DynamicBayesianNetwork {SMRUCC.genomics.Analysis.BNLearn.DBN}

.NET clr documentation

DynamicBayesianNetwork

Description

Dynamic Bayesian Network for gene regulatory network simulation.

This DBN implements a 2-slice temporal Bayesian network (2TBN) where:

• Gene/operon expression at time t+1 depends on TF and metabolite states at time t
• TF states are provided as evidence from the ODE solver (TF protein/RNA abundance)
• Metabolite concentrations are provided as evidence from the ODE solver

The DBN supports two modes:

1. Topology-only mode: Uses RegulatoryLink topology to initialize CPTs based on biological heuristics (activator/inhibitor effects via noisy-OR/AND gates). No RNAseq data required.
2. Data-fitting mode: Uses RNAseq time-series data to learn CPT parameters, with the topology-based CPTs serving as a Dirichlet prior.

Coupling with metabolic network ODEs:

• DBN -> ODEs: Predicted gene states are mapped to RNA transcript abundance change rates (expected transcription rate), which serve as transcription rate terms in the ODEs: dR/dt = k_synthesis rate - k_degradation R
• ODEs -> DBN: Metabolite concentrations and TF abundances from the ODEs are discretized (Low/Medium/High) and used as evidence for DBN inference.

Declare

            
# namespace SMRUCC.genomics.Analysis.BNLearn.DBN
export class DynamicBayesianNetwork {
   # Configuration for the DBN (discretization, smoothing, rates, etc.)
   Config: DBNConfig;
   topologySize: integer;
}

        

.NET clr type reference tree

1. use by property member Config: DBNConfig

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