Modeling microbial growth and dynamics

A Discretized Overlap Resolution Algorithm (DORA) for resolving spatial overlaps in individual-based models of microbes

Individual-based modeling (IbM) is an instrumental tool for simulating spatial microbial growth, with applications in both microbial ecology and biochemical engineering. Unlike Cellular Automata (CA), which use a fixed grid of cells with predefined rules for interactions, IbMs model the individual behaviors of cells, allowing complex population dynamics to emerge. IbMs require more detailed modeling of individual interactions, which introduces significant computational challenges, particularly in resolving spatial overlaps between cells. Traditionally, this is managed using arrays or kd-trees, which require numerous pairwise comparisons and become inefficient as population size increases. To address this bottleneck, we introduce the Discretized Overlap Resolution Algorithm (DORA), which employs a grid-based framework to efficiently manage overlaps. By discretizing the simulation space further and assigning circular cells to specific grid units, DORA transforms the computationally intensive pairwise comparison process into a more efficient grid-based operation. This approach significantly reduces the computational load, particularly in simulations with large cell populations. Our evaluation of DORA, through simulations of microbial colonies and biofilms under varied nutrient conditions, demonstrates its superior computational efficiency and ability to accurately capture microbial growth dynamics compared to conventional methods. DORA's grid-based strategy enables the modeling of densely populated microbial communities within practical computational timeframes, thereby expanding the scope and applicability of individual-based modeling.

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Turbidimetric flow analysis system for the investigation of microbial growth

The monitoring of life of microbial populations is of the uttermost importance in environmental and food analysis, agriculture, as well as in medicine. The duration of bacteria adaptation to new environmental conditions, its lifetime and the divisions' pace are the key information in many studies. It was found that the fully-mechanized flow analysis system based on solenoid valves and pumps, paired with a dedicated flow-through optoelectronic detector can be successfully applied for monitoring of bacteria growth. The applicability of the designed multicommutated flow analysis (MCFA) system was proved by analysis of solutions containing bacteria cells proceeded by tests of McFarland (McF) standards. The developed setup allowed modelling and simulation of microbial growth, as well as monitoring of the bacteria growth in real-time manner to be carried out. The monitor is useful for the quantitative estimation of the basic parameters of bacteria population like its size, the rate of bacteria multiplication, as well as the times of lag, log and stationary phases of microbial growth.

Agent-Based Modeling of Microbial Communities

Microbial communities are complex living systems that populate the planet with diverse functions and are increasingly harnessed for practical human needs. To deepen the fundamental understanding of their organization and functioning as well as to facilitate their engineering for applications, mathematical modeling has played an increasingly important role. Agent-based models represent a class of powerful quantitative frameworks for investigating microbial communities because of their individualistic nature in describing cells, mechanistic characterization of molecular and cellular processes, and intrinsic ability to produce emergent system properties. This review presents a comprehensive overview of recent advances in agent-based modeling of microbial communities. It surveys the state-of-the-art algorithms employed to simulate intracellular biomolecular events, single-cell behaviors, intercellular interactions, and interactions between cells and their environments that collectively serve as the driving forces of community behaviors. It also highlights three lines of applications of agent-based modeling, namely, the elucidation of microbial range expansion and colony ecology, the design of synthetic gene circuits and microbial populations for desired behaviors, and the characterization of biofilm formation and dispersal. The review concludes with a discussion of existing challenges, including the computational cost of the modeling, and potential mitigation strategies.

Individual-Based Modeling of Spatial Dynamics of Chemotactic Microbial Populations

One important direction of synthetic biology is to establish desired spatial structures from microbial populations. Underlying this structural development process are different driving factors, among which bacterial motility and chemotaxis serve as a major force. Here, we present an individual-based, biophysical computational framework for mechanistic and multiscale simulation of the spatiotemporal dynamics of motile and chemotactic microbial populations. The framework integrates cellular movement with spatial population growth, mechanical and chemical cellular interactions, and intracellular molecular kinetics. It is validated by a statistical comparison of single-cell chemotaxis simulations with reported experiments. The framework successfully captures colony range expansion of growing isogenic populations and also reveals chemotaxis-modulated, spatial patterns of a two-species amensal community. Partial differential equation-based models subsequently validate these simulation findings. This study provides a versatile computational tool to uncover the fundamentals of microbial spatial ecology as well as to facilitate the design of synthetic consortia for desired spatial patterns.

