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Benjamin Dominik Maier

European Bioinformatics Institute (EMBL-EBI) | Cambridge University

I am a computational biologist who enjoys exploring cell signalling through data-driven mathematical modelling. I am especially interested in understanding the dynamic interplay of systems components and how information is specifically processed and linked to cell-fate decisions despite cross-talk and noise. Recently graduated from the joint Master’s programme in Molecular Techniques in Life Science in Stockholm, Sweden, I am a PhD candidate in Evangelia Petsalaki’s research group (Whole Cell Signalling) at the European Bioinformatics Institute and the University of Cambridge as part of the EMBL International PhD Programme.

My PhD project involves the development of context-specific executable models of signalling processes. For this the current plan is to optimise an existing method in my group that derives phenotype/context-specific networks from omics data and develop a method that can translate these into executable models. Besides science, I am addicted to all sorts of ballsports (tennis, badminton, table tennis, foosball) and enjoy multi-day hiking and kayaking tours .

Benjamin Dominik Maier has added 13 preLight posts

Holimap: an accurate and efficient method for solving stochastic gene network dynamics

Chen Jia, Ramon Grima



Selected by Benjamin Dominik Maier

Digital Microbe: A Genome-Informed Data Integration Framework for Collaborative Research on Emerging Model Organisms

Iva Veseli, Zachary S. Cooper, Michelle A. DeMers, et al.



Selected by Benjamin Dominik Maier, Jennifer Ann Black

Inferring protein from mRNA concentrations using convolutional neural networks

Patrick Maximilian Schwehn, Pascal Falter-Braun



Selected by Benjamin Dominik Maier

Patterned embryonic invagination evolved in response to mechanical instability

Bruno C. Vellutini, Marina B. Cuenca, Abhijeet Krishna, et al.

AND

Divergent evolutionary strategies preempt tissue collision in fly gastrulation

Bipasha Dey, Verena Kaul, Girish Kale, et al.



Selected by Reinier Prosee et al.

Multi-pass, single-molecule nanopore reading of long protein strands with single-amino acid sensitivity

Keisuke Motone, Daphne Kontogiorgos-Heintz, Jasmine Wee, et al.



Selected by Benjamin Dominik Maier, Samantha Seah

Inference of drug off-target effects on cellular signaling using Interactome-Based Deep Learning

Nikolaos Meimetis, Douglas A. Lauffenburger, Avlant Nilsson



Selected by Benjamin Dominik Maier

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Jeanette A.I. Johnson, Genevieve L. Stein-O’Brien, Max Booth, et al.



Selected by Benjamin Dominik Maier et al.

A Phosphoproteomics Data Resource for Systems-level Modeling of Kinase Signaling Networks

Song Feng, James A. Sanford, Thomas Weber, et al.



Selected by Benjamin Dominik Maier

Similarity metric learning on perturbational datasets improves functional identification of perturbations

Ian Smith, Petr Smirnov, Benjamin Haibe-Kains



Selected by Benjamin Dominik Maier, Anna Foix Romero

Biologically informed NeuralODEs for genome-wide regulatory dynamics

Intekhab Hossain, Viola Fanfani, John Quackenbush, et al.



Selected by Benjamin Dominik Maier

Phospho-seq: Integrated, multi-modal profiling of intracellular protein dynamics in single cells

John D. Blair, Austin Hartman, Fides Zenk, et al.



Selected by Benjamin Dominik Maier

A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion

Geena V. Ildefonso, Stacey D. Finley



Selected by Benjamin Dominik Maier

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Benjamin Dominik Maier

Intekhab Hossain (ihossain@g.harvard.edu), the first author of this study, is happy to answer questions and guide users through the setup process of PHOENIX, so feel free to send him a message or comment here 🙂

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1 year

Benjamin Dominik Maier

Supplement: Current Approaches to Study Protein-Protein Interactions

*Genetic*
– Yeast Two-Hybrid (Y2H) System involves fusing one protein of interest to a DNA-binding domain (BDB) and another protein to an activation domain (AD) (Fields and Song, 1989). If the two proteins interact, the DNA-binding and activation domains come into close proximity, leading to the expression of a reporter gene. Newer versions of the assay (Weile et al., 2017; Luck et al. 2020) allow for screening of entire proteomes or random peptide libraries, which can enable unbiased identification of novel interactions.
– Protein fragment complementation assay (PCA) involves splitting a protein of interest into two fragments and fusing each fragment with a complementary protein fragment (Michnick et al., 2007). The resulting fusion proteins can only reconstitute the original protein activity if the two proteins interact and bring the two fragments together. This approach can be used to study protein-protein interactions in vivo and has been used for high-throughput screening to identify potential drug targets.
– Protein-array based methods involve immobilising large numbers of purified proteins on a solid surface (e.g. glass slide). The arrays can then be probed with (fluorescently) labelled proteins of interest to identify potential interaction partners. This approach enables the simultaneous screening of thousands of protein pairs, but it does necessitate laborious protein purification steps.

