The reconstruction and analysis of metabolic models have garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of the steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.

代谢模型的重建和分析已经获得了越来越多的关注，因为它们已被证明是实用的大量应用。随着生成的代谢模型数量的增加，用于分析它们的工具库呈指数级增长。在这项工作中，我们讨论了许多有前途的建模框架的生物学相关性，重点关注每种方法能够解决的问题和假设。为此，我们批判性地分析了专注于资源分配和热力学考虑的稳态建模方法，这些方法产生了有希望的结果，并有助于生成和实验验证大量预测。对于涉及更复杂监管的小型网络，我们讨论了动力学建模技术，这些技术在解决稳态建模范围之外的问题时显示出令人鼓舞的结果。最后，我们讨论了所讨论的框架在应变设计领域的潜在应用。采用这种方法被认为可以显着提高计算机预测的准确性，从而减少所需的设计-构建-测试周期的数量。