Cellular acclimation dynamics of Chlamydomonas reinhardtii in response to environmental changes
Within the frame of their genetic capacity, organisms are able to acclimate to changes in environmental conditions. Acclimation responses represent a complex dynamic interplay between genes, proteins and metabolites that appear to consist of general and specific response elements. These response elements can be identified by analyzing acclimation responses to different environmental changes at the levels of physiology and at molecular levels, comprising metabolites, gene expression, and protein over time. Extracting the important information from the resulting highly complex, yet fragmentary data sets and separating it from technical and biological noise is challenging. Biological noise refers to changes that do not impact the response. Describing those results in an over detailed description on the basis of single molecules may lead to the misinterpretation of response elements. This problem is also known in statistics and machine learning and there is referred to as model overfitting. To address this problem an algorithm is required that allows to extract and visualize a response-specific fingerprint from complex data sets by an intelligent combination of response descriptions at the levels of functional ontologies and single molecules. Such an algorithm needs to find the “sweet spot” between a too general description solely based on functional ontologies and a description that is only based on single molecule information and therefor too noisy and complex. We will follow two aims in this project. First, we will acquire time-resolved systems responses of the unicellular green alga Chlamydomonas reinhardtii to several environmental challenges like temperature stress or nutrient limitations. Second, we will develop an algorithm to extract response-specific molecular fingerprints that will allow a better understanding of cellular acclimation strategies of this alga to environmental changes. To this purpose, we will design a visualization strategy that combines elements of interactive visualization using multiple views with user-guided steering of algorithmic properties to allow exploration of the combined ontology/response data.