The efficient applications of native flora for phytorestoration of mine tailings: a pan-global survey.

Mine tailings are the discarded materials resulting from mining processes after minerals have been extracted. They consist of leftover mineral fragments, excavated land masses, and disrupted ecosystems. The uncontrolled handling or discharge of tailings from abandoned mine lands (AMLs) poses a threat to the surrounding environment. Numerous untreated mine tailings have been abandoned globally, necessitating immediate reclamation and restoration efforts. The limited feasibility of conventional reclamation methods, such as cost and acceptability, presents challenges in reclaiming tailings around AMLs. This study focuses on phytorestoration as a sustainable method for treating mine tailings. Phytorestoration utilizes existing native plants on the mine sites while applying advanced principles of environmental biotechnology. These approaches can remediate toxic elements and simultaneously improve soil quality. The current study provides a global overview of phytorestoration methods, emphasizing the specifics of mine tailings and the research on native plant species to enhance restoration ecosystem services.

Transcriptome-wide marker gene expression analysis of stress-responsive sulfate-reducing bacteria.

Sulfate-reducing bacteria (SRB) are terminal members of any anaerobic food chain. For example, they critically influence the biogeochemical cycling of carbon, nitrogen, sulfur, and metals (natural environment) as well as the corrosion of civil infrastructure (built environment). The United States alone spends nearly $4 billion to address the biocorrosion challenges of SRB. It is important to analyze the genetic mechanisms of these organisms under environmental stresses. The current study uses complementary methodologies, viz., transcriptome-wide marker gene panel mapping and gene clustering analysis to decipher the stress mechanisms in four SRB. Here, the accessible RNA-sequencing data from the public domains were mined to identify the key transcriptional signatures. Crucial transcriptional candidate genes of Desulfovibrio spp. were accomplished and validated the gene cluster prediction. In addition, the unique transcriptional signatures of Oleidesulfovibrio alaskensis (OA-G20) at graphene and copper interfaces were discussed using in-house RNA-sequencing data. Furthermore, the comparative genomic analysis revealed 12,821 genes with translation, among which 10,178 genes were in homolog families and 2643 genes were in singleton families were observed among the 4 genomes studied. The current study paves a path for developing predictive deep learning tools for interpretable and mechanistic learning analysis of the SRB gene regulation.

Convergence research and training in computational bioengineering: a case study on AI/ML driven biofilm-material interaction discovery.

Initially, research disciplines operated independently, but the emergence of trans-disciplinary sciences led to convergence research, impacting graduate programs and research laboratories, especially in bioengineering and material engineering as presented here. Current graduate curriculum fails to efficiently prepare students for multidisciplinary and convergence research, thus creating a gap between the students and research laboratory expectations. We present a convergence training framework for graduate students, incorporating problem-based learning under the guidance of senior scientists and collaboration with postdoctoral researchers. This case study serves as a template for transdisciplinary convergent training projects - bridging the expertise gap and fostering successful convergence learning experiences in computational biointerface (material-biology interface). The 18-month Advanced Data Science Workshop, initiated in 2019, involves project-based learning, online training modules, and data collection. A pilot solution utilized Jupyter notebook on Google collaborator and culminated in a face-to-face workshop where project presentations and finalization occurred. The program started with 9 experts in the four diverse fields creating 14 curated projects in data science (Artificial Intelligence/Machine Learning), material science, biofilm engineering, and biointerface. These were integrated into convergence research through webinars by the experts. The experts chose 8 of the 14 projects to be part of an all-day in-person workshop, where over 20 learners formed eight teams that tackled complex problems at the interface of digital image processing, gene expression analysis, and material prediction. Each team was comprised of students and postdoctoral researchers or research scientists from diverse domains including computer science, materials science, and biofilm research. Some projects were selected for presentation at the international IEEE Bioinformatics conference in 2022, with three resulting Machine Learning (ML) models submitted as a journal paper. Students engaged in problem discussions, collaborated with experts from different disciplines, and received guidance in decomposing learning objectives. Based on learner feedback, this successful experience allows for consolidation and integration of convergence research via problem-based learning into the curriculum. Three bioengineering participants, who received training in data science and engineering, have received bioinformatics jobs in biotechnology industries.

CREID - A ChemoReceptor-Effector Interaction Database.

The ChemoReceptor-Effector Interaction Database (CREID) is a collection of bacterial chemoreceptor and effector protein and interaction data to understand the process that chemoreceptors and effectors play in various environments. Our website includes terms associated with chemosensory pathways to educate users and those involved in collaborative research to help them understand this complex biological network. It includes 2,440 proteins involved in chemoreceptor and effector systems from 7 different bacterial families with 1,996 chemoeffector interactions. It is available at https://reactcreid.bicbioeng.org.

