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Duygu Dikicioglu


Department of Chemical Engineering and Biotechnology

I am interested in understanding the complex networks of interactions at the cellular level as well as those across species, and how these translate into a system-level understanding of organisms, populations or mixed-communities, which can then be exploited for biotechnological applications. A systems-based analysis integrating functional genomics and metabolomics with theoretical models allows us to seek a deeper understanding of how functional entities and modules facilitate the flow of biological information at the cellular level as well as across single-cell species. My work therefore follows three main streams of interest; (i) systems based understanding and exploitation of organisms through integration of “omics” technologies and network- / model-based analysis, (ii) optimisation and predictive-analysis of upstream processing of cell and microbial cultures in biotechnology applications, and (iii) development of methodologies and pipelines to assist these goals.

Research areas:

Systems Biology and Biotechnology

Model based analysis of biological systems

Bioprocess engineering and upstream process development for biomanufacturing pharmaceuticals


Current projects:

Understanding the beneficial role of the co-existence of multiple species in microbial communities for biotechnological applications

Many biological communities encountered in nature are highly heterogeneous systems comprised of many different species from different kingdoms. Although this heterogeneity is, at times, compulsory to maintain the survival of the participating species, in other cases, the species comprising the heterogeneous community would be able to survive as homogeneous populations, but nevertheless prefer such a symbiotic life along with other species. Understanding the beneficial aspects of this co-existence dynamics for each species and how the member species would be affected from a disturbance of this dynamic equilibrium would allow us to exploit these systems. An interesting example of such a community exists on grapes and it facilitates the alcoholic and the following malolactic fermentation during wine making. The alcohol content as well as the desirable and undesirable sensory characteristics of wine all depend on how this community of multiple species from the Bacteria and Fungi Kingdoms behave. This system is also an interesting model from an in silico perspective as it accommodates inter-species interactions at varying levels of complexity from bacteria-yeast symbiosis to more complex yeast-yeast symbiotic relationships with more subtle activity. Understanding this community will equip us with the necessary tools to interfere with the fermentation process to reverse the progress of a fermentation process with less desirable outcome due to an unexpected variation in the population dynamics, as well as to introduce new members into the community or to alter the population dynamics to improve the complexity and the palatability of the final product.


Modelling of Upstream Processing in Cell Cultures

This project involves a model-based analysis of upstream processing in mammalian cell cultures producing monoclonal antibodies. Controlled cultivations are used to generate realistic, scalable, and repeatable process conditions in bioprocesses by monitoring control parameters and outputs. Consequently, a wealth of data on process attributes is available for each bioprocess. Although such data are generally considered to be vital from a Process Analytical Technology perspective, the information embedded in these datasets is often not fully exploited and only archived. This work focuses on mining and compiling such data from cultures of recombinant protein production by Chinese hamster ovary cells, and employing model-based approaches to detect and identify general patterns in this data set. Cultivation parameters that are representative of the performance and the course of cultivations, and that may be used in the predictive control of future cultivations, will be identified.