厙ぴ勛圖

At the 厙ぴ勛圖, bioinformatics, data analysis, and modelling are essential tools in modern biological sciences research. These approaches allow scientists to make sense of vast and complex biological data, from DNA sequences to ecosystem dynamics, and to uncover patterns that would be impossible to detect by observation alone.

Researchers use bioinformatics to study the genetic makeup of organisms, track evolutionary changes, and identify genes linked to traits or diseases. This involves analyzing large datasets generated by technologies like genome sequencing and transcriptomics. Students learn these skills through courses that combine biology with computer science, preparing them for careers in research, biotechnology, and health.

In ecological modelling, scientists simulate how populations and ecosystems respond to environmental changes, such as climate shifts or habitat loss. These models help predict future scenarios and guide conservation efforts. For example, UC researchers use modelling to understand how freshwater ecosystems might respond to altered rainfall patterns or rising temperatures.

Data analysis is also central to understanding experimental results in physiology, behaviour, and molecular biology. Researchers apply statistical and computational methods to test hypotheses, visualize trends, and draw meaningful conclusions from complex experiments.

By integrating biology with data science, UC���s research in bioinformatics and modelling is helping to solve real-world problems���from protecting biodiversity to improving human health. It also equips students with the analytical skills needed in today���s data-driven world.

Our staff have extensive local and international connections and welcome contact from potential students and collaborators.��

Examples of our research include:

• Molecular phylogenetics; Species distribution patterns in the Philippine flora
• Next-generation approaches to microbial ecology
• Models of sexual selection theory to predict genomic variation
• Omics to signals: understanding encoding and decoding of early signal mechanisms
• Predicting die-offs of rimurapa (Durvillaea antarctica) using weather data and heat-budget modelling
• Decoding stress responses and phenological behaviour in plants
• Metagenomics of Antarctic microbial communities

How our resaerch has impact

• Development of bioinformatics tools for analysis of molecular ecology datasets.
• Co-led the 1000 Springs Project, a landmark study cataloguing the chemical, physical, and microbial diversity which has become a foundational dataset for geothermal ecology and microbial diversity in Aotearoa.
• Developed ecological forecasting models to better understand biodiversity and ecosystem responses to global change.
• Developed fine-scale mechanistic distribution models for intertidal animals because large scale models ignore important microhabitats.

Facilities and equipment