Regulatory Response to Carbon Starvation in Caulobacter crescentus

Bacteria adapt to shifts from rapid to slow growth, and have developed strategies for long-term survival during prolonged starvation and stress conditions. We report the regulatory response of C. crescentus to carbon starvation, based on combined high-throughput proteome and transcriptome analyses. Our results identify cell cycle changes in gene expression in response to carbon starvation that involve the prominent role of the FixK FNR/CAP family transcription factor and the CtrA cell cycle regulator. Notably, the SigT ECF sigma factor mediates the carbon starvation-induced degradation of CtrA, while activating a core set of general starvation-stress genes that respond to carbon starvation, osmotic stress, and exposure to heavy metals. Comparison of the response of swarmer cells and stalked cells to carbon starvation revealed four groups of genes that exhibit different expression profiles. Also, cell pole morphogenesis and initiation of chromosome replication normally occurring at the swarmer-to-stalked cell transition are uncoupled in carbon-starved cells

Temporal Controls of the Asymmetric Cell Division Cycle in Caulobacter crescentus

The asymmetric cell division cycle of Caulobacter crescentus is orchestrated by an elaborate gene-protein regulatory network, centered on three major control proteins, DnaA, GcrA and CtrA. The regulatory network is cast into a quantitative computational model to investigate in a systematic fashion how these three proteins control the relevant genetic, biochemical and physiological properties of proliferating bacteria. Different controls for both swarmer and stalked cell cycles are represented in the mathematical scheme. The model is validated against observed phenotypes of wild-type cells and relevant mutants, and it predicts the phenotypes of novel mutants and of known mutants under novel experimental conditions. Because the cell cycle control proteins of Caulobacter are conserved across many species of alpha-proteobacteria, the model we are proposing here may be applicable to other genera of importance to agriculture and medicine (e.g., Rhizobium, Brucella)

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness.

Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository

Understanding a Bisferrocene Molecular QCA Wire

Molecular QCA are considered among the most promising beyond CMOS devices. Frequency as well as self-assembly characteristics are the features that make them most attractive. Several challenges restrain them for being exploited from a practical point of view in the near future, not only for the difficulties at the technological level, but for the inappropriateness of the tools used when studying and predicting their behavior. In this chapter we describe our methodology to simulate and model sequences of bisferrocene molecules aimed at understanding the behavior of a realistic MQCA wire. The simulations consider as variables distances between successive molecules, as well as different electric field applied (in terms of input and of clock). The method can be used to simulate and model also other more complex structures, and perspectives are given on the exploitation of the achieved result

Understanding a Bisferrocene Molecular QCA Wire

Molecular QCA are considered among the most promising beyond CMOS devices. Frequency as well as self-assembly characteristics are the features that make them most attractive. Several challenges restrain them for being exploited from a practical point of view in the near future, not only for the difficulties at the technological level, but for the inappropriateness of the tools used when studying and predicting their behavior. In this chapter we describe our methodology to simulate and model sequences of bisferrocene molecules aimed at understanding the behavior of a realistic MQCA wire. The simulations consider as variables distances between successive molecules, as well as different electric field applied (in terms of input and of clock). The method can be used to simulate and model also other more complex structures, and perspectives are given on the exploitation of the achieved result