Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
ISSN: 0974-276X
Hao Zhu
Southern Medical University, China
Posters & Accepted Abstracts: J Proteomics Bioinform
How growth is controlled in normal tissues and organs but not in cancers is a question that has drawn wide attention but remained poorly understood. To uncover feedbacks and their properties in control mechanisms consisting of genes and their products, quantitative methods are needed but not enough, because gene expression and protein interaction are discrete events. A new model combining features of lattice models and vertex models and integrating differential equations, molecular signaling and mechanical force is developed to investigate growth and growth arrest of Drosophila wing. The model includes key elements in the Wnt, PCP and Hippo pathways, encapsulates proteins and their attributes and behaviors into objects, uses message-passing between objects to simulate signaling between proteins, simulates cell divisions in a 2D lattice, computes protein concentrations and cell polarity, captures the spatiotemporal distribution of signaling events and computes the spatiotemporal distribution of mechanical stress. The distributions of protein concentrations, cell polarity, cell division rates, and cell population agree well with experimental observations, justifying the unveiled control mechanism. Reconstructed signaling events uncover two intercellular feedbacks that jointly control growth, patterning and growth arrest. Specifically, the results indicate that the Warts repressing Yorkie event (Wts_Rep_Yki) distributes densely at the central in early stages but densely at the periphery in later stages. The spatiotemporal distribution of this critical event provides the unprecedented and most pertinent evidence suggesting that growth is gradually refrained from the periphery to the central of the wing pouch, which is a new and somewhat counter intuitive finding. The methods can be applied widely to systematically investigating signaling and patterning across the gene, molecular, cell and tissue levels.
Hao Zhu has obtained his MS in Computer Science from National University of Defense Technology, Changsha, China and PhD in Pathophysiology from Southern Medical University, Guangzhou, China. He has completed his Postdoctoral studies in Bioinformatics Institute of Singapore and School of Mathematical Sciences, University of Nottingham, UK. With a profound interest in Evo-Devo, currently he is working on mechanisms that make new genes (especially lncRNA genes) function coordinately with old ones to regulate gene expression and control developmental signaling and patterning. He developed a cellular automata style modeling tool to effectively address signaling events and lncRNA analysis tool/platform (Long Target) to analyze the evolution and function of lncRNAs.
E-mail: zhuhao@smu.edu.cn