Browsing by Author "Prasad, Ashok, advisor"

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Open Access
A kinetic model development of the M13 bacteriophage life cycle
(Colorado State University. Libraries, 2014) Smeal, Steven William, author; Fisk, Nick, advisor; Prasad, Ashok, advisor; Gentry-Weeks, Claudia, committee member
A kinetic model which can simulate the M13 bacteriophage (a virus which only infects bacteria) life-cycle was created through a set of ordinary differential equations. The M13 bacteriophage is a filamentous phage with a circular single-stranded DNA genome. The kinetic model was developed by converting the biology into ordinary differential equations through careful studying of the existing literature describing the M13 life cycle. Most of the differential equations follow simple mass-action kinetics but some have an additional function, called the Hill Function, to account for special scenarios. Whenever possible, the rate constants associated with each ordinary differential equation were based off of experimentally determined constants. The literature describing M13 viral infection did not provide all of the rate constants necessary for our model. The parameters which were not experimentally determined through literature were estimated in the model based on what is known about the process. At present, no experiments were performed by our lab to verify the model or expand on the information available in the literature. However, the M13 phage model has improved the understanding of phage biology and makes some suggestions about the unknown factors that are most important to quantitatively understanding phage biology. The kinetic model is genetically structured and simulates all well-known and major features of viral phage infection beginning when the first viral ssDNA has entered the cytoplasm and ends right before the cell is ready to divide. The model includes DNA replication, transcription, translation, mRNA processing and degradation, viral protein P2 and viral DNA interaction, viral protein P5 and viral single-strand DNA (ssDNA) interaction, P5 and mRNA interactions, and the assembly of new phage. Additionally, the model has implemented an interaction of P2 and P10, which has not been directly verified through experiments, to account for the negative effect P10 has on DNA replication. The interaction of the host cell and virus infection was not explicitly modeled, but a subset of cellular resources were set aside for phage reproduction based on experimental estimates of the metabolic burden of phage infection. Specifically, limited amount of host resources RNA polymerase, DNA polymerase 3, and ribosomes were allocated to phage reproduction. All other host resources such as nucleotides and amino acids were assumed to be in abundance and did not limit phage replication. The model was verified by comparing the output of the model to a set of existing experimental results in literature. The model reproduced both the experimentally measured levels of phage proteins and mRNA, and the timing and dynamics of virus production for the first cell cycle after infection. All of the unknown parameters were based off the model results at the end of the first cell cycle. When the model was extended to account for phage production through multiple cell divisions, the model predicts the cell has the ability to cure itself from the infection in 7 - 8 cell cycles, which we found literature supporting our results after we made the conclusions. Once the model was created we studied how host resources, RNA polymerase and ribosomes, were distributed during the infection process. We were also able to replicate an experiment describing the effects that the viral DNA binding protein P5 had on the translation of five other viral proteins in-silico. The role of P5 inhibition in the viral life-cycle is unclear and our in-depth analysis of P5 function has revealed a possible explanation of how P5 translational inhibition could be an evolutionary advantage. Additionally, we proposed a mechanism which has not been strongly suggested to exist in literature. We are anticipating the development of the model will aide in the progress of phage display on filamentous phage and we believe the current model can be easily amendable to account for other phage like phages such as Ike filamentous phage. We discuss further additions and modifications to the model that will allow more exact treatment of early events in the phage life-cycle and more explicit coupling of phage life-cycle and host biology.
Open Access
Characterizing biological systems: quantitative methods for synthetic genetic circuits in plants and intracellular mechanics
(Colorado State University. Libraries, 2018) Xu, Wenlong, author; Prasad, Ashok, advisor; Medford, June I., committee member; Reardon, Kenneth F., committee member; Munsky, Brian E., committee member
To view the abstract, please see the full text of the document.
