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Abstract Escherichia coli MLB (MG1655 ΔpflB ΔldhA), which can hardly grow on glucose with little succinate accumulation under anaerobic conditions. Two-stage fermentation is a fermentation in which the first stage is used for cell growth and the second stage is used for product production. The ability of glucose consumption and succinate production of MLB under anaerobic conditions can be improved significantly by using acetate as the solo carbon source under aerobic condition during the two-stage fermentation. Then, the adaptive laboratory evolution (ALE) of growing on acetate was applied here. We assumed that the activities of succinate production related enzymes might be further improved in this study. E. coli MLB46-05 evolved from MLB and it had an improved growth phenotype on acetate. Interestingly, in MLB46-05, the yield and tolerance of succinic acid in the anaerobic condition of two-stage fermentation were improved significantly. According to transcriptome analysis, upregulation of the glyoxylate cycle and the activity of stress regulatory factors are the possible reasons for the elevated yield. And the increased tolerance to acetate made it more tolerant to high concentrations of glucose and succinate. Finally, strain MLB46-05 produced 111 g/L of succinic acid with a product yield of 0.74 g/g glucose. Synopsis

Abstract Accurate inference and prediction of gene regulatory network are very important for understanding dynamic cellular processes. The large-scale time series genomics data are helpful to reveal the molecular dynamics and dynamic biological processes of complex biological systems. Firstly, we collected the time series data of the rat pineal gland tissue in the natural state according to a fixed sampling rate, and performed whole-genome sequencing. The large-scale time-series sequencing data set of rat pineal gland was constructed, which includes 480 time points, the time interval between adjacent time points is 3 min, and the sampling period is 24 h. Then, we proposed a new method of constructing gene expression regulatory network, named the gene regulatory network based on time series data and entropy transfer (GRNTSTE) method. The method is based on transfer entropy and large-scale time-series gene expression data to infer the causal regulatory relationship between genes in a data-driven mode. The comparative experiments prove that GRNTSTE has better performance than dynamical gene network inference with ensemble of trees (dynGENIE3) and SCRIBE, and has similar performance to TENET. Meanwhile, we proved that the performance of GRNTSTE is slightly lower than that of SINCERITIES method and better than other gene regulatory network construction methods in BEELINE framework, which is based on the BEELINE data set. Finally, the rat pineal rhythm gene expression regulatory network was constructed by us based on the GRNTSTE method, which provides an important reference for the study of the pineal rhythm mechanism, and is of great significance to the study of the pineal rhythm mechanism.

Research on forecasting methods of time series data has become one of the hot spots. More and more time series data are produced in various fields. It provides data for the research of time series analysis method, and promotes the development of time series research. Due to the generation of highly complex and large-scale time series data, the construction of forecasting models for time series data brings greater challenges. The main challenges of time series modeling are high complexity of time series data, low accuracy and poor generalization ability of prediction model. This paper attempts to cover the existing modeling methods for time series data and classify them. In addition, we make comparisons between different methods and list some potential directions for time series forecasting.