Bio+MedVis Challenge @ IEEE VIS 2022 – Program

Abstract: Post-Translational Modification (PTM) is the covalent addition or removal of functional groups from an amino acid residue in a protein. Thus, PTMs contribute greatly to the complexity of the proteome by generating millions of potential proteoforms from a few thousands of unmodified proteins. PTMs play a key regulatory role in every known biological process - from signal transduction to mRNA splicing - and their dysregulation is often a hallmark of disease. An important aspect is that multiple PTMs can co-exist on the same protein, histones being a prime example of this crosstalk. Within this context, one of the projects of the CompOmics group is reprocessing proteomics data from the PRIDE repository using “ionbot”, a machine learning-based peptide identification engine that can perform “open PTM searches” to identify all PTMs that are present in a sample. Although the project has successfully found new PTMs, the challenge we face now is finding a clear and concise visualization to communicate and interpret our results.

Bio: Enrico Massignani studied Biotechnology and Bioinformatics at the University of Milan (Italy). He learned my PhD in 2021 with a thesis on computational methods to research protein arginine methylations and other PTMs by mass spectrometry, supervised by Dr Tiziana Bonaldi at the European School of Medicine (SEMM, Milan, Italy). He is currently working as a Postdoc at the Flanders Institute for Biotechnology, in the CompOmics group led by Prof Lennart Martens. His research mainly focuses on understanding the effects of PTMs on a protein's structure and function and how different PTMs can interact with each other.

Abstract: Infectious diseases pose a threat to both public health and economies worldwide. Dedicated cell imaging research and the development of novel drugs will be critical to countering these microbial threats, such as antibiotic-resistant bacteria. However, with the increasing capabilities of imaging techniques and the design of novel biomolecular therapeutics, the corresponding data are becoming increasingly difficult to analyze, and both existing data management and visualization are reaching their limits. In this talk, I will introduce two areas where the large scale of data and its spatial complexity make sense-making difficult, and discuss the challenges and possible solutions. In DNA nanotechnology, DNA can be used as a building material for constructing nanoscale therapeutic devices. However, the spatial conformations and number of features require abstract visual representations to effectively display information relevant to a particular in silico design task. Also, novel microscopes for dynamic imaging of cells, such as the lattice light sheet microscope, achieve unprecedented temporal resolutions and acquisition durations that can produce TB-scale data with incredibly high spatial complexity. Addressing these challenges goes beyond finding suitable visual representations but also requires appropriate solutions for data access and real-time exploration.

Bio: Haichao Miao is a researcher at the Center for Applied Scientific Computing, Lawrence Livermore National Laboratory. He focuses on applying visualization and virtual reality methods to problems in dynamic microscopy and additive manufacturing. He received his PhD in computer science from TU Wien, Austria, in 2019. From 2016 to 2020, he worked at the Austrian Institute of Technology, where he focused on the development of in-silico design methods for DNA nanostructure modeling.

Abstract: In enzyme mechanistic studies and mutant designs, it is highly desirable to know the individual residue contributions to the reaction free energy barrier. In this talk, we will show that such free energy contributions from each residue can be readily obtained by post-processing ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulation trajectories. Specifically, through mean force integration along the minimum free energy pathway (MFEP), one can obtain the electrostatic, polarization, and van der Waals (vdW) contributions from each residue to the free energy barrier. Separately, a similar analysis procedure will allow us to assess the contribution from different reaction coordinates. The chorimate mutase reaction will be used to demonstrate the utilization of these two trajectory analysis tools.

Bio: Dr. Yihan Shao is an Associate Professor of Chemistry and Biochemistry at the University of Oklahoma. He develops computational methods and software for modeling enzyme reactions, such as CRISPR Cas protein catalysis, and protein covalent inhibition. He also facilitates the development of bioimaging (fluorescence, chemiluminescence, bioluminescence, positron emission tomography, and optoacoustics) probes.

Abstract: In this talk, I’ll provide my view on the perspectives of bio+medical visualization, their role in their corresponding domains, and most importantly, their commonalities and future challenges. Although the biological (biochemical) and medical domains have many common goals and challenges given by nature, from the visualization perspective they have been mostly treated as rather separate fields. In the last years, we are trying to reveal the reasons for that and to stress the commonalities in their challenges and proposed visualization techniques. Are we heading towards Bio+MedVis or rather keep them separate?

Bio: Barbora Kozlíková is an associate professor at the department of Visual Computing at Masaryk University in Brno, Czech Republic. She is the head of Visitlab, a visualization laboratory focusing on interdisciplinary research in visualization and visual analysis. She is supervising 7 PhD students. She received her PhD in 2011 and since then, she is working on many research projects and scientific visualization topics, mostly connected with biochemistry and bioinformatics. She teaches visualization and computer graphics at the Masaryk University.