Swimming bacteria are ubiquitous in aqueous environments ranging from oceans to fluidic environments within a living host. Furthermore, engineered bacteria are being increasingly utilized for a host of applications including environmental bioremediation, biosensing, and for the treatment of diseases. Often driven by chemotaxis (i.e. biased migration in response to gradients of chemical effectors) and quorum sensing (i.e. number density dependent regulation of gene expression), bacterial population dynamics and emergent behavior play a key role in regulating their own life and their impact on their immediate environment. Computational models that accurately and robustly describe bacterial population behavior and response to environmental stimuli are crucial to both understanding the dynamics of microbial communities and efficiently utilizing engineered microbes in practice. Many existing computational frameworks are finely-detailed at the cellular level, leading to extended computational time requirements, or are strictly population scale models, which do not permit population heterogeneities or spatiotemporal variability in the environment. To bridge this gap, we have created and experimentally validated a scalable, computationally-efficient, agent-based model of bacterial chemotaxis and quorum sensing (QS) which robustly simulates the stochastic behavior of each cell across a wide range of bacterial populations, ranging from a few to several hundred cells. We quantitatively and accurately capture emergent behavior in both isogenic QS populations and the altered QS response in a mixed QS and quorum quenching (QQ) microbial community. Finally, we show that the model can be used to predictively design synthetic genetic components towards programmed microbial behavior. / Master of Science

Thesis (MSc)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Bacterial chemotaxis, as observed for Escherichia coli, in a field of chemoattractant molecules is characterised by a run-and-tumble motion. The motion is effected by the clockwise (CW) or counter-clockwise (CCW) rotation of flagella; filamentous appendages attached to molecular motors on the cell body. Runs appear when all flagella turn in the CCW-direction and are used to maintain a favourable direction. Tumbles emerge as soon as one flagellum starts to turn CW and are used for reorientation. Because of the variation observed between individual bacteria displaying run-and-tumble motion, we choose to model this behaviour within a probabilistic framework. An important feature of the chemotactic ability of E.coli is that the cell increases run while moving in the right direction and shortens it in the opposite case. This underlines that tumbles are used for reorientation. It has been found from experiments that there can be significant variation in the tumble fashion depending on the fraction of CW-rotating motors (Turner et al., 2000). The change in angle produced when fewer flagella are rotating CW was found to be smaller when compared to the case for many CW-rotating flagella. In addition, the change of direction contributed by a small portion of CW-rotating flagella is rarely significant for bacteria with many flagella. Based on these observations, we have distinguished between models for the one-flagellated and the multi-flagellated cases. Furthermore, since the tumbling angle change increases with the fraction of CW-rotating motors, it would not be impossible to have some cases where the amount of turn produced by the CW-rotating motors induces the bacterium to have a change of direction greater than 2π. But, this feature could not have been observed because when the bacterium tumbles it can effectuate several revolutions before resuming to a new direction. Therefore, we do not restrict our change of direction to (0,2π) to allow the bacteria to have the possibility to effectuate change of directions of magnitude greater than 2π. To this end, we differentiate between the probability of having directional change of magnitude α and α +2π . Thus we do not use angle change distributions that are defined modulo 2π such as the von Mises distribution or the wrapped normal distribution. The chemotactic ability of the bacterium is modelled by representing the CCW-bias of a single flagellum as a function of the chemoattractant concentration. The model includes the temporal memory of chemoattractant concentration that the bacterium has, which usually spans about 4s. The information about the quality of the current direction of the bacterium is transmitted to the flagellar motor by assuming that this one varies with the chemoattractant concentration level. In addition, the saturation of the bias is incorporated by assuming that the bacterium performs a temporal comparison of the receptor occupancy. The present CCW-bias-Model accounts for the chemotactic ability of the bacterium as well as its adaptation to uniform chemoattractant environment. The models of one-flagellated and multi-flagellated bacterial motion, are used to investigate two