*Biophysical*
– Fluorescence/Bioluminescence Resonance Energy Transfer (FRET/BRET) involves labelling one protein with a donor fluorophore/luminescence and the other protein with an acceptor fluorophore (Sun et al., 2016). When the two proteins interact, the donor fluorophore transfers energy to the acceptor fluorophore, resulting in fluorescence, which can be measured by a detector.
– Mass spectrometry-based methods involve the fractionation of cell lysates by size or other physicochemical properties, followed by mass spectrometry analysis to identify co-eluting proteins (Richards et al., 2021). Although this approach can identify numerous potential interaction partners in an unbiased manner, it can be quite laborious since it requires protein purification steps.

*Biochemical*
– Immunoprecipitation-based methods such as Co-Immunoprecipitation (Co-IP) or Tandem Affinity Purification (TAP) involve the isolation of protein-protein interactions by adsorbing the protein complex onto beads using a combination of protein tags and specific antibodies (Lin & Lai, 2017), and subsequently identifying them by mass spectrometry or Western blotting.
– Surface Plasmon Resonance (SPR) involves immobilising one protein on a chip and flowing the other protein over it. The binding between the two proteins is measured in real-time based on the changes in the refractive index of the chip.
– Proximity-dependent labelling methods such as BioID and APEX enable identification of interacting proteins within a certain distance of the protein of interest and can be used to identify both stable and transient interactions in an unbiased way (Chen & Perrimon, 2017).

*Computational*
– Co-evolution Analysis makes inferences about protein-protein interactions using alignments (both sequence and structure) and phylogenetic distances. The approach can be used to predict functional residues and domains involved in the PPI.
– Molecular Docking Analysis uses structural templates of individual proteins to predict the structure of a complex. The approach is commonly used to screen large libraries of small molecule compounds for potential drug candidates that can target PPIs.
While most models require experimentally determined interactions as prior knowledge, there are also some approaches to predict interactions de novo.

Supplemental References

Chen, C. L., & Perrimon, N. (2017). Proximity‐dependent labeling methods for proteomic profiling in living cells. WIREs Developmental Biology, 6(4). https://doi.org/10.1002/wdev.272

Fields, S., & Song, O. (1989). A novel genetic system to detect protein-protein interactions. Nature, 340(6230), 245–246. https://doi.org/10.1038/340245a0

Lin, J. S., & Lai, E. M. (2017). Protein-Protein Interactions: Co-Immunoprecipitation. Methods in molecular biology (Clifton, N.J.), 1615, 211–219. https://doi.org/10.1007/978-1-4939-7033-9_17

Luck, K., Kim, DK., Lambourne, L. et al. A reference map of the human binary protein interactome. Nature 580, 402–408 (2020). https://doi.org/10.1038/s41586-020-2188-x

Michnick, S., Ear, P., Manderson, E. et al. (2007) Universal strategies in research and drug discovery based on protein-fragment complementation assays. Nat Rev Drug Discov, 6, 569–582. https://doi.org/10.1038/nrd2311

Richards, A.L., Eckhardt, M. and Krogan, N.J. (2021) Mass spectrometry‐based protein–protein interaction networks for the study of human diseases, Molecular Systems Biology, 17(1). https://doi.org/10.15252/msb.20188792

Sun, S., Yang, X., Wang, Y., & Shen, X. (2016). In Vivo Analysis of Protein–Protein Interactions with Bioluminescence Resonance Energy Transfer (BRET): Progress and Prospects. International Journal of Molecular Sciences, 17(10), 1704. https://doi.org/10.3390/ijms17101704

Weile, J., Sun, S., Cote, A. G., Knapp, J., Verby, M., Mellor, J. C., … Roth, F. P. (2017). A framework for exhaustively mapping functional missense variants. Molecular Systems Biology, 13(12), 957. https://www.doi.org/10.15252/msb.20177908

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