BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology.

Open Access ArticleLarge-scale collective behaviors such as synchronization and coordination spontaneously arise in many bacterial populations. With systems biology attempting to understand these phenomena, and synthetic biology opening up the possibility of engineering them for our own benefit, there is growing interest in how bacterial populations are best modeled. Here we introduce BSim, a highly flexible agent-based computational tool for analyzing the relationships between single-cell dynamics and population level features. BSim includes reference implementations of many bacterial traits to enable the quick development of new models partially built from existing ones. Unlike existing modeling tools, BSim fully considers spatial aspects of a model allowing for the description of intricate micro-scale structures, enabling the modeling of bacterial behavior in more realistic three-dimensional, complex environments. The new opportunities that BSim opens are illustrated through several diverse examples covering: spatial multicellular computing, modeling complex environments, population dynamics of the lac operon, and the synchronization of genetic oscillators. BSim is open source software that is freely available from http://bsim-bccs.sf.net and distributed under the Open Source Initiative (OSI) recognized MIT license. Developer documentation and a wide range of example simulations are also available from the website. BSim requires Java version 1.6 or higher.Engineering and Physical Sciences Research Council (EPSRC)Biotechnology and Biological Sciences Research Council (BBSRC

A 3D individual-based model to investigate the spatially heterogeneous response of bacterial biofilms to antimicrobial agents

The response of bacterial biofilms to treatment with antimicrobial agents is often characterized by the emergence of recalcitrant cellular microcolonies. We present an individual-based model to investigate the biophysical mechanisms of the selective resistance that arises within the biofilm and leads to a spatially heterogeneous response upon treatment with antibiotics. The response occurs in 3 distinct phases. In the first phase, the subpopulation of metabolically active cells diminishes due to antibiotic-induced cell death. Subsequently, in the second phase, increased nutrient availability allows dormant cells in the lower layers of the biofilm to transform into metabolically active cells. In the third phase, survival of the biofilm is governed by the interplay between 2 contrasting factors: (1) rate of antibiotic-induced cell death and (2) rate of transformation of dormant cells into active ones. Metabolically active cells at the distal edge of the biofilm sacrifice themselves to protect the dormant cells in the interior by (1) reducing local antibiotic concentrations and (2) increasing nutrient availability. In the presence of quorum sensing, biofilms exhibit increased tolerance compared with the quorum sensing-negative strains. Extracellular polymeric substance (EPS) forms a protective layer at the top of the biofilm, thereby limiting antibiotic penetration. The surviving cells, in turn, produce EPS resulting in a feedback-like mechanism of resistance. Whereas resistance in QS- biofilms occurs because of transformation of dormant cells into metabolically active cells, this transformation is less pronounced in QS+ biofilms, and resistance is a consequence of the sequestration of the antibiotic by EPS

Agent-based model of diffusion of N-acyl homoserine lactones in a multicellular environment of Pseudomonas aeruginosa and Candida albicans

Experimental incapacity to track microbemicrobe interactions in structures like biofilms, and the complexity inherent to the mathematical modelling of those interactions, raises the need for feasible, alternative modelling approaches. This work proposes an agent-based representation of the diffusion of N-acyl homoserine lactones (AHL) in a multicellular environment formed by Pseudomonas aeruginosa and Candida albicans. Depending on the spatial location, C. albicans cells were variably exposed to AHLs, an observation that might help explain why phenotypic switching of individual cells in biofilms occurred at different time points. The simulation and algebraic results were similar for simpler scenarios, although some statistical differences could be observed (p<0.05). The model was also successfully applied to a more complex scenario representing a small multicellular environment containing C. albicans and P. aeruginosa cells encased in a 3-D matrix. Further development of this model may help create a predictive tool to depict biofilm heterogeneity at the single-cell level.This work has been funded by a Research Grant 2014 by the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) to AL; the Portuguese Foundation for Science and Technology (FCT) [grant numbers UID/ BIO/04469/2013, UID/EQU/00511/2013] units and COMPETE 2020 [grant numbers POCI-01-0145-FEDER-006684, POCI-01-0145-FEDER-006939]; North Portugal Regional Operational Programme (NORTE 2020) [grant number NORTE‐01‐0145‐FEDER‐000005 – LEPABE-2-ECO-INNOVATION] under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

A 3-D Computational Model To Investigate The Influence of Quorum Sensing And Antibiotic Treatment On Growth Dynamics Of Poly Microbial Bio films

Bio films are the surface associated cell assemblages encased with extra-cellular polymeric substances. They form 3 Dimensional heterogeneous structures with more than one bacterial species. In poly-microbial infections, high antibiotic resistance has been observed. The potential reasons for this resistance can be, genetic mutations, antibiotic diffusion limitation and quorum sensing