main problems. The first one consists of determining the optimal tumbling angle strategy of the bacteria. The second one consists of looking at the effects of the tumble variation on the chemotactic efficiency of the bacteria. In order to address these questions, the chemotactic efficiency measure is defined in such a way that it reflects the ability of the bacteria to converge and to stay in a near neighbourhood of the source so that they gain more nutrients. Since its movement is entirely governed by its single flagellum, the one flagellated bacterium is more able to effectuate a run motion. Tumbling events are modelled to be all equivalent because there is not any fraction of flagella to consider. On the other hand, the tumble variation of the multi-flagellated bacteria is modelled by assuming that the directional change during a tumble is a function of the fraction of CW-rotating motors. By assuming that the number of CW-rotating flagella follows a binomial distribution, we suppose that the multi-flagellated bacteria are less able to effectuate a run motion. This also implies that the change of direction produced by fewer CW-rotating flagella are more likely to happen, and this compensates the lack of run. The models show that the optimal tumbling angle change for the bacteria is less than 2π and that higher flagellated bacteria have higher chemotacitc efficiency. As the number of flagella of the bacteria increases, there can be more tumble variation, in this case the bacteria are more capable of adjusting their direction. There could be some situation were the bacteria are not moving to the right direction, but do not require a large change of direction. This ability to adjust their direction accordingly allows them to converge nearer to the source and to gain more nutrients. In addition, the dependence of the tumbling angle on the fraction of CW-rotating flagella of the mutli-flagellated bacteria, implies that there is a correlation between the tumbling angle deviation and the external environment, because the rotational states CCW-CW of the flagella depends on the external cue. Consequently, it would not be impossible that the average magnitude of tumbling angle change depends on the external environment. To investigate this possibility we analyse the distribution of the tumbling tendency of a single bacterium over time, which is the distribution over time of the average positive tumbling change of the bacterium, within zerogradient environment and within non-zero-gradient environment. We defined the average of these tumbling tendency over time as the directional persistence. We observe that the directional persistence within these different nonzero- gradient environment remains the same. However, the difference between the directional persistence within zero-gradient and non-zeros gradient environment gets larger as the number of flagella of the cell increases. There is more correlation between the external environment and the tumbling tendency of the bacterium. Which is the reason why the higher flagellated bacteria responds the best to the external environment by having the higher chemotactic performance. Finally, the total directional persistence generated by the optimal tumbling angle change of the bacteria is the average directional persistence of the bacteria regardless of their number of flagella. Its value, predicted by the model is 1.54 rad within a non-zero-gradient environment and 1.63 rad within a zero-gradient environment. / AFRIKAANSE OPSOMMING: Bakteriese chemotakse, soos waargeneem word vir Escherichia coli, in ’n veld van chemiese lokmiddel molekules word gekenmerk deur ’n hardloopen- tuimel beweging. Die beweging word bewerkstellig deur die regsom of linksom rotasie van flagella; filamentagtige aanhangsels geheg aan molekulêre motors op die selliggaam. ’n Hardloop aksie kom voor as al die flagella linksom roteer en word gebruik om ’m voordelige koers te handhaaf. Tuimels kom voor sodra een van die flagella regsom draai en word gebruik vir heroriënteering. Van wee die variasie wat waargeneem word tussen individuele bakterieë wat hardloop-en-tuimel bewegiging vertoon, verkies ons ’n probabilistiese raamwerk om in te werk. ’n Belangrike eienskap van die chemotakse vermoë van E. coli is dat die sel meer gereeld hardloop terwyl dit in die regte rigting beweeg en minder gereeld in die teenoorgestelde geval. Dit beklemtoon dat tuimels gebruik word vir heroriënteering. Dit is al eksperimenteel vasgestel dat daar betekenisvolle variasie kan wees in die tuimel wyse, wat afhang van die breukdeel regsom roterende motors (Turner et al., 2000). Die hoekverskil afkomstig van minder regsom roterende flagella was vasgestel om kleiner te wees in vergelyking met die menig regsom roterende geval. Verder word die bydrae tot die hoekverskil van ’n klein breukdeel regsom roterende flagella selde beduidend vir bakterieë met baie flagella. As gevolg van hierdie waarnemings, tref ons onderskeid tussen modelle vir een-flagella en multiflagella gevalle. Aangesien die tuimel