A swarm describes a group of interacting agents exhibiting complex collective behaviors. Higher-level behavioral patterns of the group are believed to emerge from simple low-level rules of decision making at the agent-level. With the potential application of swarms of aerial drones, underwater robots, and other multi-robot systems, there has been increasing interest in approaches for specifying complex, collective behavior for artificial swarms. Traditional methods for creating artificial multi-agent behaviors inspired by known swarms analyze the underlying dynamics and hand craft low-level control logics that constitute the emerging behaviors. Deep learning methods offered an approach to approximate the behaviors through optimization without much human intervention.

This thesis proposes a graph based neural network architecture, SwarmNet, for learning the swarming behaviors of multi-agent systems. Given observation of only the trajectories of an expert multi-agent system, the SwarmNet is able to learn sensible representations of the internal low-level interactions on top of being able to approximate the high-level behaviors and make long-term prediction of the motion of the system. Challenges in scaling the SwarmNet and graph neural networks in general are discussed in detail, along with measures to alleviate the scaling issue in generalization is proposed. Using the trained network as a control policy, it is shown that the combination of imitation learning and reinforcement learning improves the policy more efficiently. To some extent, it is shown that the low-level interactions are successfully identified and separated and that the separated functionality enables fine controlled custom training.

The cell is a dense environment composes of proteins, nucleic acids, as well as other small molecules, which are constantly bombarding each other and interacting. These interactions and the diffusive motions are driven by internal thermal fluctuations. Upon collision, molecules can interact and form complexes. It is of interest to learn kinetic parameters such as reaction rates of one molecule converting to different species or two molecules colliding and form a new species as well as to learn diffusion coefficients.

Several experimental measurements can probe diffusion coefficients at the single-molecule and bulk level. The target of this thesis is on single-molecule methods, which can assess diffusion coefficients at the individual molecular level. For instance, super resolution methods like stochastic optical reconstruction microscopy (STORM) and photo activated localization microscopy (PALM), have a high spatial resolution with the cost of lower temporal resolution. Also, there is a different group of methods, such as MINFLUX, multi-detector tracking, which can track a single molecule with high spatio-temporal resolution. The problem with these methods is that they are only applicable to very diluted samples since they need to ensure existence of a single molecule in the region of interest (ROI).

In this thesis, the goal is to have the best of both worlds by achieving high spatio-temporal resolutions without being limited to a few molecules. To do so, one needs to refocus on fluorescence correlation spectroscopy (FCS) as a method that applies to both in vivo and in vitro systems with a high temporal resolution and relies on multiple molecules traversing a confocal volume for an extended period of time. The difficulty here is that the interpretation of the signal leads to different estimates for the kinetic parameters such as diffusion coefficients based on a different number of molecules we consider in the model. It is for this reason that the focus of this thesis is now on using Bayesian nonparametrics (BNPs) as a way to solve this model selection problem and extract kinetic parameters such as diffusion coefficients at the single-molecule level from a few photons, and thus with the highest temporal resolution as possible.

In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different illumination intensities or different local environments); and (3) inferring the camera gain. My general theoretical framework utilizes the Bayesian nonparametric Gaussian and beta-Bernoulli processes with a Markov chain Monte Carlo sampling scheme, which I further specify and implement for Total Internal Reflection Fluorescence (TIRF) microscopy data, benchmarking the method on synthetic data. These three frameworks are self-contained, and can be used concurrently so that the fluorescence profile and emitter locations are both considered unknown and, under some conditions, learned simultaneously. The framework I present is flexible and may be adapted to accommodate the inference of other parameters, such as emission photophysical kinetics and the trajectories of moving molecules. My TIRF-specific implementation may find use in the study of structures on cell membranes, or in studying local sample properties that affect fluorescent molecule photon emission rates.

OP50 Esherichia coli is a Gram-negative bacterium with a fast replication rate and can be easily manipulated, making it a model species for many science disciplines. To probe this bacterium’s search strategy, cultures were starved and the cell velocity was probed at various points later in time after perturbing the buffer in which the bacteria were located. To start, we added E.coli OP50 filtrate. In yet another experiment filtrate from a Bdellovibrio bacteriovorus (Gram-negative predator) culture was added to monitor the OP50’s differential response to cues from its environment. Using MATLAB code, thousands of E.coli tracks were measured.

Bdellovibrio bacteriovorus is a predatory bacterium that may serve as a living antibiotic by destroying biofilms and invading gram-negative bacteria. Swimming at over 100μm s-1, these predators collide into their prey and invade them to complete their life cycle. While previous experiments have investigated B. bacteriovorus’ motility, no study has yet collected swim speed variations over the lifespan of B. bacteriovorus. In this study, we used state-of-the-art bacterial tracking methods to record the speed of tens of thousands of bacteria. These results were used to describe their metabolic state under starvation conditions in which they lose energy in a dissipative manner by propelling themselves at high speeds through solution. In particular, we investigated the metabolic response of starved predators to the addition of prey-lysate.