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Sanjana AnanPosition/Title: Postdoctoral Scholar email: sanan@uoguelph.ca Phone: 5198295799 Office: ANNU 229
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Education
PhD, Companion Animal Nutrition (University of Guelph 2022-2024)
B.Sc. Honours, major in Animal Biology (University of Guelph 2016-2020)
Background
Growing up in Bangladesh, I was fascinated to see how quickly the general public embraced pet ownership over the last decade and how people altered their lifestyles around taking care of cats and dogs. This interest influenced my decision to pursue a major Animal Biology at the University of Guelph. I was happy to be received by a welcoming community here in Guelph which pushed me to join various extra curricular programs revolving around student wellness advocacy and assisting other international students within campus. I gained an interest in research after completing two independent course projects and working with Dr. Eduardo Ribeiro's team on the effects of organic trace minerals in transition dairy cow reproduction. After completing my undergraduate degree at the University of Guelph, I felt motivated to pursue research projects related to companion animals under the guidance of my advisor Dr. Anna Kate Shoveller. Initially I started working in her laboratory as a Masters by thesis student, however I was offered a chance to transfer into a PhD after a year, a prospect that excited me to no end. My project was focused on quantifying net energy within high protein and fibre diets fed to cats using indirect calorimetry. Net energy systems for feed formulation have been well established in agricultural species such as cows and pigs, and was proposed to be included in other production animals such as chickens and rainbow trout. The studies established new net energy values for diets intended for cats, and highlighted the unique metabolic adaptations in the carnivore model.
Research Interest
Currently I am utilizing the meta-reactions tool developed by Dr. Jaap van Milgen to model nutrient metabolism using feline cat data. The nutrient fluxes can be determined using feed, plasma and urinary amino acid data which can be plugged into the tool for the respective metabolic pathway. The tool can mimic the gluconeogenic state cats enter right after feeding, and the respective carbondioxide production and oxygen consumption stoichiometry is calculated by the software. This can be used to obtain a predicted respiratory quotient value which can be compared to in vivo data measured in cats undergoing calorimetry. This can help us understand if the tool can sufficiently model the metabolic pathways cats undergo after feeding and highlight their unique adaptations.
When I’m not busy with research work, I like to spend time pursuing my hobbies like reading books, watching films and embroidery.