Cross-Spectral Factor Analysis
What is being modeled?
We are modeling multi-region electrophysiological recordings (Local Field Potentials)
Description & purpose of resource
The purpose of this resource is a computational framework of a machine learning technique to analyze multi-region electrophysiological recordings and learn electrical connectome networks that are related to outcomes of interest (e.g., mouse model of depression). The learned networks are visualizable and explainable.
Spatial scales
organ
Temporal scales
10-3 - 1 s
1 - 103 s
This resource is currently
mature and useful in ongoing research
Has this resource been validated?
No
Link to Resource Credibility Assessment
Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)
No
Key publications (e.g. describing or using resource)
Talbot, A., Dunson, D., Dzirasa, K., & Carlson, D. (2020). Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity. arXiv preprint arXiv:2004.05209.
Gallagher, N., Ulrich, K. R., Talbot, A., Dzirasa, K., Carin, L., & Carlson, D. E. (2017). Cross-spectral factor analysis. In Advances in Neural Information Processing Systems (pp. 6842-6852).
Hultman, R., Ulrich, K., Sachs, B. D., Blount, C., Carlson, D. E., Ndubuizu, N., ... & Dzirasa, K. (2018). Brain-wide electrical spatiotemporal dynamics encode depression vulnerability. Cell, 173(1), 166-180.
DOI link to publication describing this resource
Link to resource
Collaborators
David Carlson
PI contact information
david.carlson@duke.edu
Keywords
BRAIN TMM
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Elementary Flux Mode Workshop
What is being modeled?
Biochemical pathways, cellular- and microbial community-level metabolism
Description & purpose of resource
Hands on workshop for learning or teaching genome-enabled, metabolic modeling. Materials include presentations, step-by-step guides, example metabolic models and completed exercises.
Spatial scales
molecular
cellular
Temporal scales
10-3 - 1 s
1 - 103 s
This resource is currently
mature and useful in ongoing research
a demonstration or a framework to be built upon (perhaps with a sample implementation)
Has this resource been validated?
No
How has the resource been validated?
Material has been used for educating 4 classes of graduate students in elementary flux mode analysis.
Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)
No
Key publications (e.g. describing or using resource)
Beck, A.E., Hunt, K.A., Carlson, R.P. (2018) Measuring Cellular Biomass Composition for Computational Biology Applications. Processes 6(5), 38. 10.3390/pr6050038
Hunt, K.A., Jennings, R. deM. Inskeep, W.P., Carlson, R. P. (2018) Multiscale analysis of autotroph-heterotroph interactions in a high-temperature microbial community. PLoS Computational Biology, 14(9):e1006431. 10.1371/journal.pcbi.1006431
Carlson, R.P., Beck, A.E., Phalak, P., Fields, M.W., Gedeon, T., Hanley, L., Harcombe, W.R., Henson, M.A., Heys, J.J. (2018) Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross feeding microbial consortia. Biochemical Transactions. 46: 269. 10.1042/BST20170242
Beck, A.E., Bernstein, H.C., Carlson, R.P. (2017) Stoichiometric network analysis of cyanobacterial acclimation to photosynthesis-associated stresses identifies heterotrophic niches. Processes 5 (2), 32. 10.3390/pr5020032
Carlson, R.P., Beck, A.E., Phalak, P., Fields, M.W., Gedeon, T., Hanley, L., Harcombe, W.R., Henson, M.A., Heys, J.J. (2018) Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross feeding microbial consortia. Biochemical Transactions. 46: 269. 10.1042/BST20170242
Beck, A.E., Bernstein, H.C., Carlson, R.P. (2017) Stoichiometric network analysis of cyanobacterial acclimation to photosynthesis-associated stresses identifies heterotrophic niches. Processes 5 (2), 32. 10.3390/pr5020032
Taffs, R., Aston, J.E., Brileya, K., Jay, Z., Klatt, C.G., McGlynn, S., Mallette, N., Montross, S., Gerlach, R., Inskeep, W.P., Ward, D.M., Carlson R.P. (2009) In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study. BMC Systems Biology 3:114.
Carlson, R.P. (2009) Decomposition of complex microbial behaviors into resource-based stresses. Bioinformatics. 25: 90-97.
Carlson, R.P. (2007) Metabolic systems cost-benefit analysis for interpreting network structure and regulation. Bioinformatics. 23: 1258-1264.
DOI link to publication describing this resource
graduate course publication based on material in workshop:
https://doi.org/10.1186/1752-0509-3-114
Link to resource
Collaborators
Ross Carlson
PI contact information
rossc@montana.edu
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1st Annual Multiscale Modeling (MSM) PI Consortium Meeting
Flash Presentations of all 24 projects
Meeting Materials includes links to:
Award Listing
Projects Grouped by Scientific Areas
Comparison of Scales Modeled
Model Sharing Strategies - One Day Mini-Workshop
Background:
Verifiability and reproducibility of scientific results and the ability to share models are key to collaborative scientific work. However, the typical model description published in peer-reviewed literature is not sufficient for model duplication. When it is, reproducing models is extremely labor intensive. Existing model frameworks are generally not designed to support interoperability or model sharing.
Thermodynamics and Learning
American Physical Society Topical Group on Data Science is hosting its 11th webinar on Friday, May 29, 2020. Please register and join us for another series of exciting talks at the intersection of machine learning and physics:
Schedule:
* 1:00 - 1:05pm ET | Welcome!
* 1:05 - 1:41pm ET | Alex Alemi - Thermodynamics of Machine Learning
* 1:41 - 2:17pm ET | Nicole Yunger Halpern - Learning about learning by many-body systems
Reverse Diauxie Phenotype in Pseudomonas aeruginosa Biofilm Revealed by Exometabolomics and Label-Free Proteomics.
Microorganisms enhance fitness by prioritizing catabolism of available carbon sources using a process known as carbon catabolite repression (CCR). Planktonically grown Pseudomonas aeruginosa is known to prioritize the consumption of organic acids including lactic acid over catabolism of glucose using a CCR strategy termed “reverse diauxie.” P. aeruginosa is an opportunistic pathogen with well-documented biofilm phenotypes that are distinct from its planktonic phenotypes.
Special Issue: Methods in Computational Biology.
Biological systems are multiscale with respect to time and space, exist at the interface of biological and physical constraints, and their interactions with the environment are often nonlinear. These systems are being quantified in ever increasing detail using rapidly developing omics technologies; yet, it is difficult to predict the dynamic and spatial behavior of even the simplest model systems. Computational biology approaches are essential for leveraging the omics data to develop and test new theories on biological organization.