Stochastic modeling for the expression of a gene regulated by competing transcription factors

Advances in the technologies for studying consortia of bacteria and cyanobacteria/microalgae in wastewaters

The excessive generation and discharge of wastewaters have been serious concerns worldwide in the recent past. From an environmental friendly perspective, bacteria, cyanobacteria and microalgae, and the consortia have been largely considered for biological treatment of wastewaters. For efficient use of bacteria‒cyanobacteria/microalgae consortia in wastewater treatment, detailed knowledge on their structure, behavior and interaction is essential. In this direction, specific analytical tools and techniques play a significant role in studying these consortia. This review presents a critical perspective on physical, biochemical and molecular techniques such as microscopy, flow cytometry with cell sorting, nanoSIMS and omics approaches used for systematic investigations of the structure and function, particularly nutrient removal potential of bacteria‒cyanobacteria/microalgae consortia. In particular, the use of specific molecular techniques of genomics, transcriptomics, proteomics metabolomics and genetic engineering to develop more stable consortia of bacteria and cyanobacteria/microalgae with their improved biotechnological capabilities in wastewater treatment has been highlighted.

Noise and information transmission in promoters with multiple internal States

Based on the measurements of noise in gene expression performed during the past decade, it has become customary to think of gene regulation in terms of a two-state model, where the promoter of a gene can stochastically switch between an ON and an OFF state. As experiments are becoming increasingly precise and the deviations from the two-state model start to be observable, we ask about the experimental signatures of complex multistate promoters, as well as the functional consequences of this additional complexity. In detail, we i), extend the calculations for noise in gene expression to promoters described by state transition diagrams with multiple states, ii), systematically compute the experimentally accessible noise characteristics for these complex promoters, and iii), use information theory to evaluate the channel capacities of complex promoter architectures and compare them with the baseline provided by the two-state model. We find that adding internal states to the promoter generically decreases channel capacity, except in certain cases, three of which (cooperativity, dual-role regulation, promoter cycling) we analyze in detail.

FOUNDATIONS FOR MODELING THE DYNAMICS OF GENE REGULATORY NETWORKS: A MULTILEVEL-PERSPECTIVE REVIEW

A promising alternative for unraveling the principles under which the dynamic interactions among genes lead to cellular phenotypes relies on mathematical and computational models at different levels of abstraction, from the molecular level of protein-DNA interactions to the system level of functional relationships among genes. This review article presents, under a bottom–up perspective, a hierarchy of approaches to modeling gene regulatory network dynamics, from microscopic descriptions at the single-molecule level in the spatial context of an individual cell to macroscopic models providing phenomenological descriptions at the population-average level. The reviewed modeling approaches include Molecular Dynamics, Particle-Based Brownian Dynamics, the Master Equation approach, Ordinary Differential Equations, and the Boolean logic abstraction. Each of these frameworks is motivated by a particular biological context and the nature of the insight being pursued. The setting of gene network dynamic models from such frameworks involves assumptions and mathematical artifacts often ignored by the non-specialist. This article aims at providing an entry point for biologists new to the field and computer scientists not acquainted with some recent biophysically-inspired models of gene regulation. The connections promoting intuition between different abstraction levels and the role that approximations play in the modeling process are highlighted throughout the paper.