Cooperation between autotrophic and heterotrophic denitrifiers under low C/N ratios revealed by individual-based modelling.

Denitrifying biofilms, in which autotrophic denitrifiers (AD) and heterotrophic denitrifiers (HD) coexist, play a crucial role in removing nitrate from water or wastewater. However, it is difficult to elucidate the interactions between HD and AD through sequencing-based experimental methods. Here, we developed an individual-based model to describe the interspecies dynamics and priority effects between sulfur-based AD (Thiobacillus denitrificans) and HD (Thauera phenylcarboxya) under different C/N ratios. In test I (coexistence simulation), AD and HD were initially inoculated at a ratio of 1:1. The simulation results showed excellent denitrification performance and a coaggregation pattern of denitrifiers, indicating that cooperation was the predominant interaction at a C/N ratio of 0.25 to 1.5. In test II (invasion simulation), in which only one type of denitrifier was initially inoculated and the other was added at the invasion time, denitrifiers exhibited a stratification pattern in biofilms. When HD invaded AD, the final HD abundance decreased with increasing invasion time, indicating an enhanced priority effect. When AD invaded HD, insufficient organic carbon sources weakened the priority effect by limiting the growth of HD populations. This study reveals the interaction between autotrophic and heterotrophic denitrifiers, providing guidance for optimizing wastewater treatment process.

Predicting partner fitness based on spatial structuring in a light-driven microbial community.

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology (e.g., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.

Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis.

Despite increased understanding about psoriasis pathophysiology, currently there is a lack of predictive computational models. We developed a personalisable ordinary differential equations model of human epidermis and psoriasis that incorporates immune cells and cytokine stimuli to regulate the transition between two stable steady states of clinically healthy (non-lesional) and disease (lesional psoriasis, plaque) skin. In line with experimental data, an immune stimulus initiated transition from healthy skin to psoriasis and apoptosis of immune and epidermal cells induced by UVB phototherapy returned the epidermis back to the healthy state. Notably, our model was able to distinguish disease flares. The flexibility of our model permitted the development of a patient-specific "UVB sensitivity" parameter that reflected subject-specific sensitivity to apoptosis and enabled simulation of individual patients' clinical response trajectory. In a prospective clinical study of 94 patients, serial individual UVB doses and clinical response (Psoriasis Area Severity Index) values collected over the first three weeks of UVB therapy informed estimation of the "UVB sensitivity" parameter and the prediction of individual patient outcome at the end of phototherapy. An important advance of our model is its potential for direct clinical application through early assessment of response to UVB therapy, and for individualised optimisation of phototherapy regimes to improve clinical outcome. Additionally by incorporating the complex interaction of immune cells and epidermal keratinocytes, our model provides a basis to study and predict outcomes to biologic therapies in psoriasis.

Coupled CFD-DEM modeling to predict how EPS affects bacterial biofilm deformation, recovery and detachment under flow conditions.

The deformation and detachment of bacterial biofilm are related to the structural and mechanical properties of the biofilm itself. Extracellular polymeric substances (EPS) play an important role on keeping the mechanical stability of biofilms. The understanding of biofilm mechanics and detachment can help to reveal biofilm survival mechanisms under fluid shear and provide insight about what flows might be needed to remove biofilm in a cleaning cycle or for a ship to remove biofilms. However, how the EPS may affect biofilm mechanics and its deformation in flow conditions remains elusive. To address this, a coupled computational fluid dynamic- discrete element method (CFD-DEM) model was developed. The mechanisms of biofilm detachment, such as erosion and sloughing have been revealed by imposing hydrodynamic fluid flow at different velocities and loading rates. The model, which also allows adjustment of the proportion of different functional groups of microorganisms in the biofilm, enables the study of the contribution of EPS toward biofilm resistance to fluid shear stress. Furthermore, the stress-strain curves during biofilm deformation have been captured by loading and unloading fluid shear stress to study the viscoelastic properties of the biofilm. Our predicted emergent viscoelastic properties of biofilms were consistent with relevant experimental measurements.

CFD-DEM modelling of biofilm streamer oscillations and their cohesive failure in fluid flow.

