SBML2HYB: a Python']Python interface for SBML compatible hybrid modeling

被引:4
作者
Pinto, Jose [1 ]
Costa, Rafael S. [1 ]
Alexandre, Leonardo [1 ,2 ]
Ramos, Joao [1 ]
Oliveira, Rui [1 ]
机构
[1] Univ NOVA Lisboa, NOVA Sch Sci & Technol, Dept Chem, LAQV REQUIMTE, Lisbon, Portugal
[2] INESC ID, Lisbon, Portugal
关键词
PROTEIN; PATHWAY;
D O I
10.1093/bioinformatics/btad044
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Here, we present sbml2hyb, an easy-to-use standalone Python tool that facilitates the conversion of existing mechanistic models of biological systems in Systems Biology Markup Language (SBML) into hybrid semiparametric models that combine mechanistic functions with machine learning (ML). The so-formed hybrid models can be trained and stored back in databases in SBML format. The tool supports a user-friendly export interface with an internal format validator. Two case studies illustrate the use of the sbml2hyb tool. Additionally, we describe HMOD, a new model format designed to support and facilitate hybrid models building. It aggregates the mechanistic model information with the ML information and follows as close as possible the SBML rules. We expect the sbml2hyb tool and HMOD to greatly facilitate the widespread usage of hybrid modeling techniques for biological systems analysis.
引用
收藏
页数:4
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共 24 条
[21]   A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria [J].
Vijayakumar, Supreeta ;
Rahman, Pattanathu K. S. M. ;
Angione, Claudio .
ISCIENCE, 2020, 23 (12)
[22]   A novel identification method for hybrid (N)PLS dynamical systems with application to bioprocesses [J].
von Stosch, M. ;
Oliveira, R. ;
Peres, J. ;
de Azevedo, S. Feyo .
EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (09) :10862-10874
[23]   Hybrid semi-parametric modeling in process systems engineering: Past, present and future [J].
von Stosch, Moritz ;
Oliveira, Rui ;
Peres, Joana ;
de Azevedo, Sebastiao Feyo .
COMPUTERS & CHEMICAL ENGINEERING, 2014, 60 :86-101
[24]   A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action [J].
Yang, Jason H. ;
Wright, Sarah N. ;
Hamblin, Meagan ;
McCloskey, Douglas ;
Alcantar, Miguel A. ;
Schrubbers, Lars ;
Lopatkin, Allison J. ;
Satish, Sangeeta ;
Nili, Amir ;
Palsson, Bernhard O. ;
Walker, Graham C. ;
Collins, James J. .
CELL, 2019, 177 (06) :1649-+