Browsing by Subject "Systems biology"

Although antibiotics have proven to be one of the great achievements of modern medicine, their efficacy has dramatically decreased over the past several decades. This is due, in part, to the rapid pace of natural bacterial evolution, but also to the overuse and misuse of antibiotics in general. This often selects for drug-resistant pathogens, and allows them to flourish in the face of antibiotic treatment. In addition to the emergence of genetic resistance, bacteria often utilize a number of population-level behaviors to survive antibiotic treatment. This is referred to as collective antibiotic tolerance (CAT). Taken together, antibiotic resistance and tolerance have led to the re-emergence of infectious diseases throughout the world. In general, there are two strategies to combat this risk: develop novel antibiotics, and/or use existing drugs more effectively, so as to minimize the chance of resistance emergence. Novel drug development is a time- and resource-intensive process, and pharmaceutical companies are not financially incentivized to develop these types of drugs. Therefore, it is of increasing importance to understand the population dynamics underlying various bacterial survival mechanisms, and exploit this knowledge to design better antibiotic treatment protocols.

My dissertation research focuses on a prevalent phenomenon called the postantibiotic effect (PAE), which refers to the transient suppression of bacterial growth following antibiotic treatment. Although PAE has been empirically observed in a wide variety of antibiotics and microbial species, heretofore there has not been a definitive mechanistic explanation for this pervasive observation.

In this work, I use a combination of high-throughput microfluidic experiments and computational modeling to examine the relationship between dosing parameters and the degree of bacterial inhibition, quantified by population recovery time. I found that recovery time is a function of total antibiotic, regardless of how the dose profile. Moreover, a minimal model of transport and binding kinetics was sufficient to recapture this trend, suggesting a unifying explanation for historical observations of PAE in a variety of contexts. I validated this modeling using both in silico and in vitro perturbation studies.

Moreover, I showed that efflux inhibition, a common strategy in antibiotic treatment, is effective in certain dynamic-dependent situations. This work puts forth a possible mechanism for PAE, which could serve as a clinical aid in selecting effective antibiotic/adjuvant combinations, as well as in designing periodic antibiotic treatments.

Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally efficient methods for Bayesian inference for simultaneous estimation of latent states and unknown fixed parameters.

Chapter 1 introduces state space models and methods of inference in these models. Chapter 2 describes a novel method for jointly sampling the entire latent state vector in a nonlinear Gaussian state space model using a computationally efficient adaptive mixture modeling procedure. This method is embedded in an overall Markov chain Monte Carlo algorithm for estimating fixed parameters as well as states. In Chapter 3 the method of the previous chapter is implemented in a few illustrative

nonlinear models and compared to standard existing methods. This chapter also looks at the effect of the number of mixture components as well as length of the time series on the efficiency of the method. I then turn to an biological application in Chapter 4. I discuss modeling choices as well as derivation of the state space model to be used in this application. Parameter and state estimation are analyzed in these models for both simulated and real data. Chapter 5 extends the methodology introduced in Chapter 2 from nonlinear Gaussian models to general state space models. The method is then applied to a financial

stochastic volatility model on US $ - British £ exchange rates. Bayesian inference in the previous chapter is accomplished through Markov chain Monte Carlo which is suitable for batch analyses, but computationally limiting in sequential analysis. Chapter 6 introduces sequential Monte Carlo. It discusses two methods currently available for simultaneous sequential estimation of latent states and fixed parameters and then introduces a novel algorithm that reduces the key, limiting degeneracy issue while being usable in a wide model class. Chapter 7 implements the novel algorithm in a disease surveillance context modeling influenza epidemics. Finally, Chapter 8 suggests areas for future work in both modeling and Bayesian inference. Several appendices provide detailed technical support material as well as relevant related work.

Cell division and growth are complex processes fundamental to all living organisms. In the budding yeast, Saccharomyces cerevisiae, these two processes are known to be coordinated with one another as a cell's mass must roughly double before division. Moreover, cell-cycle progression is dependent on cell size with smaller cells at birth generally taking more time in the cell cycle. This dependence is a signature of size control. Systems biology is an emerging field that emphasizes connections or dependencies between biological entities and processes over the characteristics of individual entities. Statistical models provide a quantitative framework for describing and analyzing these dependencies. In this dissertation, I take a statistical systems biology approach to study cell division and growth and the dependencies within and between these two processes, drawing on observations from richly informative microscope images and time-lapse movies. I review the current state of knowledge on these processes, highlighting key results and open questions from the biological literature. I then discuss my development of machine learning and statistical approaches to extract cell-cycle information from microscope images and to better characterize the cell-cycle progression of populations of cells. In addition, I analyze single cells to uncover correlation in cell-cycle progression, evaluate potential models of dependence between growth and division, and revisit classical assertions about budding yeast size control. This dissertation presents a unique perspective and approach towards comprehensive characterization of the coordination between growth and division.