Density-dependent effects are the main determinants of variation in growth dynamics between closely related bacterial strains

Although closely related, bacterial strains from the same species show significant diversity in their growth and death dynamics. Yet, our understanding of the relationship between the kinetic parameters that dictate these dynamics is still lacking. Here, we measured the growth and death dynamics of 11 strains of Escherichia coli originating from different hosts and show that the growth patterns are clustered into three major classes with typical growth rates, maximal fold change, and death rates. To infer the underlying phenotypic parameters that govern the dynamics, we developed a phenomenological mathematical model that accounts not only for growth rate and its dependence on resource availability, but also for death rates and density-dependent growth inhibition. We show that density-dependent growth is essential for capturing the variability in growth dynamics between the strains. Indeed, the main parameter determining the dynamics is the typical density at which they slow down their growth, rather than the maximal growth rate or death rate. Moreover, we show that the phenotypic landscape resides within a two-dimensional plane spanned by resource utilization efficiency, death rate, and density-dependent growth inhibition. In this phenotypic plane, we identify three clusters that correspond to the growth pattern classes. Overall, our results reveal the tradeoffs between growth parameters that constrain bacterial adaptation.

Investigating the Ability of Growth Models to Predict In Situ Vibrio spp. Abundances

Vibrio spp. have an important role in biogeochemical cycles; some species are disease agents for aquatic animals and/or humans. Predicting population dynamics of Vibrio spp. in natural environments is crucial to predicting how the future conditions will affect the dynamics of these bacteria. The majority of existing Vibrio spp. population growth models were developed in controlled environments, and their applicability to natural environments is unknown. We collected all available functional models from the literature, and distilled them into 28 variants using unified nomenclature. Next, we assessed their ability to predict Vibrio spp. abundance using two new and five already published longitudinal datasets on Vibrio abundance in four different habitat types. Results demonstrate that, while the models were able to predict Vibrio spp. abundance to an extent, the predictions were not reliable. Models often underperformed, especially in environments under significant anthropogenic influence such as aquaculture and urban coastal habitats. We discuss implications and limitations of our analysis, and suggest research priorities; in particular, we advocate for measuring and modeling organic matter.

Application of machine learning to the monitoring and prediction of food safety: A review

Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.

Bioanalytical insight into the life of microbial populations: A chemical monitoring of ureolytic bacteria growth

In this publication an alternative approach to investigations of bacterial growth is proposed. Contrary to the conventional physical methods it is based on enzyme activity detection. The procedure for real-time and on-line monitoring of microbial ureolytic activity (applied as a model experimental biosystem) in the flow analysis format is presented. The developed fully-mechanized bioanalytical flow system is composed of solenoid micropumps and microvalves actuated by Arduino microcontroller. The photometric detection based on Nessler reaction is performed using dedicated flow-through optoelectronic detector made of paired light emitting diodes. The developed bioanalytical system allows discrete assaying of microbial urease in the wide range of activity up to 5.4 U mL^-1 with detection limit below 0.44 U mL^-1, a high sensitivity in the linear range of response (up to 200 mV U^-1 mL and relatively high throughput (9 detection per hour). The proposed differential procedure of measurements (i.e. a difference between peaks register for sample with and without external addition of urea is treated as an analytical signal) allows elimination of interfering effects from substrate and products of biocatalysed reaction as well as other components of medium used for microbial growth. The developed bioanalytical system was successfully applied for the control of growth of urease-positive bacteria strains (Proteus vulgaris, Klebsiella pneumoniae and Paracoccus yeei) including examination of effects from various microbial cultivation conditions like temperature, composition of culture medium and amount of substrate required for induction of bacterial enzymatic activity. The developed bioanalytical flow system can be applied for metabolic activity-based estimation of parameters of lag and log phases of microbial growth as well as for detection of decline phase.

Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism

Systems biology approaches are extensively used to model and reverse-engineer gene regulatory networks from experimental data. Indoleamine 2,3-dioxygenases (IDOs)-belonging in the heme dioxygenase family-degrade l-tryptophan to kynurenines. These enzymes are also responsible for the de novo synthesis of nicotinamide adenine dinucleotide (NAD+). As such, they are expressed by a variety of species, including fungi. Interestingly, Aspergillus may degrade l-tryptophan not only via IDO but also via alternative pathways. Deciphering the molecular interactions regulating tryptophan metabolism is particularly critical for novel drug target discovery designed to control pathogen determinants in invasive infections. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling l-tryptophan metabolism. The method unravels a possible novel approach to target fungal virulence factors during infection. Furthermore, this study represents the first application of continuous-time Bayesian networks as a gene network reconstruction method in Aspergillus metabolism. The experiment showed that the applied computational approach may improve the understanding of metabolic networks over traditional pathways.

A Predictive Model for Survival of Escherichia coli O157:H7 and Generic E. coli in Soil Amended with Untreated Animal Manure

This study aimed at developing a predictive model that captures the influences of a variety of agricultural and environmental variables and is able to predict the concentrations of enteric bacteria in soil amended with untreated Biological Soil Amendments of Animal Origin (BSAAO) under dynamic conditions. We developed and validated a Random Forest model using data from a longitudinal field study conducted in mid-Atlantic United States investigating the survival of Escherichia coli O157:H7 and generic E. coli in soils amended with untreated dairy manure, horse manure, or poultry litter. Amendment type, days of rain since the previous sampling day, and soil moisture content were identified as the most influential agricultural and environmental variables impacting concentrations of viable E. coli O157:H7 and generic E. coli recovered from amended soils. Our model results also indicated that E. coli O157:H7 and generic E. coli declined at similar rates in amended soils under dynamic field conditions.The Random Forest model accurately predicted changes in viable E. coli concentrations over time under different agricultural and environmental conditions. Our model also accurately characterized the variability of E. coli concentration in amended soil over time by providing upper and lower prediction bound estimates. Cross-validation results indicated that our model can be potentially generalized to other geographic regions and incorporated into a risk assessment for evaluating the risks associated with application of untreated BSAAO. Our model can be validated for other regions and predictive performance also can be enhanced when data sets from additional geographic regions become available.

Dynamics of bacterial population growth in biofilms resemble spatial and structural aspects of urbanization

Biofilms develop from bacteria bound on surfaces that grow into structured communities (microcolonies). Although surface topography is known to affect bacterial colonization, how multiple individual settlers develop into microcolonies simultaneously remains underexplored. Here, we use multiscale population-growth and 3D-morphometric analyses to assess the spatiotemporal development of hundreds of bacterial colonizers towards submillimeter-scale microcolony communities. Using an oral bacterium (Streptococcus mutans), we find that microbial cells settle on the surface randomly under sucrose-rich conditions, regardless of surface topography. However, only a subset of colonizers display clustering behavior and growth following a power law. These active colonizers expand three-dimensionally by amalgamating neighboring bacteria into densely populated microcolonies. Clustering and microcolony assembly are dependent on exopolysaccharides, while population growth dynamics and spatial structure are affected by cooperative or antagonistic microbes. Our work suggests that biofilm assembly resembles certain spatial-structural features of urbanization, where population growth and expansion can be influenced by type of settlers, neighboring cells, and further community merging and scaffolding occurring at various scales.

Strain Variability of Listeria monocytogenes under NaCl Stress Elucidated by a High-Throughput Microbial Growth Data Assembly and Analysis Protocol