Classification of bacterial nanowire proteins using Machine Learning and Feature Engineering model.

Nanowires (NW) have been extensively studied for Shewanella spp. and Geobacter spp. and are mostly produced by Type IV pili or multiheme c-type cytochrome. Electron transfer via NW is the most studied mechanism in microbially induced corrosion, with recent interest in application in bioelectronics and biosensor. In this study, a machine learning (ML) based tool was developed to classify NW proteins. A manually curated 999 protein collection was developed as an NW protein dataset. Gene ontology analysis of the dataset revealed microbial NW is part of membranal proteins with metal ion binding motifs and plays a central role in electron transfer activity. Random Forest (RF), support vector machine (SVM), and extreme gradient boost (XGBoost) models were implemented in the prediction model and were observed to identify target proteins based on functional, structural, and physicochemical properties with 89.33%, 95.6%, and 99.99% accuracy. Dipetide amino acid composition, transition, and distribution protein features of NW are key important features aiding in the model's high performance.

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review.

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.

Graphene as Thinnest Coating on Copper Electrodes in Microbial Methanol Fuel Cells.

Dehydrogenation of methanol (CH3OH) into direct current (DC) in fuel cells can be a potential energy conversion technology. However, their development is currently hampered by the high cost of electrocatalysts based on platinum and palladium, slow kinetics, the formation of carbon monoxide intermediates, and the requirement for high temperatures. Here, we report the use of graphene layers (GL) for generating DC electricity from microbially driven methanol dehydrogenation on underlying copper (Cu) surfaces. Genetically tractable Rhodobacter sphaeroides 2.4.1 (Rsp), a nonarchetypical methylotroph, was used for dehydrogenating methanol at the GL-Cu surfaces. We use electrochemical methods, microscopy, and spectroscopy methods to assess the effects of GL on methanol dehydrogenation by Rsp cells. The GL-Cu offers a 5-fold higher power density and 4-fold higher current density compared to bare Cu. The GL lowers charge transfer resistance to methanol dehydrogenation by 4 orders of magnitude by mitigating issues related to pitting corrosion of underlying Cu surfaces. The presented approach for catalyst-free methanol dehydrogenation on copper electrodes can improve the overall sustainability of fuel cell technologies.

Artificial Intelligence-Driven Image Analysis of Bacterial Cells and Biofilms.

The current study explores an artificial intelligence framework for measuring the structural features from microscopy images of the bacterial biofilms. Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a model for sulfate reducing bacteria that are implicated in microbiologically influenced corrosion problems. Our goal is to automate the process of extracting the geometrical properties of the DA-G20 cells from the scanning electron microscopy (SEM) images, which is otherwise a laborious and costly process. These geometric properties are a biofilm phenotype that allow us to understand how the biofilm structurally adapts to the surface properties of the underlying metals, which can lead to better corrosion prevention solutions. We adapt two deep learning models: (a) a deep convolutional neural network (DCNN) model to achieve semantic segmentation of the cells, (d) a mask region-convolutional neural network (Mask R-CNN) model to achieve instance segmentation of the cells. These models are then integrated with moment invariants approach to measure the geometric characteristics of the segmented cells. Our numerical studies confirm that the Mask-RCNN and DCNN methods are 227x and 70x faster respectively, compared to the traditional method of manual identification and measurement of the cell geometric properties by the domain experts.

An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms.

Microbially induced corrosion (MIC) of metal surfaces caused by biofilms has wide-ranging consequences. Analysis of biofilm images to understand the distribution of morphological components in images such as microbial cells, MIC byproducts, and metal surfaces non-occluded by cells can provide insights into assessing the performance of coatings and developing new strategies for corrosion prevention. We present an automated approach based on self-supervised deep learning methods to analyze Scanning Electron Microscope (SEM) images and detect cells and MIC byproducts. The proposed approach develops models that can successfully detect cells, MIC byproducts, and non-occluded surface areas in SEM images with a high degree of accuracy using a low volume of data while requiring minimal expert manual effort for annotating images. We develop deep learning network pipelines involving both contrastive (Barlow Twins) and non-contrastive (MoCoV2) self-learning methods and generate models to classify image patches containing three labels-cells, MIC byproducts, and non-occluded surface areas. Our experimental results based on a dataset containing seven grayscale SEM images show that both Barlow Twin and MoCoV2 models outperform the state-of-the-art supervised learning models achieving prediction accuracy increases of approximately 8 and 6%, respectively. The self-supervised pipelines achieved this superior performance by requiring experts to annotate only ~10% of the input data. We also conducted a qualitative assessment of the proposed approach using experts and validated the classification outputs generated by the self-supervised models. This is perhaps the first attempt toward the application of self-supervised learning to classify biofilm image components and our results show that self-supervised learning methods are highly effective for this task while minimizing the expert annotation effort.