We work on constructing mathematical models of gene regulatory networks for periodic processes, such as the cell cycle in budding yeast, using biological data sets and applying or developing analysis methods in the areas of mathematics, statistics, and computer science. We identify genes with periodic expression and then the interactions between periodic genes, which defines the structure of the network. This network is then translated into a mathematical model, using Ordinary Differential Equations (ODEs), to describe these entities and their interactions. The models currently describe gene regulatory interactions, but we are expanding to capture other events, such as phosphorylation and ubiquitination. To model the behavior, we must then find appropriate parameters for the mathematical model that allow its dynamics to approximate the biological data.

This pipeline for model construction is not focused on a specific algorithm or data set for each step, but instead on leveraging several sources of data and analysis from several algorithms. For example, we are incorporating data from multiple time series experiments, genome-wide binding experiments, computationally predicted binding, and regulation inference to identify potential regulatory interactions.

These approaches are designed to be applicable to various periodic processes in different species. While we have worked most extensively on models for the cell cycle in Saccharomyces cerevisiae, we have also begun working with data sets for the metabolic cycle in S. cerevisiae, and the circadian rhythm in Mus musculus.

Many factors, including gene networks, developmental processes, and the environment mediate the link between the activity of genes and complex phenotypes in higher organisms. While genetic variants are the raw material for evolution, these other factors are critical for determining which variants are actually exposed to natural selection. In this dissertation, I describe three projects in which I investigate how developmental mechanisms and the environment interact to shape phenotypic variation. In each project, I use gene expression as a window into the activity of genes, and as a tool to measure variation in and among developmental mechanisms. Two projects are experimental, focusing on early development in sea urchins, and how environmental stress caused by climate change impacts the expression of genetic variation in phenotypic traits. In these projects, I explicitly incorporate information about the biochemical functions of genes and how they interact in development, and test how such mechanisms shape the impact of genetic and environmental perturbations to development. The third project is methodological, in which I propose a unified statistical framework for inferring previously unknown developmental constraints that may underlie gene expression phenotypes. Together, these projects demonstrate that an understanding of developmental mechanisms can enhance our understanding of the processes that shape variation in populations, and can help predict the biological effects of climate change.

Bacteria are found throughout the environment, from the air to the soil, but more importantly, they reside within the human body. Crucial to their survival in each of these environments is the constant interplay between these organisms and their surroundings. Inadvertently, the ways in which these stimuli are processed can have a profound impact on human health. With potentially negative or positive consequences, it becomes critical to understand how microorganisms encode and decode signals.

Understanding bacterial signal processing is crucial to tackling the treatment of infectious diseases, especially with the rise of antibiotic resistant organisms. Antibiotic resistance has become a global health issue as bacteria have developed or acquired genes that confer resistance to all antibiotics currently in use today. This has serious implications for the future treatment of infectious diseases, potentially limiting options to those from a pre-antibiotic era. However, as with other external factors, antibiotics are just another signal that bacteria need to decode and encode a response to. As such, it is of utmost importance to better understand how bacteria process stimuli.

In my dissertation, I analyzed the ways in which bacteria both encode and decode information. In particular, I focused on how information is processed from signals with a temporal domain. To start, I developed a computational framework to understand how organisms decode signals, specifically oscillatory signals. With this model, I examined the capability of an incoherent feedforward loop motif to exhibit temporal adaptation, in which a network becomes desensitized to sustained stimuli. I discovered that this property is crucial for networks to distinguish signals of varying temporal dynamics.