Biofilm streamer motion under different flow conditions is important for a wide range of industries. The existing work has largely focused on experimental characterisations of these streamers, which is often time-consuming and expensive. To better understand the physics of biofilm streamer oscillation and their interactions in fluid flow, a computational fluid dynamics-discrete element method model has been developed. The model was used to study the flow-induced oscillations and cohesive failure of single and multiple biofilm streamers. We have studied the effect of streamer length on the oscillation at varied flow rates. The predicted single biofilm streamer oscillations in various flow rates agreed well with experimental measurements. We have also investigated the effect of the spatial arrangement of streamers on interactions between two oscillating streamers in parallel and tandem arrangements. Furthermore, cohesive failure of streamers was studied in an accelerating fluid flow, which is important for slowing down biofilm-induced clogging.

Silicosis in finishing workers in quartz conglomerates processing.

INTRODUCTION: Outbreaks of silicosis have bene recently reported in artificial stone workers. AIM: To describe the features of silicosis in quartz conglomerate workers in North-Eastern Italy. METHODS: Active search of pneumoconiosis was performed in 11 companies of North-Eastern Italy involved in the fabrication of quartz conglomerate countertops. Occupational history, lung function tests, chest X-ray and high resolution computed tomography (HRCT) were performed. In selected cases, trans-bronchial biopsies were taken for histological evaluation and identification of silica crystals in the tissue. Cumulative exposure to crystalline silica was estimated. RESULTS: We recruited 45 workers and 24 cases of silicosis were diagnosed. Mean age at diagnosis was 43 years and duration of exposure to quartz conglomerate dust was 3.5 to 20 years. The average silica cumulative exposure was 4.3 mg/m3/y. Abnormal findings were detected in 42% of chest X-rays, in 33% of spirometry and 50% of carbon monoxide lung diffusion (DLco). HRCTs were abnormal in all cases showing well-defined rounded opacities, irregular/linear intralobular opacities and bilateral enlarged mediastinal lymph-nodes. Histological findings consistent with silicosis were observed in 24 cases. Numerous silica particles (diameter 0.1-5 µm) were identified in lung tissue. CONCLUSIONS: We reported an unexpected high incidence of silicosis in Italian workers exposed to quartz conglomerate dust. The results suggest that chest HRCT is indicated for screening of workers with high exposure to silica and DLco should be added to spirometry in health surveillance. More rigorous application of safety regulations and more effective preventive interventions at work are necessary.

NUFEB: A massively parallel simulator for individual-based modelling of microbial communities.

We present NUFEB (Newcastle University Frontiers in Engineering Biology), a flexible, efficient, and open source software for simulating the 3D dynamics of microbial communities. The tool is based on the Individual-based Modelling (IbM) approach, where microbes are represented as discrete units and their behaviour changes over time due to a variety of processes. This approach allows us to study population behaviours that emerge from the interaction between individuals and their environment. NUFEB is built on top of the classical molecular dynamics simulator LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), which we extended with IbM features. A wide range of biological, physical and chemical processes are implemented to explicitly model microbial systems, with particular emphasis on biofilms. NUFEB is fully parallelised and allows for the simulation of large numbers of microbes (107 individuals and beyond). The parallelisation is based on a domain decomposition scheme that divides the domain into multiple sub-domains which are distributed to different processors. NUFEB also offers a collection of post-processing routines for the visualisation and analysis of simulation output. In this article, we give an overview of NUFEB's functionalities and implementation details. We provide examples that illustrate the type of microbial systems NUFEB can be used to model and simulate.

Pathways to cellular supremacy in biocomputing.

Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the "genetic circuit" metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of "cellular supremacy" to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found.

Modelling bacterial twitching in fluid flows: a CFD-DEM approach.