Sustainable valorization of asphaltenes via flash joule heating.

The refining process of petroleum crude oil generates asphaltenes, which poses complicated problems during the production of cleaner fuels. Following refining, asphaltenes are typically combusted for reuse as fuel or discarded into tailing ponds and landfills, leading to economic and environmental disruption. Here, we show that low-value asphaltenes can be converted into a high-value carbon allotrope, asphaltene-derived flash graphene (AFG), via the flash joule heating (FJH) process. After successful conversion, we develop nanocomposites by dispersing AFG into a polymer effectively, which have superior mechanical, thermal, and corrosion-resistant properties compared to the bare polymer. In addition, the life cycle and technoeconomic analysis show that the FJH process leads to reduced environmental impact compared to the traditional processing of asphaltene and lower production cost compared to other FJH precursors. Thus, our work suggests an alternative pathway to the existing asphaltene processing that directs toward a higher value stream while sequestering downstream emissions from the processing.

Molecular regulation of conditioning film formation and quorum quenching in sulfate reducing bacteria.

Sensing surface topography, an upsurge of signaling biomolecules, and upholding cellular homeostasis are the rate-limiting spatio-temporal events in microbial attachment and biofilm formation. Initially, a set of highly specialized proteins, viz. conditioning protein, directs the irreversible attachment of the microbes. Later signaling molecules, viz. autoinducer, take over the cellular communication phenomenon, resulting in a mature microbial biofilm. The mandatory release of conditioning proteins and autoinducers corroborated the existence of two independent mechanisms operating sequentially for biofilm development. However, both these mechanisms are significantly affected by the availability of the cofactor, e.g., Copper (Cu). Generally, the Cu concentration beyond threshold levels is detrimental to the anaerobes except for a few species of sulfate-reducing bacteria (SRB). Remarkably SRB has developed intricate ways to resist and thrive in the presence of Cu by activating numerous genes responsible for modifying the presence of more toxic Cu(I) to Cu(II) within the periplasm, followed by their export through the outer membrane. Therefore, the determinants of Cu toxicity, sequestration, and transportation are reconnoitered for their contribution towards microbial adaptations and biofilm formation. The mechanistic details revealing Cu as a quorum quencher (QQ) are provided in addition to the three pathways involved in the dissolution of cellular communications. This review articulates the Machine Learning based data curing and data processing for designing novel anti-biofilm peptides and for an in-depth understanding of QQ mechanisms. A pioneering data set has been mined and presented on the functional properties of the QQ homolog in Oleidesulfovibrio alaskensis G20 and residues regulating the multicopper oxidase properties in SRB.

Dispersion and Aggregation Fate of Individual and Co-Existing Metal Nanoparticles under Environmental Aqueous Suspension Conditions.

The use of diverse metal nanoparticles (MNPs) in a wide range of commercial products has led to their co-existence in the aqueous environment. The current study explores the dispersion and aggregation fate of five prominent MNPs (silver, copper, iron, nickel, and titanium), in both their individual and co-existing forms. We address a knowledge gap regarding their environmental fate under turbulent condition akin to flowing rivers. We present tandem analytical techniques based on dynamic light scattering, ultraviolet-visible spectroscopy, and inductively coupled plasma atomic emission spectroscopy for discerning their dispersion behavior under residence times of turbulence, ranging from 0.25 to 4 h. The MNPs displayed a multimodal trend for dispersion and aggregation behavior with suspension time in aqueous samples. The extent of dispersion was variable and depended upon intrinsic properties of MNPs. However, the co-existing MNPs displayed a dominant hetero-aggregation effect, independent of the residence times. Further research with use of real-world environmental samples can provide additional insights on the effects of sample chemistry on MNPs fate.

Photosynthetic microbial fuel cells for methanol treatment using graphene electrodes.

