Resource Patch Formation and Exploitation throughout the Marine Microbial Food Web

Exploitation of microscale (?m?mm) resource patches by planktonic microorganisms may influence oceanic trophodynamics and nutrient cycling. However, examinations of microbial behavior within patchy microhabitats have been precluded by methodological limitations. We developed a microfluidic device to generate microscale resource patches at environmentally realistic spatiotemporal scales, and we examined the exploitation of these patches by marine microorganisms. We studied the foraging response of three sequential levels of the microbial food web: a phytoplankton (Dunaliella tertiolecta), a heterotrophic bacterium (Pseudoalteromonas haloplanktis), and a phagotrophic protist (Neobodo designis). Population-level chemotactic responses and single-cell swimming behaviors were quantified. Dunaliella tertiolecta accumulated within a patch of , simulating a zooplankton excretion, within 1 min of its formation. Pseudoalteromonas haloplanktis cells also exhibited a chemotactic response to patches of D. tertiolecta exudates within 30 s, whereas N. designis shifted swimming behavior in response to bacterial prey patches. Although they relied on different swimming strategies, all three organisms exhibited behaviors that permitted efficient and rapid exploitation of resource patches. These observations imply that microscale nutrient patchiness may subsequently trigger the sequential formation of patches of phytoplankton, heterotrophic bacteria, and protozoan predators in the ocean. Enhanced uptake and predation rates driven by patch exploitation could accelerate carbon flux through the microbial loop

Bacterial relay for energy efficient molecular communications

In multi-cellular organisms, molecular signaling spans multiple distance scales and is essential to tissue structure and functionality. Molecular communications is increasingly researched and developed as a key subsystem in the Internet-of-Nano-Things paradigm. While short range microscopic diffusion communications is well understood, longer range channels can be inefficient and unreliable. Static and mobile relays have been proposed in both conventional wireless systems and molecular communication contexts. In this paper, our main contribution is to analyze the information delivery energy efficiency of bacteria mobile relays. We discover that these mobile relays offer superior energy efficiency compared with pure diffusion information transfer over long diffusion distances. This paper has widespread implications ranging from understanding biological processes to designing new efficient synthetic biology communication systems

Target search of active particles in complex environments

Microswimmers are microscopic active agents capable of harvesting energy from the surrounding environment and converting it into self-propulsion and directed motion. This peculiar feature characterizes them as out-of-equilibrium systems that break microscopic reversibility. The problem of finding a specific target in a complex environment is essential for these agents since it is employed for a variety of purposes, from foraging nourishment to escaping potential threats. Here, we provide a detailed study of the target search process for microswimmers exploring complex environments. To this end, we generalize Transition Path Theory, the rigorous statistical mechanics description of transition processes, to the target-search problem. One of the main results of this thesis is the generalization to non-equilibrium systems of the Transition Path Sampling (TPS) algorithm, which was originally designed to simulate rare transitions in passive systems. The TPS algorithm relies on microscopic reversibility for its functioning, therefore its generalization to out-of-equilibrium systems lacking detailed balance and microscopic reversibility has remained a major challenge. Within this work, we generalize the TPS algorithm to the case of an active Brownian particle, i.e. a paradigmatic model for microswimmers, and we obtain a first insight into the counterintuitive target-search pathways explored by these agents. The second result of this thesis is a systematic characterization of the target-search path ensemble for an active particle exploring an energy landscape. The third and final original contribution of this Ph.D. thesis is the generalization of the concept of the committor function to target-search problems, with a validation of our theory against experiments of a camphor self-propelled disk.Comment: Ph.D. thesi

Accounting for Diffusion in Agent Based Models of Reaction-Diffusion Systems with Application to Cytoskeletal Diffusion

Diffusion plays a key role in many biochemical reaction systems seen in nature. Scenarios where diffusion behavior is critical can be seen in the cell and subcellular compartments where molecular crowding limits the interaction between particles. We investigate the application of a computational method for modeling the diffusion of molecules and macromolecules in three-dimensional solutions using agent based modeling. This method allows for realistic modeling of a system of particles with different properties such as size, diffusion coefficients, and affinity as well as the environment properties such as viscosity and geometry. Simulations using these movement probabilities yield behavior that mimics natural diffusion. Using this modeling framework, we simulate the effects of molecular crowding on effective diffusion and have validated the results of our model using Langevin dynamics simulations and note that they are in good agreement with previous experimental data. Furthermore, we investigate an extension of this framework where single discrete cells can contain multiple particles of varying size in an effort to highlight errors that can arise from discretization that lead to the unnatural behavior of particles undergoing diffusion. Subsequently, we explore various algorithms that differ in how they handle the movement of multiple particles per cell and suggest an algorithm that properly accommodates multiple particles of various sizes per cell that can replicate the natural behavior of these particles diffusing. Finally, we use the present modeling framework to investigate the effect of structural geometry on the directionality of diffusion in the cell cytoskeleton with the observation that parallel orientation in the structural geometry of actin filaments of filopodia and the branched structure of lamellipodia can give directionality to diffusion at the filopodia-lamellipodia interface

Data-driven models for cell motility in complex 2- and 3-dimensional environments

