Jonathan Dubois

Systrip: A Visual Environment for the Investigation of Time-series Data in the Context of Metabolic Networks

Technological advances in biology lead to a profusion of quantitative data, raising analytical challenges. Visual analytics is particularly well suited to address these difficulties. It helps to interactively move through the different levels of analysis and to simultaneously investigate data with different point of views. It is especially the case when dealing with biological networks that can contain hundreds of elements. In these studies biologists generally follow the same analytic process which consists in first getting an overview of the data before focussing on a few relevant subnetworks. In this article we present, Systrip, a visual environment for the analysis of time-series data in the context of biological networks. In particular we focus on the study of metabolism. Systrip gathers bioinformatics and graph theoretical algorithms that can be assembled in different ways to help biologists in their visual mining process. This framework had been used to analyse various real biological data. In this article we describe how it helped in understanding drug effects on the metabolism of the parasite of the tsetse fly causing sleeping sickness.

Solving the traffic and flitter challenges with tulip

We present our visualization systems and findings for the badge and network traffic as well as the social network and geospatial challenges of the 2009 VAST contest. The summary starts by presenting an overview of our time series encoding of badge information and network traffic. Our findings suggest that employee 30 may be of interest. In the second part of the paper, we describe our system for finding subgraphs in the social network subject to degree constraints. Subsequently, we present our most likely candidate network which is similar to scenario B.