We observe different biological movement behaviors around us in nature everyday. Most organisms in biology move randomly and our main question is how can we model their behavior. There are two distinct goals to this project that we hope to accomplish; one, modeling a physical representation of a biological random walking organism, and two, observe and understand its characteristic motion in different environments. We use a Hexbug Nano as our random walker (RW) and confinement of varying diameters to contain the RW. We modify the Hexbug Nano to perform movement behaviors similar to RWs, which are more diffusive in nature. We use imaging tools such as ImageJ, Ctrax, Matlab, and Python Programming Language to analyze data obtained from sequence of images from different experiments. We analyze our data by looking at the mean squared displacement of trajectories of different bugs to understand their characteristic motion and behavior. We further aim to modify the environment of the confinement by changing the properties such as coefficient of friction between the surface and the RW, the type, amount, shape, and size of the barriers that the RW interacts with, and the varying size of the confinement that contains the RW to understand and compare the RW's diffusion behavior in different types of experimental setup conditions.

This project combined novel techniques from ecosystem ecology and data science to develop a data product that computes half-hourly soil carbon fluxes. Understanding soil carbon fluxes provides baseline metrics for monitoring changes in soil carbon for future climate scenarios. I acquired nearly-continuous precipitation, soil temperature, air pressure and other ancillary data from the National Ecological Observatory Network (NEON, www.neonscience.org) at six different locations throughout the continental United States. These locations represent a range of different ecosystems present in the United States. The data are inputs to the flux-gradient method, which applies Fick’s law of diffusion in the soil. The flux-gradient method is a simplified method of numerical computation technique of a partial differential equation (the Crank-Nicolson method). I modified previously existing code from NEON to acquire, compute, and produce “tidy” output of soil carbon fluxes with the flux-gradient method. Additionally, I contributed to an interactive web interface to visualize model results in daily, seasonal, and annual patterns in soil fluxes. Our results showed rapid changes in soil moisture is a challenge to model dynamic patterns in soil fluxes, especially prevalent at water-limited sites. Water content increases the activity of microbes inside the soil, thereby increasing soil fluxes and their associated uncertainty. Using statistical modeling we investigated how this rapid change and recovery in soil fluxes varied between sites.

Microorganisms affect many key biological processes like diseases, reproduction and digestion. Because of the complexity of the fluids in which they swim, and the shape and stroke of the microorganisms, it is difficult to solve for its swimming speeds analytically. Hence experimental model systems are necessary to capture the essential features of their swimming. I engineered such a model swimming apparatus - "Taylor swimming sheet" on macroscale, operating in the same Reynold's number as microorganisms. The advantage of such a system includes better control over different variables and parameters that impact swimming behavior of this artificial swimmer. Theoretically, Taylor swimming sheet works only for a prescribed stroke in an infinitely long sheet, imposed through a uniform deformation of the sheet such that the swimmer remains force/torque free. I implemented these aspects in the design experimentally. The assembly of the machine led to collection of data to characterize our system using different fluids of different properties. Our current data exhibits successful implementation of the theoretical Taylor swimming sheet in Newtonian fluids. I obtained images using the Pixelink CMOS camera and wrote Python code to analyze stacks of these images to obtain the swimming speed. The outcome of this research will contribute towards ongoing investigations of consolidating fundamental principles that govern engineering micro and nano swimmers for impactful applications like the treatment of bacterial infections, drug delivery, and microsurgery, etc.

Modern Great Salt Lake (GSL) resulted from the mass evaporation of Lake Bonneville, the most recent large lake episode within the Great Basin of the Western United States that formed during the last ice age. Today, mineral deposits surround the lake, including hydrated calcium sulfate (gypsum) and sodium chloride (halite). GSL is a hypersaline environment, hosting a complex community of microbiota in the water column and sediment in which minerals precipitate. This raises the question: What microorganisms get entrapped as the crystals form? This project will generate an optimal surface sterilization for gypsum, the first protocol of its kind for this mineral, as well as the first community-wide genomic isolation and identification of gypsum entombed microorganisms in a modern hypersaline environment. I collected recently precipitated gypsum crystals at the GSL shoreline enriched with clay deposits. I hypothesize that clay inclusions in GSL gypsum crystals will be enriched with archaea, bacteria, and fungi from the lake's microbial community, and this entrapped community may differ from the surrounding clay. I developed a multi-step surface sterilization protocol accounting for the unique chemical makeup of gypsum, and a protocol for optimal release of entombed microorganisms within clay inclusions. Results have provided evidence for the presence of numerous entombed organisms in isolated clay inclusions within gypsum crystals. These data are relevant to the gypsum that has been mapped on Mars by recent space missions, suggesting the evaporation of large salt-lake systems, rendering GSL as a potential analogue to evaporative lake regions on Mars.