hoeksverskil vergroot saam met die breukdeel regsom roterende motore, is dit nie onmoontlik om gevalle te hê waar die hoeveelheid draaiaksie gegenereer deur die regsom roterende motore ’n rigtingsverskil groter as 2π kan bewerkstellig nie. Dit was nie moontlik om hierdie eienskap waar te neem nie aangesien die bakterieë ’n paar keer kan tuimel voordat ’n nuwe rigting vasgestel word. Vir hierdie rede beperk ons nie die hoeksverskil tot (0,2π) nie om die bakterieë toe te laat om rigtings veranderinge groter as 2π te ondergaan. Vir hierdie doel, onderskei ons tussen die waarskynlikheid van ’n rigtinsverskil met grootte α en α + 2π. Dus, gebruik ons nie hoekverskil verspreidings wat modulo 2 gedefinieer is nie, soos die von Mises verspreiding of omwinde normaalverdeling. Die chemotakse vermoë van die bakterium word gemodelleer deur die linksom sydigheid van ’n enkele flagellum as ’n funksie van die chemotakse lokmiddel konsentrasie voor te stel. Die model sluit in die tydelike geheue wat die bakterium besit oor chemotakse lokmiddel konsentrasie, wat gewoonlik oor 4s strek. Die informasie oor die kwaliteit van die huidige rigting van die bakterium word deur gegee na die flagella motor toe deur die aanname te maak dat dit wissel met die chemotakse lokmiddel konsentrasie vlak. Die versadiging van die sydigheid word geinkorporeer deur aan te neem dat die bakterium ’n temporale vergelyking maak tussen reseptor okkupasie. Die huidige linksom sydige model neem die bakterium chemotakse vermoë in ag, as ook aanpassing tot ’n uniforme chemotakse lokmiddel omgewing. Die modelle van een-flagella en multi-flagella bakteriële beweging word gebruik om twee hoof probleme te bestudeer. Die eerste, bestaan daaruit om vas te stel wat die optimale tuimel hoek strategie van die bakterieë is. Die tweede kyk na die uitwerking van tuimel variasie op chemotakse effektiwiteit. In orde om hierdie vra te adreseer word die chemotakse effektiwiteit op so mannier gedefinieer dat dit die bakteriese vermoë om die buurt om die oorsprong te nader en daar te bly. Aangesien die beweging heeltemal vasgestel word deur een flagella, in die een-flagella geval, is ’n bakterium meer in staat daartoe om ’n hardloop aksie te bewerkstellig. Tuimel voorvalle word as ekwivalent gemodeleer omdat daar geen breukdeel roterende flagella is om in ag te neem nie. In teenstelling, word die tuimel variasie van multi-flagella bakterieë gemodeleer deur die aanname te maak dat rigtingsverandering gedurende ’n tuimel ’n funksie is van die breukdeel regsom roterende motore. Deur die aanname te maak dat die getal regsom roterende flagella ’n binomiese verspreiding volg, veronderstel ons dat multi-flagella bakterieë minder in staat daartoe is om ’n hardloop aksie te onderneem. Hierdie impliseer ook dat rigtingverandering wat geproduseer word deur minder regsom roterende flagella meer geneig is om voor te kom en dan kompenseer vir ’n tekortkoming aan hardloop gebeure. Die modelle wys dat die optimale tuimelhoek verandering minder as 2 is en dat bakterieë met meer flagella meer chemotaksies effektief is. Soos die getal flagella vermeder, kan daar meer tuimel variasie wees, en in die geval is die bakterieë meer in staat om hul rigting te verander. Daar kan omstandighede wees waar die bakterieë nie in die regtige rigting beweeg nie, maar nie ’n groot rigtingsverskil nodig het nie. Hierdie vermoë om hul rigting byvolglik te verander stel hul in staat om nader aan die oorsprong te konvergeer en dus meer voedingstowwe op te neem. Die afhanklikheid van die tuimel hoek op die breukdeel regsom roterende flagella van multi-flagella bakterieë dui daarop dat daar ’n korrelasie is tussen die tuimel hoek afwyking en die eksterne omgewing, omdat die roterings toestand, regs- of linksom, van die flagella afhanklik is van die eksterne sein. As ’n gevolg, is dit nie onmoontlik dat die gemiddelde grootte van die tuimel hoek verandering van die eksterne omgewing afhang nie. Om hierdie moontlikheid te bestudeer, analiseer ons die verspreiding van die tuimel neiging van ’n enkele bakterium oor tyd, wat die verspreiding oor tyd van die gemiddelde positiewe tuimel verandering is, in ’n nulgradient en nie-nul-gradient omgewing. Ons het hierdie gemiddelde tuimel neigings oor tyd gedefinieer as die rigtings volharding. Ons het waargeneem dat die rigtings volharding binne verskillende nienul- gradient omgewings dieselfde bly. Nogtans is die verskil tussen die rigtings volharding binne nul-gradient en nie-nul-gradient omgewings groter soos die getal flagella vermeder. Daar is meer korrelasie tussen die eksterne omgewing en tuimel neiging van die bakterium. Dit is die rede hoekom bakterieë met meer flagella die beste reageer op die eksterne omgewing deur beter chemotakse effektiwiteit. Ten slotte, die totale rigtings volharding gegenereer deur die optimale tuimel hoek verandering is die gemiddelde rigtings volharding ongeag van die getal flagella. Die waarde wat deur die model voorspel word is 1.54 rad binne ’n nie-nul-gradient omgewing en 1.63 rad binne ’n nul-gradient omgewing.

