The Braun lab is a world leading lab in plant network analysis at the Technische Universität München (TUM). We aim to understand biological systems by an interdisciplinary approach that includes high-throughput protein-interaction mapping, data integration and statistics, mechanistic studies, and quantitative modeling. We are seeking computational post-docs (m/f) for two recently funded high-profile projects.

Network integration for metabolic modeling (BMBF e:Bio)

The project aims to functionally integrate metabolic and protein interaction networks for a biotechnologically relevant unicellular alga. Chlamydomonas reinhardtii can convert sunlight into potential biofuels including molecular hydrogen. First quantitative models of Chlamydomonas metabolism only account for a portion of genome-encoded enzymes and more complete experimental data are necessary. Protein interactions play a central role in structuring and regulating metabolic processes. This interdisciplinary project aims to leverage systematic protein interaction data to expand and refine genome-scale metabolic models. A map of the C.re. protein-protein interaction network that is currently being generated will be analyzed using biochemical and computational approaches to define novel biochemical reactions and pathways, which form the basis for an expanded quantitative metabolic model. Model predictions will be tested experimentally to obtain an increasingly precise mathematical representation of algal metabolism. This project will produce second generation genome-scale metabolic model and a reference network for biotechnologically important algae, and will profoundly contribute to our understanding of the intersection of metabolic and physical networks.

Network evolution in drought tolerance (ERC-CoG)

The project aims to identify evolutionary network changes that mediate drought tolerance. Even closely related species can show highly different tolerance to drought stress. In this project we experimentally map the physical stress-response networks of four related brassicaceae, and analyze their dynamic regulatory behaviour. Using a combination of graph theoretical and statistical analyses, data integration and literature-informed criteria, a ranked candidate list of stress response regulators will be assembled, which will be genetically and biotechnologically validated. This project is expected to provide fundamental insights into the evolution of biological networks and lead to a proof-of-concept for network-informed biotechnological strategies.

Qualifications/Application

Candidates should be highly motivated, hold a Ph.D. in computational biology, biological modelling or related fields. Experience in interdisciplinary work with experimental scientists, in plant metabolism and network analysis is highly desirable. Salary is according to TV-L E13. TU Munich is consistently one of the top ranking German universities and one of eleven German Elite Universities. TUM is an equal opportunity employer; applications by female scientists are encouraged. Equally qualified handicapped applicants will be preferentially considered.

Selected references

Wessling R et al.; Convergent targeting of a common host protein-network by pathogen effectors from three kingdoms of life. Cell Host & Microbe. (2014): 16(3), 364-375.
Arabidopsis Interactome Mapping Consortium: Vidal M*, Ecker JR*, Hill DE*, Braun P* (Chair) et al., Evidence for Network Evolution in a Plant Interactome Map. Science. 2011; 596-601
Mukhtar MS et al.; Independently Evolved Virulence Effectors Converge onto Hubs in a Plant Immune System Network. Science. 2011; 333:601-7…