Photosynthetic microbial fuel cells (pMFC) represent a promising approach for treating methanol (CH3OH) wastewater. However, their use is constrained by a lack of knowledge on the extracellular electron transfer capabilities of photosynthetic methylotrophs, especially when coupled with metal electrodes. This study assessed the CH3OH oxidation capabilities of Rhodobacter sphaeroides 2.4.1 in two-compartment pMFCs. A 3D nickel (Ni) foam modified with plasma-grown graphene (Gr) was used as an anode, nitrate mineral salts media (NMS) supplemented with 0.1% CH3OH as anolyte, carbon brush as cathode, and 50 mM ferricyanide as catholyte. Two simultaneous pMFCs that used bare Ni foam and carbon felt served as controls. The Ni/Gr electrode registered a two-fold lower charge transfer resistance (0.005 kΩ cm2) and correspondingly 16-fold higher power density (141 mW/m2) compared to controls. The underlying reasons for the enhanced performance of R. sphaeroides at the graphene interface were discerned. The real-time polymerase chain reaction (PCR) analysis revealed the upregulation of cytochrome c oxidase, aa3 type, subunit I gene, and Flp pilus assembly protein genes in the sessile cells compared to their planktonic counterparts. The key EET pathways used for sustaining CH3OH oxidation were discussed.

Modeling graphene oxide transport and retention in biochar.

Experimental data from fixed-bed column studies and a numerical model based on convection-dispersion equations were used to describe transport and retention of Graphene Oxide (GO) in sand, biochar (BC), and BC modified with nanoscale zero-valent iron (BC-nZVI). Three blocking functions, namely no blocking, site-blocking, and depth-dependent blocking, were used to analyze GO transport and retention behavior in each media as a function of Ionic Strength (IS). An inverse modeling approach was implemented to determine the attachment coefficient (Ka) and maximum solid-phase retention capacity (Smax). The Langmuirian attachment model with site-blocking function effectively described experimental GO breakthrough curves (R2 ~ 0.70-0.99) compared to other models, indicating the importance of introducing a limit on the attachment capacity of the media. The Ka values for BC and BC-nZVI were significantly higher than sand, attributable to high porosity, roughness, and surface chemical properties. The models predicted an increasing trend in Ka (0.065 to 0.615 min-1) in BC with increasing IS (0.1 to 10 mM), while Ka values decreased (2.26 to 0.349 min-1) for BC-nZVI. A consistent increase in Smax was observed for both BC and BC-nZVI with increasing IS. Scenario analysis was conducted to further understand the effect of influent IS, GO concentration, and treatment depth. BC-nZVI exhibited a higher Ka and Smax and as a result, higher GO retention than BC at lower IS (0.1 and 1.0 mM). BC-nZVI had a relatively lower Ka (0.349 min-1) at 10 mM IS, however, it outperformed BC when GO retention capacities are compared over a longer period attributable to a higher Smax (6.47). Complete GO breakthrough occurred in a 5 cm media after 350 and 465 days for BC and BC-nZVI, respectively at 10 mM IS and influent concentration of 0.1 mg·L-1. GO breakthrough time increased with increasing treatment depth, however, the relation was non-linear.

A comprehensive review on the use of algal-bacterial systems for wastewater treatment with emphasis on nutrient and micropollutant removal.

The scarcity of water resources and environmental pollution have highlighted the need for sustainable wastewater treatment. Existing conventional treatment systems are energy-intensive and not always able to meet stringent disposal standards. Recently, algal-bacterial systems have emerged as environmentally friendly sustainable processes for wastewater treatment and resource recovery. The algal-bacterial systems work on the principle of the symbiotic relationship between algae and bacteria. This paper comprehensively discusses the most recent studies on algal-bacterial systems for wastewater treatment, factors affecting the treatment, and aspects of resource recovery from the biomass. The algal-bacterial interaction includes cell-to-cell communication, substrate exchange, and horizontal gene transfer. The quorum sensing (QS) molecules and their effects on algal-bacterial interactions are briefly discussed. The effect of the factors such as pH, temperature, C/N/P ratio, light intensity, and external aeration on the algal-bacterial systems have been discussed. An overview of the modeling aspects of algal-bacterial systems has been provided. The algal-bacterial systems have the potential for removing micropollutants because of the diverse possible interactions between algae-bacteria. The removal mechanisms of micropollutants - sorption, biodegradation, and photodegradation, have been reviewed. The harvesting methods and resource recovery aspects have been presented. The major challenges associated with algal-bacterial systems for real scale implementation and future perspectives have been discussed. Integrating wastewater treatment with the algal biorefinery concept reduces the overall waste component in a wastewater treatment system by converting the biomass into a useful product, resulting in a sustainable system that contributes to the circular bioeconomy.

Oxygen Reduction Reaction with Manganese Oxide Nanospheres in Microbial Fuel Cells.