Listeria monocytogenes causes the severe foodborne illness listeriosis and survives in food-associated environments due to its high stress tolerance. A data assembly and analysis protocol for microbial growth experiments was compiled to elucidate the strain variability of L. monocytogenes stress tolerance. The protocol includes measurement of growth ability under stress (step 1), selection of a suitable method for growth parameter calculation (step 2), comparison of growth patterns between strains (step 3), and biological interpretation of the discovered differences (step 4). In step 1, L. monocytogenes strains (n = 388) of various serovars and origins grown on media with 9.0% NaCl were measured using a Bioscreen C microbiology reader. Technical variability of the growth measurements was assessed and eliminated. In step 2, the growth parameters determined by Gompertz, modified-Gompertz, logistic, and Richards models and model-free splines were compared, illustrating differences in the suitability of these methods to describe the experimental data. In step 3, hierarchical clustering was used to describe the NaCl tolerance of L. monocytogenes measured by strain-specific variation in growth ability; tolerant strains had higher growth rates and maximum optical densities and shorter lag phases than susceptible strains. The spline parameter area under the curve best classified "poor," "average," and "good" growers. In step 4, the tested L. monocytogenes lineage I strains (serovars 4b and 1/2b) proved to be significantly more tolerant toward 9.0% NaCl than lineage II strains (serovars 1/2a, 1/2c, and 3a). Our protocol provides systematic tools to gain comparable data for investigating strain-specific variation of bacterial growth under stress.IMPORTANCE The pathogen Listeria monocytogenes causes the foodborne disease listeriosis, which can be fatal in immunocompromised individuals. L. monocytogenes tolerates several environmental stressors and can persist in food-processing environments and grow in foodstuffs despite traditional control measures such as high salt content. Nonetheless, L. monocytogenes strains differ in their ability to withstand stressors. Elucidating the intraspecies strain variability of L. monocytogenes stress tolerance is crucial for the identification of particularly tolerant strains. To enhance reliable identification of variability in bacterial stress tolerance phenotypes, we compiled a large-scale protocol for the entire data assembly and analysis of microbial growth experiments, providing a systematic approach and checklist for experiments on strain-specific growth ability. Our study illustrated the diversity and strain-specific variation of L. monocytogenes stress tolerance with an unprecedented scope and discovered biologically relevant serovar- and lineage-dependent phenotypes of NaCl tolerance.

Guiding Mineralization Co-Culture Discovery Using Bayesian Optimization

Many disciplines rely on testing combinations of compounds, materials, proteins, or bacterial species to drive scientific discovery. It is time-consuming and expensive to determine experimentally, via trial-and-error or random selection approaches, which of the many possible combinations will lead to desirable outcomes. Hence, there is a pressing need for more rational and efficient experimental design approaches to reduce experimental effort. In this work, we demonstrate the potential of machine learning methods for the in silico selection of promising co-culture combinations in the application of bioaugmentation. We use the example of pollutant removal in drinking water treatment plants, which can be achieved using co-cultures of a specialized pollutant degrader with combinations of bacterial isolates. To reduce the experimental effort needed to discover high-performing combinations, we propose a data-driven experimental design. Based on a dataset of mineralization performance for all pairs of 13 bacterial species co-cultured with MSH1, we built a Gaussian process regression model to predict the Gompertz mineralization parameters of the co-cultures of two and three species, based on the single-strain parameters. We subsequently used this model in a Bayesian optimization scheme to suggest potentially high-performing combinations of bacteria. We achieved good performance with this approach, both for predicting mineralization parameters and for selecting effective co-cultures, despite the limited dataset. As a novel application of Bayesian optimization in bioremediation, this experimental design approach has promising applications for highlighting co-culture combinations for in vitro testing in various settings, to lessen the experimental burden and perform more targeted screenings.

Effect of low doses of biocides on the antimicrobial resistance and the biofilms of Cronobacter sakazakii and Yersinia enterocolitica

