Metabolic pathways are often engineered in single microbial populations. However, the introduction of heterologous circuits into the host can create a substantial metabolic burden that limits the overall productivity of the system. This limitation could be overcome by metabolic division of labor (DOL), whereby distinct populations perform different steps in a metabolic pathway, reducing the burden each population will experience. While conceptually appealing, the conditions when DOL is advantageous have not been rigorously established. Here, we have analyzed 24 common architectures of metabolic pathways in which DOL can be implemented. Our analysis reveals general criteria defining the conditions that favor DOL, accounting for the burden or benefit of the pathway activity on the host populations as well as the transport and turnover of enzymes and intermediate metabolites. These criteria can help guide engineering of metabolic pathways and have implications for understanding evolution of natural microbial communities.

Identifying active prophages is critical for studying coevolution of phage and bacteria, investigating phage physiology and biochemistry, and engineering designer phages for diverse applications. We present Prophage Hunter, a tool aimed at hunting for active prophages from whole genome assembly of bacteria. Combining sequence similarity-based matching and genetic features-based machine learning classification, we developed a novel scoring system that exhibits higher accuracy than current tools in predicting active prophages on the validation datasets. The option of skipping similarity matching is also available so that there's higher chance for novel phages to be discovered. Prophage Hunter provides a one-stop web service to extract prophage genomes from bacterial genomes, evaluate the activity of the prophages, identify phylogenetically related phages, and annotate the function of phage proteins. Prophage Hunter is freely available at https://pro-hunter.bgi.com/.

The length of the G1 phase in the cell cycle shows significant variability in different cell types and tissue types. To gain insights into the control of G1 length, we generated an E2F activity reporter that captures free E2F activity after dissociation from Rb sequestration and followed its kinetics of activation at the single-cell level, in real time. Our results demonstrate that its activity is precisely coordinated with S phase progression. Quantitative analysis indicates that there is a pre-S phase delay between E2F transcriptional dynamic and activity dynamics. This delay is variable among different cell types and is strongly modulated by the cyclin D/CDK4/6 complex activity through Rb phosphorylation. Our findings suggest that the main function of this complex is to regulate the appropriate timing of G1 length.

For many biological applications, exploration of the massive parametric space of a mechanism-based model can impose a prohibitive computational demand. To overcome this limitation, we present a framework to improve computational efficiency by orders of magnitude. The key concept is to train a neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training. The trained neural network can then be used to explore a much larger parametric space. We demonstrate this notion by training neural networks to predict pattern formation and stochastic gene expression. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can be a platform for fast parametric space screening of biological models with user defined objectives.

The Rb/E2F network has a critical role in regulating cell cycle progression and cell fate decisions. It is dysfunctional in virtually all human cancers, because of genetic lesions that cause overexpression of activators, inactivation of repressors, or both. Paradoxically, the downstream target of this network, E2F1, is rarely strongly overexpressed in cancer. E2F1 can induce both proliferation and apoptosis but the factors governing these critical cell fate decisions remain unclear. Previous studies have focused on qualitative mechanisms such as differential cofactors, posttranslational modification or state of other signaling pathways as modifiers of the cell fate decisions downstream of E2F1 activation. In contrast, the importance of the expression levels of E2F1 itself in dictating the downstream phenotypes has not been rigorously studied, partly due to the limited resolution of traditional population-level measurements. Here, through single-cell quantitative analysis, we demonstrate that E2F1 expression levels have a critical role in determining the fate of individual cells. Low levels of exogenous E2F1 promote proliferation, moderate levels induce G1, G2 and mitotic cell cycle arrest, and very high levels promote apoptosis. These multiple anti-proliferative mechanisms result in a strong selection pressure leading to rapid elimination of E2F1-overexpressing cells from the population. RNA-sequencing and RT-PCR revealed that low levels of E2F1 are sufficient to induce numerous cell cycle-promoting genes, intermediate levels induce growth arrest genes (i.e., p18, p19 and p27), whereas higher levels are necessary to induce key apoptotic E2F1 targets APAF1, PUMA, HRK and BIM. Finally, treatment of a lung cancer cell line with a proteasome inhibitor, MLN2238, resulted in an E2F1-dependent mitotic arrest and apoptosis, confirming the role of endogenous E2F1 levels in these phenotypes. The strong anti-proliferative activity of moderately overexpressed E2F1 in multiple cancer types suggests that targeting E2F1 for upregulation may represent an attractive therapeutic strategy in cancer.