Bakdrive

Bakdrive identifies driver species in microbial communities from metagenomic sequencing data to characterize and control microbial community dynamics.

Key Features:

• Ecological Network Inference: Bakdrive infers ecological networks directly from metagenomic samples without requiring a pre-existing network model.
• Driver Species Identification: Using principles of control theory, Bakdrive identifies minimal sets of driver species that can influence community dynamics.
• Host-Specific Variation Consideration: The method accounts for host-specific variation when identifying influential species.
• FMT Simulation: Bakdrive simulates fecal microbial transplantation by introducing identified driver species from healthy donor samples into disease-affected samples, including simulations for recurrent Clostridioides difficile infection (rCDI).

Scientific Applications:

• Simulated and empirical datasets: Applied to both simulated and real metagenomic datasets for demonstration and validation.
• Recurrent Clostridioides difficile infection (rCDI): Identifies driver species from healthy donors and simulates their introduction into rCDI samples to assess potential microbiome restoration.
• Crohn’s disease: Applied to datasets from Crohn’s disease patients to uncover driver species consistent with prior research on microbial dysbiosis.

Methodology:

Bakdrive leverages information in metagenomic sequencing samples to infer ecological networks without a pre-existing model, applies control theory to identify minimal driver-species sets, and simulates fecal microbial transplantation by introducing identified drivers from donor to recipient samples.