Speaker: Chris Illingworth (3.15pm-4pm)

Title: The bioinformatics of short-read viral sequence data

Abstract: Genome sequence data has the potential to generate insights into understanding both the biology and treatment of viral organisms.  However, many of the basic concepts of how to process genome sequence data were derived for very different species.  In the first part of the talk I outline four bioinformatic challenges (or perhaps, opportunities) posed by viral sequence data and how these can be addressed.  In turn I consider the extreme depth of sequencing, the extent of noise in sequence data, the length of reads compared to that of the genome, and the potential to collect data from an evolving population.  In the second part of the talk I describe some outcomes of applying novel bioinformatic tools to data from experimental and clinical infection, and discuss the potential for new research to combat the public health threat of infectious disease.

Bio: Chris Illingworth is a visiting researcher from the UK, hosted by the University of Helsinki.  Following studies in mathematics and research training in computational chemistry, he found a number of interesting problems in evolutionary biology that have since kept him occupied (and, just about, employed).  Since his PhD, Chris has worked at the University of Oxford, the Wellcome Trust Sanger Institute, and, most recently, the University of Cambridge.  Chris is in Finland in order to build new research links and collaborations and would look forward to hearing more about what you are working on: Do introduce yourself while he is here.

Coffee, tea and biscuits (4pm-4.30pm)

Speaker: Jing Tang (4.30pm – 5pm)

Title:  Making sense of functional precision medicine with mathematical and statistical models

Abstract: To reach sustainable clinical responses, cancer patients who become resistant to standard treatments urgently need combinatorial therapies, which shall effectively inhibit the cancer cells and block the emergence of drug resistance. Recent advances in next generation sequencing has revealed the intrinsic heterogeneity in cancer survival pathways, which partly explains why patients respond differently to the same therapy. To help fill in the major gap between the vast knowledge of cancer genetics and effective anticancer treatments, my research group aims to accelerate the discovery of drug combination therapies using computational approaches to (i) predict and prioritize individualized drug combinations and pinpoint their effective target interactions (ii) to evaluate the degree of synergy in the drug combination screens and (iii) to understand and translate the mechanisms of drug combinations into treatment suggestions for patients.
My talks will brief several recent publications on this topic. First, I will introduce a computational model called CES (combined essentiality score) to integrate the genetic essentiality profiles from RNAi and CRISPR-Cas9 screens, while accounting for the molecular features of the genes. The CES method provided an integrated framework to leverage both functional genetic screen and molecular feature data in identifying more reliable drug targets for cancer cells. Secondly, I will describe the FAIRification of drug combination screen data by a series of mathematical models including the CSS and ZIP methods, all of which are made freely available, together with the harmonized dataset at the DrugComb portal (https://drugcomb.eu/). Lastly, I will present several case studies in breast cancer and hematological cancer where the informatics approaches have led to the discovery of personalized combination therapies that warrant further investigations.

Bio: Dr. Jing Tang is an assistant professor at the Faculty of Medicine of the University of Helsinki. He obtained a PhD in statistics at the Department of Mathematics and Statistics at the University of Helsinki and has been working in life sciences for more than ten years. His research interests are mathematical, statistical and informatics tools to tackle biomedical questions that may potentially lead to breakthroughs in drug discovery. These methods offer an improved efficiency to identify more effective cancer treatments for personalized medicine.