Open Access
Computational approaches to predict drug response to cytotoxic chemotherapy
(Colorado State University. Libraries, 2020) Mannheimer, Joshua D., author; Gustafson, Daniel, advisor; Prasad, Ashok, advisor; Krapf, Diego, committee member; Thamm, Douglas, committee member
Cancer is the second leading cause of death in the United States. Statistically, within a lifetime there is slightly above a one-third chance of developing some form of cancer and a one in five chance of dying from the disease. Thus, it is no hyperbole that the understanding and treatment of cancer is one of the most pressing issues in medical research of the current era. Cytotoxic chemotherapies are a class of anti-cancer drugs that are widely used to treat a number of cancers. While cytotoxic chemotherapies are extremely effective in treating a subset of individuals for some cancers, drug resistance resulting in failure of treatment is a prominent obstacle in many cancer patients. Precision medicine, a novel concept to the 21st century, is the application of disease treatments that are specifically tailored to an individual and the specific attributes of their disease. In oncology, precision medicine particularly refers to the use of gene expression and other biological factors to inform an individual's treatment. Because cancer and its response to treatment result from many complex biological interactions, computational methods have become an essential tool to identify the molecular signatures that are the basis for precision treatment. In this thesis, a systematic analysis of the computational approaches is performed to gain insight necessary for the development of novel computational approaches in precision medicine in cancer. Statistical learning models are a class of computational modeling methods that identify and extrapolate complex patterns from large amounts of data. Specifically, this involves applying statistical learning approaches on in vitro data from cell lines and patient tumor data to predict drug response, particularly for cytotoxic chemotherapies, with an emphasis on understanding the fundamental modeling principles and data attributes driving model performance. The first chapter serves as an introduction to chemotherapy and the advancements that have driven computational approaches to precision applications in cancer. The second chapter serves as a technical introduction to statistical learning models and approaches. In the third chapter a systematic assessment of linear and non-linear modeling approaches are applied to in vitro cell lines panel including the National Cancer Institute's 60 cancer cell lines (NCI60) and cell lines of Genomics of Drug Sensitivity in Cancer (GDSC) to predict drug response in several cytotoxic chemotherapies. With in-depth analysis it is shown that the relationship between tumor tissue histotype and drug response is the major driver of model performance and can be maintained in as little as 250 random genes. The fourth chapter utilizes statistical models to explore the influence of drug induced gene perturbations on drug response models in comparison with basal gene expression. The findings indicate that drug induced changes in gene expression are superior predictors of drug response. Second, it is demonstrated that Boolean network representation of gene interactions show distinct topological differences between drug induced changes in gene expression and basal gene expression. Finally, in the fifth chapter, drug induced gene changes demonstrating high levels of connectivity in the previously developed networks are applied to derive a basal gene expression signature to predict response to combined gemcitabine and cisplatin chemotherapy treatment in patients with bladder cancer. These models show that this derived signature performs better than a random cohort of genes and in some situations genes derived directly from basal gene expression.
Open Access
Data analysis and predictive modeling for synthetic and naturally occurring biological switches
(Colorado State University. Libraries, 2016) Schaumberg, Katherine A., author; Prasad, Ashok, advisor; Medford, June, advisor; Shipman, Patrick, committee member; Antunes, Mauricio, committee member; Krapf, Diego, committee member
Biological switches are biochemical network motifs responsible for determining the chemical state of cells, and are a key part of every biological system. The impact of these biological switches on cell behavior is broad. For example, many diseases such as cancer are thought to be caused by a misregulation of the bio-chemical state in a cell or group of cells. Also cell fates in differentiating stem cells are controlled by biological switches. Because of their general importance the synthetic biology community has also constructed synthetic biological switches in living organisms. While there are different kinds of possible switches, in my thesis I study switches capable of stably generating two unique molecular states, also called bi-stable switches. Here these switches are studied from two perspectives. In Chapters 1-4 I present theoretical and experimental work on analysis of specific circuits that act like biological switches. In Chapter 5 I employ a data mining perspective to identify gene expression signatures of switches that are sensitive to cytotoxic cancer drugs. This dissertation starts with a computational analysis of the effect of leaky promoter expression on bi-stable biological switches. In several biological and synthetic systems gene transcription is never completely off, even when repressed. This residual expression is referred to here as leaky expression. Bi-stable systems would be expected to have some amount of leaky expression in their off state. However, the impact of leaky expression on the functioning and properties of biological switches has not been well studied. To help fill this gap we conducted a theoretical analysis of leaky expression’s effect on biological switches. Two switches, a positive feedback and negative inhibition-based switch were studied. We found that the different circuit topologies showed different advantages in terms of their ability to handle leaky expression. Next this dissertation describes work done in collaboration with the Medford lab at Colorado State University, to construct and characterize a library of genetic plant parts. These parts would later be used in construction of perhaps the first synthetic bi-stable toggle switch in a plant. As part of this study, experiments were designed and conducted for finding the nature of the experimental noise associated with the assays used to test these plant parts. A mathematical normalization was developed to estimate quantitative information on the performance of each part. Validation experiments were done to assess the usefulness of this method for predicting the behavior of stably transformed plants from higher throughput transient assays. In the end a library of over one hundred quantitatively characterized plant parts in both Arabidopsis and Sorghum was constructed. The quantitative parameters of this library of genetic parts were then used in combination with a probabilistic bootstrap method we developed to predict optimal part combinations for construction of a bi-stable switch in Arabidopsis. The dissertation concludes with a study of biological networks in cancer cells from a data mining perspective. A large amount of data exists in the public domain on the sensitivity of cancer cell lines to cytotoxic drugs. Some cancers appear to be in a "sensitive state" while others are in a "resistant state". We would like to be able to know the gene expression signatures of these two states in order to predict cancer drug sensitivity from gene expression data. As a first step towards this goal we assessed the repeatability of predictions between the two standard databases of cancer cell lines, the NCI60 and the GDSC. This lead to identification of a preprocessing method needed to combine data from multiple databases. This was then followed up with the development of a comparative analysis platform. This platform was used to test the accuracy of models designed to predict drug sensitivity, when different model construction methods were used.