In terms of information encoding, I utilized the complexity of this process to predict bacterial characteristics of interest. The fundamental premise behind this work is to increase the information content of phenotypes for the prediction of bacterial characteristics. Specifically, I used the temporal domain of growth for the prediction of genetic identity and traits of interest. I demonstrated that temporal growth dynamics under standardized conditions can differentiate among hundreds of strains, even strains of the same species. While growth dynamics could, with high accuracy, differentiate between unique strains, it was insufficient to quantify how genetically different these strains were. This absence highlighted the challenges in using genomics to infer phenotypes and vice versa. Bypassing this complexity, I showed that growth dynamics alone could robustly predict antibiotic responses. Together, my findings demonstrate the ability to develop applications that take advantage of the complexity of bacterial information encoding.

This work highlights the importance of understanding how bacteria decode signals with temporal dynamics. Additionally, I demonstrated one application for utilizing bacterial signal encoding, the prediction of bacterial characteristics.

Bacteria are found throughout the environment, from the air to the soil, but more importantly, they reside within the human body. Crucial to their survival in each of these environments is the constant interplay between these organisms and their surroundings. Inadvertently, the ways in which these stimuli are processed can have a profound impact on human health. With potentially negative or positive consequences, it becomes critical to understand how microorganisms encode and decode signals.

Understanding bacterial signal processing is crucial to tackling the treatment of infectious diseases, especially with the rise of antibiotic resistant organisms. Antibiotic resistance has become a global health issue as bacteria have developed or acquired genes that confer resistance to all antibiotics currently in use today. This has serious implications for the future treatment of infectious diseases, potentially limiting options to those from a pre-antibiotic era. However, as with other external factors, antibiotics are just another signal that bacteria need to decode and encode a response to. As such, it is of utmost importance to better understand how bacteria process stimuli.

In my dissertation, I analyzed the ways in which bacteria both encode and decode information. In particular, I focused on how information is processed from signals with a temporal domain. To start, I developed a computational framework to understand how organisms decode signals, specifically oscillatory signals. With this model, I examined the capability of an incoherent feedforward loop motif to exhibit temporal adaptation, in which a network becomes desensitized to sustained stimuli. I discovered that this property is crucial for networks to distinguish signals of varying temporal dynamics.

In terms of information encoding, I utilized the complexity of this process to predict bacterial characteristics of interest. The fundamental premise behind this work is to increase the information content of phenotypes for the prediction of bacterial characteristics. Specifically, I used the temporal domain of growth for the prediction of genetic identity and traits of interest. I demonstrated that temporal growth dynamics under standardized conditions can differentiate among hundreds of strains, even strains of the same species. While growth dynamics could, with high accuracy, differentiate between unique strains, it was insufficient to quantify how genetically different these strains were. This absence highlighted the challenges in using genomics to infer phenotypes and vice versa. Bypassing this complexity, I showed that growth dynamics alone could robustly predict antibiotic responses. Together, my findings demonstrate the ability to develop applications that take advantage of the complexity of bacterial information encoding.

This work highlights the importance of understanding how bacteria decode signals with temporal dynamics. Additionally, I demonstrated one application for utilizing bacterial signal encoding, the prediction of bacterial characteristics.

The physics of complex systems has grown considerably as a field in recent decades, largely due to improved computational technology and increased availability of systems level data. One area in which physics is of growing relevance is molecular biology. A new field, systems biology, investigates features of biological systems as a whole, a strategy of particular importance for understanding emergent properties that result from a complex network of interactions. Due to the complicated nature of the systems under study, the physics of complex systems has a significant role to play in elucidating the collective behavior.

In this dissertation, we explore three problems in the physics of complex systems, motivated in part by systems biology. The first of these concerns the applicability of Boolean models as an approximation of continuous systems. Studies of gene regulatory networks have employed both continuous and Boolean models to analyze the system dynamics, and the two have been found produce similar results in the cases analyzed. We ask whether or not Boolean models can generically reproduce the qualitative attractor dynamics of networks of continuously valued elements. Using a combination of analytical techniques and numerical simulations, we find that continuous networks exhibit two effects -- an asymmetry between on and off states, and a decaying memory of events in each element's inputs -- that are absent from synchronously updated Boolean models. We show that in simple loops these effects produce exactly the attractors that one would predict with an analysis of the stability of Boolean attractors, but in slightly more complicated topologies, they can destabilize solutions that are stable in the Boolean approximation, and can stabilize new attractors.

