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Using statistical physics principles to solve problems in biology is one of the most promising directions due to the complexity and non-equilibrium fluctuations in biological systems. In this work, we try to describe the dynamics at both cellular and molecular levels. Microtubule dynamics and dynamic disorder of enzyme proteins are two of the examples we investigated. The dynamics of microtubules and the mechanical properties of these polymers are essential for many key cellular processes. However, critical discrepancies between experimental observations and existing models need to be resolved before further progress towards a complete model can be made. We carried out computational studies to compare the mechanical properties of two alternative models, one corresponding to the existing, conventional model, and the other considering an additional type of tubulin lateral interaction described in a cryo-EM structure of a proposed trapped intermediate in the microtubule assembly process. Our work indicates that a class of sheet structures is transiently trapped as an intermediate during the assembly process in physiological conditions. In the second part of the work, we analyzed enzyme slow conformational changes in the context of regulatory networks. A single enzymatic reaction with slow conformational changes can serve as a basic functional motif with properties normally discussed with larger networks in the field of systems biology. The work on slow enzyme dynamics fills the missing gap between studies on intramolecular and network dynamics. We also showed that enzyme fluctuations could be amplified into fluctuations in phosphorylation networks. This can be used as a novel biochemical "reporter" for measuring single enzyme conformational fluctuation rates. / Ph. D.

Agronomic and water quality impacts of urea particle size were evaluated through field and laboratory experiments and mathematical modeling. In a two-year field study, corn silage yield, corn nitrogen (N) removal, and nitrate-N (NO₃⁻-N) leaching from urea pellets (1.5 g each) and granules (0.01-0.02 g each) applied at 184 kg-N/ha were compared. A control treatment (no N) and two other N application rates (110 and 258 kg-N/ha) were also included. Urea particle size impact on dissolution rate, dissolved urea movement, mineralization, and N0³-N leaching were evaluated in the laboratory. A two-dimensional (2-D) mathematical model was developed to simulate the fate of subsurface-banded urea and its transformation products, ammonium (NH₄⁺)and NO₃⁻. With 184 kg-N/ha, corn silage yield was 15% higher (p = 0.02) and corn N removal was 19% higher (p = 0.07) with pellets than granules in the second year of the field study. In the absence of yield response at 110 kg-N/ha, reason for higher yield at 184 kg-N/ha with pellets was unclear. Greater N removal reduced NO₃⁻-N leaching potential from pellets compared to granules during the over-winter period. No urea form response to yield or corn N removal was observed in the first year. In 23 of 27 sampling events, granules had higher NO₃⁻-N concentration in the root zone than pellets, with average nitrate-N concentrations of 2.6 and 2.2 mg-N/L, respectively. However, statistically, NO₃⁻-N leaching from the root zone was unaffected by urea form, probably due to high variability within treatments masking the treatment effects. In October 1997, pellets retained 16% more (p = 0.04) inorganic-N in the top half of the root zone than granules, due to slower nitrification in pellets as was determined in the mineralization study. Slower NO₃⁻-N leaching allowed for greater N extraction by plants. Pellets had lower dissolution, urea hydrolysis, and nitrification rates than granules; however, nitrification inhibition was the dominant mechanism controlling N fate. The model took into account high substrate concentration effects on N transformations, important for simulating the fate of band-applied N. The model exhibited good mass conservative properties, robustness, and expected moisture and N distribution profiles. Differences in measured field data and model outputs were likely due to uncertainties and errors in measured data and input parameters. Model calibration results indicated that moisture-related parameters greatly affected N fate simulation. Sensitivity analyses indicated the importance of nitrification-related parameters in N simulation, particularly, their possible multiplicative effects. Need for extensive model testing and validation was recognized. The validated 2-D N model could be incorporated into a management model for better management of subsurface-banded granular N. However, the 2-D model is not appropriate for simulating the three dimensional N movement from pellets. / Ph. D.

<i>Caulobacter crescentus</i> is an important model organism for studying regulation of cell growth, division and differentiation in prokaryotes. <i>C. crescentus</i> undergoes asymmetric division producing two progeny cells with identical genome but different developmental programs: the "swarmer" cell is flagellated and motile, and the "stalked" cell is sessile (attached to a surface by its stalk and holdfast). Only stalked cells undergo chromosome replication and cell division. A swarmer cell must shed its flagellum and grow a stalk before it can enter the replication-division cycle. Based on published experimental evidence, we propose a molecular mechanism controlling the cell division cycle in this bacterium. Our quantitative model of the mechanism illustrates detailed temporal dynamics of regulatory proteins and corresponding physiological changes during the process of cell cycle progression and differentiation of wild-type cells (both stalked cells and swarmer cells) and of a number of known and novel mutant strains. Our model presents a unified view of temporal regulation of protein activities during the asymmetric cell division cycle of <i>C. crescentus</i> and provides an opportunity to study and analyze the system dynamics of the <i>Caulobacter</i> cell cycle (as opposed to the dynamics of individual steps). The model can serve as a starting point for investigating molecular regulations of cell division and differentiation in other genera of alpha-proteobacteria, such as <i>Brucella</i> and <i>Rhizobium</i>, because recent experimental data suggest that these alpha-proteobacteria share similar genetic mechanisms for cell cycle control. / Ph. D.

