Computing has a rich history in biological discovery, and the needs of biologists are continuing to drive algorithm development in multiple domains. Close alignment between developers and deep biolocial domain expertise is crucial in this field and a programming langauge such as Julia provides an ideal plattform for such tight collaborations that require a range of access points to the software. Julia provides interactive and notebook-based interfaces commonly used by experimentalists whilst also giving algorithm developers the opportunity create more complex workflows and structures, and optimize performance as needed.
In this minisymposium, we aim to highlight ways in which computational biologists* are using Julia to advance our understanding of living systems, in academia and industry, while also contributing to the package ecosystem and core language on multiple fronts.
* "Computational biology" is a broad tent. Topics presented include algorithms for text matching and search (bioinformatics), modeling and machine learning (systems biology and quantiative pharmacology),
statistics and data science (experimental analysis, epidemiology), and imaging and geospatial statistics (microscopy), among others.
Summary
Computational biology spans diverse domains from bioinformatics to systems biology, and this minisymposium highlights how Julia’s unique blend of interactive workflows and high-performance capabilities empowers biologists to advance discovery while enriching the language ecosystem.
Background
Biology is the study of living systems.
Computing can help tackle the inherent complexities and advance our understanding in the field.
We believe, this is done best when the development of computational approaches closely aligned with domain expertise. An inclusive and flexible programming langauge such as Julia is a key for such close alignment.
Julia's success in addressing the two-language problem is particularly important here. It means that biologists with great domain knowledge, but often without formal computer science training, can develop and contribute to packages. Furthermore, it allows for prototype code (often developed in an academic setting) to gradually transition all the way to becoming core packages used by many, both within and outside of the biology community. Indeed, work on parsing, low-level IO and memory access, and support for file formats such as HDF5, are a few examples of work stemming from the Julia biology community that has improved the Julia package ecosystem as a whole, and core contributors to biology-facing packages are also active in the data and machine learning stacks, statistics, imaging, and more.
Objective
In this minisymposium, we want to bring together Julia developers and users intrested in computational biology. We want to highlight and celebrate how Julia’s unique blend of interactive workflows and high-performance capabilities empowers biologists to advance discovery while enriching the language ecosystem.
Planned contents
We welcome talk submissions on anything related to doing biology in Julia, including, but not limited to, the following topics:
* Core Bioinformatics
* Systems Biology and Quantiative Pharmacology
* Transcriptomics
* Genomics
* Ecology
* Epidemiology
* Imaging
* File reading/parsing
* Machine learning
* Multidimensional statistics
* Visualization
Confirmed Talks:
* Jakob Nybo Andersen - MemoryViews.jl or BufferIO.jl or parsing
* Tim Holy - Imaging
* Kevin Bonham - Spatial single cell
* Rasmus Henningsson - Reproducibility in practice, with focus on single cell transcriptomics
* Richard Reeves (or postdoc) - Ecology
* Elisabeth Roesch (or colleague) - Quantitative Pharmacology
Tentative Talks (we have reached out, but not yet gotten confirmation of attendance):
* More Quantitative Pharmacology
* Tidier
* Neurotherapeutics
* Electron dynamics
I am an assistant professor at Tufts Medical Center with nearly 11 years in computational biology and bioinformatics, much of that time spent coding in Julia. I study the relationship between the gut microbiome and human development. I am a co-maintainer of the BioJulia organization and maintain or contribute to packages in the Biology, Data, Ecology, and Statistics ecosystems, and have worked on educational material for Pumas.ai and JuliaHub.
