Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan.
Data Science Center, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan.
Sci Rep. 2024 Nov 18;14(1):27252. doi: 10.1038/s41598-024-76029-1.
The discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a substantial challenge in bioprocess optimization. We previously introduced a hybrid in silico/in-cell controller (HISICC) that combines model-based optimization with cell-based feedback to address this problem. Here, we extended this approach to regulate a key enzyme level using intracellular biosensing. The extended HISICC was implemented using an Escherichia coli strain engineered for fatty acid production (FA3). This strain contains a genetically encoded feedback controller that decelerates the expression of acetyl-CoA carboxylase (ACC) in response to malonyl-CoA synthesized through the enzymatic reaction. We modeled FA3 to allow the HISICC to optimize an inducer input that accelerates the enzyme expression. Simulations showed that the HISICC slowed the unexpectedly rapid accumulation of ACC resulting from PMMs before it reached cytotoxic levels, thereby improving fatty acid yields. These results highlight the potential of our approach, particularly in cases where monitoring intracellular biomolecules is required to handle PMMs.
模型预测与实际过程之间的差异，即过程模型失配（PMM），仍然是生物过程优化的一个重大挑战。我们之前引入了一种混合的计算/细胞内控制器（HISICC），它将基于模型的优化与基于细胞的反馈相结合，以解决这个问题。在这里，我们扩展了这种方法，使用细胞内生物传感来调节关键酶的水平。扩展的 HISICC 是使用大肠杆菌工程菌（FA3）来实现脂肪酸生产的。该菌株包含一个遗传编码的反馈控制器，该控制器响应通过酶反应合成的丙二酰辅酶 A 来减缓乙酰辅酶 A 羧化酶 (ACC) 的表达。我们对 FA3 进行建模，使 HISICC 能够优化加速酶表达的诱导剂输入。模拟表明，HISICC 在 ACC 因 PMM 而意外快速积累达到细胞毒性水平之前减缓了其积累，从而提高了脂肪酸产量。这些结果突出了我们方法的潜力，特别是在需要监测细胞内生物分子以处理 PMM 的情况下。