The susceptibility of Cronobacter sakazakii ATCC 29544 (CS) and Yersinia enterocolitica ATCC 9610 (YE) to sodium hypochlorite (10% of active chlorine; SHY), peracetic acid (39% solution of peracetic acid in acetic acid; PAA) and benzalkonium chloride (BZK) was tested. Minimum inhibitory concentration (MIC) values (planktonic cells; microdilution broth method) of 3,800 ppm (SHY), 1,200 ppm (PAA) and 15 ppm (BZK) for CS, and 2,500 ppm (SHY), 1,275 ppm (PAA) and 20 ppm (BZK) for YE, were found. In some instances, an increase in growth rate was observed in presence of sub-MICs (0.25MIC, 0.50MIC or 0.75MIC) of biocides relative to the samples without biocides. The cultures exhibited an acquired tolerance to biocides and an increase in antibiotic resistance after exposure to sub-MICs of such disinfectants. Strains were able to form strong biofilms on polystyrene after 48 hours (confocal laser scanning microscopy), with average biovolumes in the observation field (14,161 µm2) of 242,201.0 ± 86,570.9 µm3 (CS) and 190,184.5 ± 40,860.3 µm3 (YE). Treatment of biofilms for 10 minutes with disinfectants at 1MIC or 2MIC reduced the biovolume of live cells. PAA (YE) and BZK (CS and YE) at 1MIC did not alter the percentage of dead cells relative to non-exposed biofilms, and their effect of countering biofilm was due principally to the detachment of cells. These results suggest that doses of PAA and BZK close to MICs might lead to the dissemination of live bacteria from biofilms with consequent hazards for public health.

Survival of Microorganisms on Filtering Respiratory Protective Devices Used at Agricultural Facilities

Bioaerosol is a threat at workplaces, therefore the selection and safe use of filtering facepiece respirators (FFRs) is important in preventive activities. The aim of the study was to assess the survival of microorganisms on materials used for FFRs construction. The parameters for microorganism growth under model conditions were described using the Gompertz equation, model verification was also carried out using FFRs at the farmers' workplaces. We found that the factors determining a high survival of microorganisms were as follows: moisture corresponding to the conditions of use and storage of FFRs at workplaces, the presence of sweat and organic dust; inorganic dust and addition of biocide in nonwovens limited the growth of microorganisms, resulting in a shortening of the stationary growth phase and decreased cell numbers (5-6 log). Dust concentration at workplaces was higher than EU occupational exposure limit values and WHO recommendations for airborne particulate matter. Microbial contaminations of the air (103-104 CFU/m3), settled dust (104-106 CFU/g) and FFRs (105 CFU/4cm2) during the grain harvest were high, the main contamination being bacteria (actinomycetes, Pseudomonas fluorescens) and xerophilic fungi. A high correlation was found between the number of microorganisms and the weight of dust on FFRs (R2 = 0.93-0.96).

The potential of quantitative models to improve microalgae photosynthetic efficiency

The massive increase in carbon dioxide concentration in the atmosphere driven by human activities is causing huge negative consequences and new sustainable sources of energy, food and materials are highly needed. Algae are unicellular photosynthetic microorganisms that can provide a highly strategic contribution to this challenge as alternative source of biomass to complement crops cultivation. Algae industrial cultures are commonly limited by light availability, and biomass accumulation is strongly dependent on their photon-to-biomass conversion efficiency. Investigation of algae photosynthetic metabolism is thus strategic for the generation of more efficient strains with higher productivity. Algae are cultivated at industrial scale in conditions highly different from the natural niches they adapted to and strains development efforts must fully consider the seminal influence on productivity of regulatory mechanism of photosynthesis as well as of cultivation parameters like cells concentration, light distribution in the culture, mixing, nutrients and carbon dioxide availability. In this review we will focus in particular on how mathematical models can account for the complex influence of all environmental parameters and can be exploited for development of improved algae strains.

A Multiscale Agent-Based Model for the Investigation of E. coli K12 Metabolic Response During Biofilm Formation

