Phosphatidylinositol (4,5)-bisphosphate turnover by INP51 regulates the cell wall integrity pathway in "Saccharomyces cerevisiae"

Signal transduction pathways are important for the cell to transduce external or internal stimuli where second messengers play an important role as mediators of the stimuli. One important group of second messengers are the phosphoinositide family present in organisms ranging from yeast to mammals. The dephosphorylation and phosphorylation cycle of the phosphatidylinositol species are thought to be important in signaling for recruitment or activation of proteins involved in vesicular transport and/or to control the organization of the actin cytoskeleton. In mammals, phosphatidylinositol (4,5)bisphosphate (PI(4,5)P2) signaling is essential and regulated by various kinases and phosphatases. In the model organism Saccharomyces cerevisiae PI(4,5)P2 signaling is also essential but the regulation remains unclear. My dissertation focuses on the regulation of PI(4,5)P2 signaling in Saccharomyces cerevisiae. The organization of the actin cytoskeleton in Saccharomyces cerevisiae is regulated by different proteins such as calmodulin, CMD1, and here I present data that CMD1 plays a role in the regulation of the only phosphatidylinositol 4-phosphate 5-kinase, MSS4, in Saccharomyces cerevisiae. CMD1 regulates MSS4 activity through an unknown mechanism and thereby controls the organization of the actin cytoskeleton. MSS4 and CMD1 do not physically interact but MSS4 seems to be part of a large molecular weight complex as shown by gel filtration chromatography. This complex could contain regulators of the MSS4 activity. The complex is not caused by dimerization of MSS4 since MSS4 does not interact with itself. Two pathways, the cell wall integrity pathway and TORC2 (target of rapamycin complex 2) signaling cascade are important for the organization of the actin cytoskeleton. Loss of TOR2 function results in a growth defect that can be suppressed by MSS4 overexpression. To further characterize the link between MSS4 and the TORC2 signaling pathway and the cell wall integrity pathway we looked for targets of PI(4,5)P2. The TORC2 pathway and the cell wall integrity pathway signal to the GEF ROM2, an activator of the small GTPase RHO1. In our study we identified ROM2 as a target of PI(4,5)P2 signaling. We observed that the ROM2 localization changes in an mss4 conditional mutant. This suggests that the proper localization needs PI(4,5)P2. This could be mediated by the putative PI(4,5)P2 binding pleckstrin homology (PH) domain of ROM2. To better understand the regulation of PI(4,5)P2 levels in Saccharomyces cerevisiae we focused on one of the PI(4,5)P2 5-phosphatases, INP51. Here we present evidence that INP51 is a new negative regulator of the cell wall integrity pathway as well as the TORC2 pathway. INP51 probably regulates these two pathways by the turnover of PI(4,5)P2 thereby inactivating the effector/s. The deletion of INP51 does not result in any phenotype, but when combined with mutations of the cell wall integrity pathway we observe synthetic interaction. INP51 together with the GTPase activating protein (GAP) SAC7, responsible for the negative regulation of RHO1, negatively regulates the cell wall integrity pathway during vegetative growth. One of the targets of cell wall integrity pathway, the cell wall component chitin, which is normally deposited at the bud end, bud neck and forms bud scars, is delocalized in the mother cell in the sac7 inp51 double deletion mutant. In addition, another downstream component of the cell wall integrity pathway, the MAP kinase MPK1, has increased phosphorylation and protein level in the sac7 inp51 double deletion mutant. This suggests that INP51 is important for the negative regulation of the cell wall integrity pathway. Furthermore, we show evidence that INP51 forms a complex with TAX4 or IRS4, with two EH-domain containing proteins, that positively regulates the activity of INP51 and in this manner negatively regulate the cell wall integrity pathway. The EH-domain is known to bind the NPF-motif. This motif is present in INP51 and is important for INP51 interaction with TAX4 or IRS4. The EH-NPF interaction is a conserved mechanism to build up protein networks. The interaction between an EH-domain containing protein and a PI(4,5)P2 5-phosphatase is conserved. This is demonstrated by the epidermal growth factor substrate EPS15 (EH) interaction with the PI(4,5)P2 5-phosphatase synaptojanin the mammalian orthologue of the Saccharomyces cerevisiae INP proteins. In summary, INP51 together with TAX4 and IRS4, forms complexes important for regulation of PI(4,5)P2 levels. The complexes are linked to the TORC2 signaling pathway and the cell wall integrity pathway, specifically regulating MPK1 activation and chitin biosynthesis. The work presented in this dissertation facilitates the development of a model of the complex regulation of PI(4,5)P2 signaling in Saccharomyces cerevisiae

