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首页> 外文期刊>Bioprocess and Biosystems Engineering >A dual-parameter identification approach for data-based predictive modeling of hybrid gene regulatory network-growth kinetics in Pseudomonas putida mt-2
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A dual-parameter identification approach for data-based predictive modeling of hybrid gene regulatory network-growth kinetics in Pseudomonas putida mt-2

机译：杂交基因监管网络生长动力学基于数据的基于数据预测建模的双参数鉴定方法PiDoda MT-2

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Data integration to model-based description of biological systems incorporating gene dynamics improves the performance of microbial systems. Bioprocess performance, typically predicted using empirical Monod-type models, is essential for a sustainable bioeconomy. To replace empirical models, we updated a hybrid gene regulatory network-growth kinetic model, predicting aromatic pollutants degradation and biomass growth in Pseudomonas putida mt-2. We modeled a complex biological system including extensive information to understand the role of the regulatory elements in toluene biodegradation and biomass growth. The updated model exhibited extra complications such as the existence of oscillations and discontinuities. As parameter estimation of complex biological models remains a key challenge, we used the updated model to present a dual-parameter identification approach (the 'dual approach') combining two independent methodologies. Approach I handled the complexity by incorporation of demonstrated biological knowledge in the model-development process and combination of global sensitivity analysis and optimisation. Approach II complemented Approach I handling multimodality, ill-conditioning and overfitting through regularisation estimation, global optimisation, and identifiability analysis. To systematically quantify the biological system, we used a vast amount of high-quality time-course data. The dual approach resulted in an accurately calibrated kinetic model (NRMSE: 0.17055) efficiently handling the additional model complexity. We tested model validation using three independent experimental data sets, achieving greater predictive power (NRMSE: 0.18776) than the individual approaches (NRMSE I: 0.25322, II: 0.25227) and increasing model robustness. These results demonstrated data-driven predictive modeling potentially leading to bioprocess' model-based control and optimisation.
机译：数据集成到基于模型的生物系统描述，其纳入基因动力学提高了微生物系统的性能。通常使用经验Monod型模型预测的生物过程性能对于可持续的生物经济来说至关重要。为了替换经验模型，我们更新了一种杂交基因调节网络 - 增长动力学模型，预测芳香污染物的降解和生物量生长在假单胞菌MT-2中。我们建模了复杂的生物系统，包括广泛的信息，了解调节元件在甲苯生物降解和生物质生长中的作用。更新的模型表现出额外的并发症，例如存在振荡和不连续性。随着复杂生物模型的参数估计仍然是一个关键挑战，我们使用更新的模型来呈现双参数识别方法（“双方法”）组合两个独立方法。方法我通过在模型开发过程中掺入显示的生物学知识以及全球敏感性分析和优化的组合来处理复杂性。方法II补充方法我通过正则化估计，全局优化和可识别性分析处理多层性，不成分调节和过度装备。为了系统地量化生物系统，我们使用了大量的高质量时间课程数据。双方法导致精确校准的动力学模型（NRMSE：0.17055）有效处理额外的模型复杂性。我们使用三个独立的实验数据集测试了模型验证，实现了比各个方法更高的预测功率（NRMSE：0.18776）（NRMSE I：0.25322,i：0.25227）和增加模型鲁棒性。这些结果表明，数据驱动的预测建模可能导致生物过程基于模型的控制和优化。

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