http://hdl.handle.net/1721.1/54823 Wed, 19 Jun 2013 03:11:40 GMT 2013-06-19T03:11:40Z http://hdl.handle.net/1721.1/79208 Computational modeling techniques for biological network productivity increases : optimization and rate-limiting reaction detection Cui, Yuanyuan, Ph.D. Massachusetts Institute of Technology The rapid development and applications of high throughput measurement techniques bring the biological sciences into a 'big data' era. The vast available data for enzyme and metabolite concentrations, fluxes, and kinetics under normal or perturbed conditions in biological networks provide unprecedented opportunities to understand the cell functions. On the other hand, it brings new challenges of handling, integrating, and interpreting the large amount of data to acquire novel biological knowledge. In this thesis, we address this problem with a new ordinary differential equation (ODE) model based on the mass-action rate law (MRL) of the biochemical reactions. It describes the detailed biochemical mechanisms of the enzyme reactions, and therefore reflects closely of how the enzymes work in the systems. Because the MRL models are constructed with elementary enzyme reaction steps, it is also much more flexible than the aggregated rate law (ARL) model to incorporate new enzyme interactions and regulations. Two versions of the MRL model ensembles for the central carbon metabolic network, which generates most of the precursors for the secondary metabolite, were constructed. The E. coli version contains the basic reactions in this network and was applied to optimize the aromatic amino acid production which requires fine-tuned flux partition between glycolysis pathway and the pentose phosphate pathway. The S. cerevisiae version is more sophisticated with the incorporated dynamics of the NAD/NADH and NADP/NADPH, as well as the automatic switch from aerobic to anaerobic condition. It was applied to maximize the ethanol production yield, for which the NAD/NADH ratio is a crucial regulating factor. In order to develop methodologies to understand the intrinsic network properties and optimize the network behavior, we further explored approaches for the identification of pathway bottlenecks. Four computational assays were studied, including metabolite accumulation, conditional Vmax, increased glucose input, and decreased E₀, which were applied to the ethanol model ensemble to discover their effectiveness in bottleneck identification in this network. The TDH reaction was detected as a major bottleneck restricting carbon flow towards the ethanol pathway and affecting NADH availability. To manipulate the network for desired production rates of target metabolites, we developed an optimization technique for mass-action rate law ODE models that allows parallel or sequential combinations of enzyme knock-out and over-/under-expression strategies to be conducted on the model. Many strategies were suggested to improve the aromatic amino acid production and help identify the two-direction flux feature of the pentose phosphate pathway. Strategies were also found to enhance the ethanol production yield above 95% of the theoretical yield. Although the two applications studied here are both in the field of metabolic engineering, it is anticipated that the mass-action rate law models for the central carbon metabolism can be extended to study the cancer metabolism. Preliminary studies show promising results for designing cancer clinical trial simulations with a combined model incorporating high level cancer progression and detailed cancer biochemical metabolism. Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2013.; Cataloged from PDF version of thesis.; Includes bibliographical references. Tue, 01 Jan 2013 00:00:00 GMT http://hdl.handle.net/1721.1/79208 2013-01-01T00:00:00Z http://hdl.handle.net/1721.1/79145 Predicting and testing determinants of histidine-kinase functions by leveraging protein sequence information Ashenberg, Orr All cells sense and respond to their environments using signal transduction pathways. These pathways control a sweeping variety of cellular processes across the domains of life, but the pathways are often built from a small, shared set of protein domains. At the core of tens of thousands of signal transduction networks in bacteria is a pair of proteins, a histidine kinase and a response regulator. Upon receiving an input signal, a histidine kinase autophosphorylates and then catalyzes transfer of its phosphoryl group to a cognate response regulator, which often activates a transcriptional response. Bacteria typically encode dozens of kinases and regulators, and the kinases function as dimers in all known examples. This dimeric state raises two functional questions. Do histidine kinases specifically form dimers? Once a kinase has dimerized, does a chain in the dimer phosphorylate itself (cis) or its partner chain (trans)? Specific kinase dimerization is likely important to avoid detrimental crosstalk between separate signaling pathways, and how autophosphorylation occurs is central to kinase activity. In my thesis, I have taken biochemical and evolutionary approaches to identify molecular determinants for both dimerization specificity and autophosphorylation. To study dimerization specificity, I developed an in vitro binding assay to measure