Prof. Dr. Heike Siebert

Projects as a project leader

• CH-AP28

  A Synthetic Approach Towards Understanding the Formation of Robust Turing Patterns in Developmental Biology

  Prof. Dr. Heike Siebert

  Project heads: Prof. Dr. Heike Siebert
  Project members: -
  Duration: 01.10.2017 - 30.09.2021
  Status: running
  Located at: Freie Universität Berlin
  

  Description

  The development of complex multicellular organisms from a fertilized egg cell continues to pose some of the most intriguing and challenging problems in modern biology. Life at this level is governed by complex regulatory processes and disentangling these has proved difficult. Yet there are a few physical processes that are believed to underlie the differentiation into different cell types, tissue formation, organogenesis and form and function of life more generally. The outcome of these processes can be shown to be highly replicable, robust and capable of producing the complexity we observe in nature. Here we propose to reconstruct, rationally and using only biological components, such pattern generating processes de novo. To do so we use a combination of developmental, systems and synthetic biology and mathematical modelling. Ability to forward engineer such pattern forming processes will fundamentally alter our understanding of the processes underpinning life, and ultimately our ability to affect developmental processes in health and disease.
  
  http://www.mi.fu-berlin.de/en/math/groups/dibimath/projects/synturpat/index.html

• CH5

  Model classification under uncertainties for cellular signaling networks

  Prof. Dr. Alexander Bockmayr / Prof. Dr. Susanna Röblitz / Prof. Dr. Heike Siebert

  Project heads: Prof. Dr. Alexander Bockmayr / Prof. Dr. Susanna Röblitz / Prof. Dr. Heike Siebert
  Project members: Stefanie Kasielke / Adam Streck
  Duration: -
  Status: completed
  Located at: Freie Universität Berlin / Konrad-Zuse-Zentrum für Informationstechnik Berlin
  

  Description

  Mathematical modelling in biological and medical applications is almost always faced with the problem of incomplete and noisy data. Rather than adding unsupported assumptions to obtain a unique model, a different approach generates a pool of models in agreement with all available observations. Analysis and classification of such models allow linking the constraints imposed by the data to essential model characteristics and showcase different implementations of key mechanisms. Within the project, we aim at combining the advantages of logical and continuous modeling to arrive at a comprehensive system analysis under data uncertainty. Model classification will integrate qualitative aspects such as characteristics of the network topology with more quantitative information extracted from clustering of joint parameter distributions derived from Bayesian approaches. The theory development is accompanied by and tested in application to oncogenic signaling networks.
  
  http://www.mi.fu-berlin.de/en/math/groups/dibimath/projects/A-CH5/index.html