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 # iDEC 2024 | iTidetron
 
-This wiki template was built using [Material for MkDocs](https://squidfunk.github.io/mkdocs-material/). You are by no means confined to use this template. In fact, you are encouraged to build your own unique **static site** that best suits your project, and you are free to use any framework you like.
+## About iTidetron  
+**iTidetron** is a multidisciplinary team from [Tidetron Bioworks Technology](https://www.tidetronbio.com/en/), based in Guangzhou, China. Our team consists of researchers at various stages of their careers, bringing together expertise from computational biology, biotechnology, and experimental research. Our mission is to advance the academic and industrial applications of directed evolution and deep learning technologies in synthetic biology, with a particular focus on bio-functional peptides. By integrating cutting-edge computational approaches and innovative lab techniques, we aim to push the boundaries of protein engineering and peptide discovery, making significant contributions to biotechnology and medicine. 
+
+## Project Mission and Vision
+At iTidetron, our mission is to harness the power of directed evolution and deep learning to revolutionize the discovery and engineering of bio-functional peptides. By combining computational biology with experimental innovation, we strive to create advanced solutions that address critical challenges in biotechnology, from antimicrobial peptide discovery to improving industrial processes. We aim to lead the way in developing more efficient, sustainable, and scalable methods for peptide-based therapies and applications, with a particular focus on enhancing human health and industrial productivity.  
 
-Should you decide to use this wiki, however, we have a [tutorial](https://wiki.idec.io/team_wiki/mkdocs/) to walk you through the basics.
+Our vision for this project is to transform the landscape of antimicrobial peptide (AMP) discovery and bio-functional peptide engineering through the integration of deep learning and directed evolution technologies. We aim to set a new standard for AMP prediction and synthesis by developing innovative computational models, named [COMDEL2](https://ai.tidetronbio.com:7782/COMDEL2.html) and high-throughput experimental approaches. 
+
+<div align="center">
+  <img src="docs/documentation/experiment_record/Fig1_COMDEL_application.png" alt="COMDEL2" />
+</div>
+
+By leveraging these advanced tools, we aspire to improve the efficiency and accuracy of identifying peptides with potent antimicrobial properties, such as those from Lactobacillus plantarum, for real-world applications in biotechnology, medicine, and industrial-scale production. Ultimately, we seek to contribute to the development of novel therapeutics and biotechnological solutions that address pressing global health and industrial challenges.
+
+## Team members  
+**Collabration:** Ao Jiang (Advisor).  
+**Experiment:** Xuemei Zhao, Shijing Lai, Yucheng Hu, Jinde Chen, Qiuru Zhong, Qing He, Min Yang, YiYang He.  
+**Computational Biology:** Yu Zhang (Team Leader), Qijun Ge, Wei Lei, Houliang Lin.  
+**FADS:** Yang Zhang.  
+**Mentor:** Yirui Wu.  
+
+## Links and Contact Information
+Tidetron Bioworks: https://www.tidetronbio.com/en/  
+Web server of COMDEL2, the deep-learning based BioPep identifier: https://ai.tidetronbio.com:7782/COMDEL2.html