sybil

sybil performs constraint-based analysis of genome-scale metabolic networks within R to compute and optimize metabolic flux distributions for systems- and synthetic-biology applications.

Key Features:

• Flux analysis methods: Implements flux-balance analysis (FBA), minimization of metabolic adjustment (MOMA), and regulatory on/off minimization (ROOM).
• Computational efficiency: Optimized implementations enable analyses up to ten times faster in benchmarks such as whole-genome single gene deletion simulations on a complete E. coli metabolic model.
• Simulation of genetic perturbations: Supports in silico whole-genome single gene deletions and genome-scale pairwise knock-out computations.
• Object-oriented extensibility in R: Provides an object-oriented architecture within R that enables custom constraint-based algorithms and construction of analysis pipelines while leveraging R's statistical computing and graphics capabilities.
• Solver integration: Interfaces with external mathematical optimization programs to explore high-dimensional optimization problems efficiently.

Scientific Applications:

• Genome-wide knockout analysis: Enables large-scale computation of whole-genome and pairwise gene knock-outs in metabolic models.
• Model refinement and curation: Facilitates automated searches for model improvements and identification of inconsistencies in genome-scale metabolic reconstructions.
• Systems and synthetic biology studies: Supports analysis of metabolic responses to genetic modifications for systems biology and synthetic biology research.

Methodology:

Constraint-based modeling that optimizes network fluxes under stoichiometric and other constraints, implementing FBA, MOMA and ROOM, simulating single gene deletions, and interfacing with mathematical optimization solvers within R.