Adaptive fuzzy interpolation

Fuzzy interpolative reasoning strengthens the power of fuzzy inference by the enhancement of the robustness of fuzzy systems and the reduction of the systems' complexity. However, after a series of interpolations, it is possible that multiple object values for a common variable are inferred, leading to inconsistency in interpolated results. Such inconsistencies may result from defective interpolated rules or incorrect interpolative transformations. This paper presents a novel approach for identification and correction of defective rules in interpolative transformations, thereby removing the inconsistencies. In particular, an assumption-based truth-maintenance system (ATMS) is used to record dependences between interpolations, and the underlying technique that the classical general diagnostic engine (GDE) employs for fault localization is adapted to isolate possible faulty interpolated rules and their associated interpolative transformations. From this, an algorithm is introduced to allow for the modification of the original linear interpolation to become first-order piecewise linear. The approach is applied to a realistic problem, which predicates the diarrheal disease rates in remote villages, to demonstrate the potential of this study

Adaptive Fuzzy Interpolation and Extrapolation with Multiple-antecedent Rules

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning owning to its efficient identification and correction of defective interpolated rules during the interpolation process. This approach assumes that: i) two closest adjacent rules which flank the observation or a previously inferred result are always available; ii) only single-antecedent rules are involved. In practice, however, variable values of these rules may lie just on one side of the observation or inferred result. Also, there may be certain rules with multiple antecedents in the rule base. This paper extends the adaptive approach, in order to cover fuzzy extrapolation and to support rule base with multiple-antecedent rules. Adaptive fuzzy interpolation and extrapolation complement each other, which jointly improve the applicability of fuzzy interpolative reasoning, as it significantly reduces the restriction over the given rule base

Adaptive Fuzzy Interpolation with Uncertain Observations and Rule Base

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It views interpolation procedures as artificially created system components, and identifies all possible sets of faulty components that may each have led to all detected contradictory results. From this, a modification procedure takes place, which tries to modify each of such components, termed candidates, in an effort to remove all the contradictions and thus restore consistency. This approach assumes that the employed interpolation mechanism is the only cause of contradictions, that is all given observations and rules are believed to be true and fixed. However, this may not be the case in certain real situations. It is common in fuzzy systems that each observation or rule is associated with a certainty degree. This paper extends the adaptive approach by taking into consideration both observations and rules also, treating them as diagnosable and modifiable components in addition to interpolation procedures. Accordingly, the modification procedure is extended to cover the cases of modifying observations or rules in a given rule base along with the modification of fuzzy reasoning components. This extension significantly improves the robustness of the existing adaptive approach

Exploring resilience for effective learning in computer science education

Background and context: Many factors have been shown to be important for supporting effective learning and teaching – and thus progression and success – in formal educational contexts. While factors such as key introductory-level computer science knowledge and skills, as well as pre-university learning and qualifications, have been extensively explored, the impact of measures of positive psychology are less well understood for the discipline of computer science. This preliminary work investigates the relationships between effective learning and success, and two measures of positive psychology, Grit (Duckworth’s 12-item Grit scale) [6] and the Nicolson McBride Resilience Quotient (NMRQ) [3], in success in first-year undergraduate computer science to provide insight into the factors that impact on the transition from secondary education into tertiary education