In the post-"omic" era the analysis of high-throughput data is regarded as one of the major challenges faced by researchers. One focus of this data analysis is uncovering biological network topologies and dynamics. It is believed that this kind of research will allow the development of new mathematical models of biological systems as well as aid in the improvement of already existing ones. The work that is presented in this dissertation addresses the problem of the analysis of highly complex data sets with the aim of developing a methodology that will enable the reconstruction of a biological network from time series data through an iterative process. The first part of this dissertation relates to the analysis of existing methodologies that aim at inferring network structures from experimental data. This spans the use of statistical tools such as correlations analysis (presented in Chapter 2) to more complex mathematical frameworks (presented in Chapter 3). A novel methodology that focuses on the inference of biological networks from time series data by least squares fitting will then be introduced. Using a set of carefully designed inference rules one can gain important information about the system which can aid in the inference process. The application of the method to a data set from the response of the yeast Saccharomyces cerevisiae to cumene hydroperoxide is explored in Chapter 5. The results show that this method can be used to generate a coarse-level mathematical model of the biological system at hand. Possible developments of this method are discussed in Chapter 6. / Ph. D.

Vector-borne diseases (VBDs) cause significant harm to humans, plants, and animals worldwide. For instance, VBDs are very difficult to manage, as they are governed by complex interactions. VBD transmission depends on the pathogen itself, vector-host movement, and environmental conditions. Mosquito-borne diseases are a perfect example of how all these factors contribute to changes in VBD dynamics. Although vectors are highly sensitive to climate, modeling studies tend to ignore climate effects. Here, I am interested in the arthropod small vectors that are sensitive to climate factors such as temperature, precipitation, and drought. In particular, I am looking at the effect of temperature on vector traits for two VBDs, namely, dengue, caused by a virus that infects humans and bluetongue disease, caused by a virus that infects ruminants. First, I collect data on mosquito traits' response to temperature changes, this includes adult traits as well as juvenile traits. Next, I use these traits to model mosquito density, and then I incorporate the density into our mathematical models to investigate the effect it has on the basic reproductive ratio R0, a measure of how contagious the disease is. I use R0 to determine disease risk. For dengue, my results show that using mosquito life stage traits response to temperature improves our vector density approximation and disease risk estimates. For bluetongue, I use midge traits response to temperature to show that the suitable temperature for bluetongue risk is between 21.5 °C and 30.7 °C. These results can inform future control and prevention strategies. / Doctor of Philosophy / Infectious diseases are a type of illness that occurs when microorganisms spread between hosts. Some infectious diseases are directly transmitted and some require indirect transmission such as vector-borne diseases (VBDs). Each VBD requires the presence of a vector for the disease to be transmitted. For example, dengue that puts 40% of the world population at risk, requires mosquitoes to transmit the disease between humans. My research aims to investigate how the main climate factor, temperature, influences the spread of VBDs. I develop mathematical and statistical models that explain the effect of temperature on vector traits of a mosquito-borne disease (dengue) and a midge-borne disease (bluetongue) and investigate modeling formulas to improve our estimates for dengue mosquito densities. My results can be used to inform future prevention and control strategies.

Yes / The coupling of thermal (Multi Stage Flash, MSF) and membrane processes (Reverse Osmosis, RO) in desalination systems has been widely presented in the literature to achieve an improvement of performance compared to an individual process. However, very little study has been made to the combined Multi Effect Distillation (MED) and Reverse Osmosis (RO) processes. Therefore, this research investigates several design options of MED with thermal vapor compression (MED_TVC) coupled with RO system. To achieve this aim, detailed mathematical models for the two processes are developed, which are independently validated against the literature. Then, the integrated model is used to investigate the performance of several configurations of the MED_TVC and RO processes in the hybrid system. The performance indicators include the fresh water productivity, energy consumption, fresh water purity, and recovery ratio. Basically, the sensitivity analysis for each configuration is conducted with respect to seawater conditions and steam supply variation. Most importantly, placing the RO membrane process upstream in the hybrid system generates the overall best configuration in terms of the quantity and quality of fresh water produced. This is attributed to acquiring the best recovery ratio and lower energy consumption over a wide range of seawater salinity.