kinase dimerization, and I then showed that a paralogous pair of kinases from E. coli specifically formed homodimers over heterodimers. Residues important for dimerization specificity were predicted by measuring amino acid coevolution within kinases, which leverages the enormous amount of sequence information available for the kinase family. Experimental verification of these predictions showed that a set of residues at the base of the kinase dimerization domain was sufficient to establish homospecificity. This same region of the kinase, in particular the loops at the base of the kinase dimer, was also important for determining autophosphorylation mechanism. Recent work showed that kinases could autophosphorylate either in cis or in trans, and I found that a trans kinase could be made to autophosphorylate in cis by replacing its loop with the loop from a cis kinase. I also found that two sets of orthologs, despite having significantly diverged loop sequences, had conserved their autophosphorylation mechanisms. This raised the possibility that kinase loops may be under selection to maintain the same autophosphorylation mechanism. Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, February 2013.; This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.; Cataloged from student-submitted PDF version of thesis. "September 2012."; Includes bibliographical references. Tue, 01 Jan 2013 00:00:00 GMT http://hdl.handle.net/1721.1/79145 2013-01-01T00:00:00Z http://hdl.handle.net/1721.1/77783 Integrative approaches for systematic reconstruction of regulatory circuits in mammals Santos Botelho Oliveira Leite, Ana Paula The reconstruction of regulatory networks is one of the most challenging tasks in systems biology. Although some models for inferring regulatory networks can make useful predictions about the wiring and mechanisms of molecular interactions, these approaches are still limited and there is a strong need to develop increasingly universal and accurate approaches for network reconstruction. This problem is particularly challenging in mammals, due to the higher complexity of mammalian regulatory networks and limitations in experimental manipulation. In this thesis, I present three systematic approachs to reconstruct, analyse and refine models of gene regulation. In Chapter 1, I devise a method for deriving an observational model from temporal genomic profiles. I use it to choose targets for perturbation experiments in order to determine a network controlling the responses of mouse primary dendritic cells to stimulation with pathogen components. In Chapter 2, I introduce the algorithm Exigo, for identifying essential interactions in regulatory networks reconstructed from experimental data where regulators have been silenced, using a network reduction strategy. Exigo outperforms previous approaches on simulated data, uncovers the core network structure when applied to real networks derived from perturbation studies in mammals, and improves the performance of network inference methods. Lastly, I introduce in Chapter 3 an approach to learn a module network from multiple highthroughput assays. Analysis of a diffuse large B-cell lymphoma dataset identifies candidate regulator genes, microRNAs and copy number aberrations with biological, and possibly therapeutic, importance. Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012.; Cataloged from PDF version of thesis.; Includes bibliographical references (p. 141-149). Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/77783 2012-01-01T00:00:00Z http://hdl.handle.net/1721.1/77782 c-Myc regulates transcriptional pause release and is a global amplifier of transcription Lin, Charles Yang Elevated expression of the c-Myc transcription factor occurs frequently in human cancers and is associated with tumor aggression and poor clinical outcome. However, the predominant mechanism by which c-Myc regulates global transcription in both healthy and tumor cells is poorly understood. In this thesis, I present evidence that c-Myc is a global regulator of RNA Polymerase II (RNA Pol II) transcriptional pause release. Transcriptional pausing occurs when additional regulatory steps are required to promote elongation of genes after transcription has initiated. Chapter 2 identifies transcriptional pausing as a general feature of transcription by RNA Pol II in mammalian cells. c-Myc is identified as having a major role in promoting release from pause at its target genes. Chapter 3 finds in tumor cells with elevated c-Myc, the transcription factor binds to promoters and enhancers of most actively transcribed genes. The predominant effect of c-Myc binding is to produce higher levels of transcription by promoting RNA Pol II transcriptional pause release. Thus, c-Myc accumulates in the promoter regions of active genes across the cancer cell genome and causes transcriptional amplification, producing increased levels of transcripts within the cells gene expression program. These results imply that transcriptional amplification can reduce rate-limiting constraints for tumor cell growth and proliferation. Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012.; Cataloged from PDF version of thesis.; Includes bibliographical references (p. 203-226). Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/77782 2012-01-01T00:00:00Z