This work explores the effects of body shape and configuration of flagella on motility of Helicobacter pylori, a helical-shaped bacterium that inhabits the viscoelastic gastric mucosa and causes gastritis, ulcers and gastric cancer. Although it is well known that different shapes produce different hydrodynamic drag thus altering the speed and that helical shapes generate additional thrust this has not been quantitatively established for flagellated bacteria. Using fast time-resolution and high-magnification two-dimensional phase-contrast microscopy to simultaneously image and track individual H. pylori and its rod-shaped isogenic mutant in broth and mucin solutions, the shape as well as rotational and translational speed was determined. In collaboration with Professor Henry Fu and Mehdi Jabbarzadeh the experimental data was used to validate the method of regularized Stokeslets by directly comparing the observed speeds to numerical calculations. The results show that due to relatively slow body rotation rates, the helical shape makes at most a 15% contribution to speeds. In order to explore the effects of arrangement of flagella on motility three different Helicobacter spp. were examined: H. suis (bipolar, multiple flagella), H. cetorum (bipolar, single flagellum) and H. pylori (unipolar, multiple flagella) swimming in broth and mucin. Results show that regardless of media, the flagella bundles of bipolar bacteria can assume one of two configurations interchangeably: extended away from the body or wrapped around it. H. suis predominantly swims with the lagging flagella extended behind the body and the leading flagella wrapped around it, but cases where both bundles are extended or both are wrapped have also been observed. In addition the effects of varying pH on motility of H. suis in broth and mucin were investigated. In broth the rotational speed is not significantly affected by varying pH and the peak of the speed distribution shifts to lower values as the pH decreases. However in mucin the rotational speed decreases by a factor of 20 from pH5 to 4 and the motion is completely hindered below pH4. This indicates that H. suis is unable to move below pH4, in agreement with previous findings on H. pylori, due to gelation of mucin below pH4.

Quorum sensing (QS) is a type of microbial communication used by bacteria to coordinate their behavior based on population density, regulating complex processes like biofilm formation and virulence, among other behaviors. Quorum quenching (QQ), on the other hand, disrupts this communication, usually by degradation of the QS signaling molecule. QQ offers a potential strategy for controlling bacterial behaviors linked to pathogenicity and biofouling. Despite significant advances in understanding and modeling the spatial-temporal behavior of QS, predictive modeling of QQ remains nascent, with a notable gap in the quantitative assessment of QQ's impact on QS. Here we show quantitative evaluation and characterization of the effect of QQ on QS in agar-based experiments, combined with an experimentally validated computational model. This research utilizes green fluorescence in E. coli MG 1655 as an indicator of QS activation, focusing on the degradation of Acyl-Homoserine Lactone (AHL), a key QS molecule in Gram-negative bacteria linked to pathogenicity, by the AiiA enzyme in engineered AiiA-producing Salmonella Typhimurium 14028. Our findings suggest that QQ more effectively influences QS in spatial configurations of the populations with larger interaction surfaces and shorter diffusion distances. Contrary to our initially held hypothesis, the primary effect of QQ is not a delay in QS onset but rather an attenuation of QS activity, with the area-under-the-curve of fluorescence serving as a quantitative metric. This study also introduces, to the best of our knowledge, one of the first instances of experimentally validated predictive modeling for QQ, applied to agar-based experimental setups. We posit that the quantitative experimental characterization and modeling framework presented in this research will enhance the understanding of bacterial community interactions. Enhanced comprehension of QQ and QS behaviors holds significant promise for advancing practical applications, particularly in mitigating or diminishing undesirable QS-associated activities. This is especially relevant in areas like biofouling, waste treatment, and the reduction of infections and progression of diseases in plants and animals, areas increasingly important as concerns about drug resistance in microbes and food security escalates. / Master of Science / One of the ways bacteria communicate with each other is called quorum sensing (QS), where they use chemical signals to organize and time group behavior, including forming communities encapsulated in protective layers, called biofilms, and engaging in virulent attacks against hosts. Quorum quenching (QQ) in bacteria, however, disrupts this communication system, usually by breaking down the chemical signals that bacteria use to send messages to each other. Even though QS has been studied extensively, determining how to predict and control QQ is still a nascent area of research. Here, we studied and characterized how QQ affects QS by doing experiments with bacteria populations in agar (a jelly-like substance) and applied a computational model to explain and ultimately predict the experimental observations. Engineered QS population (E. coli MG 1655) produced Acyl-Homoserine Lactone (AHL) signaling molecules, and engineered QQ bacteria (S. Tm 14028) used the Autoinducer Inactivation A (AiiA) enzyme to break down the AHL. According to our results, QQ doesn't delay the QS bacteria's group behaviors (in our case, green fluorescent signal production); it weakens the signal instead. Understanding QQ and QS better, especially through measurements and modeling, could lead to expanded methods of deterring harmful bacterial behavior, managing waste better, and stopping diseases in plants, animals, and humans, especially with the concerning rise of drug-resistant microbes and food security. One exciting possibility is using QQ to protect plants from bacterial infections. This could be a way to shield our crops without always relying on antibiotics.