandez-Guzmán G, Chang TH, Farr KM, Barbazuk WB, Chamala S, Mutwil M, Shivhare D, Alvarez-Ponce D, Mitter N, Hayward A, Fletcher S, Rozas J, Sánchez Gracia A, Kuhn D, Barrientos-Priego AF, Salojärvi J, Librado P, Sankoff D, Herrera-Estrella A, *ALBERT VA, *Herrera-Estrella L. (2019). The avocado genome informs deep angiosperm phylogeny, highlights introgressive hybridization, and reveals pathogen-influenced gene space adaptation. Proceedings of the National Academy of Sciences of the United States of America (PNAS), Aug 6, 10.1073/pnas.1822129116.