Continuum and discrete approach in modeling biofilm development and structure: a review

The scientific community has recognized that almost 99% of the microbial life on earth is represented by biofilms. Considering the impacts of their sessile lifestyle on both natural and human activities, extensive experimental activity has been carried out to understand how biofilms grow and interact with the environment. Many mathematical models have also been developed to simulate and elucidate the main processes characterizing the biofilm growth. Two main mathematical approaches for biomass representation can be distinguished: continuum and discrete. This review is aimed at exploring the main characteristics of each approach. Continuum models can simulate the biofilm processes in a quantitative and deterministic way. However, they require a multidimensional formulation to take into account the biofilm spatial heterogeneity, which makes the models quite complicated, requiring significant computational effort. Discrete models are more recent and can represent the typical multidimensional structural heterogeneity of biofilm reflecting the experimental expectations, but they generate computational results including elements of randomness and introduce stochastic effects into the solutions

Modeling and Computations of Cellular Dynamics Using Complex-fluid Models

Cells are fundamental units in all living organisms as all living organisms are made up of cells of different varieties. The study of cells is therefore an essential part of research in life science. Cells can be classified into two basic types: prokaryotic cells and eukaryotic cells. One typical organisms of prokaryotes is bacterium. And eukaryotes mainly consist of animal cells. In this thesis, we focus on developing predictive models mathematically to study bacteria colonies and animal cell mitotic dynamics. Instead of living alone, bacteria usually survive in a biofilm, which is a microorganism where bacteria stick together by extracellular matrix primarily made up of extracellular polymeric substances (EPS) that the bacteria excrete. By treating the biofilm and solvent as a fluid mixture, we have developed a mathematical modeling framework and computational tool to investigate the mechanisms of biofilm formation and function. The bacteria in biofilms can be categorized into various types either by their persistence to antimicrobial treatments or by their reactions to quorum sensing molecules. We have studied dynamics of 3D heterogeneous biofilm formation under hydrodynamic stress, investigated the pros and cons of quorum sensing mechanism in an aqueous environment subject to hydrodynamic impact, explored the mechanism of antimicrobial persistence, looked into optimal dosing strategies, and examined the impact of cell motility on the development of biofilm morphology. As an integral part of the study, we have also validated our model of biofilm persistence to antimicrobial treatment against the experimental results obtained in our collaborators’ laboratory. Using the validated model, we then have probed the scenario of biofilm relapse after the antimicrobial treatment. These studies have demonstrated that our model and computational package can be an effective tool for analyzing the mechanism of biofilm formation and function. During an eukaryotic cell cycle, mitosis is a process in which a mother cell divides into two genetically identical daughter cells. In the initial stage of mitosis, the mother cell, spreaded on a substrate, undergoes a dramatic shape change by detaching from the substance and forming a spherical shape. During the late stage of mitosis, a contractile ring forms on the cell division plane, splitting the mother cell into two identical daughter cells. This late stage of mitotic process is also known as cytokinesis for eukaryotic cells. We have developed a modeling framework for simulating the space-time evolution of cell morphology, cell motility and mitotic dynamics of eukaryotic cells by a multiphase field complex fluids approach. In order to solve the complex cellular dynamics models, we have developed a series of efficient, energy law preserving, stable schemes and implemented them on GPU clusters for high-performance computing. The models have shown qualitative agreement with experiments on cell rounding, movement, wrinkling, blebbing, and dividing processes

The emergence of biofilms:Computational and experimental studies

The response of biofilms to any external stimuli is a cumulative response aggregated from individual bacteria residing within the biofilm. The organizational complexity of biofilm can be studied effectively by understanding bacterial interactions at cell level. The overall aim of the thesis is to explore the complex evolutionary behaviour of bacterial biofilms. This thesis is divided into three major studies based on the type of perturbation analysed in the study. The first study analyses the physics behind the development of mushroom-shaped structures from the influence of nutrient cues in biofilms. Glazier-Graner-Hogeweg model is used to simulate the cell characteristics. From the study, it is observed that chemotaxis of bacterial cells towards nutrient source is one of the major precursors for formation of mushroom-shaped structures. The objective of the second study is to analyse the impact of environmental conditions on the inter-biofilm quorum sensing (QS) signalling. Using a hybrid convection-diffusion-reaction model, the simulations predict the diffusivity of QS molecules, the spatiotemporal variations of QS signal concentrations and the competition outcome between QS and quorum quenching mutant bacterial communities. The mechanical effects associated with the fluid-biofilm interaction is addressed in the third study. A novel fluid-structure interaction model based on fluid dynamics and structural energy minimization is developed in the study. Model simulations are used to analyse the detachment and surface effects of the fluid stresses on the biofilm. In addition to the mechanistic models described, a separate study is carried out to estimate the computational efficiency of the biofilm simulation models