Bacterial biofilm formation is an organized collective response to biochemical cues that enables bacterial colonies to persist and withstand environmental insults. We developed a multiscale agent-based model that characterizes the intracellular, extracellular, and cellular scale interactions that modulate Escherichia coli MG1655 biofilm formation. Each bacterium's intracellular response and cellular state were represented as an outcome of interactions with the environment and neighboring bacteria. In the intracellular model, environment-driven gene expression and metabolism were captured using statistical regression and Michaelis-Menten kinetics, respectively. In the cellular model, growth, death, and type IV pili- and flagella-dependent movement were based on the bacteria's intracellular state. We implemented the extracellular model as a three-dimensional diffusion model used to describe glucose, oxygen, and autoinducer 2 gradients within the biofilm and bulk fluid. We validated the model by comparing simulation results to empirical quantitative biofilm profiles, gene expression, and metabolic concentrations. Using the model, we characterized and compared the temporal metabolic and gene expression profiles of sessile versus planktonic bacterial populations during biofilm formation and investigated correlations between gene expression and biofilm-associated metabolites and cellular scale phenotypes. Based on our in silico studies, planktonic bacteria had higher metabolite concentrations in the glycolysis and citric acid cycle pathways, with higher gene expression levels in flagella and lipopolysaccharide-associated genes. Conversely, sessile bacteria had higher metabolite concentrations in the autoinducer 2 pathway, with type IV pili, autoinducer 2 export, and cellular respiration genes upregulated in comparison with planktonic bacteria. Having demonstrated results consistent with in vitro static culture biofilm systems, our model enables examination of molecular phenomena within biofilms that are experimentally inaccessible and provides a framework for future exploration of how hypothesized molecular mechanisms impact bulk community behavior.

Spatiotemporal disturbance characteristics determine functional stability and collapse risk of simulated microbial ecosystems

Terrestrial microbial ecosystems are exposed to many types of disturbances varying in their spatial and temporal characteristics. The ability to cope with these disturbances is crucial for maintaining microbial ecosystem functions, especially if disturbances recur regularly. Thus, understanding microbial ecosystem dynamics under recurrent disturbances and identifying drivers of functional stability and thresholds for functional collapse is important. Using a spatially explicit ecological model of bacterial growth, dispersal, and substrate consumption, we simulated spatially heterogeneous recurrent disturbances and investigated the dynamic response of pollutant biodegradation – exemplarily for an important ecosystem function. We found that thresholds for functional collapse are controlled by the combination of disturbance frequency and spatial configuration (spatiotemporal disturbance regime). For rare disturbances, the occurrence of functional collapse is promoted by low spatial disturbance fragmentation. For frequent disturbances, functional collapse is almost inevitable. Moreover, the relevance of bacterial growth and dispersal for functional stability also depends on the spatiotemporal disturbance regime. Under disturbance regimes with moderate severity, microbial properties can strongly affect functional stability and shift the threshold for functional collapse. Similarly, networks facilitating bacterial dispersal can delay functional collapse. Consequently, measures to enhance or sustain bacterial growth/dispersal are promising strategies to prevent functional collapses under moderate disturbance regimes.

Functional Resistance to Recurrent Spatially Heterogeneous Disturbances Is Facilitated by Increased Activity of Surviving Bacteria in a Virtual Ecosystem

Bacterial degradation of organic compounds is an important ecosystem function with relevance to, e.g., the cycling of elements or the degradation of organic contaminants. It remains an open question, however, to which extent ecosystems are able to maintain such biodegradation function under recurrent disturbances (functional resistance) and how this is related to the bacterial biomass abundance. In this paper, we use a numerical simulation approach to systematically analyze the dynamic response of a microbial population to recurrent disturbances of different spatial distribution. The spatially explicit model considers microbial degradation, growth, dispersal, and spatial networks that facilitate bacterial dispersal mimicking effects of mycelial networks in nature. We find: (i) There is a certain capacity for high resistance of biodegradation performance to recurrent disturbances. (ii) If this resistance capacity is exceeded, spatial zones of different biodegradation performance develop, ranging from no or reduced to even increased performance. (iii) Bacterial biomass and biodegradation dynamics respond inversely to the spatial fragmentation of disturbances: overall biodegradation performance improves with increasing fragmentation, but bacterial biomass declines. (iv) Bacterial dispersal networks can enhance functional resistance against recurrent disturbances, mainly by reactivating zones in the core of disturbed areas, even though this leads to an overall reduction of bacterial biomass.

