OUTside-IN and INside-OUT

Maize plants with improved growth characteristics in the greenhouse are tested in field trials. Unfortunately, the knowledge gained in the laboratory cannot easily be transferred to the field. One of the reasons for the low success rate in translating laboratory findings into field applications is the observation that laboratory-bred plants have very pronounced phenotypic and molecular differences compared to the same genotypes grown in the field.

From cell to canopy

To facilitate the different irrigation regimes required for drought studies and to increase the resolution and sensitivity of phenotyping, we use the automated irrigation and imaging platforms for plant phenotyping, called Phenovision. The Phenovision platform is equipped with three camera systems that enable the three-dimensional reconstruction of plants, the measurement of growth-related phenotypic characteristics, water consumption and plant physiology.

Research question

The size control of multicellular organisms is an old biological question that has always fascinated scientists. Growth, per definition is a dynamic process and it becomes more and more evident that its regulation is highly coordinated in time and space. Our long-term goal is therefore to decipher the dynamics of the molecular pathways and networks that determine plant organ size, using maize as a model system.

Wytynck Pieter

Wytynck Pieter - Postdoctoral fellow
Joined the group in 2022

I studied Bioscience Engineering at the university of Ghent focusing on Cell and Gene Biotechnology. After obtaining my masters, I did a PhD on improving the abiotic stress tolerance in crop species. Subsequently, I worked for 2,5 years at Biogazelle which is a Contract Research Organization (CRO) specializing in DNA- and RNA-based applications to support pharmaceutical research, clinical trials and diagnostic test development. As a scientist I provided scientific guidance to ongoing projects and R&D activities. Currently, I work as a postdoctoral researcher on the use of AI/Machine Learning in plant breeding on the BREEDIT project.