Systematic Design of a Quorum Sensing-Based Biosensor for Enhanced Detection of Metal Ion in Escherichia Coli

An Allee-based distributed algorithm for microbial whole-cell sensors

Reliable detection of substances present at potentially low concentrations is a problem common to many biomedical applications. Complementary to well-established enzyme-, antibody-antigen-, and sequencing-based approaches, so-called microbial whole-cell sensors, i.e., synthetically engineered microbial cells that sense and report substances, have been proposed as alternatives. Typically these cells operate independently: a cell reports an analyte upon local detection.In this work, we analyze a distributed algorithm for microbial whole-cell sensors, where cells communicate to coordinate if an analyte has been detected. The algorithm, inspired by the Allee effect in biological populations, causes cells to alternate between a logical 0 and 1 state in response to reacting with the particle of interest. When the cells in the logical 1 state exceed a threshold, the algorithm converts the remaining cells to the logical 1 state, representing an easily-detectable output signal. We validate the algorithm through mathematical analysis and simulations, demonstrating that it works correctly even in noisy cellular environments.

Quorum sensing in biofilms: a key mechanism to target in ecotoxicological studies

Our environment is heavily contaminated by anthropogenic compounds, and this issue constitutes a significant threat to all life forms, including biofilm-forming microorganisms. Cell-cell interactions shape microbial community structures and functions, and pollutants that affect intercellular communications impact biofilm functions and ecological roles. There is a growing interest in environmental science fields for evaluating how anthropogenic pollutants impact cell-cell interactions. In this review, we synthesize existing literature that evaluates the impacts of quorum sensing (QS), which is a widespread density-dependent communication system occurring within many bacterial groups forming biofilms. First, we examine the perturbating effects of environmental contaminants on QS circuits; and our findings reveal that QS is an essential yet underexplored mechanism affected by pollutants. Second, our work highlights that QS is an unsuspected and key resistance mechanism that assists bacteria in dealing with environmental contamination (caused by metals or organic pollutants) and that favors bacterial growth in unfavourable environments. We emphasize the value of considering QS a critical mechanism for monitoring microbial responses in ecotoxicology. Ultimately, we determine that QS circuits constitute promising targets for innovative biotechnological approaches with major perspectives for applications in the field of environmental science.

Unraveling the complex regulatory networks in biofilm formation in bacteria and relevance of biofilms in environmental remediation

Biofilms are assemblages of bacteria embedded within a matrix of extracellular polymeric substances (EPS) attached to a substratum. The process of biofilm formation is a complex phenomenon regulated by the intracellular and intercellular signaling systems. Various secondary messenger molecules such as cyclic dimeric guanosine 3',5'-monophosphate (c-di-GMP), cyclic adenosine 3',5'-monophosphate (cAMP), and cyclic dimeric adenosine 3',5'-monophosphate (c-di-AMP) are involved in complex signaling networks to regulate biofilm development in several bacteria. Moreover, the cell to cell communication system known as Quorum Sensing (QS) also regulates biofilm formation via diverse mechanisms in various bacterial species. Bacteria often switch to the biofilm lifestyle in the presence of toxic pollutants to improve their survivability. Bacteria within a biofilm possess several advantages with regard to the degradation of harmful pollutants, such as increased protection within the biofilm to resist the toxic pollutants, synthesis of extracellular polymeric substances (EPS) that helps in the sequestration of pollutants, elevated catabolic gene expression within the biofilm microenvironment, higher cell density possessing a large pool of genetic resources, adhesion ability to a wide range of substrata, and metabolic heterogeneity. Therefore, a comprehensive account of the various factors regulating biofilm development would provide valuable insights to modulate biofilm formation for improved bioremediation practices. This review summarizes the complex regulatory networks that influence biofilm development in bacteria, with a major focus on the applications of bacterial biofilms for environmental restoration.

In vivo biosensors: mechanisms, development, and applications

In vivo biosensors can recognize and respond to specific cellular stimuli. In recent years, biosensors have been increasingly used in metabolic engineering and synthetic biology, because they can be implemented in synthetic circuits to control the expression of reporter genes in response to specific cellular stimuli, such as a certain metabolite or a change in pH. There are many types of natural sensing devices, which can be generally divided into two main categories: protein-based and nucleic acid-based. Both can be obtained either by directly mining from natural genetic components or by engineering the existing genetic components for novel specificity or improved characteristics. A wide range of new technologies have enabled rapid engineering and discovery of new biosensors, which are paving the way for a new era of biotechnological progress. Here, we review recent advances in the design, optimization, and applications of in vivo biosensors in the field of metabolic engineering and synthetic biology.

Understanding Robust Adaptation Dynamics of Gene Regulatory Network

Robust adaptation is a critical attribute for gene regulatory network (GRN), understanding the relationship between adaptation and the GRN topology, and corresponding parameters is a challenging issue. The work in this paper includes: first, seven constraint multiobjective optimization algorithms are used to find sufficient solutions to get more reliable statistic rules. Meanwhile, the algorithms are compared to facilitate the future algorithm selection; second, a fuzzy c-mean algorithm is used to analyze solutions and to classify the solutions into different groups; third, the histogram analysis for all satisfactory solutions shows the preferred parameter range, i.e., parameter motif. The contributions of this paper includes: 1) Two new adaptation indices i.e., peak time and settle down time, are proposed for the first time to give more accurate description of the robust adaptation. Our conclusion is that some solutions even with satisfactory sensitivity and precision are not practically of robust adaptation because of too long time needed. 2) The relationship between topology, parameter set, and robust adaptation of GRN is discovered in the sense of both preferred topology and parameter motif. Our conclusion is that the robust adaptation depends more on the GRN topology than the model parameter set in two feasible topologies.