Open Access
Development of single cell shape measures and quantification of shape changes with cancer progression
(Colorado State University. Libraries, 2018) Alizadeh, Elaheh, author; Prasad, Ashok, advisor; DeLuca, Jennifer, committee member; Munsky, Brian, committee member; Snow, Christopher D., committee member
In spite of significant recent progress in cancer diagnostics and treatment, it is still the second leading cause of death in the United States. Some of the complexity of cancer arises from its heterogeneity. Cancer tumors in each patient are different than other patients. Even different tumors from one patient could differ from each other. Such a high diversity of tumors makes it challenging to correctly characterize cancer and come up with the best treatment plan for each patient. In order to do that, a complex combination of clinical and histopathological data need to be collected. This dissertation provides the evidence that the shape of the cells can be used in conjunction with other methods for a more reliable cancer characterization. In this study, experimental studies, numerical representation of the cell shape, big data analysis methods, and machine learning techniques are combined to provide a tool to better characterize cancer cells using their shape information. It provides evidence that cell shape encodes information about the cell phenotype, and demonstrates that the former can be used to predict the latter. This dissertation proposes detailed quantitative methods for quantifying the shape and structure of a cell and its nucleus. These features are classified into three main categories of textural, spreading and irregularity measures, which are then sub-categorized into nine different shape categories. Textural measures are used to quantify changes in actin organization for the cells perturbed with cytoskeletal drugs. Using the spreading and irregularity measures, it is shown that the changes in actin structure lead to significant changes in irregularity of the boundary of a cell and spreading of the cell and nuclei. Using these methods, the shape of retina, breast, and osteosarcoma cancer cells are quantified and it is shown that the majority of cells have similar changes in their shape once they become cancerous. Then, a neural network is trained on the shape of the cells which leads to an excellent prediction of class of cancer cells. This study shows that even though cancer cells have different characteristics, they can be categorized into clinically relevant subgroups using their shape information alone.
