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Professor

I am a professor at the Organismal and Evolutionary Biology Research Programme, Department of Computer Science, Institute of Biotechnology and Helsinki Institute for Information Technology HIIT. I develop evolutionary theory and its applications to solve problems such as drug resistance. In particular, I try to understand how predictable evolution is and how and to what extent evolving populations can be controlled. We further develop algorithms needed to analyse big biological data sets. Our work is basic science that can lead to applications relevant to human health, for example, in the context of cancer and infectious disease and evolution of drug resistance. My group works collaboratively and across different scientific disciplines. We have a record of successful research collaborations working together with clinicians and experimentalists.

Postdoctoral Researcher, PhD

I am interested in modelling the ecology and evolution of populations in both microscopic and macroscopic scales. My current research projects are: i) controlling driver mutations and metastases in cancer, and ii) developing tools for analysing mutational rate heterogeneity in cancer genomes.

Postdoctoral Researcher, PhD

Dr Otto Pulkkinen is a University Researcher working on machine learning and its applications to cancer research. He is affiliated with Helsinki Institute for Information Technology (HIIT) and works jointly with the Mustonen group and Tero Aittokallio group at Institute for Molecular Medicine Finland (FIMM).

Postdoctoral Researcher, PhD

Research interests: Bayesian statistics and modelling in application to ecology, evolution and epidemiology; data visualization;

Current project: applying machine learning to cancer

Postdoctoral Researcher, PhD

I envision that quantitative predictions will be crucial for solving the global antibiotic resistance crisis on many levels. My current research on this topic uses the powerful combination of laboratory guided evolutionary experiments and predictive modelling to ask how much information the drug exposure history of a bacterial population contains about its future phenotypic and genomic status. This work will allow identifying treatment strategies with a high risk to promote the evolution of multidrug resistance as well as understanding fundamental factors contributing to the predictability of rapid evolution. I am also highly interested in the population genetics of microbes in complex settings (including but not limited to the presence of antibiotics), including ecological interactions and eco-evolutionary feedbacks, and am involved in a number of experimental projects on this theme.

Postdoctoral Researcher, PhD

Past environmental conditions leave marks on genomes and epigenomes. Some of these traces of past adaptations are passed to future generations. I investigate, how genetic memory will influence on adaptation in future environments. This information can be used to predict, for example, the most probable evolutionary trajectories of a certain bacterial strain. Ultimately, this research focuses on designing optimal control strategies for preventing drug resistant bacterial strains or cancer cells.

PhD Candidate

The recent development of evolutionary models showing learning capabilities has opened an opportunity to connect the fields of evolution and machine learning. I am interested in exploiting this connection by bringing knowledge from the second field to the first one, thus creating ways to predict various characteristics of evolution. Predicting its rate, in particular, can yield estimations of adaptation, which in turn allows for interesting applications, such as in resistance to antibiotics or cancer treatment.

PhD Candidate

I am Qingli Guo, a PhD student in the Computer Science Doctoral Programme. My research focuses on tumour progression using multi-layers of data. Firstly, the genomic changes that occur during tumorigenesis are a combination of various mutagenic processes. I am interested in inferring potential mutational signatures from noisy, low-depth, sequencing data that can be applicable in a translational setting.

Secondly, drug resistance driven by tumour heterogeneity is often responsible for therapeutic failure. Therefore, it would be of great advantage to use this low-depth data to reconstruct clonal structures of cancer cell populations within individuals. Finally, the tumour microenvironment (TME) promotes cancer cell proliferation as well as migration of metastasis through reciprocal interactions with tumour cells. Identification and quantification of the principle components of tumour progression within TME  will provide new insights for the design of novel efficient anticancer therapies. My PhD project will focus on the above-mentioned topics in order to improve our current understanding of tumorigenesis.