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Software developed by the Louca lab
 
Audio processing
Python scripts for working with bioacoustic audio files (WAV or FLAC), such as extracting clips, converting annotation tables, plotting spectrograms and computing acoustic parameter differences.
audio_spectrograms
audio_waveform
Cariaco metabolic modeling
MATLAB code for inverse linear transport modeling (ILTM), estimation of eddy diffusivity based on the spatiotemporal distribution of a conserved tracer, and spatial metabolic flux (SMF) analysis of microbial systems near steady state, exemplified for the Cariaco Basin sub-euphotic water column.
Reference: Louca, S., Scranton, M. I., Taylor, G. T., Astor, Y. M., Crowe, S. A., Doebeli, M. (2019). Circumventing kinetics in biogeochemical modeling. PNAS 116:11329-11338
ILTM Cariaco estimated diffusivity
SMF idealizing chemical transition zones as hotspots
ILTM Cariaco estimated net metabolite production rates
SMF Cariaco predicted metabolite concentrations vs data
castor
An R package for efficient phylogenetics on large trees.
Reference: Louca, S., Doebeli, M. (2018). Efficient comparative phylogenetics on large trees. Bioinformatics 34:1053-1055
Ancestral state reconstruction - schematic
castor benchmarks - set 1
castor benchmarks - set 2
castor benchmarks - set 3
Hidden state prediction - schematic
FAPROTAX
Database and software for mapping prokaryotic taxa to metabolic functional groups.
Reference: Louca, S., Parfrey, L. W., Doebeli, M. (2016). Decoupling function and taxonomy in the global ocean microbiome. Science 353:1272-1277
FAPROTAX schematic
FAPROTAX usage overview
Fast-reaction-transport modeling
MATLAB code demonstrating fast-reaction-transport biogeochemical models in 1-dimensional water and sediment columns.
Reference: Louca, S., Taylor, G. T, Astor, Y. M., Buck, K., Muller-Karger, F. E. (2022). Transport-limited reactions in microbial systems. Environmental Microbiology 25:268-282
Cariaco_model_vs_data
Numerical_iteration
Simple_examples
MCM (Microbial Community Modeler)
A computational framework for modeling microbial communities using dynamic flux balance analysis of genome-based cell models, in the context of a dynamical environment. Classical functional group models are also supported.
Reference: Louca, S., Doebeli, M. (2015). Calibration and analysis of genome-based models for microbial ecology. eLife 4:e08208
MCM model-data comparison
MCM overview
MCM simulation - schematic
MCM simulation of methanogenic community
Metabolic fluxes predicted by MCM
Metabolic control analysis of biogeochemical systems
Python code for simulating and performing metabolic control analysis of biogeochemical reaction-advection-diffusion models, at steady state.
Reference: Louca, S. (2025). Metabolic control analysis of biogeochemical systems. in review
FIG_Black_Sea_sediments
FIG_Saanich_GCM
peacots (Periodogram Peaks in Correlated Time Series)
An R package for detecting cyclicity in time series using the Ornstein-Uhlenbeck state-space model (rather than simple white noise) as a null hypothesis.
Reference: Louca, S., Doebeli, M. (2015). Detecting cyclicity in ecological time series. Ecology 96:1724-1732
peacots principle
Saanich GCM (Gene Centric Model)
Gene-centric biogeochemical model (MATLAB code) for the Saanich Inlet seasonally anoxic water column, under steady state conditions.
Reference: Louca, S., Hawley, A. K., Katsev, S., Torres-Beltran, M., Bhatia, M. P., Kheirandish, S., Michiels, C. C., Capelle, D., Lavik, G., Doebeli, M., Crowe, S. A., Hallam, S. J (2016). Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone. PNAS 113:E5925-E5933
Saanich predicted mRNA and protein profiles
Saanich metabolic model
Saanich predicted chemical profiles
 

Louca lab. Department of Biology, University of Oregon, Eugene, USA
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