Synthetic Turing Patterns - A Synthetic Approach Towards Understanding the Formation of Robust Turing Patterns in Developmental Biology
01.09.2017 - 31.05.2023
Prof. Dr. Heike Siebert, Dr. Elisa Tonello (2018-2021), Laura Cifuentes Fontanals (2021-2022)
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 ...
Spatio-temporal modeling of bioregions (MATH+ EF5-5)
01.01.2021 - 31.12.2022
Prof. Dr. Heike Siebert, Dr. Natasa Djurdjevac Conrad, Dr. Sara Varela, Dr. Elisa Tonello
Biodiversity can be analyzed using so-called bioregions, geographical areas characterized by specific species composition that can be identified using clustering in ecological networks. Analyzing the evolution of bioregions through time and space is key to understanding the basic principles ...
Algebraic methods for investigating cell fate decisions (MATH+ AA1-4)
01.01.2019 - 31.12.2021
Prof. Dr. Heike Siebert, Prof. Dr. Christian Haase, Robert Schwieger (2019-2020), Hannes Klarner (2020-2021)
The project aims at establishing a new application field for algebraic methods in systems biology. We plan to develop methods utilizing Gröbner bases and related concepts for the characterization of attractor sets representing different cell fates in Boolean models of molecular networks and ...
Evaluierung von Unsicherheiten mathematischer Modelle in den Bereichen Infektionskrankheiten und Systembiologie
Prof. Dr. Heike Siebert, Dr. Sheetal Prakash Silal (University of Cape Town)
Feb 01, 2018 — Jan 31, 2019
With recent technological advancement, mathematical models are being widely applied to support decision-making in the life sciences, e.g. for planning experiments or intervention strategies for disease spreading. As such it is increasingly important that sources of errors are investigated and ...
MoSTNet - Modular Modeling Methods for Signal Transduction Networks
Prof. Dr. Heike Siebert, Dr. Hannes Klarner, Robert Schwieger, Kirsten Thobe, Firdevs Topcu-Alici
Mar 01, 2012 — Nov 30, 2017
Signal transduction networks play a major role in ensuring normal function of living cells. Understanding the interplay of signaling cascades, regulatory and crosstalk effects as well as the impact of perturbations on a system level, is a strong research focus in fields ranging from biotechnology ...
Model classification under uncertainties for cellular signaling networks (Project A-CH5)
Prof. Dr. Heike Siebert, Prof. Dr. Susanna Röblitz (ZIB), Prof. Dr. Alexander Bockmayr, Adam Streck, Stefanie Kasielke (ZIB)
Jun 01, 2014 — May 31, 2017
Mathematical modeling in biological and medical applications is almost always faced with the problem of incomplete and often 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 ...
A18 Mathematical Systems Biology
Prof. Dr. Alexander Bockmayr, Dr. Heike Siebert, Dr.-Ing. Steffen Waldherr, Dr. Yaron Goldstein, Lászlo David, Hannes Klarner
Jun 01, 2010 — May 31, 2014
Systems biology is a lively interdisciplinary research field that has received considerable attention in recent years. While traditional molecular biology studies the various components of a biological system (genes, RNAs, proteins,...) in isolation, systems biology aims to understand how these ...
A24 Top-down modeling and experimental design for molecular networks
Prof. Dr. Heike Siebert, Adam Streck
Mar 01, 2013 — May 31, 2015
Top-down modeling methods are based on the idea of collecting all known information on a system in a list of constraints. Rather than in one particular model this generally results in a set of models that cannot be further distinguished using the available information. Properties shared by all ...