SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization

TL;DR

SBML2Julia is a tool that translates systems biology models from SBML and TSV formats into Julia code, enabling efficient parameter optimization with Julia's nonlinear solvers, significantly accelerating computational tasks in systems biology.

Contribution

The paper introduces SBML2Julia, a novel interface that connects SBML models with Julia's optimization tools, enhancing computational efficiency for systems biology parameter estimation.

Findings

Reduces optimization time in systems biology models by a factor of 140.

Provides a user-friendly command line and Python API for model translation and optimization.

Enables access to Julia's high-performance nonlinear solvers for systems biology applications.

Abstract

Motivation: Estimating model parameters from experimental observations is one of the key challenges in systems biology and can be computationally very expensive. While the Julia programming language was recently developed as a high-level and high-performance language for scientific computing, systems biologists have only started to realise its potential. For instance, we have recently used Julia to cut down the optimization time of a microbial community model by a factor of 140. To facilitate access of the systems biology community to the efficient nonlinear solvers used for this optimisation, we developed SBML2Julia. SBML2Julia translates optimisation problems specified in SBML and TSV files (PEtab format) into Julia for Mathematical Programming (JuMP), executes the optimization and returns the results in tabular format. Availability and implementation: SBML2Julia is freely available under the MIT license. It comes with a command line interface and Python API. Internally, SBML2Julia calls the Julia LTS release v1.0.5 for optimisation. All necessary dependencies can be pulled from Docker Hub (https://hub.docker.com/repository/docker/paulflang/sbml2julia). Source code and documentation are available at https://github.com/paulflang/SBML2Julia.