Second, we investigate ensembles of large, random networks. Of particular interest is the transition between ordered and disordered dynamics, which is well characterized in Boolean systems. Networks at the transition point, called critical, exhibit many of the features of regulatory networks, and recent studies suggest that some specific regulatory networks are indeed near-critical. We ask whether certain statistical measures of the ensemble behavior of large continuous networks are reproduced by Boolean models. We find that, in spite of the lack of correspondence between attractors observed in smaller systems, the statistical characterization given by the continuous and Boolean models show close agreement, and the transition between order and disorder known in Boolean systems can occur in continuous systems as well. One effect that is not present in Boolean systems, the failure of information to propagate down chains of elements of arbitrary length, is present in a class of continuous networks. In these systems, a modified Boolean theory that takes into account the collective effect of propagation failure on chains throughout the network gives a good description of the observed behavior. We find that propagation failure pushes the system toward greater order, resulting in a partial or complete suppression of the disordered phase.

Finally, we explore a dynamical process of direct biological relevance: asymmetric cell division in A. thaliana. The long term goal is to develop a model for the process that accurately accounts for both wild type and mutant behavior. To contribute to this endeavor, we use confocal microscopy to image roots in a SHORTROOT inducible mutant. We compute correlation functions between the locations of asymmetrically divided cells, and we construct stochastic models based on a few simple assumptions that accurately predict the non-zero correlations. Our result shows that intracellular processes alone cannot be responsible for the observed divisions, and that an intercell signaling mechanism could account for the measured correlations.

Antibiotic discovery and widespread usage has revolutionized the treatment of infectious diseases. However, this golden age of modern-day medicine is threatened by the increasing prevalence of antibiotic-resistant pathogens. As the antibiotic development pipeline increasingly slows, we find ourselves falling behind in the race between innovation and evolution.

Among the various means of bacterial evolution, horizontal gene transfer (HGT) – or the non-genealogical transmission of DNA between organisms – is the dominant mode responsible for the acquisition of antibiotic resistance genes. Combined with antibiotic overuse and misuse, HGT primarily via conjugation, has compromised the efficacy of nearly every single antimicrobial available. Tight coupling between HGT and antibiotic-mediated selection, along with a lack of quantitative experiments, has led to the general belief that antibiotics themselves promote gene transfer; however, antibiotic action could modulate the rate of gene transfer (as is assumed), the resulting population dynamics, or both. Therefore, it is critical to decouple these two processes to definitively determine the influence of antibiotics on conjugation.

In my dissertation, I quantified the extent to which antibiotics influence conjugation in the presence and absence of antibiotic-mediated selection dynamics. To do so, I implemented a synthetically engineered conjugation system, which facilitates precise quantification of conjugation dynamics. Using this platform, I quantified the rate of gene transfer, or the conjugation efficiency, in the absence of confounding selection dynamics. I discovered that, in contrast to conventional wisdom, antibiotics did not significantly increase the conjugation efficiency. This finding was general to 10 antibiotics, as well as nine native and clinically relevant plasmids. Instead, antibiotic selection dynamics alone could account for conjugation dynamics.

I next investigated the potential strategies to minimize, or ideally reverse, plasmid-mediated resistance. Traditionally, reducing overall antibiotic use has been the primary approach to reversing resistance; minimizing selection takes advantage of costly resistance genes to competitively displace resistant bacteria with their sensitive counterparts. However, despite widespread antibiotic stewardship initiatives, even costly resistance persists for long periods of time. One potential explanation is that sufficiently fast conjugation enables plasmid persistence in the absence of selection. Similar challenges in quantifying conjugation have prevented general conclusions, and overall, the extent to which conjugation enables persistence is unknown.

Using the same platform, I showed that conjugation enables the persistence of costly plasmids, even in the absence of selection. Conjugation-assisted persistence was true for nine common conjugal plasmids, and in microbial populations consisting of varying degrees of complexity. Finally, I showed that by reducing the conjugation efficiency and promoting plasmid loss, it is possible to reverse resistance. Together, these findings contribute to basic understanding of the propagation and persistence plasmids, and elucidate a novel therapeutic strategy by taking advantage of ecological/evolutionary dynamics to to reduce or even reverse the spread of resistance.

Cellular dynamics arise from intricate interactions among diverse components, such as metabolites, RNAs, and proteins. An in-depth understanding of these interactions requires an integrated approach to the investigation of biological systems. This task can benefit from a combination of mathematical modeling and experimental validations, which is becoming increasingly indispensable for basic and applied biological research.

