# Applied Mathematics

## Managing Patients with Chronic Conditions

## PIMS-SFU 20th Anniversary Celebration: Nataša Pržulj - Data Driven Medicine

The Pacific Institute for the Mathematical Sciences (PIMS) was founded in 1996, and Simon Fraser University is a founding member. The members of PIMS now include all the major Canadian research universities west of Ontario, as well as universities in Washington and Oregon. Please join us to celebrate 20 years of productive collaboration, with a lecture by SFU alumna and professor at UCL Nataša Pržulj on Data Driven Medicine followed by a reception.

We are faced with a flood of molecular and clinical data. Various biomolecules interact in a cell to perform biological function, forming large, complex systems. Large amounts of patient-specific datasets are available, providing complementary information on the same disease type. The challenge is how to model and mine these complex data systems to answer fundamental questions, gain new insight into diseases and improve therapeutics. Just as computational approaches for analyzing genetic sequence data have revolutionized biological and medical understanding, the expectation is that analyses of networked “omics” and clinical data will have similar ground-breaking impacts. However, dealing with these data is nontrivial, since many questions we ask about them fall into the category of computationally intractable problems, necessitating the development of heuristic methods for finding approximate solutions.

We develop methods for extracting new biomedical knowledge from the wiring patterns of large networked biomedical data, linking network wiring patterns with function and translating the information hidden in the wiring patterns into everyday language. We introduce a versatile data fusion (integration) framework that can effectively integrate somatic mutation data, molecular interactions and drug chemical data to address three key challenges in cancer research: stratification of patients into groups having different clinical outcomes, prediction of driver genes whose mutations trigger the onset and development of cancers, and re-purposing of drugs for treating particular cancer patient groups. Our new methods stem from network science approaches coupled with graph-regularised non-negative matrix tri-factorization, a machine learning technique for co-clustering heterogeneous datasets.

## About Irreversibility in Rarefied Gas Dynamics

## 2016 Graduate Mathematical Modelling in Industry Workshop

## A glamorous Hollywood star, a renegade composer, and the mathematical development of spread spectrum communications.

## Bayesian study design for nonlinear systems: an animal disease transmission experiment case study

## Optimal Strategic Sizing of Energy Storage Facilities in Restructured Electricity Markets

## The long road to 0.075: a statistician’s perspective of the process for setting ozone standards

## Conference on the Mathematics of Sea Ice

Interesting mathematics arises in many areas of the study of sea ice and its role in climate. Partial differential equations, numerical analysis, dynamical systems and bifurcation theory, diffusion processes, percolation theory, homogenization and statistical physics represent a broad range of active fields in applied mathematics and theoretical physics which are relevant to important issues in climate science and the analysis of sea ice in particular.