Duygu Dikicioglu | University College London (original) (raw)

Papers by Duygu Dikicioglu

Education for Chemical Engineers

Genome-scale stoichiometric models, con-strained to optimise biomass production are often used to... more Genome-scale stoichiometric models, con-strained to optimise biomass production are often used to predict mutant phenotypes. However, for Saccharomyces cerevisiae, the representation of biomass in its metabolic model has hardly changed in over a decade, despite major advances in analytical technologies. Here, we use the sto-ichiometric model of the yeast metabolic network to show that its ability to predict mutant phenotypes is particularly poor for genes encoding enzymes involved in energy generation. We then identify apparently inefficient energy-generating pathways in the model and demonstrate that the network suffers from the high energy burden associated with the generation of biomass. This is tightly connected to the availability of phosphate since this macronutrient links energy generation and structural biomass components. Variations in yeast’s biomass composition, within experi-mentally-determined bounds, demonstrated that flux dis-tributions are very sensitive to such chan...

Scientific Reports

Process analytical technology (PAT) has demonstrated huge potential to enable the development of ... more Process analytical technology (PAT) has demonstrated huge potential to enable the development of improved biopharmaceutical manufacturing processes by ensuring the reliable provision of quality products. However, the complexities associated with the manufacture of advanced therapy medicinal products have resulted in a slow adoption of PAT tools into industrial bioprocessing operations, particularly in the manufacture of cell and gene therapy products. Here we describe the applicability of a novel refractometry-based PAT system (Ranger system), which was used to monitor the metabolic activity of HEK293T cell cultures during lentiviral vector (LVV) production processes in real time. The PAT system was able to rapidly identify a relationship between bioreactor pH and culture metabolic activity and this was used to devise a pH operating strategy that resulted in a 1.8-fold increase in metabolic activity compared to an unoptimised bioprocess in a minimal number of bioreactor experiments;...

This dataset contains 1) Appendix I: four xml format genome-scale metabolic models for S. cerevis... more This dataset contains 1) Appendix I: four xml format genome-scale metabolic models for S. cerevisiae, O. oeni, K. phaffii, and L. mesenteroides. 2) Appendix II: The py format files containing commands to load the four models and set constraints to the models in python environment. 3) Appendix III: The py format files to call commands to implement FBA and FVA to the models. 4) Appendix IV: The py format files containing commands to rename all the metabolites and give each one a unique ID according to its KEGG ID and its compartment. 5) Appendix V: The flux distribution after implementing FBA and FVA for each of the four models. 6) Appendix VI: The py format files containing commands to set constraints according to the concentration right after alcoholic fermentation. 7) Appendix VII: The flux distribution of the community model built by sc and kp models

Topological analysis of large networks, which focus on a specific biological process or on relate... more Topological analysis of large networks, which focus on a specific biological process or on related biological processes, where functional coherence exists among the interacting members, may provide a wealth of insight into cellular functionality. This work presents an unbiased systems approach to analyze genetic, transcriptional regulatory, and physical interaction networks of yeast genes possessing such functional coherence to gain novel biological insight. The present analysis identified only a few transcriptional regulators amongst a large gene cohort associated with the protein metabolism and processing in yeast. These transcription factors are not functionally required for the maintenance of these tasks in growing cells. Rather, they are involved in rewiring gene transcription in response to such major challenges as starvation, hypoxia, DNA damage, heat shock, or the accumulation of unfolded proteins. Indeed, only a subset of these proteins were captured empirically in the nucl...

Growth characteristics and exometabolite levels of air±pH ± cultures of yeast inoculated into 2 n... more Growth characteristics and exometabolite levels of air±pH ± cultures of yeast inoculated into 2 nM rapamycin-containing medium (.xlsx format, seven worksheets including legend; data on fermentation characteristics, optical density, biomass density, exometabolite levels, exometabolite yields, and comparison of agitation at 400 rpm vs 800 rpm provided in separate worksheets). (XLSX 35 kb)

(.xlsx format, spreadsheet)

$Research paper thumbnail of Percent difference in killed fraction of population between the population estimate and mean observations for different sizes of cell population represented as box plots from Dynamic modelling of the killing mechanism of action by virus-infected yeasts$

(.tif format, image file)

Killer yeasts are microorganisms, which can produce and secrete proteinaceous toxins, a character... more Killer yeasts are microorganisms, which can produce and secrete proteinaceous toxins, a characteristic gained via infection by a virus. These toxins are able to kill sensitive cells of the same or a related species. From a biotechnological perspective, killer yeasts are beneficial due to their antifungal/antimicrobial activity, but also regarded as problematic for large-scale fermentation processes, whereby those yeasts would kill species off starter cultures and lead to stuck fermentations. Here, we propose a mechanistic model of the toxin-binding kinetics pertaining to the killer population coupled with the toxin-induced death kinetics of the sensitive population to study toxic action. The dynamic model captured the transient toxic activity starting from the introduction of killer cells into the culture at the time of inoculation through to induced cell death. The kinetics of K1/K2 activity via its primary pathway of toxicity was 5.5 times faster than its activity at low concentra...

Methods in Molecular Biology, 2019

The dynamics of eukaryotic systems provide us with a signature of their response to stress, pertu... more The dynamics of eukaryotic systems provide us with a signature of their response to stress, perturbations, or sustained, cyclic, or periodic variations and fluctuations. Studying the dynamic behavior of such systems is therefore elemental in achieving a mechanistic understanding of cellular behavior. This conceptual chapter discusses some of the key aspects that need to be considered in the study of dynamic responses of eukaryotic systems, in particular of eukaryotic networks. However, it does not aim to provide an exhaustive evaluation of the existing methodologies. The discussions in the chapter primarily relate to the cellular networks of eukaryotes and essentially leave higher dynamic community structures such as social networks, epidemic spreading, or ecological networks out of the scope of this argument.