Open Access
Genome-scale metabolic modeling of cyanbacteria: network structure, interactions, reconstruction and dynamics
(Colorado State University. Libraries, 2016) Joshi, Chintan Jagdishchandra, author; Prasad, Ashok, advisor; Peebles, Christie A. M., committee member; Reardon, Kenneth, committee member; Peers, Graham, committee member
Metabolic network modeling, a field of systems biology and bioengineering, enhances the quantitative predictive understanding of cellular metabolism and thereby assists in the development of model-guided metabolic engineering strategies. Metabolic models use genome-scale network reconstructions, and combine it with mathematical methods for quantitative prediction. Metabolic system reconstructions, contain information on genes, enzymes, reactions, and metabolites, and are converted into two types of networks: (i) gene-enzyme-reaction, and (ii) reaction-metabolite. The former details the links between the genes that are known to code for metabolic enzymes, and the reaction pathways that the enzymes participate in. The latter details the chemical transformation of metabolites, step by step, into biomass and energy. The latter network is transformed into a system of equations and simulated using different methods. Prominent among these are constraint-based methods, especially Flux Balance Analysis, which utilizes linear programming tools to predict intracellular fluxes of single cells. Over the past 25 years, metabolic network modeling has had a range of applications in the fields of model-driven discovery, prediction of cellular phenotypes, analysis of biological network properties, multi-species interactions, engineering of microbes for product synthesis, and studying evolutionary processes. This thesis is concerned with the development and application of metabolic network modeling to cyanobacteria as well as E. coli. Chapter 1 is a brief survey of the past, present, and future of constraint-based modeling using flux balance analysis in systems biology. It includes discussion of (i) formulation, (ii) assumption, (iii) variety, (iv) availability, and (v) future directions in the field of constraint based modeling. Chapter 2, explores the enzyme-reaction networks of metabolic reconstructions belonging to various organisms; and finds that the distribution of the number of reactions an enzyme participates in, i.e. the enzyme-reaction distribution, is surprisingly similar. The role of this distribution in the robustness of the organism is also explored. Chapter 3, applies flux balance analysis on models of E. coli, Synechocystis sp. PCC6803, and C. reinhardtii to understand epistatic interactions between metabolic genes and pathways. We show that epistatic interactions are dependent on the environmental conditions, i.e. carbon source, carbon/oxygen ratio in E. coli, and light intensity in Synechocystis sp. PCC6803 and C. reinhardtii. Cyanobacteria are photosynthetic organisms and have great potential for metabolic engineering to produce commercially important chemicals such as biofuels, pharmaceuticals, and nutraceuticals. Chapter 4 presents our new genome scale reconstruction of the model cyanobacterium, Synechocystis sp. PCC6803, called iCJ816. This reconstruction was analyzed and compared to experimental studies, and used for predicting the capacity of the organism for (i) carbon dioxide remediation, and (ii) production of intracellular chemical species. Chapter 5 uses our new model iCJ816 for dynamic analysis under diurnal growth simulations. We discuss predictions of different optimization schemes, and present a scheme that qualitatively matches observations.
Open Access
Investigating the osteogenic potential of multipotent mesenchymal stromal cells through the use of DNA microarray technology and biomaterial nanotopography
(Colorado State University. Libraries, 2011) Berger, Dustin, author; Prasad, Ashok, advisor; Popat, Ketul, advisor; Deluca, Jennifer, committee member
To view the abstract, please see the full text of the document.
Open Access
Mathematical and experimental studies in cellular decision making
(Colorado State University. Libraries, 2017) Lyons, Samanthe Merrick, author; Prasad, Ashok, advisor; Medford, June, committee member; Kisiday, John, committee member; Snow, Chris, committee member
The biological sciences are undergoing an epistemological revolution. Mathematical modeling, quantitative experiments and data analysis, machine learning and other methods of "big-data" modeling are slowly but surely changing the way the biological and biomedical sciences and engineering are being carried out. This thesis presents work that seeks to advance understanding of biological processes using mathematical modeling as well as experiments coupled with sophisticated quantitative analysis. The central theme of the research presented is cellular decision-making. A cellular decision is defined here as a transition from one cell state, or phenotype, to another, based upon information received from an external or internal signal. This work explores the mechanisms behind cellular decisions with three specific systems and a variety of mathematical and modeling techniques. This dissertation begins with a brief survey of the use of mathematical modeling in cellular biology, utilizing specific example of various approaches. This reviews the diversity of techniques available from detailed mechanistic models to simplified phenomenological representations, and notes some applications demonstrating the utility of such models. The first exploration of cellular decisions is concerned with the question of how cells can make decisions in the face of cross-talk from multiple signals. The real cellular environment is noisy, with stochastically varying levels of external signals and cellular decisions required in spite of this noise. In Chapter 3 the ubiquitous bacterial two-component signaling system and the similarly structured mammalian TGF-β pathway are modeled with stochastic simulations of the chemical master equation. Information theory is utilized to quantify the amount of information transmitted by these signaling systems in the presence of competing signals from cross-talk, revealing that the mammalian TGF- pathway was able to transmit information accurately despite high levels of cross-talk, while