Mechanisms underlying T. b. brucei infection-associated Follicular B cells dysfunction. Promotor: Prof. Magdalena Radwanska Centre for Biomedical Research
African trypanosomes belonging to T. brucei species cause African Trypanosomosis in humans and Nagana in cattle. In humans, the disease is caused by the sub-species T. b. rhodesiense (acute form) in East and South Africa and T. b. gambiense (chronic form) in West and Central Africa. This project targets T. b. brucei parasite isolated from a domestic animal and adapted to an experimental model in mice. Start date: October 2016
Development of the single-layered 2D semiconductors by atomic layer deposition Promotor: Prof. Dr. Serge Zhuiykov Center for Environmental & Energy Research
Single fundamental layer semiconductors represent new class of 2D nano-materials with unique physical-chemical properties. However, there are challenges in their development. Atomic layer deposition technique represents unique opportunity to help in making those 2D layered semiconductors as thin as less than 1.0 nm. Start date: April 2016 PhD: Ultra-thin oxide semiconductors for optoelectronic and bioinspired optoelectronic applications (2020)
Development of the single-layered 2D semiconductors by atomic layer deposition Promotor: Prof. Dr. Serge Zhuiykov Center for Environmental & Energy Research
Reaction-Diffusion Equations and their Applications in Chemistry Biology Promotor: Prof. Dr. Shodhan Rao and Prof. Dr. Bernard De Baets Center for Biotech Data Science
Reaction-Diffusion equations are semi-linear Partial differential equations that are commonly used as mathematical models for biochemical reaction networks and also in the mathematical studies of biological pattern formation. We aim to derive some efficient numerical techniques that are used to solve such equations and also investigate some of their applications in biology. Start Date: September 2015
Breght Vandenberghe
Towards 3D phenotyping of rice plants via structure from motion Promotor: Prof. Dr. Arnout Van Messem Center for Biotech Data science
The aim of the project is to develop methods and automatic workflows to process 3D point clouds of rice plants obtained through the MVS-SFM (Multi-View Stereo and Structure From Motion) algorithm, to estimate phenotypic parameters related to growth. The idea is to apply 3D-data processing techniques and machine learning, to achieve accurate measurements on single plants, and to monitor growth over time. Start Date: November 2016
Increasing and optimizing plant biomass: in search of novel, natural compounds and their modus operandi Promotor: Prof. dr. ir. Stephen Depuydt co-supervisor: Prof. dr. ir. Bartel Vanholme Lab of Plant Growth Analysis
In a way to feed the rapidly increasing world population and in the frame of a carbon-negative biobased economy there is a need for improving crop growth without compromising the environment. In that regard, the use of natural compounds in the form of biostimulants is globally becoming more important and might add to further reducing/replacing the use of chemical fertilizers and pesticides. The focus of this project is to test natural compounds with a focus on phenylpropanoids (from terrestrial plants) for their growth promoting effects when applied to plants. We mainly focus on coumarin, a derivative of the phenylpropanoid pathway and an allelochemical present in both land plants as well as algae. Coumarin has been linked to many phenotypic effects (influencing germination, root growth, shoot growth, hypocotyl elongation etc.) for which its interplay with auxin has been suggested as crucial, but fundamental research is lacking. The aims of the project are (1) to clarify how natural compounds can enhance plant growth and (2) to unravel the molecular mechanism behind the coumarin induced elongation effects on Arabidopsis thaliana. Start date: October 2015 PhD: Phenotypic effects and molecular mode-of-action of coumarin on Arabidopsis thaliana (2020)
Exploring novel biostimulants and their modus operandi to improve growth and yield in rice Promotor: Prof. dr. ir. Stephen Depuydt and Prof. dr. Godelieve Gheysen Lab of Plant Growth Analysis
We focus on how novel and perhaps unanticipated natural compounds influence plant growth and yield, with a specific focus on rice as a model crop. Our research can lead to new, marketable biostimulants, but will also contribute to understanding and linking plant development and yield in rice. To this end, we aim to use a combination of phenotype based bioassays and molecular marker based assays to find novel plant growth promoting compounds. Subsequently, we intend to unravel the mode of action of selected molecules at the phenotypical and molecular level regarding leaf growth and (in future research) also on grain yield and/or nutritional value of rice seeds by using several –omics approaches as well as directed analysis of gene regulatory circuits that determine cell elongation. Start date: October 2016 PhD: Bioassays and bioactive compounds for improving rice elongation growth (2020)
Towards reading the genome using interpretable convolutional neural networks Promotor: Prof. Dr. Wesley De Neve Center for Biotech Data Science
With ever-evolving genome sequencing methods, more and more genomic information is becoming available. The next big thing in healthcare is to use all that data to produce personalized genome-based medicine. However, our understanding of the way DNA works is incomplete. Therefore, in our research, we want to automate the analysis of DNA sequences by making use of deep machine learning (that is, deep neural networks).
Additionally, as the reasoning process underlying deep neural networks is often not well-understood, we want to use visualization techniques to get more insight into why certain decisions have been made. In our ongoing research and development efforts, we leverage the aforementioned tools to predict gene splice sites and translation initiation sites, with results showing that deep machine learning is a good fit for making sense of genomic data. Indeed, we are able to automate the targeted prediction processes, and at the same time, we are able to outperform older techniques that make use of manual feature extraction by human experts. Moreover, through advanced visualization techniques, we are able to demonstrate that our novel deep machine learning techniques automatically learn biologically relevant features, including typical splice site patterns, individual nucleotide frequencies in exons and introns, and the lack of other splice sites in each other’s proximity. Start date: September 2015 PhD: Automated learning of biologically relevant features from primary sequence using convolutional neural networks (2020)
