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Research
Overview
The goal of the Computational Biosciences Group is to innovate data analytics, data management, and statistical machine learning methods for experimental data and mechanistic models towards addressing our nation's energy, environment, and health needs.
Our key research areas include:
- Neuroscience and neuromorphic computing
- Data management
- AI for biomolecular imaging
- Computational predictive biomanufacturing
- Quantum data analysis for biology
How we work
We are an interdisciplinary team with diverse expertise in biological topics and computational approaches, and our members span across the Computational Sciences and Biosciences Areas. Our general approach is to nucleate integrated teams around large, collaborative research programs.
Interested in collaborating?
Please contact Kristofer Bouchard or the individual project contact if you are interested in working with us. You can find a list of ongoing collaborations and key projects here:
BRAVE Taskforce 5
This project is supported by the DOE BRaVE program under the research focus of providing innovations in DOE’s user facility instrumentation, experimental techniques, and data analytics that will support biopreparedness and response. Taskforce 5 is a team of scientists from multiple national labs and universities whose expertise is being utilized to prepare for the next biothreat. The goal of the Taskforce 5 project is to create the infrastructure and capabilities to transition from being… Read More »
Quantitative Metabolic Modeling Group
We create the tools necessary to predict biological behavior, so as to unlock the full potential of bioengineering. To this end, we combine machine learning, synthetic biology and automation with mathematical modeling. We use these tools to enable the production of renewable biofuels and bioproducts, and combat climate change. Website: Quantitative Metabolic Modeling Group Website Contact: Héctor García… Read More »
The Neural Systems and Machine Learning Lab
We are an interdisciplinary team that focuses on understanding how distributed neural circuits give rise to coordinated behaviors and perceptions. We take a multi-pronged approach to this problem by developing novel theoretical frameworks for neural circuit function, conducting in vivo neuroscience experiments, and developing state of the art machine learning tools to address diverse systems biology questions. Website: Neural Systems and Machine Learning Lab Contact: Kristofer… Read More »
Neurodata Without Borders (NWB)
Neurodata Without Borders (NWB) is a data standard for neurophysiology, providing neuroscientists with a common ‘language’ to share, archive, use, and build analysis tools for neurophysiology data. NWB is designed to store a variety of neurophysiology data, including data from intracellular and extracellular electrophysiology experiments, data from optical physiology experiments, and tracking and stimulus data. Website: Neurodata Without Borders Contacts: Oliver Rübel, Ryan Ly, Stephanie… Read More »
Feedback control with a brain-in-a-box
Our long-term goal is to create a capability to use brain-organoids as low-power computing platforms for various nonproliferation and nuclear security applications. Such a platform could revolutionize field applications where low-power edge computing is needed for feedback control and/or data processing. To develop this platform, we will use remote detection of isotopic signatures with lasers as our exemplar mission scenario. Contacts: Kristofer Bouchard, Ankit… Read More »
JAMO 2.0
A collaborative rewrite effort between CBG and JGI to produce a generic, petabyte-scale data management and archival service. JAMO is JGI’s central Data Management System for collecting and disseminating genomic metadata and files. The service acts as a clearinghouse for data and associated metadata produced by JGI’s array of sequencing divisions and accessible via specialized portals. All raw and processed data is stored on a HPSS, with the raw sequencing data redundantly archived at a… Read More »
LinkML: a linked data modeling framework
LinkML is an open, extensible framework that allows domain experts, data scientists, and engineers to work cooperatively to model, validate, and share scientific data in reusable and interoperable formats, leveraging appropriate ontologies and vocabularies. LinkML helps researchers transform human-centered scientific knowledge into computable data models while providing tools that simplify development for software engineers and scientists, including a semantically rich data modeling language,… Read More »
Bioenergy.org
A collaborative educational and informational platform providing access to published data sets produced by the four US Department of Energy funded Bioenergy Research Centers: CABBI, CBI, GLBRC, and JBEI. The DOE Bioenergy Research Centers (BRCs) support a viable and sustainable domestic biofuels and bioproducts industry derived from nonfood lignocellulosic plant biomass. Website: https://bioenergy.org/ Contacts: Oliver Rübel, Charles… Read More »
Data Citation Explorer / DANDI
A collaborative effort between CBG and JGI to extend JGI’s Data Citation Explorer to support neuroscience data through the DANDI project. The Data Citation Explorer (DCE), discovers literature incorporating scientific data sets whether or not provenance was clearly indicated. Website: https://dce.jgi.doe.gov/about (accessible via Lab VPN only) Contacts: Ryan Ly, Charles… Read More »