Models de terraformació. Estudi de la dinàmica de poblacions entre microorganismes sintètics i autòctons per a la degradació de plàstic oceànic

Our planet is experiencing an accelerated process of change associated to a variety of anthropogenic phenomena. The presence of vast amount of plastic in the open ocean has generated great concern to its potential ecological conseqüences. The cutting edge area of biology, synthetic biology, leads to new approaches and promises solutions to some of today's most pressing challenges in environmental protection in order to restore the ecosystem-level homeostasis in affected areas. Under the supervision of the research group of ICREA-Complex Systems Lab (CSL, http://complex.upf.edu/) the present project offers a theoretical aproach which aims to understand the dynamics of different mathematical and computational models about the oceanic plastic debris degradation potential from a synthetic microbial strain based on the wild type present in the affected ecosystem. We have been analyzing the problem assuming that many important problems in complex systems are related in one way or another with the presence of phase transition phenomena. Going through a scaled set of computational and mathematical models based on the "simplest model" philosopy, we have been validating population dymnamics strategies, at first stage level, as "fuction and die" degradation process or the biofilm formation capacity to increase the control over microplastic dispersion, as well as determining the key aspects of the studied system, always from a qualitative point of view. The different models explored have been performed using cellular automata (CA), individual based models (IBM), Mean Field, as stochastic models in 1 and 2 dimensions (1D and 2D) and contrasting them with analytical models. Most of them in MATLAB programming language. As a final addressing, there have been initiated laboratory experiments to explore the assumed biofilm capacity to catch plastic as a primary experimental approach to the proposed population dynamics. Furthermore the results, the present work suppose a huge learning process that has allowed me the possibility to get into the world of complex systems and synthetic biology.El planeta està experimentant un procés accelerat de canvi associat a una gran varietat de fenòmens antropogènics. La presència de grans quantitats de plàstic a mar obert ha generat una forta preocupació envers les potencials conseqüències ecològiques. L'àrea innovadora en el camp de la biologia -la biologia sintètica- condueix a noves aproximacions i promet solucions a alguns dels reptes actuals mes urgents en l'àmbit de protecció mediambiental, amb l'objectiu de restaurar l'homeostàsis dels ecosistemes en les àrees afectades. Sota la tutela del grup de recerca ICREA-Complex Systems Lab (CSL, http://complex.upf.edu/ ), el projecte ofereix una aproximació teòrica amb l'objectiu d'entendre les dinàmiques de diferents models matemàtics i computacionals sobre el potencial de degradació de plàstic oceànic per part d'una soca sintètica basada en la soca autòctona present en el ecosistema afectat. S'ha realitzat una anàlisi del problema assumint que, d'una manera o altre, molts dels problemes importants en els sistemes complexos estan relacionats amb els fenòmens de transició de fases. A través d'un conjunt escalat de models matemàtics i computacionals, basats en la "filosofia del model més simple", s'han validat estratègies de dinàmica de poblacions com ara: els processos de degradació "funció i mort", la capacitat de creació de biofilm per aconseguir un increment en el control sobre la dispersió de microplàstics, així com la determinació d'aspectes clau del sistema proposat, sempre des d'un punt de vista qualitatiu. Els diferents models suggerits han estat implementats com a autòmats cel·lulars (CA), Models Basats en l'Individu (IBM) i models de camp mig (Mean Field). Aquests models estocàstics en 1 i 2 dimensions (1D i 2D) s'han contrastat amb models analítics. La gran majoria s'han escrit en llenguatge de programació MATLAB. Com a direccionalitat final del projecte, s'han iniciat experiments de laboratori per a l'exploració de la capacitat del biofilm per a atrapar plàstic, sota l'enfocament d'una aproximació experimental inicial a les dinàmiques de poblacions proposades. Més enllà dels resultats, el present treball suposa un gran procés d'aprenentatge que m'ha brindat la possibilitat d'entrar en el món dels sistemes complexos i la biologia sintètica.El planeta está experimentando un proceso acelerado de cambio asociado a una gran variedad de fenómenos antropogenicos. La presencia de grandes cantidades de plástico a mar abierto ha generado una fuerte preocupación hacia las potenciales consecuencias ecológicas. El área innovadora en el campo de la biología -la biología sintética- conduce a nuevas aproximaciones y promete soluciones a algunos de los retos actuales más urgentes en el ámbito de protección medioambiental, con el objetivo de restaurar la homeostasis de los ecosistemas en las áreas afectadas. Bajo el tutelaje del grupo de investigación ICREA-Complex Systems Lab (CSL, http://complex.upf.edu/), el proyecto ofrece una aproximación teórica con el objetivo de entender las dinámicas de diferentes modelos matemáticos y computacionales sobre el potencial de degradación de plástico por parte de una cepa sintética basada en la cepa autóctona presente en el ecosistema afectado. Se ha realizado un análisis del problema asumiendo que, de una manera o de otra, muchos de los problemas importantes en los sistemas complejos están relacionados con los fenómenos de transiciones de fases. A través de un conjunto escalado de modelos matemáticos y computacionales, basados en la "filosofía del modelo más simple", se han validado estrategias de dinámica de poblaciones como: los procesos de degradación "función y muerte" o la capacidad de creación de biofilm para conseguir un incremento en el control sobre la dispersión de microplasticos, así como la determinación de aspectos clave del sistema propuesto, siempre des de un punto de vista cualitativo. Los diferentes modelos sugeridos han sido implementados como autómatas celulares (CA), Modelos basados en el Individuo (IBM), campo medio (Mean Field), los modelos estocásticos en 1 i 2 dimensiones (1D y 2D) y contrastándolos con modelos analíticos. La gran mayoría se han escrito en lenguaje de programación MATLAB. Como direccionalidad final del proyecto, se han iniciado experimentos de laboratorio para la exploración de la capacidad del biofilm para atrapar plástico, bajo el enfoque de una aproximación experimental inicial a las dinámicas de población propuestas. Más allá de los resultados, el presente trabajo supone un gran proceso de aprendizaje que me ha brindado la posibilidad de entrar en el mundo de los sistemas complejos y la biología sintética