Adaptive Fuzzy Interpolation with Prioritized Component Candidates

Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It first identifies all possible sets of faulty fuzzy reasoning components, termed the candidates, each of which may have led to all the contradictory interpolations. It then tries to modify one selected candidate in an effort to remove all the contradictions and thus restore interpolative consistency. This approach assumes that all the candidates are equally likely to be the real culprit. However, this may not be the case in real situations as certain identified reasoning components may be more liable to resulting in inconsistencies than others. This paper extends the adaptive approach by prioritizing all the generated candidates. This is achieved by exploiting the certainty degrees of fuzzy reasoning components and hence of derived propositions. From this, the candidate with the highest priority is modified first. This extension helps to quickly spot the real culprit and thus considerably improves the approach in terms of efficiency

Dendritic Cell Algorithm with Optimised Parameters using Genetic Algorithm

Intrusion detection systems are developed with the abilities to discriminate between normal and anomalous traffic behaviours. The core challenge in implementing an intrusion detection systems is to determine and stop anomalous traffic behavior precisely before it causes any adverse effects to the network, information systems, or any other hardware and digital assets which forming or in the cyberspace. Inspired by the biological immune system, Dendritic Cell Algorithm (DCA) is a classification algorithm developed for the purpose of anomaly detection based on the danger theory and the functioning of human immune dendritic cells. In its core operation, DCA uses a weighted sum function to derive the output cumulative values from the input signals. The weights used in this function are either derived empirically from the data or defined by users. Due to this, the algorithm opens the doors for users to specify the weights that may not produce optimal result (often accuracy). This paper proposes a weight optimisation approach implemented using the popular stochastic search tool, genetic algorithm. The approach is validated and evaluated using the KDD99 dataset with promising results generated

Generalized Adaptive Fuzzy Rule Interpolation

As a substantial extension to fuzzy rule interpolation that works based on two neighbouring rules flanking an observation, adaptive fuzzy rule interpolation is able to restore system consistency when contradictory results are reached during interpolation. The approach first identifies the exhaustive sets of candidates, with each candidate consisting of a set of interpolation procedures which may jointly be responsible for the system inconsistency. Then, individual candidates are modified such that all contradictions are removed and thus interpolation consistency is restored. It has been developed on the assumption that contradictions may only be resulted from the underlying interpolation mechanism, and that all the identified candidates are not distinguishable in terms of their likelihood to be the real culprit. However, this assumption may not hold for real world situations. This paper therefore further develops the adaptive method by taking into account observations, rules and interpolation procedures, all as diagnosable and modifiable system components. Also, given the common practice in fuzzy systems that observations and rules are often associated with certainty degrees, the identified candidates are ranked by examining the certainty degrees of its components and their derivatives. From this, the candidate modification is carried out based on such ranking. This work significantly improves the efficacy of the existing adaptive system by exploiting more information during both the diagnosis and modification processes

A Comparative Study of Genetic Algorithm and Particle Swarm optimisation for Dendritic Cell Algorithm

Dendritic cell algorithm (DCA) is a class of artificial immune systems that was originally developed for anomaly detection in networked systems and later as a general binary classifier. Conventionally, in its life cycle, the DCA goes through four phases including feature categorisation into artificial signals, context detection of data items, context assignment, and finally labeling of data items as either abnormal or normal class. During the context detection phase, the DCA requires users to manually pre-define the parameters used by its weighted function to process the signals and data items. Notice that the manual derivation of the parameters of the DCA cannot guarantee the optimal set of weights being used, research attention has thus been attracted to the optimisation of the parameters. This paper reports a systematic comparative study between Genetic algorithm (GA) and Particle Swarm optimisation (PSO) on parameter optimisation for DCA. In order to evaluate the performance of GADCA and PSO-DCA, twelve publicly available datasets from UCI machine learning repository were employed. The performance results based on the computational time, classification accuracy, sensitivity, F-measure, and precision show that, the GA-DCA overall outperforms PSO-DCA for most of the datasets