CamOptimus is a tool for applying Genetic Algorithm (GA) to solve multi-parametric optimisation p... more CamOptimus is a tool for applying Genetic Algorithm (GA) to solve multi-parametric optimisation problems and Symbolic Regression (SR) to obtain models using the data generated during optimisation procedure to investigate the effect of individual parameters on the system of interest. The source code for the compiled software, and the Graphical User Interface (GUI) of the application are available under free licensing (GNU General Public License v3.0). The user manual is supplied in the compressed folder. textbfImportantinformation\textbf{Important information}textbfImportantinformation: access to the files for this software has been restricted as they are out of date. The software is available on Github, where updated documentation and new releases are available. href[https://github.com/DuyguDtexthttps://github.com/DuyguD\href{[https://github.com/DuyguD}{\\text{https://github.com/DuyguD}}hrefhttps://github.com/DuyguDtexthttps://github.com/DuyguD.

fnyzer: A Python tool for the modelling and analysis of flexible nets. Flexible net examples

Kp.1.0 can also be accessible through BIOMODELS database (MODEL1703150000)

1. p-values of the genes and the significantly expressed genes with their directions of regulatio... more 1. p-values of the genes and the significantly expressed genes with their directions of regulations in response to rapamycin and caffeine 2. Core proteins of the constructed TOR signaling network 3. Annotation Collection Table (ACT) of the core proteins of TOR Signaling protein interaction network 4. Interactions of the constructed TOR signaling protein interaction network 5. Modules found by MCODE plugin of Cytoscape 6. GO Term enrichment results of the 25 modules identified by MCODE plugin of Cytoscape 7. S. Cerevisiae Receptor List 8. Yeast Transcriptional Regulatory Network 9. Key Transcription Factors that regulates a common set of significantly expressed genes in response to rapamycin and caffeine

BMC Systems Biology, 2011

Background A microorganism is able to adapt to changes in its physicochemical or nutritional envi... more Background A microorganism is able to adapt to changes in its physicochemical or nutritional environment and this is crucial for its survival. The yeast, Saccharomyces cerevisiae, has developed mechanisms to respond to such environmental changes in a rapid and effective manner; such responses may demand a widespread re-programming of gene activity. The dynamics of the re-organization of the cellular activities of S. cerevisiae in response to the sudden and transient removal of either carbon or nitrogen limitation has been studied by following both the short- and long-term changes in yeast's transcriptomic profiles. Results The study, which spans timescales from seconds to hours, has revealed the hierarchy of metabolic and genetic regulatory switches that allow yeast to adapt to, and recover from, a pulse of a previously limiting nutrient. At the transcriptome level, a glucose impulse evoked significant changes in the expression of genes concerned with glycolysis, carboxylic acid...

Biochemical Engineering Journal

Increase in the adoption of liquid handling devices (LHD) can facilitate experimental activities.... more Increase in the adoption of liquid handling devices (LHD) can facilitate experimental activities. Initially adopted by businesses and industry-based laboratories, the practice has also moved to academic environments, where a wide range of non-standard/non-typical experiments can be performed. Current protocols or laboratory analyses require researchers to transfer liquids for the purpose of dilution, mixing, or inoculation, among other operations. LHD can render laboratories more efficient by performing more experiments per unit of time, by making operations robust and resilient against external factors and unforeseen events such as the COVID-19 pandemic, and by remote operation. The present work reviews literature that reported the adoption and utilisation of LHD available in the market and presents examples of their practical use. Applications demonstrate the critical role of automation in research development and its ability to reduce human intervention in the experimental workflow. Ultimately, this work will provide guidance to academic researchers to determine which LHD can fulfil their needs and how to exploit their use in both conventional and non-conventional applications. Furthermore, the breadth of applications and the scarcity of academic institutions involved in research and development that utilise these devices highlights an important area of opportunity for shift in technology to maximize research outcomes. .

(.cps format, COPASI working file)

... Larger image in new window Strategy to generate reciprocal translocations in yeast (Delneri e... more ... Larger image in new window Strategy to generate reciprocal translocations in yeast (Delneri et al. Nature 422, 68-72, 2004). ... Larger image in new window Tests in different media show that gene interactions are context-dependent (Harrison et al. PNAS 104, 2307-2312, 2007). ...

Computers & Chemical Engineering

SummaryParkinson’s Disease is the second most common neurodegenerative disease after Alzheimer’s ... more SummaryParkinson’s Disease is the second most common neurodegenerative disease after Alzheimer’s disease. Despite extensive research, the initial cause of the disease is still unknown, although substantial advances were made in understanding of its genetics and the cognate neurophysiological mechanisms. Determining the causality relationships and the chronological steps pertaining to Parkinson’s Disease is essential for the discovery of novel drug targets. We developed a systematic in silico model based on available data, which puts the possible sequence of events occurring in a neuron during disease onset into light. This is the first ever attempt, to our knowledge, to model comprehensively the primary modifications in the molecular pathways that manifest in compromised neurons from the commencement of the disease to the consequences of its progression. We showed that our proposed disease pathway was relevant for unveiling yet incomplete knowledge on calcium homeostasis in mitochon...