Computational systems biology methods for functional classification of membrane proteins and modeling of quorum sensing in Pseudomonas aeruginosa

Due to the function of membrane proteins and the effort required for experimental annotations, bioinformatical approaches to functionally classify uncharacterized sequences are desirable. For this, the similarity between sequences of different membrane proteins was statistically analyzed based on several amino acid compositions. To discriminate between functional classes, a ranking method was developed. We showed that including further information in the amino acid composition and filtering into different sequence regions improved the classification quality. Subsets based on function achieved sensitivities of about 80%, whereas those of random subsets are in the range of 30--35%. The pathogen Pseudomonas aeruginosa produces many virulence factors that are regulated by Quorum sensing. The number of infecting strains with antibiotic resistance is growing. Thus, new strategies focus on Quorum sensing inhibitors that target the regulatory pathways of virulence factors. Pseudomonas aeruginosa contains three Quorum sensing systems that were simulated with an extended multi--level logical formalism to study the influence of Quorum sensing inhibitors on the autoinducer and virulence factor formation. A topology analysis suggested that the proteins PqsR and PqsE act as receptors. Both are required together with an autoinducer to form pyocyanin. Enzyme inhibitors were more useful to block the autoinducer formation, whereas PqsR antagonists inhibited the pyocyanin biosynthesis stronger.Aufgrund der Funktionen von Membranproteinen und dem Aufwand experimenteller Charakterisierungen sind bioinformatische Ansätze zur Klassifizierung unbekannter Sequenzen sinnvoll. Daher wurde deren Ähnlichkeit basierend auf verschiedenen Aminosäurenkompositionen bestimmt und statistisch analysiert. Eine Ranking--Methode wurde zur Einteilung in funktionelle Klassen entwickelt. Wir konnten zeigen, dass die Vorhersagegenauigkeit durch Hinzunahme weiterer Informationen und durch Unterscheidung verschiedener Sequenzregionen verbessert werden kann. Proteingruppen mit derselben Funktion erzielten Sensitivitäten von etwa 80%, während zufällig zusammengestellte Gruppen nur 30--35% erreichten. Der Krankheitserreger Pseudomonas aeruginosa produziert viele durch Quorum Sensing regulierte Virulenzfaktoren. Wegen der wachsenden Anzahl Antibiotika--resistenter Stämme greifen neue antibakterielle Strategien gezielt diese Regulationsmechanismen an. Die drei Quorum Sensing--Systeme von Pseudomonas aeruginosa wurden mit einem erweiterten logischen Formalismus modelliert um den Einfluss von Quorum Sensing--Inhibitoren auf die Bildung von Autoinducern und Virulenzfaktoren zu untersuchen. Eine Topologie--Analyse zeigte, dass die Proteine PqsR und PqsE anscheinend als Rezeptoren zusammen mit einem Autoinducer Pyocyanin regulieren. Enzym--Hemmstoffe waren besser geeignet, die Bildung von Autoinducern zu blockieren, während PqsR--Antagonisten die Pyocyanin--Biosynthese besser hemmten