Studying cell motility is of vital importance for health, for knowing how cells behave and are affected by, and can themselves cause, disease. Mathematical modelling of such behaviour has proved beneficial for furthering knowledge of important motility processes in many different cell types. This work aims to define and analyse data-integrated mathematical models for cell motility in 2 and 3 dimensions, specifically applied to glioblastoma tumour cells and surface-attached P. aeruginosa bacterial cells. Models are outlined, tested on in silico data, parametrized where possible and assumptions are studied in detail. As a result, recommendations are made for how subsequent data could be collected to further improve the prediction and validation of these models. A comprehensive framework is developed for the analysis of cell tracking data in 2 and 3 dimensions which allows a user to study various aspects of the Persistent Random Walk model as applied to these tracks, looking at speeds, persistence time, mean squared displacement and root mean squared speed. In silico simulations show good agreement with model predictions, however the model is incapable of describing the experimental data, as evidenced by lack of agreement in speed distributions and the speed parameter changing with time. A Bayesian approach to estimating these parameters is also considered, with estimates of persistence time seen to be inflated here compared to those from the frequentist approach. A newly-observed twiddling mechanism used in chemotaxis by P. aeruginosa is also studied, through rigorous hypothesis testing of assumptions about this motion. An individual-based model is employed to simulate the resulting chemotactic motion, which shows good agreement with results from the specified analytic model, though the model cannot currently be validated against experimental data due to lack of appropriate data for parameter estimation

An integrative modelling framework for multicellular systems

Ph. D. Thesis.Multicellular systems exhibit complex population scale behaviour that emerge from the interactions between constituent cells. Integrative modelling (IM) techniques are a valuable tool for studying these systems capturing processes that occur at many temporal and spatial scales. The application of IM to multicellular systems is challenging as it is knowledge and resource intensive, additionally there do not exist effective frameworks or tools, inhibiting its wider application in Systems and Synthetic biology. This thesis presents Simbiotics, a novel IM framework for the modelling of mixed species bacterial consortia. Simbiotics is a spatially explicit multi-scale modelling platform for the design, simulation and analysis of bacterial populations. A library of modules simulating features such as cell geometries, physical force dynamics, genetic circuits, metabolic pathways, chemical diffusion and cell interactions is implemented, that the modeller may compose into their own custom models. Common modelling methods such as Boolean networks, differential equations, Gillespie models and SBML are implemented. With the platform in-silico experiments can be conducted with programmed experiment interactions, data collection and analysis. The framework is extendable and modular, allowing for the library to be updated as knowledge progresses. A novel file format for the reuse and communication of multicellular models and simulation methods is also implemented. Additionally an intuitive graphical user interface, Easybiotics, has been developed allowing for multicellular modelling with minimal programming experience. Four novel case studies are pursued with Simbiotics studying the emergent behaviours of multicellular systems. The effect of physical cell interactions are characterised in the first two studies. Investigation into how chemical signalling and intracellular dynamics influence population dynamics and patterns are studied in the final two case studies. These studies demonstrate how Simbotics can be integrated into a Systems/ Synthetic biology workflow, facilitating the studying of natural systems and as a CAD tool for developing novel synthetic systems.EPSR

A comprehensive survey of recent advancements in molecular communication

With much advancement in the field of nanotechnology, bioengineering and synthetic biology over the past decade, microscales and nanoscales devices are becoming a reality. Yet the problem of engineering a reliable communication system between tiny devices is still an open problem. At the same time, despite the prevalence of radio communication, there are still areas where traditional electromagnetic waves find it difficult or expensive to reach. Points of interest in industry, cities, and medical applications often lie in embedded and entrenched areas, accessible only by ventricles at scales too small for conventional radio waves and microwaves, or they are located in such a way that directional high frequency systems are ineffective. Inspired by nature, one solution to these problems is molecular communication (MC), where chemical signals are used to transfer information. Although biologists have studied MC for decades, it has only been researched for roughly 10 year from a communication engineering lens. Significant number of papers have been published to date, but owing to the need for interdisciplinary work, much of the results are preliminary. In this paper, the recent advancements in the field of MC engineering are highlighted. First, the biological, chemical, and physical processes used by an MC system are discussed. This includes different components of the MC transmitter and receiver, as well as the propagation and transport mechanisms. Then, a comprehensive survey of some of the recent works on MC through a communication engineering lens is provided. The paper ends with a technology readiness analysis of MC and future research directions

Integrating In-Silico Models with In-Vitro Data to Generate Novel Insights into Biological Systems

Models and computational predictions are useful in identifying certain key parameters that play a central role in defining the overall behavior of the system, and thus lead to new and more informative experiments. In this thesis, in-silico models are developed over a range of individual biological scales (macroscopic, mesoscopic and microscopic) for a range of cellular phenomena (cellular interactions, migration and signalling pathways) in order to highlight the importance of combined in-vitro – in-silico investigations. It is widely accepted that Systems Biology aims to provide a simpler and more abstract framework to explain complex biological phenomena. However, integration of these models with experimental data is often underutilised. Incorporation of experimentally derived data sets into the mathematical framework of in-silico modelling results in reliable, well parameterised systems capable of replicating dynamical properties of the biological systems. Work in this thesis includes the development of a continuous macroscopic in-silico model to identify the key mechanisms of interaction between cells present within the gastric tumour microenvironment. This model of discovery is used in a predictive capacity to accept or reject hypotheses. Next, the construction of a discrete cell based model of fibroblast migration is used to determine the degree of bias fibroblast cells experience when migrating over different surface topologies. The key results from this model show that particular surface topographies can have an effect on migratory cell behaviour. Then, the parameterisation of a differential equation model is used to quantify the key mechanisms of Nrf2 regulation in the cytoplasm and nucleus. Validation with experimentally derived datasets results in the quantification of rate ratios important to the dynamics of this signalling pathway. Finally, a stochastic Petri-net model capable of simulating the dynamical behaviour of functional cross-talk between the Nrf2 and NF-κB pathways is developed. This approach allows for the evaluation of a wide array of network responses, without the need for computationally expensive parameterisation. Together, these models exhibit how integration of in-silico models with in-vitro datasets can be used to generate new knowledge, or testable hypotheses