Synthetic biology tools for environmental protection.

Synthetic biology transforms the way we perceive biological systems. Emerging technologies in this field affect many disciplines of science and engineering. Traditionally, synthetic biology approaches were commonly aimed at developing cost-effective microbial cell factories to produce chemicals from renewable sources. Based on this, the immediate beneficial impact of synthetic biology on the environment came from reducing our oil dependency. However, synthetic biology is starting to play a more direct role in environmental protection. Toxic chemicals released by industries and agriculture endanger the environment, disrupting ecosystem balance and biodiversity loss. This review highlights synthetic biology approaches that can help environmental protection by providing remediation systems capable of sensing and responding to specific pollutants. Remediation strategies based on genetically engineered microbes and plants are discussed. Further, an overview of computational approaches that facilitate the design and application of synthetic biology tools in environmental protection is presented.

Gut-on-a-chip models for dissecting the gut microbiology and physiology.

Microfluidic technologies have been extensively investigated in recent years for developing organ-on-a-chip-devices as robust in vitro models aiming to recapitulate organ 3D topography and its physicochemical cues. Among these attempts, an important research front has focused on simulating the physiology of the gut, an organ with a distinct cellular composition featuring a plethora of microbial and human cells that mutually mediate critical body functions. This research has led to innovative approaches to model fluid flow, mechanical forces, and oxygen gradients, which are all important developmental cues of the gut physiological system. A myriad of studies has demonstrated that gut-on-a-chip models reinforce a prolonged coculture of microbiota and human cells with genotypic and phenotypic responses that closely mimic the in vivo data. Accordingly, the excellent organ mimicry offered by gut-on-a-chips has fueled numerous investigations on the clinical and industrial applications of these devices in recent years. In this review, we outline various gut-on-a-chip designs, particularly focusing on different configurations used to coculture the microbiome and various human intestinal cells. We then elaborate on different approaches that have been adopted to model key physiochemical stimuli and explore how these models have been beneficial to understanding gut pathophysiology and testing therapeutic interventions.

Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases.

The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.

Engineering biology approaches for food and nutrient production by cyanobacteria.

As photoautotrophic organisms, cyanobacteria capture and store solar energy in the form of biomass. Cyanobacterial biomass has been an important component of diet and nutrition in several regions for centuries. Synthetic biology strategies are currently being applied to increase the yield and productivity of cyanobacterial biomass by optimizing solar energy utilization and CO2 fixation rates for carbon storage. Likewise, engineering cyanobacteria as cellular factories to synthesize carbohydrates, amino acids, proteins, lipids and fatty acids is providing an attractive way to sustainably produce food and nutrients for human consumption. In this review, we have summarized recent progress in both aspects and prospective trends under development.

A novel computational simulation approach to study biofilm significance in a packed-bed biooxidation reactor.

Fe (II) biooxidation has recently gained significant interest. It plays a key role in a number of environmental and industrial processes such as bioleaching, acid mine drainage treatment, desulphurization of sour gases, and coal desulphurization. In this work, a three-dimensional CFD model for gas-liquid flow in a lab-scale packed-bed biooxidation reactor is used. The reactor is randomly packed with spherical particles, and the particles are covered with Leptospirillum ferrooxidans biofilm for Fe (II) biooxidation. A modified Jodrey-Tory algorithm is used to generate random packing with actual porosity of 0.42, and biofilm layer with constant thickness is considered over the particles. A simplified Eulerian-Eulerian model is used to obtain detailed flow field. The concentration profile in the reactor and the conversion of Fe (II) from the present simulations are obtained and validated using experimental data reported in the literature. The results of the study indicate that about three-quarters of the conversion occurs in the upper half of the reactor and Fe (II) concentration on the biofilm surface at the lower quarter of the reactor does not exceed 5 mM (The inlet concentration is 89.6 mM). The findings reveal that rate-limiting phenomena may vary in different parts of the reactor. The results obtained through the simulations represent advantages for the design and optimization of packed-bed biofilm reactors.

A Systems-Based Approach for Cyanide Overproduction by Bacillus megaterium for Gold Bioleaching Enhancement.

With the constant accumulation of electronic waste, extracting precious metals contained therein is becoming a major challenge for sustainable development. Bacillus megaterium is currently one of the microbes used for the production of cyanide, which is the main leaching agent for gold recovery. The present study aimed to propose a strategy for metabolic engineering of B. megaterium to overproduce cyanide, and thus ameliorate the bioleaching process. For this, we employed constraint-based modeling, running in silico simulations on iJA1121, the genome-scale metabolic model of B. megaterium DSM319. Flux balance analysis (FBA) was initially used to identify amino acids to be added to the culture medium. Considering cyanide as the desired product, we used growth-coupled methods, constrained minimal cut sets (cMCSs) and OptKnock to identify gene inactivation targets. To identify gene overexpression targets, flux scanning based on enforced objective flux (FSEOF) was performed. Further analysis was carried out on the identified targets to determine compounds with beneficial regulatory effects. We have proposed a chemical-defined medium for accelerating cyanide production on the basis of microplate assays to evaluate the components with the greatest improving effects. Accordingly, the cultivation of B. megaterium DSM319 in a chemically-defined medium with 5.56 mM glucose as the carbon source, and supplemented with 413 µM cysteine, led to the production of considerably increased amounts of cyanide. Bioleaching experiments were successfully performed in this medium to recover gold and copper from telecommunication printed circuit boards. The results of inductively coupled plasma (ICP) analysis confirmed that gold recovery peaked out at around 55% after 4 days, whereas copper recovery continued to increase for several more days, peaking out at around 85%. To further validate the bioleaching results, FESEM, XRD, FTIR, and EDAX mapping analyses were performed. We concluded that the proposed strategy represents a viable route for improving the performance of the bioleaching processes.

Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319.

Bacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.