3 research outputs found

    Evolutionary computation applied to combinatorial optimisation problems

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    This thesis addresses the issues associated with conventional genetic algorithms (GA) when applied to hard optimisation problems. In particular it examines the problem of selecting and implementing appropriate genetic operators in order to meet the validity constraints for constrained optimisation problems. The problem selected is the travelling salesman problem (TSP), a well known NP-hard problem. Following a review of conventional genetic algorithms, this thesis advocates the use of a repair technique for genetic algorithms: GeneRepair. We evaluate the effectiveness of this operator against a wide range of benchmark problems and compare these results with conventional genetic algorithm approaches. A comparison between GeneRepair and the conventional GA approaches is made in two forms: firstly a handcrafted approach compares GAs without repair against those using GeneRepair. A second automated approach is then presented. This meta-genetic algorithm examines different configurations of operators and parameters. Through the use of a cost/benefit (Quality-Time Tradeoff) function, the user can balance the computational effort against the quality of the solution and thus allow the user to specify exactly what the cost benefit point should be for the search. Results have identified the optimal configuration settings for solving selected TSP problems. These results show that GeneRepair when used consistently generates very good TSP solutions for 50, 70 and 100 city problems. GeneRepair assists in finding TSP solutions in an extremely efficient manner, in both time and number of evaluations required

    Single-cell analysis of cell competition using quantitative microscopy and machine learning

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    Cell competition is a widely conserved, fundamental biological quality control mechanism. The cell competition assay of MDCK wild-type versus mutant MDCK Scribble-knockdown (ScribKD) relies on a mechanical mechanism of competition, which posits that the emergence of compressing stresses within the tissue at high confluency drive the competitive outcome. According to this mechanism, proliferating wild-type cells out-compete mutant ScribKD cells, resulting in their apoptosis and apical extrusion. Previous studies show that there is an increased division rate of wild-type cells in neighbourhoods with high numbers of ScribKD cells, but what still remains a mystery is whether this is a cause or consequence of increased apoptosis in the “loser” cell population. This project also interrogated the competitive assay of wild-type versus RasV12 , which is hypothesized to operate on a biochemical mechanism and results in the apical extrusion (but not apoptosis) of the loser RasV12 population. For both these mechanisms of competition it is still unknown which population of cells are driving the winner/loser outcome. Is the winner cell proliferation prompting the loser cell demise? Or is an autonomous loser elimination prompting a subsequent winner cell proliferation? In my research, I have employed multi-modal, time-lapse microscopy to image competition assays continuously for several days. These data were then segmented into wild-type or mutant instances using a Convolutional Neural Network (CNN) that can differentiate between the cell types, after which they were tracked across cellular generations using a Bayesian multi-object tracker. A conjugate analysis of fluorescent cell-cycle indicator probes was then utilised to automatically identify key time points of cellular fate commitment using deep-learning image classification. A spatio-temporal analysis was then conducted in order to quantify any correlation between wild-type proliferation and mutant cell demise. For the case of wild-type versus ScribKD , there was no clear evidence for the wild-type cells mitoses directly impacting upon the ScribKD cell apoptotic elimination. Instead, a subsequent analysis found that a more subtle mechanism of pre-emptive, local density increases around the apoptosis site appeared to be determining the eventual ScribKD fate. On the other hand, there was clear evidence of a direct impact of wild-type mitoses on the subsequent apical extrusion and competitive elimination of RasV12 cells. Both of these conclusions agree with the prevailing classification of cell competition types: mechanical interactions are more diffuse and occur over a larger spatio-temporal domain, whereas biochemical interactions are constrained to nearest neighbour cells. The hypothesized density-dependency of ScribKD elimination was further quantified on a single-cell scale by these analyses, as well as a potential new understanding of RasV12 extrusion. Most interestingly, it appears that there is a clear biophysical mechanism to the elimination in the biochemical RasV12 cell competition. This suggests that perhaps a new semantic approach is needed in the field of cell competition in order to accurately classify different mechanisms of elimination
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