State of the art on granular sludge by using bibliometric analysis

With rapid industrialization and urbanization in the nineteenth century, the activated sludge process (ASP) has experienced significant steps forward in the face of greater awareness of and sensitivity toward water-related environmental problems. Compared with conventional flocculent ASP, the major advantages of granular sludge are characterized by space saving and resource recovery, where the methane and hydrogen recovery in anaerobic granular and 50% more space saving, 30-50% of energy consumption reduction, 75% of footprint cutting, and even alginate recovery in aerobic granular. Numerous engineers and scientists have made great efforts to explore the superiority over the last 40 years. Therefore, a bibliometric analysis was desired to trace the global trends of granular sludge research from 1992 to 2016 indexed in the SCI-EXPANDED. Articles were published in 276 journals across 44 subject categories spanning 1420 institutes across 68 countries. Bioresource Technology (293, 11.9%), Water Research (235, 9.6%), and Applied Microbiology and Biotechnology (127, 5.2%) dominated in top three journals. The Engineering (991, 40.3%), China (906, 36.9%), and Harbin Inst Technol, China (114, 4.6%) were the most productive subject category, country, and institution, respectively. The hotspot is the emerging techniques depended on granular reactors in response to the desired removal requirements and bio-energy production (primarily in anaerobic granular sludge). In view of advanced and novel bio-analytical methods, the characteristics, functions, and mechanisms for microbial granular were further revealed in improving and innovating the granulation techniques. Therefore, a promising technique armed with strengthened treatment efficiency and efficient resource and bio-energy recovery can be achieved.

Modeling marine oily wastewater treatment by a probabilistic agent-based approach

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization.

A System Dynamics Model to Predict the Human Monocyte Response to Endotoxins

System dynamics is a powerful tool that allows modeling of complex and highly networked systems such as those found in the human immune system. We have developed a model that reproduces how the exposure of human monocytes to lipopolysaccharides (LPSs) induces an inflammatory state characterized by high production of tumor necrosis factor alpha (TNFα), which is rapidly modulated to enter into a tolerant state, known as endotoxin tolerance (ET). The model contains two subsystems with a total of six states, seven flows, two auxiliary variables, and 14 parameters that interact through six differential and nine algebraic equations. The parameters were estimated and optimized to obtain a model that fits the experimental data obtained from human monocytes treated with various LPS doses. In contrast to publications on other animal models, stimulation of human monocytes with super-low-dose LPSs did not alter the response to a second LPSs challenge, neither inducing ET, nor enhancing the inflammatory response. Moreover, the model confirms the low production of TNFα and increased levels of C-C motif ligand 2 when monocytes exhibit a tolerant state similar to that of patients with sepsis. At present, the model can help us better understand the ET response and might offer new insights on sepsis diagnostics and prognosis by examining the monocyte response to endotoxins in patients with sepsis.

Developmental Design of Synthetic Bacterial Architectures by Morphogenetic Engineering

Synthetic biology is an emerging scientific field that promotes the standardized manufacturing of biological components without natural equivalents. Its goal is to create artificial living systems that can meet various needs in health care or energy domains. While most works are focused on the individual bacterium as a chemical reactor, our project, SynBioTIC, addresses a novel and more complex challenge: shape engineering; that is, the redesign of natural morphogenesis toward a new kind of developmental 3D printing. Potential applications include organ growth, natural computing in biocircuits, or future vegetal houses. To create in silico multicellular organisms that exhibit specific shapes, we construe their development as an iterative process combining fundamental collective phenomena such as homeostasis, patterning, segmentation, and limb growth. Our numerical experiments rely on the existing Escherichia coli simulator Gro, a physicochemical computation platform offering reaction-diffusion and collision dynamics solvers. The synthetic bioware of our model executes a set of rules, or genome, in each cell. Cells can differentiate into several predefined types associated with specific actions (divide, emit signal, detect signal, die). Transitions between types are triggered by conditions involving internal and external sensors that detect various protein levels inside and around the cell. Indirect communication between bacteria is relayed by morphogen diffusion and the mechanical constraints of 2D packing. Starting from a single bacterium, the overall architecture emerges in a purely endogenous fashion through a series of developmental stages, inlcuding proliferation, differentiation, morphogen diffusion, and synchronization. The genome can be parametrized to control the growth and features of appendages individually. As exemplified by the L and T shapes that we obtain, certain precursor cells can be inhibited while others can create limbs of varying size (divergence of the homology). Such morphogenetic phenotypes open the way to more complex shapes made of a recursive array of core bodies and limbs and, most importantly, to an evolutionary developmental exploration of unplanned functional forms.