Operating microbial fuel cells (MFCs) under extreme pH conditions offers a substantial benefit. Acidic conditions suppress the growth of undesirable methanogens and increase redox potential for oxygen reduction reactions (ORRs), and alkaline conditions increase the electrocatalytic activity. However, operating any fuel cells, including MFCs, is difficult under such extreme pH conditions. Here, we demonstrate a pH-universal ORR ink based on hollow nanospheres of manganese oxide (h-Mn3O4) anchored with multiwalled carbon nanotubes (MWCNTs) on planar and porous forms of carbon electrodes in MFCs (pH = 3-11). Nanospheres of h-Mn3O4 (diameter ∼ 31 nm, shell thickness ∼ 7 nm) on a glassy carbon electrode yielded a highly reproducible ORR activity at pH 3 and 10, based on rotating disk electrode (RDE) tests. A phenomenal ORR performance and long-term stability (∼106 days) of the ink were also observed with four different porous cathodes (carbon cloth, carbon nanofoam paper, reticulated vitreous carbon, and graphite felt) in MFCs. The ink reduced the charge transfer resistance (R ct) to the ORR by 100-fold and 45-fold under the alkaline and acidic conditions, respectively. The current study promotes ORR activity and subsequently the MFC operations under a wide range of pH conditions, including acidic and basic conditions.

Wind Turbine Blades Using Recycled Carbon Fibers: An Environmental Assessment.

Polymers reinforced with virgin carbon fibers (VCF) are being used to make spar caps of wind turbine (WT) blades and polymers with glass fibers (GF) to make skins of the blade components. Here, we assess the life cycle environmental performance of the hybrid blades with spar caps based on VCF and the shells and shear webs based on RCF (recycled CF) composites (RCF-hybrid). The production of the WT blades and associated reinforced polymers is assumed to occur in Sweden, with their uses and end-of-life management in the European region. The functional unit is equivalent to three blades in an offshore WT with the market incumbent blades solely based on the GF composite or the hybrid option. The RCF-hybrid blades offer 12-89% better environmental performance in nine out of 10 impact categories and 6-26% better in six out of 10 impact categories. The RCF-hybrid blades exhibit optimum environmental performance when the VCF manufacturing facilities are equipped with pollution abatement systems including regenerative thermal oxidizers to reduce ammonia and hydrogen cyanide emissions; spar caps are made using VCF epoxy composites through pultrusion and resin infusion molding, and the blade scrap is mechanically recycled at the end of life. The energy and carbon payback times for the RCF-hybrid blades were found to be 5-13% lower than those of the market incumbents.

Enhanced biohydrogen production with low graphene oxide content using thermophilic bioreactors.

Modern society envisions hydrogen (H2) fuel to drive the transportation, industrial, and domestic sectors. Here, we explore use of graphene oxide nanoparticles (GO NPs) for greatly enhancing bio-H2 production by a consortium based on Thermoanaerobacterium thermosaccharolyticum spp. Thermophilic batch bioreactors were set up at 60 OC and initial pH of 8.5 to assess the effects of GO NPs supplements on biohydrogen production. Under optimal GO NPs loading of 10 mg/L, the supplemented system yielded âˆ¼ 300% higher H2 yield (6.78 mol H2/mol sucrose) than control. Such an optimized system offered 73% H2 purity and 85% conversion efficiency by promoted the desirable acetate fermentation pathway. Miseq Illumina sequencing tests revealed that the optimal levels of GO NPs did not alter the microbial composition of consortium.

Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion.

Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings.

Gene Sets and Mechanisms of Sulfate-Reducing Bacteria Biofilm Formation and Quorum Sensing With Impact on Corrosion.

Sulfate-reducing bacteria (SRB) have a unique ability to respire under anaerobic conditions using sulfate as a terminal electron acceptor, reducing it to hydrogen sulfide. SRB thrives in many natural environments (freshwater sediments and salty marshes), deep subsurface environments (oil wells and hydrothermal vents), and processing facilities in an industrial setting. Owing to their ability to alter the physicochemical properties of underlying metals, SRB can induce fouling, corrosion, and pipeline clogging challenges. Indigenous SRB causes oil souring and associated product loss and, subsequently, the abandonment of impacted oil wells. The sessile cells in biofilms are 1,000 times more resistant to biocides and induce 100-fold greater corrosion than their planktonic counterparts. To effectively combat the challenges posed by SRB, it is essential to understand their molecular mechanisms of biofilm formation and corrosion. Here, we examine the critical genes involved in biofilm formation and microbiologically influenced corrosion and categorize them into various functional categories. The current effort also discusses chemical and biological methods for controlling the SRB biofilms. Finally, we highlight the importance of surface engineering approaches for controlling biofilm formation on underlying metal surfaces.