Enum: ComputationalModelTypeEnum

Type of computational or in-silico model

Permissible Values

Value	Meaning	Description
GENOME_SCALE_METABOLIC	None	Genome-scale metabolic reconstruction (e
FLUX_BALANCE_ANALYSIS	None	Constraint-based FBA model
KINETIC	None	ODE-based kinetic model with rate equations
AGENT_BASED	None	Agent-based simulation model
BOOLEAN_NETWORK	None	Boolean gene regulatory network
PHYSIOLOGICAL	None	Physiologically-based pharmacokinetic (PBPK) or organ model
DIGITAL_TWIN	None	Patient-specific computational model
MACHINE_LEARNING	None	ML/AI predictive model trained on disease data
PERTURBATION_PREDICTION	None	Cell-based perturbation models (CRISPR screens, chemical perturbations) with ...
FOUNDATION_MODEL	None	Pre-trained single-cell foundation models (scGPT, Geneformer, scGenePT) for p...

Slots

Name	Description
model_type	Type of computational model

Identifier and Mapping Information

Schema Source

from schema: https://w3id.org/monarch-initiative/dismech

LinkML Source

name: ComputationalModelTypeEnum
description: Type of computational or in-silico model
from_schema: https://w3id.org/monarch-initiative/dismech
rank: 1000
permissible_values:
  GENOME_SCALE_METABOLIC:
    text: GENOME_SCALE_METABOLIC
    description: Genome-scale metabolic reconstruction (e.g., Recon3D, Harvey)
  FLUX_BALANCE_ANALYSIS:
    text: FLUX_BALANCE_ANALYSIS
    description: Constraint-based FBA model
  KINETIC:
    text: KINETIC
    description: ODE-based kinetic model with rate equations
  AGENT_BASED:
    text: AGENT_BASED
    description: Agent-based simulation model
  BOOLEAN_NETWORK:
    text: BOOLEAN_NETWORK
    description: Boolean gene regulatory network
  PHYSIOLOGICAL:
    text: PHYSIOLOGICAL
    description: Physiologically-based pharmacokinetic (PBPK) or organ model
  DIGITAL_TWIN:
    text: DIGITAL_TWIN
    description: Patient-specific computational model
  MACHINE_LEARNING:
    text: MACHINE_LEARNING
    description: ML/AI predictive model trained on disease data
  PERTURBATION_PREDICTION:
    text: PERTURBATION_PREDICTION
    description: Cell-based perturbation models (CRISPR screens, chemical perturbations)
      with gene expression readouts
  FOUNDATION_MODEL:
    text: FOUNDATION_MODEL
    description: Pre-trained single-cell foundation models (scGPT, Geneformer, scGenePT)
      for perturbation response prediction