2:30 pm Wednesday, September 26, 2018 
North Campus Building, Room 114

Modeling and Simulation for Drug Development

Anita T. Layton

Canada 150 Research Chair in Mathematical Biology and Medicine, Professor of Applied Mathematics and Pharmacy, University of Waterloo


Computational modeling can be used to reveal insights into the mechanisms and potential side effects of a new drug. Here we will focus on two major diseases: diabetes, which affects 1 in 10 people in North America, and hypertension, which affects 1 in 3 adults.

For diabetes, we are interested in a class of relatively novel drug treatment, the SGLT2 inhibitors (sodium-glucose co-transporter 2 inhibitors). E.g., Dapagliflozin, Canagliflozin, and Empagliflozin. We conduct simulations to better understand any side effect these drugs may have on our kidneys (which are the targets of SGLT2 inhibitors). Interestingly, these drugs may have both positive and negative side effects.

For hypertension, we want to better understand the sex differences in the efficacy of some of the drug treatments. Women generally respond better to ARBs (angiotensin receptor blockers) than ACE inhibitors (angiontensin converting enzyme inhibitors), whereas the opposite is true for men. We have developed the first sex-specific computational model of blood pressure regulation, and applied that model to assess whether the "one-size-fits-all" approach to blood pressure control is appropriate with regards to sex.

2:30 pm Wednesday, October 24, 2018 
North Campus Building, Room 114

Machine Learning and the Mathematics of Genomes

Lila Kari (Waterloo)

In the same way we use the twenty-six letters of the alphabet to write text, and the two bits 0 and 1 to write computer code, the four basic DNA units (Adenine, Cytosine, Guanine, Thymine) are used by Nature to encode information as DNA strands. Theoretically, a DNA strand can be viewed as a "word” over the four-letter alphabet {A, C, G, T}, and the mathematical structure of such words has implications for their biological structure and function.
This talk describes our research into the mathematical properties of genomic DNA sequences by exploring the connection between word frequencies in a genome and the type of organism that the genome belongs to. In particular, I describe our investigation into the Chaos Game Representation of a DNA sequence as a potential "genomic signature” of its species. Moreover, I describe how we combine supervised machine learning techniques with such genomic signatures for ultrafast, accurate, and scalable algorithms for species identication and classication. The potential impact of such alignment-free universal classication algorithms could be signicant, given that 86% of existing species on Earth and 91% of species in the oceans still await classication.

2:30 pm Wednesday, November 21, 2018 
North Campus Building, Room 114

Kaisa Miettinen (U of Jyvaskyla, Finland)