Advanced Search

Analysis of robustness and stochasticity in biochemical networks

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.advisor Alexander van Oudenaarden. en_US Ong, Mei-Lyn en_US
dc.contributor.other Massachusetts Institute of Technology. Computational and Systems Biology Program. en_US 2012-04-26T18:50:55Z 2012-04-26T18:50:55Z 2012 en_US 2012 en_US
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references. en_US
dc.description.abstract Cells are constantly faced with the challenge of functioning reliably while being subject to unpredictable changes from within and outside. Here, I present two studies in which I analyze how biochemical circuits that regulate signaling and gene expression can generate robustness or phenotypic variability between otherwise identical yeast cells. Using the osmosensing signaling pathway which consists of a phosphorelay connected to a MAPK cascade, we predict signaling robustness to changes in kinetic rate constants by employing a computational sensitivity analysis. Consistent with the model predictions, we find that the input-output relation of signaling activation is severely impacted by protein coding sequence changes in the MAPK cascade genes, but not the phosphorelay genes. By decoupling the network into two separate modules, we show that an input-output analysis of each of the modules can generate the observed disparity in their tolerance to kinetic parameter variations. Our analysis suggests that the input-output relation of catalytic signaling pathways i.e. MAPK cascade are intrinsically sensitive to kinetic rate perturbations. By contrast, signaling governed by stoichiometric biochemical reactions i.e. phosphorelay exhibit robust input-output functions. We further find that cells challenged to alter their input-output function mostly recovered by gaining mutations in the MAPK cascade genes, which further supports our model. We next explore how HAC1 RNA splicing contributes to heterogeneity in the unfolded protein response (UPR). We adapt the single molecule FISH (sm-FISH) method to count endogenous spliced and unspliced HAC1 transcripts in single cells. We use a stochastic bursting-transcription-and-splicing model to determine the kinetic rates from the single cell measurements. We find that the cell-to-cell variability in the degree of splicing is tightly regulated in the presence of a UPR-inducing chemical agent, but is compromised under heat stress. By considering models including extrinsic noise at the splicing or transcriptional level, we show that the increased variability in the degree of splicing under heat stress can be generated by increased fluctuations in the splicing rate. Lastly, we present an approach using sm-FISH and protein synthesis inhibitors to measure translation and we show preliminary results suggesting its feasibility. en_US
dc.description.statementofresponsibility by Mei-Lyn Ong. en_US
dc.format.extent 131 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri en_US
dc.subject Computational and Systems Biology Program. en_US
dc.title Analysis of robustness and stochasticity in biochemical networks en_US
dc.type Thesis en_US Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Computational and Systems Biology Program. en_US
dc.identifier.oclc 784153952 en_US

Files in this item

Name Size Format Description
784153952-MIT.pdf 13.42Mb PDF Full printable version

This item appears in the following Collection(s)

Show simple item record