Chemical & Biomolecular Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/2219
Formerly known as the Department of Chemical Engineering.
Browse
Search Results
Item Impact of Stochasticity on Gene Regulation Networks(2007-05-21) Fang, Xin; Zafiriou, Evanghelos; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We studied the impact of stochasticity on gene regulation networks, using the cell-to-cell communication mechanism in Escherichia coli as an example. First we explored signal mediated positive autoregulation networks and their stochastic bistability, in the presence of which an initially homogeneous cell population would evolve into two distinct subpopulations. We proposed the simplification of the full network into one that can be theoretically studied. Simulation results indicate the simplifications retain the bistability and the distribution shapes so that the simplified network can be used to predict the bistable behavior of the full network. Moreover, it was shown that the bistability can be influenced by the signal molecule number, and that stochastic simulation is necessary for bistable systems. The self-promotion network for SdiA protein, with the autoinducer-2 (AI-2) signal molecule, was used as an example. The results further motivate the need for modeling of the AI-2 uptake mechanism. We next explored cell age distribution in the case where the number of a key protein for cell division has a stochastic bifurcation. With this bifurcation, the alive probability function (the probability that the cell has not divided) can be written in a double-exponential form. This analytical form allow the use of Laplace transform to calculate an analytical cell age distribution from the population balance model. The computation results indicate that if the key division protein number has a bifurcation, there is likely to be a significant fraction of first-generation cells in the cell population. Finally, we developed deterministic and stochastic models for the regulation network of the AI-2 uptake in Escherichia coli . This network is regulated by a set of lsr genes, and we proposed that the LsrD protein needs to reach a threshold for uptake to take place. Based on the deterministic model, kinetic parameter values were estimated by fitting to experimental data from the literature. During the step-by-step fitting procedure, data for mutant cells and effective data for wild type cells were used to avoid the complexity of the full wild-type network. With the estimated parameters, the deterministic simulation results matched experimental data well, except for a steep change and spike. A stochastic model was also developed and simulation results showed a mild change and no spike for the population means. The difference between stochastic means and deterministic paths is due to the LsrD protein number threshold and indicates that stochastic simulation may be necessary for a monostable system if it has a threshold mechanism.Item Time-Series Transcriptomic Analysis of a Systematically Perturbed Arabidopsis thaliana Liquid Culture System: A Systems Biology Perspective(2007-05-16) Dutta, Bhaskar; Klapa, Maria I; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Revealing the gene regulation network has been one of the main objectives of biological research. Studying such a complex, multi-scale and multi-parametric problem requires educated fingerprinting of cellular physiology at different molecular levels under systematically designed perturbations. Conventional biology lacked the means for holistic analysis of biological systems. In the post-genomic era, advances in robotics and biology lead to the development of high-throughput molecular fingerprinting technologies. Transcriptional profiling analysis using DNA microarrays has been the most widely used among them. My Ph.D. thesis concerns the dynamic, transcriptional profiling analysis of a systematically perturbed plant system. Specifically, Arabidopsis thaliana liquid cultures were subjected to three different stresses, i.e. elevated CO2 stress, salt (NaCl) stress and sugar (trehalose) applied individually, while the latter two stresses were also applied in combination with the CO2 stress. The transcriptional profiling of these conditions involved carrying out 320 microarray hybridizations, generating thus a vast amount of transcriptomic data for Arabidopsis thaliana liquid culture system. To upgrade the dynamic information content in the data, I developed a statistical analysis strategy that enables at each time point of a time-series the identification of genes whose expression changes in statistically significant amount due to the applied stress. Additional algorithms allow for further exploration of the dynamic significance analysis results to extract biologically relevant conclusions. All algorithms have been incorporated in a software suite called MiTimeS, written in C++. MiTimeS can be applied accordingly to analyze time-series data from any other high-throughput molecular fingerprint. The experimental design combined with existing multivariate statistical analysis techniques and MiTimeS revealed a wealth of biologically relevant dynamic information that had been unobserved before. Due to the high-throughput nature of the analysis, the study enabled the simultaneous identification and correlation of parallel-occurring phenomena induced by the applied stress. Stress responses comparisons indicated that transcriptional response of the biological system to combined stresses is usually not the cumulative effect of individual responses. In addition to the significance of the study for the analysis of the particular plant system, the experimental and analytical strategies used provide a systems biology methodological framework for any biological system, in general.