Coupled Reversible and Irreversible Bistable Switches Underlying TGF-\beta-induced Epithelial to Mesenchymal Transition

Epithelial to mesenchymal transition (EMT) plays important roles in embryonic development, tissue regeneration and cancer metastasis. While several feedback loops have been shown to regulate EMT, it remains elusive how they coordinately modulate EMT response to TGF-\beta treatment. We construct a mathematical model for the core regulatory network controlling TGF-\beta-induced EMT. Through deterministic analyses and stochastic simulations, we show that EMT is a sequential two-step program that an epithelial cell first transits to partial EMT then to the mesenchymal state, depending on the strength and duration of TGF-\beta stimulation. Mechanistically the system is governed by coupled reversible and irreversible bistable switches. The SNAIL1/miR-34 double negative feedback loop is responsible for the reversible switch and regulates the initiation of EMT, while the ZEB/miR-200 feedback loop is accountable for the irreversible switch and controls the establishment of the mesenchymal state. Furthermore, an autocrine TGF-\beta/miR-200 feedback loop makes the second switch irreversible, modulating the maintenance of EMT. Such coupled bistable switches are robust to parameter variation and molecular noise. We provide a mechanistic explanation on multiple experimental observations. The model makes several explicit predictions on hysteretic dynamic behaviors, system response to pulsed stimulation and various perturbations, which can be straightforwardly tested.Comment: 32 pages, 8 figures, accepted by Biophysical Journa

An empirical study of negation in datalog programs

Datalog is the fusion of prolong and database technologies aimed at producing an difficultly logic-based, declarative language for databases. Since negation was added to Datalog, Datalog has become more expressive. In this thesis, I focus my attention on adding negation to DatalogIC which is a language which has been implemented by Mark P. Wassell, a past MSc student in the Department of Computer Science at UCT. I analyse and compare stratified, well-founded and inflationary semantics for negation, each of which has been implemented on top of INFORMIX; we call the resulting system NDatalog. According to the test results, we find that some results are unexpected. For example, when we evaluate a recursive stratified program, the results show that NDatalogstra is slower than NDatalogwellf although NDatalogwellf is more complex. After further investigation, I find the problem is that the NDatalog system has to spend a lot of time imitating the MINUS function, which does not exist in INFORMIX-SQL. So the running time depends on what kind of database system is used as backend. When we consider the time spent on pure evaluation, excluding auxiliary functions, we find that the results support our expectations, namely, that NDatalogstra is faster than NDatalogwellf which is faster than NDataloginf

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table