Utilizing a combination of modeling and experimentation, we investigate mammalian cell cycle entry. We begin our investigation by making predictions with a mathematical model, which is constructed based on the current knowledge of biology. To test these predictions, we develop experimental platforms for validations, which in turn can be used to further refine the model. Such iteration of model predictions and experimental validations has allowed us to gain an in-depth understanding of the cell cycle entry dynamics.

In this dissertation, we have focused on the Myc-Rb-E2F signaling pathway and its associated pathways, dysregulation of which is associated with virtually all cancers. Our analyses of these signaling pathways provide insights into three questions in biology: 1) regulation of the restriction point (R-point) in cell cycle entry, 2) regulation of the temporal dynamics in cell cycle entry, and 3) post-translational regulation of Myc by its upstream signaling pathways. The well-studied pathways can serve as a foundation for perturbations and tight control of cell cycle entry dynamics, which may be useful in developing cancer therapeutics.

We conclude by demonstrating how a combination of mathematical modeling and experimental validations provide mechanistic insights into the regulatory networks in cell cycle entry.

Diverse mechanisms have been proposed to explain natural pattern formation processes, such as slime mold aggregation, feather branching, and tissue stratification. Regardless of the specific molecular interactions, the vast majority of these mechanisms invoke morphogen gradients, which are either predefined or generated as part of the patterning processes. However, using E. coli programmed by a simple synthetic gene circuit, I demonstrate here the generation of robust, self-organized ring patterns of gene expression in the absence of an apparent morphogen gradient. Interestingly, modeling and experimental tests show that the temporal dynamics of the global morphogen concentration serve as a timing mechanism to trigger formation and maintenance of these ring patterns, which are readily tunable by experimentally controllable environmental factors. This mechanism represents a novel mode of pattern formation that has implications for understanding natural developmental processes. In addition, the system can be coupled with inkjet printing technology and metabolic engineering approaches to develop future complex patterned biomaterials.

This dissertation focuses on Boolean networks with a view to their applications in Systems Biology. We study two notions of stability, based on Hamming distance and on maintenance of a stable period length. Algorithms are given for the determination of Boolean networks from both complete and partial dynamics. The dynamics of ring networks are systematically studied. An algebraic structure is developed for derivation of adjacency matrices for the dynamics of Boolean networks from simple building blocks, both by edge-swapping and by gluing simple building blocks. Some results are implemented in Python and conclusions drawn for theta networks, a class of networks only slightly more complex than rings. A short section on applications to a known biological system closes the dissertation.

How organisms adapt and survive in continuously fluctuating environments is a central question of evolutionary biology. Additionally, organisms have to deal with the inherent stochasticity in all cellular processes. The purpose of this thesis is to gain insights into how organisms can use epigenetics and the stochasticity of gene expression to deal with a fluctuating environment. To accomplish this, two cases at different temporal and structural scales were explored: (1) the early transcriptional response to an environmental stimulus in single cells, and (2) the evolutionary dynamics of a population adapting to a recurring fluctuating environment. Mathematical models of stochastic gene expression, population dynamics, and evolution were developed to explore these systems.

First, the information available in sparse single cell measurements was analyzed to better characterize the intrinsic stochasticity of gene expression regulation. A mathematical and statistical model was developed to characterize the kinetics of a single cell, single gene behavior in response to a single environmental stimulus. Bayesian inference approach was used to deduce the contribution of multiple gene promoter states on the experimentally measured cell-to-cell variability. The developed algorithm robustly estimated the kinetic parameters describing the early gene expression dynamics in response a stimulus in single neurons, even when the experimental samples were small and sparse. Additionally, this algorithm allowed testing and comparing different biological hypotheses, and can potentially be applied to a variety of systems.

Second, the evolutionary adaptation dynamics of epigenetic switches in a recurrent fluctuating environment were studied by observing the evolution of gene regulatory circuit in a population under multiple environmental cycles. The evolutionary advantage of using epigenetics to exploit the natural noise in gene expression was tested by competing this strategy against the classical genetic adaptation through mutations in a variety of evolutionary conditions. A trade-off between minimizing the adaptation time after each environmental transition and increasing the robustness of the phenotype during the constant environment between transitions was observed. Surviving lineages evolved bistable, epigenetic switching to adapt quickly in fast fluctuating environments, whereas genetic adaptation with high robustness was favored in slowly fluctuating environments.