van den Bogaard Samira, Saa Pedro A, Alter Tobias B
Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Aachen 52074, Germany.
Departamento de Ingeniería Química y Bioprocesos, Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.
Bioinformatics. 2024 Nov 28;40(12). doi: 10.1093/bioinformatics/btae691.
Expanding on constraint-based metabolic models, protein allocation models (PAMs) enhance flux predictions by accounting for protein resource allocation in cellular metabolism. Yet, to this date, there are no dedicated methods for analyzing and understanding the growth-limiting factors in simulated phenotypes in PAMs.
Here, we introduce a systematic framework for identifying the most sensitive enzyme concentrations (sEnz) in PAMs. The framework exploits the primal and dual formulations of these models to derive sensitivity coefficients based on relations between variables, constraints, and the objective function. This approach enhances our understanding of the growth-limiting factors of metabolic phenotypes under specific environmental or genetic conditions. Compared to other traditional methods for calculating sensitivities, sEnz requires substantially less computation time and facilitates more intuitive comparison and analysis of sensitivities. The sensitivities calculated by sEnz cover enzymes, reactions and protein sectors, enabling a holistic overview of the factors influencing metabolism. When applied to an Escherichia coli PAM, sEnz revealed major pathways and enzymes driving overflow metabolism. Overall, sEnz offers a computational efficient framework for understanding PAM predictions and unraveling the factors governing a particular metabolic phenotype.
sEnz is implemented in the modular toolbox for the generation and analysis of PAMs in Python (PAModelpy; v.0.0.3.3), available on Pypi (https://pypi.org/project/PAModelpy/). The source code together with all other python scripts and notebooks are available on GitHub (https://github.com/iAMB-RWTH-Aachen/PAModelpy).
基于约束的代谢模型不断扩展，蛋白质分配模型（PAM）通过考虑细胞代谢中的蛋白质资源分配来增强通量预测。然而，迄今为止，尚无专门用于分析和理解PAM模拟表型中生长限制因素的方法。
在此，我们引入了一个系统框架，用于识别PAM中最敏感的酶浓度（sEnz）。该框架利用这些模型的原始和对偶公式，根据变量、约束和目标函数之间的关系推导灵敏度系数。这种方法增强了我们对特定环境或遗传条件下代谢表型生长限制因素的理解。与其他计算灵敏度的传统方法相比，sEnz所需的计算时间大幅减少，并且便于更直观地比较和分析灵敏度。通过sEnz计算的灵敏度涵盖了酶、反应和蛋白质部分，能够全面了解影响代谢的因素。当应用于大肠杆菌PAM时，sEnz揭示了驱动溢流代谢的主要途径和酶。总体而言，sEnz为理解PAM预测和揭示控制特定代谢表型的因素提供了一个计算高效的框架。
sEnz在用于生成和分析Python中PAM的模块化工具箱（PAModelpy；v.0.0.3.3）中实现，可在Pypi（https://pypi.org/project/PAModelpy/）上获取。源代码以及所有其他Python脚本和笔记本可在GitHub（https://github.com/iAMB-RWTH-Aachen/PAModelpy）上获取。
