Dagmar Iber

Prof. Dr. Dagmar Iber, ETH Zürich, D-​BSSE

Dep. of Biosystems Science and Eng.

Professor in Computational Biology

Basel, Switzerland

(ORCID)

Principles of development: how to develop complex shapes & functionalities from single cells

Reproduction of complex life would not be possible without the deterministic self-organising dynamics that allow for the reliable translation of the linear information that is contained in our DNA into complex 3D shapes and functions. My group is interested in uncovering the underlying chemical and biophysical principles. In my talk, I will focus on the development of the neural tube, the precursor of the  central nervous system. I will discuss the mechanical principles that allow the neural tube to fold and close, the chemical principles that ensure the precise patterning of different neural progenitor domains along the neural tube, and the biophysical principles that govern the cellular organization in the neural epithelium. I will close with an overview of how these principles generalize to many other developmental phenomena, and how they can be harnessed in bioengineering approaches.

Short bio

Dagmar Iber studied mathematics and biochemistry in Regensburg, Cambridge, and Oxford. She holds Master degrees and PhDs in both disciplines. After three years as a Junior Research Fellow in St John’s College, Oxford, Dagmar became a lecturer in Applied Mathematics at Imperial College London. Dagmar has joined ETH Zurich in 2008 after returning from an investment bank where she worked as an oil option trader for one year.

Dagmar Iber’s group develops data-​based, predictive models to understand the spatio-​temporal dynamics of signaling networks. Close collaborations with experimental laboratories permit a cycle of model testing and improving. Her recent work focuses on mouse organogenesis (limb and brain development, lung and kidney branching morphogenesis) and simpler patterning systems to address more fundamental questions regarding the control of organ growth and the robustness of signalling mechanisms to evolutionary change.

Group website