published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / Zoology / Doctoral / Doctor of Philosophy

Modeling gene regulation is an important problem in genomic research. The Boolean network (BN) and its generalization Probabilistic Boolean network (PBN) have been proposed to model genetic regulatory interactions. BN is a deterministic model while PBN is a stochastic model. In a PBN, on one hand, its stationary distribution gives important information about the long-run behavior of the network. On the other hand, one may be interested in system synthesis which requires the construction of networks from the observed stationary distribution. This results in an inverse problem of constructing PBNs from a given stationary distribution and a given set of Boolean Networks (BNs), which is ill-posed and challenging, because there may be many networks or no network having the given properties and the size of the inverse problem is huge. The inverse problem is first formulated as a constrained least squares problem. A heuristic method is then proposed based on the conjugate gradient (CG) algorithm, an iterative method, to solve the resulting least squares problem. An estimation method for the parameters of the PBNs is also discussed. Numerical examples are then given to demonstrate the effectiveness of the proposed methods. However, the PBNs generated by the above algorithm depends on the initial guess and is not unique. A heuristic method is then proposed for generating PBNs from a given transition probability matrix. Unique solution can be obtained in this case. Moreover, these algorithms are able to recover the dominated BNs and therefore the major structure of the network. To further evaluate the feasible solutions, a maximum entropy approach is proposed using entropy as a measure of the fitness. Newton’s method in conjunction with the CG method is then applied to solving the inverse problem. The convergence rate of the proposed method is demonstrated. Numerical examples are also given to demonstrate the effectiveness of our proposed method. Another important problem is to find the optimal control policy for a PBN so as to avoid the network from entering into undesirable states. By applying external control, the network is desired to enter into some state within a few time steps. For PBN CONTROL, people propose to find a control sequence such that the network will terminate in the desired state with a maximum probability. Also, the problem of minimizing the maximum cost is considered. Integer linear programming (ILP) and dynamic programming (DP) in conjunction with hard constraints are then employed to solve the above problems. Numerical experiments are given to demonstrate the effectiveness of our algorithms. A hardness result is demonstrated and suggests that PBN CONTROL is harder than BN CONTROL. In addition, deciding the steady state probability in PBN for a specified global state is demonstrated to be NP-hard. However, due to the high computational complexity of PBNs, DP method is computationally inefficient for a large size network. Inspired by the state reduction strategies studied in [86], the DP method in conjunction with state reduction approach is then proposed to reduce the computational cost of the DP method. Numerical examples are given to demonstrate both the effectiveness and the efficiency of our proposed method. / published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy

Protein-protein interactions are central to their biological functions in cells. Many approaches have been applied to study protein-protein interactions in a genomic-scale. In an attempt to develop new strategies to study protein-protein interactions, FRET by using ECFP and EYFP as the donor and receptor was evaluated for possible application in protein-protein interaction study in a high-throughput fashion. Due to the intrinsic properties of ECFP and EYFP, FRET-based protein-protein interaction assay is not suitable for large-scale studies. Instead, tandem affinity purification coupled with mass spectrometry approach proved to be a useful strategy to identify protein interacting partners. Several transcription factor complexes in yeast were successfully purified and novel components in the complexes were identified by combining a shotgun mass spectrometry approach and a differential analysis of the mass spectrometry data. In particular, a negative regulator of G1 to S phase transition during cell cycle, Whi5p, was identified to be a component of SBF complex; a regulator of nitrogen metabolism, Gln3p, was identified to be a component of Hap2/3/5 complex that regulates carbon metabolism, suggesting a crosstalk between nitrogen and carbon metabolism. Additionally, one-step purification coupled with shotgun mass spectrometry analysis was applied to simplify and improve the affinity purification approach used for protein-protein interaction studies. In order to map protein complexes in their native state, a sucrose density gradient was used to separate protein complexes in cells. The proteins within each fraction from the sucrose density gradient were analyzed and quantified with mass spectrometry to obtain the protein abundance profiles across the gradient. The known protein complexes were identified by clustering the protein abundance profiles. This method could possibly be improved to become a generic approach to mapping protein complexes. The goal of protein-protein interaction studies is to determine the protein functions. In an effort to identify ribosome biogenesis genes from a yeast gene network reconstructed from diverse large-scale interaction data sets, at least 25 new ribosome biogenesis genes were confirmed by extensive experimental validations, underscoring the value of proteinprotein interaction studies and gene interaction network.

In unstimulated cells, the transcription factor NF-κB is held in the cytoplasm in an inactive state by IκB inhibitor proteins. Activation of NF--KB is mediated by signal induced degradation of IκBα via the ubiquitin proteasome-dependent pathway. Targeting the proteins for ubiquitin-mediated proteolysis is an irrevocable decision, and as such, the process needs to be highly specific and tightly regulated. This task is achieved by conjugation and deconjugation enzymes that act in a dynamic and coordinated mechanism. In a yeast two hybrid screen designed to identify proteins involved in IκBα signalling Ubch9 was found to interact with the N-terminal regulatory region of IκBα. Although Ubch9 is an enzyme homologous to E2 ubiquitin conjugating enzymes we have shown that is unable to form a thioester with ubiquitin but it is capable to form a thioester with the small ubiquitin-like protein SUMO- 1. To fully characterise the SUMO-1 modification reaction we have purified the proteins and cloned the genes encoding the SUMO-1 activating enzyme (SAEl/SAE2) and shown that it is homologous to enzymes involved in the activation of ubiquitin, Smt3p, the yeast SUMO-1 homologue, and Rublp/Nedd8, another ubiquitin-like protein. SUMO-1 is conjugated to target proteins by a pathway that is distinct from, but analogous to, ubiquitin conjugation. SUMO-1 was efficiently conjugated, both in vivo and in vitro, to IκBα on lysine 21, which is also utilised for ubiquitin modification. Thus, by blocking ubiquitination SUMO-1 modification acts antagonistically to generate a pool of IκBα resistant to proteasome-mediated degradation which consequently inhibits NF-κB dependent transcription activation. In view of several lines of similarity between NF-kB and p53, the involvement of SUMO-1 modification in the metabolism of the tumour supressor p53 was investigated. We have shown that p53 is modified by SUMO-1 at a single site, lysine 386 in the C-terminus of p53. Although p53 is regulated by ubiquitination, SUMO-1 and ubiquitin modification do not compete for the same lysine in p53. However, overexpression of SUMO-1 activates the transcriptional activity of wild type p53, but not K386R p53 where the SUMO-1 acceptor site has been mutated. A consensus sequence was obtained by comparison of the sequences surrounding the SUMO-1 acceptor lysine in proteins that have been shown to be modified by SUMO-1 and revealed a possible recognition site for SUMO-1 conjugation machinery. Tagging of proteins with SUMO-1 regulates transcriptional activation, either by interfering with subcellular location or with the ubiquitination pathway. The pathway may represent a novel target for drug development.

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / abstract / toc / Biochemistry / Master / Master of Philosophy

published_or_final_version / abstract / toc / Zoology / Doctoral / Doctor of Philosophy

published_or_final_version / Obstetrics and Gynaecology / Master / Master of Philosophy