Computational Biology

Overview

Our methods are computer-based and lab-based analyses of „big data“. Our topic revolves around primary productivity of plants, especially photosynthesis.

Almost all live on earth depends directly or indirectly on photosynthesis. This complex process uses sunlight to convert CO₂, NH₄⁺, SO₄^2-, and H₂O to organic molecules. CO₂ is taken up from the environment into the leaves in a controlled process, initially fixed by Rubisco, and converted to sugars and other building blocks in the Calvin-Benson-Bassham cycle. Photosynthesis is finely tuned to acclimate to pathogen attack, herbivory, and varying drought, salt, cold, heat, and light conditions, and it has evolved adaptations such as C4 photosynthesis or CAM to make it more efficient.

C4 photosynthesis is a turbocharger of photosynthesis and its presence leads to highly productive plants or to plants which are drought and/or salt resistant. The pathway evolved to combat the key weakness of Rubisco, its temperature-dependent sensitivity to O₂. Using transcriptomics we analyze the molecular identity of the enzymes, transporter and regulators involved in the trait. We kinetically model subsystems to understand anatomical adaptations. We use stoichiometric models to analyze the trait evolution in silico. Ultimately, we would like to be able to reconstruct.

When we study transcriptional responses to changing environments or mutations, we do it to understand not only the photosynthetic part of the response but all changes occurring in the transcriptome. Essentially, we try to understand the „wiring“, the plant’s information processing which controls the response and we try to resolve which biochemical and anatomical modules are activated to produce the visible phenotypes. Most of these analyses are collaborative and depend on the interests of our collaborators. After finishing the analyses we integrate the results into our larger models and networks.