Regulation Mechanisms of Caspase-Independent Cell Death under Hypoxia and Potential Therapeutic Strategies for Triple-Negative Breast Cancer

Professor Introduction

Y. Z | Ph.D. in Physiology

Home Institute：National University of Singapore

Project Description

This study aims to investigate the regulatory mechanisms of caspase-independent cell death under hypoxic conditions and explore potential therapeutic strategies based on data analysis for triple-negative breast cancer (TNBC). By designing experiments and employing cell culture techniques, we will simulate the hypoxic state of TNBC cells and observe caspase-independent cell death phenomena. Additionally, using artificial intelligence (AI) technology, we will employ data analysis methods to identify key genes, signaling pathways, and regulatory factors associated with this process. This research will provide significant insights into the regulation mechanisms of caspase-independent cell death under hypoxia and, with the aid of AI, contribute to the development of data-driven therapeutic strategies in basic medical research. This integration of basic medicine and AI knowledge will guide the development of novel therapeutic strategies.

Project Keywords

Project Outline

Part 1 : Introduction to Triple-Negative Breast Cancer and Hypoxia
• Overview of TNBC and its challenges in treatment
• Importance of understanding hypoxia-induced cell death mechanisms

Part 2 : Research Objectives and Hypotheses
• Investigating the regulatory mechanisms of caspase-independent cell death under hypoxia
• Exploring AI-driven therapeutic strategies for TNBC

Part 3 : Review of Existing Research and Technologies
• Current understanding of cell death mechanisms in hypoxic conditions
• Overview of AI applications in biomedical research and data analysis

Part 4:  Experimental Design and Cell Culture Techniques
• Designing experiments to simulate hypoxic conditions in TNBC cells
• Techniques for culturing TNBC cells under low oxygen environments

Part 5 :  Observation and Analysis of Caspase-Independent Cell Death
• Methods for detecting caspase-independent cell death phenomena
• Analyzing cell death patterns and identifying key regulatory factors

Part 6 : AI and Data Analysis Integration
• Utilizing AI tools to analyze experimental data
• Identifying key genes, signaling pathways, and regulatory factors associated with caspase-independent cell death

Part 7:  Validation of Key Findings
• Experimental validation of AI-predicted key genes and pathways
• Using molecular biology techniques to confirm regulatory mechanisms

Part 8: Development of Potential Therapeutic Strategies
• Exploring AI-driven approaches to develop therapeutic strategies
• Evaluating the effectiveness of proposed strategies in preclinical models

Part 9: Potential Applications in TNBC Treatment
• Discussing the therapeutic potential of identified strategies
• Evaluating the benefits and challenges of implementing these strategies in clinical settings

Part 10: Results and Discussion
• Presenting experimental and computational results
• Discussing the significance and potential applications of the research findings
• Comparing new therapeutic strategies with existing TNBC treatments

Part 11: Conclusion and Future Research Directions
• Summarizing key findings and their importance
• Identifying research limitations and suggesting future research directions
• Proposing practical applications of AI-driven therapeutic strategies in various fields

Part 12: Reporting and Presentation
• Writing a comprehensive research report with clear structure, concise language, and accurate data presentation
• Preparing and delivering an engaging oral presentation of the research background, methods, results, and conclusions

Suitable for

High School Students:  
• Interest in Biomedical Research and AI: Students strongly interested in biomedical engineering, cancer biology, and computational technologies.
• Basic Knowledge:  Students with basic knowledge of biology, chemistry, and introductory AI concepts.

University Students:
• Relevant Major:  Students majoring in biomedical engineering, computer science, bioinformatics, or related fields.
• Technical Skills:  Familiarity with cell culture techniques, molecular biology methods, and data analysis tools.

Researchers and Educators:
• Advanced Knowledge: Professionals with deep knowledge in cancer biology, cell death mechanisms, and AI applications in biomedical research.
• Teaching Integration: Ability to integrate research findings and methods into their courses and research work.