the bacterial two-component system, due to a smaller system size and the structure of phospho-transfer rather than phospho-relay, was poor at discriminating from competing cross-talk. This work presents a novel thesis: many signal transduction systems suffer less from cross-talk than was commonly imagined, and may actually make use of cross-talk for cross-regulation. The second system of cellular decisions studied in this work is a bistable synthetic toggle switch network motif composed of mutually repressible promoters in Chapter 4. This motif has been widely studied in isolation for its dynamical and static properties. However, the behavior of these switches has never previously been analyzed when coupled with a downstream binding partner, termed a "load". Real toggle switches, whether synthetic or natural, always have loads connected with them. The toggle-switch system was modeled mathematically with ordinary differential equations as well as using stochastic simulations of the chemical master equation to determine the effect of a load. The quasi-potential energy landscape of the bistable switch was calculated utilizing a novel method which revealed that, in some parameter spaces, a downstream component can significantly alter the stability of the switch; addition of a positive feedback loop could provide for a tunable switch. Chapter 5 is concerned with developing methods for identifying a complex cellular transition from less metastatic to more metastatic cancer cells. The importance of metastatic disease in the pathology of cancer cannot be understated as it is the cause of 90% of deaths from cancer. The process by which cancerous cells become metastatic is complex, but requires specific cellular mechanical conditions in order to occur. The use of cancer cell shape to predict metastatic behavior in pathology samples is a key component of prognostication, however in vitro cancer cell shape is less commonly studied. This work developed a mathematical algorithm to extract shape parameters from images of cancer cells and applied multiple statistical techniques to elucidate differences between metastatic and non-metastatic cancer cells. While both simple and complex statistical techniques including t-tests, principle component analysis (PCA) and non-metric multidimensional scaling (NMDS) revealed distinct changes, the population of cells from highly metastatic and less metastatic paired osteosarcoma cells showed significant overlap. Machine learning algorithms were, however, able to successfully classify samples of cells to high or low metastatic lines with high accuracy. The concluding chapter presents a brief analysis of the new questions that this research has elucidated, and delineates some future tasks to address them.
Open Access
Optimizing a synthetic signaling system, using mathematical modeling to direct experimental work
(Colorado State University. Libraries, 2014) Havens, Keira, author; Medford, June, advisor; Prasad, Ashok, advisor; Antunes, Mauricio, committee member; Peersen, Olve, committee member
Synthetic biology uses engineering principles and biological parts to probe existing biological networks and build new biological systems. As biological components become better characterized, synthetic biology can make use of predictive mathematical models to analyze the activity of biological systems. This thesis demonstrates the utility of modeling in optimizing a synthetic signaling system for a bacterial testing platform and advances the use of model-based bacterial systems as an effective tool of plant synthetic biology. Using models in combination with experimental data, I showed that increasing the concentration of a single component of the synthetic signaling system, the PBP, results in a 100 fold increase in sensitivity, and an order of magnitude increase in fold change response in the response of the bacterial testing platform. Additional mathematical exploration of the system identified another component, the number of PhoB inducible promoters, which could be adjusted to further increase maximum signal. In addition, our model has suggested additional avenues of research, including the potential to introduce new functions, such as memory, to the existing circuit. In this way the prototype synthetic signaling system developed by the Medford Lab has been refined to improve detection and generate substantial response, moving the technology closer to real-world use. Once validated, this modeling based protocol, using a microbial platform for developing and optimizing plant synthetic systems, will serve as a foundation for engineering advanced plant synthetic systems.
Open Access
Slow and noisy: developmental time and gene expression kinetics in big cells
(Colorado State University. Libraries, 2023) Taylor, Alexandra, author; Mueller, Rachel, advisor; Prasad, Ashok, advisor; Hoke, Kim, committee member; Krapf, Diego, committee member
Evolutionary increases in genome size, cell volume, and nuclear volume have been observed across the tree of life, with positive correlations documented between all three traits. It is well documented that developmental tempo slows as genomes, nuclei, and cells increase in size, yet the driving mechanisms are poorly understood. Meanwhile, the dramatic increases in cell volume seen across the tree of life pose interesting questions about a potential relationship between cell volume and stochastic noise at the single cell level, but this remains an underexplored area of research. To bridge these knowledge gaps, we use a mix of deterministic and stochastic, as well as species-specific and more general, models of the somitogenesis clock. In doing so, we explore the impact of changing intra-cellular gene expression kinetics induced by increasing genome size, nuclear volume, and cell volume on developmental tempo and gene expression noise. Results suggest that longer transcriptional and nuclear export times act to slow cell and developmental processes down as genome size and cell volume increase, and that "search processes" undergone by gene products within a cell become noisier with increasing volume. Analyses of stochastic model simulations and existing empirical data bring into question whether or not cell-autonomous oscillations can truly exist in the absence of cell-to-cell signaling.