Daniel Vis

Introduction

Hello, I am Daniel Vis. I started my training in Medical Biology back in 1996 and in 1997 I augmented this with a training in Computer Science. Soon after that I became partner in a company called BioMedia. After five beautiful years of being self-employed I felt that needed to finish my studies. I stopped working and finally came around to finishing my degree. In 2005 I received my doctoral degree with a major in Medical Biology and a minor in Computer Science. Gripped by the many challenges in Science I felt fasinated by exploring science to the next level. To my utter joy I was offered this great PhD position in this wonderful group that leads me into the world of time resolved biology; chronobiology. This offer I could not refuse. Here is what I will do in this PhD project.

PhD Project

In the field of systems biology there is an interest in obtaining and analyzing time-resolved X-omics data. Such data can shed light on issues like homeostasis, biorhythms and self-regulation. There is a growing awareness that these issues are fundamental in understanding the onset of diseases and the reaction of an organism (e.g. human beings) to food-intake and external disturbances.

Methods for quantifying homeostasis, biorhythms and reactions to disturbances from X-omics data is largely lacking. In this project the emphasis will be on developing methods to analyze dynamic metabolomics data. A very important issue is to develop methods for finding metabolites marking the dynamic state of the system. Such dynamic biomarkers are a fingerprint of the dynamic state of the system. The developed methods can also be applied to other types of time-resolved X-omics data.

The goal of my PhD training is to develop methods to quantify homeostasis and biorhythms from metabolomics data and to find dynamic biomarkers.

In collaboration with TNO Quality of Life and Universiteit Leiden experiments will be performed in which time-resolved metabolomics data become available. Mild interventions will be part of the design, to explore the dependence of biorhythms on these interventions.

Next, data analysis methods will be developed to analyze the dynamic data and select dynamic biomarkers. Ideas will be borrowed from systems identification and other types of dynamic modeling. The developed methods will be tested and validated with follow-up designed experiments.