Research is centred on modelling biological networks. In particular, we focus on two main areas of research:
(i) Numerical simulation and mathematical modelling of cancer.
Both development of computational tools and experiment-oriented simulations are carried out to understand and predict the emergence of cancer resistance to drugs. We mainly focus on the metastasis promoter protein S100A4 as well as the ROS1 and ALK tyrosine kinases.
Our modelling framework relies on principles of physical biochemistry and enzyme kinetics to represent activation/inhibition networks mathematically. The simplifying assumptions in our models are aimed at facilitating integration and delivery of experimentally accessible quantities (i.e., being close to experimental reality) on one hand, and on the other hand to take into consideration complexity and tractability according to state-of-the-art computational tools.
Control theory and sensitivity analysis are used to identify control points in the biological networks that underlie the resistance mechanisms of a tumour to therapies taking into account the heterogeneity of tumour cells. Our methodology facilitates access to high-performance computing (HPC) for experimental biologists. In silico studies of metastasis allow to optimize the information retrieved experimentally and to improve the design of experiments also supporting novel directions for the development of alternatives to animal testing.
(ii) Modelling of (mixed) microbial communities: stress adaptation and gene regulation of extremophile microorganisms in biotechnological applications.
In this research area we perform wet lab experiments using next-generation sequencing (NGS) and analyse data with standard bioinformatics tools as well as with some of our in-house developed software. Due to the general assumptions of the modelling framework described above, we apply similar methodology to simulate and analyse data from both areas (i) & (ii).
After extracting nucleic acids and proteins from microorganisms living under conditions of interest, we retrieve raw information about gene regulatory processes using NGS that we reverse-engineer into biological networks. This allows us to access information about microbial life in extreme conditions, on gene regulation and mechanisms of adaptation of individual and mixed microbial communities. We subsequently analyse network models by means of simulation aiming at optimizing key processes for biotechnological applications.
Research is in association with the following groups:
- Scientific Computing Group (Group Igor Pivkin), USI, Switzerland.
Article in journal (Refereed)
- Buetti-Dinh, A., Herold, M., Christel, S., El Hajjami, M., Delogu, F., et al. (2020). Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations. BMC Bioinformatics. 21. 1-15.
- Buetti-Dinh, A., Galli, V., Bellenberg, S., Ilie, O., Herold, M., et al. (2019). Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition. Biotechnology Reports. 22. 1-5.
- Buetti-Dinh, A., Friedman, R. (2018). Computer simulations of the signalling network in FLT3+-acute myeloid leukaemia : indications for an optimal dosage of inhibitors against FLT3 and CDK6. BMC Bioinformatics. 19. 1-13.
- Christel, S., Herold, M., Bellenberg, S., El Hajjami, M., Buetti-Dinh, A., et al. (2018). Multi-omics reveal the lifestyle of the acidophilic, mineral-oxidizing model species Leptospirillum ferriphilumT. Applied and Environmental Microbiology. 4.
- Christel, S., Herold, M., Bellenberg, S., Buetti-Dinh, A., El Hajjami, M., et al. (2018). Weak Iron Oxidation by Sulfobacillus thermosulfidooxidans Maintains a Favorable Redox Potential for Chalcopyrite Bioleaching. Frontiers in Microbiology. 9.
- Bellenberg, S., Buetti-Dinh, A., Galli, V., Ilie, O., Herold, M., et al. (2018). Automated Microscopic Analysis of Metal Sulfide Colonization by Acidophilic Microorganisms. Applied and Environmental Microbiology. 84.
- Buetti-Dinh, A., Jensen, R., Friedman, R. (2018). A computational study of hedgehog signalling involved in basal cell carcinoma reveals the potential and limitation of combination therapy. BMC Cancer. 18. 1-8.
- Ahlstrand, E., Buetti-Dinh, A., Friedman, R. (2018). An interactive computer lab of the galvanic cell for students in biochemistry. Biochemistry and molecular biology education. 46. 58-65.
- Christel, S., Fridlund, J., Buetti-Dinh, A., Buck, M., Watkin, E.L., et al. (2016). RNA transcript sequencing reveals inorganic sulfur compound oxidation pathways in the acidophile Acidithiobacillus ferrivorans. FEMS Microbiology Letters. 363.
- Buetti-Dinh, A., O'hare, T., Friedman, R. (2016). Sensitivity Analysis of the NPM-ALK Signalling Network Reveals Important Pathways for Anaplastic Large Cell Lymphoma Combination Therapy. PLoS ONE. 11.
- Buetti-Dinh, A., Dethlefsen, O., Friedman, R., Dopson, M. (2016). Transcriptomic analysis reveals how a lack of potassium ions increases Sulfolobus acidocaldarius sensitivity to pH changes. Microbiology. 162. 1422-1434.
- Buetti-Dinh, A., Pivkin, I., Friedman, R. (2015). S100A4 and its role in metastasis – computational integration of data on biological networks. Molecular Biosystems. 11. 2238-2246.
- Buetti-Dinh, A., Pivkin, I.V., Friedman, R. (2015). S100A4 and its role in metastasis : simulations of knockout and amplification of epithelial growth factor receptor and matrix metalloproteinases. Molecular Biosystems. 11. 2247-2254.
- Chieh, H., Scherrer, S., Buetti-Dinh, A., Ratna, P., Pizzolato, J., et al. (2012). Stochastic signalling rewires the interaction map of a multiple feedback network during yeast evolution.. Nature Communications. 3. Article ID: 682.
- Buetti-Dinh, A., Ungricht, R., Kelemen, J.Z., Shetty, C., Ratna, P., et al. (2009). Control and signal processing by transcriptional interference. Molecular Systems Biology. 5. Article ID: 300.
Conference paper (Refereed)
- Christel, S., Dopson, M., Vera, M., Sand, W., Herold, M., et al. (2015). Systems Biology of Acidophile Biofilms for Efficient Metal Extraction. Biotechnologies in Mining Industry and Environmental Engineering. 312-315.
Doctoral thesis, monograph (Other academic)
- Buetti-Dinh, A. (2012). Gene Regulation by Numbers: Steady-State and Equilibrium Binding Applied to Gene Regulation Systems.. Doctoral Thesis. Saarbrücken, Südwestdeutscher Verlag für Hochschulschriften.