همایش بین المللی هوش مصنوعی

صفحه اصلی / اولین همایش بین المللی هوش مصنوعی

Predictive Modeling of Escherichia coli Growth: The Role of Key Cellular Features

نویسندگان :

Sajedeh Farahbod¹ Masoud Tohidfar²

1- Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran. 2- Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran.

کلمات کلیدی :

Escherichia coli،Cell Growth Prediction،Metabolism Regulation،Microbial Cell Dynamics،Deep Learning in Biology

چکیده :

This study investigates the influence of four key features on the added length of Escherichia coli cells using a fully connected neural network (FCNN), based on data collected from 1,220 samples. The data comprises observations of individual cells and 10-minute sliding window averages from simulated data. Results show that removing the feature fluorescence intensity (YFP) led to the highest increase in Loss (0.3711) and root mean square error (RMSE) (0.6092). Removing cycle duration (Tcyc) also significantly reduced model accuracy, increasing Loss (0.2811) and RMSE (0.5302). In contrast, eliminating size at birth (Lb) and growth rate (Mu) had less impact. These findings highlight the importance of effective feature selection in predicting cell growth (0.2811) and RMSE (0.5302). In contrast, eliminating size at birth (Lb) and growth rate (Mu) had less impact. These findings highlight the importance of effective feature selection in predicting cell growth.