Bacterial habitats are often associated with fluid flow environments. Bacterial twitching is important for initial bacterial colonization and biofilm formation. The existing research about bacteria twitching is largely experimental orientated. There is a lack of models of twitching motility of bacteria in shear flows, which could provide fundamental understanding about how bacterial twitching would be affected by bacteria associated properties such as number of pili and their distribution on the cell body and environmental factors such as flow and surface patterns. In this work, a three-dimensional modelling approach of Computational Fluid Dynamics (CFD) coupled with the Discrete Element Method (DEM) proposed to study bacterial twitching on flat and groove surfaces under shear flow conditions. Rod-shaped bacteria are modelled as groups of spherical particles and Type IV pili attached to bacteria are modelled as dynamic springs which can elongate, retract, attach and detach. The CFD-DEM model of rod-shape bacteria is validated against orbiting of immotile bacteria in shear flows. The effects of fluid flow rate and surface topography on twitching motility are studied. The model can successfully predict upstream twitching motility of rod-shaped bacteria in shear flows. Our model can predict that there would be an optimal range of wall shear stress in which bacterial upstream twitching is most efficient. The results also indicate that when bacteria twitch on groove surfaces, they are likely to accumulate around the downstream side of the groove walls.

Individual Based Model Links Thermodynamics, Chemical Speciation and Environmental Conditions to Microbial Growth.

Individual based Models (IbM) must transition from research tools to engineering tools. To make the transition we must aspire to develop large, three dimensional and physically and biologically credible models. Biological credibility can be promoted by grounding, as far as possible, the biology in thermodynamics. Thermodynamic principles are known to have predictive power in microbial ecology. However, this in turn requires a model that incorporates pH and chemical speciation. Physical credibility implies plausible mechanics and a connection with the wider environment. Here, we propose a step toward that ideal by presenting an individual based model connecting thermodynamics, pH and chemical speciation and environmental conditions to microbial growth for 5·105 individuals. We have showcased the model in two scenarios: a two functional group nitrification model and a three functional group anaerobic community. In the former, pH and connection to the environment had an important effect on the outcomes simulated. Whilst in the latter pH was less important but the spatial arrangements and community productivity (that is, methane production) were highly dependent on thermodynamic and reactor coupling. We conclude that if IbM are to attain their potential as tools to evaluate the emergent properties of engineered biological systems it will be necessary to combine the chemical, physical, mechanical and biological along the lines we have proposed. We have still fallen short of our ideals because we cannot (yet) calculate specific uptake rates and must develop the capacity for longer runs in larger models. However, we believe such advances are attainable. Ideally in a common, fast and modular platform. For future innovations in IbM will only be of use if they can be coupled with all the previous advances.

Multiorgan accelerated silicosis misdiagnosed as sarcoidosis in two workers exposed to quartz conglomerate dust.

INTRODUCTION: Clusters of silicosis cases have been reported in the fabrication of quartz conglomerate, a new high-silica-content artificial stone for kitchen and bathroom benchtops (countertops). AIM: We describe two cases of accelerated-type silicosis with hepatic granulomas arising in workers exposed to artificial quartz conglomerates. METHODS: A confident diagnosis of multiorgan silicosis was based on high level of respirable silica in the workplace, typical radiological alterations in chest high-resolution CT, histological findings in the lung and liver, and detection of silica crystals in both tissues by phase-contrast polarising light microscopy and scanning electron microscopy and energy dispersive spectroscopy. RESULTS: The development of the disease <10 years after the first exposure is consistent with an accelerated-type of silicosis. Compared with other studies related to quartz conglomerate exposure, we determined that the levels of airborne crystalline silica during activity in the finishing area were between 0.260 and 0.744 mg/m3, that is, much higher than the threshold limit value according to American Conference of Governmental Industrial Hygienists (0.025 mg/m3). Moreover, liver granulomas were associated with accumulation of crystalline silica particles in the hepatic tissue. CONCLUSIONS: Quartz conglomerate fabrication is a potentially dangerous occupation. General practitioners and physicians should have awareness of this newly described occupational hazard. Accurate occupational history is critical in avoiding misdiagnosis, as silicosis caused by inhalation of dust from artificial quartz conglomerates may exhibit atypical presentation. These features seem to be related to the extremely high level of silica exposure and, possibly, to an increased toxicity of the dust generated in this process.

A Bayesian approach to modelling the impact of hydrodynamic shear stress on biofilm deformation.

We investigate the feasibility of using a surrogate-based method to emulate the deformation and detachment behaviour of a biofilm in response to hydrodynamic shear stress. The influence of shear force, growth rate and viscoelastic parameters on the patterns of growth, structure and resulting shape of microbial biofilms was examined. We develop a statistical modelling approach to this problem, using combination of Bayesian Poisson regression and dynamic linear models for the emulation. We observe that the hydrodynamic shear force affects biofilm deformation in line with some literature. Sensitivity results also showed that the expected number of shear events, shear flow, yield coefficient for heterotrophic bacteria and extracellular polymeric substance (EPS) stiffness per unit EPS mass are the four principal mechanisms governing the bacteria detachment in this study. The sensitivity of the model parameters is temporally dynamic, emphasising the significance of conducting the sensitivity analysis across multiple time points. The surrogate models are shown to perform well, and produced ≈ 480 fold increase in computational efficiency. We conclude that a surrogate-based approach is effective, and resulting biofilm structure is determined primarily by a balance between bacteria growth, viscoelastic parameters and applied shear stress.

A mechanistic Individual-based Model of microbial communities.

Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. However, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly combine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for "bottom up" prediction of the emergent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacteria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria interaction is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentration, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup.

Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization.

MOTIVATION: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. RESULTS: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. AVAILABILITY AND IMPLEMENTATION: The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf CONTACT: anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk.

Probabilistic latent semantic analysis applied to whole bacterial genomes identifies common genomic features.

The spread of drug resistance amongst clinically-important bacteria is a serious, and growing, problem [1]. However, the analysis of entire genomes requires considerable computational effort, usually including the assembly of the genome and subsequent identification of genes known to be important in pathology. An alternative approach is to use computational algorithms to identify genomic differences between pathogenic and non-pathogenic bacteria, even without knowing the biological meaning of those differences. To overcome this problem, a range of techniques for dimensionality reduction have been developed. One such approach is known as latent-variable models [2]. In latent-variable models dimensionality reduction is achieved by representing a high-dimensional data by a few hidden or latent variables, which are not directly observed but inferred from the observed variables present in the model. Probabilistic Latent Semantic Indexing (PLSA) is an extention of LSA [3]. PLSA is based on a mixture decomposition derived from a latent class model. The main objective of the algorithm, as in LSA, is to represent high-dimensional co-occurrence information in a lower-dimensional way in order to discover the hidden semantic structure of the data using a probabilistic framework. In this work we applied the PLSA approach to analyse the common genomic features in methicillin resistant Staphylococcus aureus, using tokens derived from amino acid sequences rather than DNA. We characterised genome-scale amino acid sequences in terms of their components, and then investigated the relationships between genomes and tokens and the phenotypes they generated. As a control we used the non-pathogenic model Gram-positive bacterium Bacillus subtilis.

Analysis and verification of the HMGB1 signaling pathway.

BACKGROUND: Recent studies have found that overexpression of the High-mobility group box-1 (HMGB1) protein, in conjunction with its receptors for advanced glycation end products (RAGEs) and toll-like receptors (TLRs), is associated with proliferation of various cancer types, including that of the breast and pancreatic. RESULTS: We have developed a rule-based model of crosstalk between the HMGB1 signaling pathway and other key cancer signaling pathways. The model has been simulated using both ordinary differential equations (ODEs) and discrete stochastic simulation. We have applied an automated verification technique, Statistical Model Checking, to validate interesting temporal properties of our model. CONCLUSIONS: Our simulations show that, if HMGB1 is overexpressed, then the oncoproteins CyclinD/E, which regulate cell proliferation, are overexpressed, while tumor suppressor proteins that regulate cell apoptosis (programmed cell death), such as p53, are repressed. Discrete, stochastic simulations show that p53 and MDM2 oscillations continue even after 10 hours, as observed by experiments. This property is not exhibited by the deterministic ODE simulation, for the chosen parameters. Moreover, the models also predict that mutations of RAS, ARF and P21 in the context of HMGB1 signaling can influence the cancer cell's fate - apoptosis or survival - through the crosstalk of different pathways.