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Publication Embargo Integrative dynamics of action potential in Axon Initial Segment (AIS) of neurons: A novel computational model : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University(Lincoln University, 2023) Pashang, AbolghasemThe Axon Initial Segment (AIS) plays a critical role in neuronal excitability and action potential initiation. Computational modelling of the AIS can provide valuable insights into the biophysical mechanisms underlying these processes and their relevance to various AIS-related disorders. The Hodgkin-Huxley model laid the foundation for understanding action potential initiation, utilising empirical differential equations to describe sodium and potassium ion conductance and their gating functions. However, the specific role of the scaffold protein Ankrin-G (AnkG) in regulating ion channel function and ion currents in the AIS remains poorly understood. This thesis investigates the impact of AnkG concentration on sodium (Nav) and potassium (Kv) channel gating and ion current properties using electrophysiology, molecular biology, and computational modelling approaches. AnkG acts as a molecular bridge between the Nav and Kv channels and the cytoskeleton, ensuring proper channel localisation and density in the AIS. Disruptions to AnkG expression can lead to alterations in channel localisation and function, affecting neuronal excitability and firing properties. This thesis endeavors to unravel the mechanisms underlying AnkG's influence on Nav and Kv channel function and its subsequent impact on ion currents in the AIS. Understanding these interactions is crucial for comprehending the pathogenesis of AIS-related disorders and shed light on the complexities of action potential initiation and propagation in neurons.Publication Embargo Novel numerical methods for stochastic ordinary and partial differential equations in modelling complex systems : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University(Lincoln University, 2023) Tiwari, ParulMany natural and engineered systems are complex due to inherent uncertainty. Stochastic Differential Equations (SDEs) and Stochastic Partial Differential equations (SPDEs) provide a rigorous mathematical foundation for modelling these systems. Understanding the dynamics of complex systems under stochastic influences is crucial for predicting system behaviour. Numerical techniques often struggle to handle the complexity and stochastic nature of these equations. This research focuses on adapting and enhancing numerical methods to provide efficient and reliable solutions. The numerical accuracy and stability of these methods are assessed through simulations and examples. This study introduces the synthesis of stochastic spectral methods to solve complex systems by representing random variables as a sum of orthogonal polynomials. We applied Polynomial Chaos Expansion (PCE) methods to contaminant transport problem and to differential equations with random forcing term. We compute the Wick exponentials and show that Wick product coincides with the ordinary product for deterministic functions. We use Malliavin calculus to find the derivatives of a stochastic quantity and are visualised through graphs. We discuss numerical challenges associated with the PCE methods and their solution strategies. In all examples, our chosen method does better and allows us to lead the way in developing robust and efficient strategies to deal with randomness, ultimately enhancing the reliability and resilience of complex systems across various scientific and engineering domains.Publication Open Access Computational techniques in mathematical modelling of biological switches(MSSANZ, 2015-12) Chong, KH; Samarasinghe, Sandhya; Kulasiri, Don; Zheng, J; Weber, T; McPhee, MJ; Anderssen, RSMathematical models of biological switches have been proposed as a means to study the mechanism of decision making in biological systems. These conceptual models are abstract representations of the key components involved in the crucial cell fate decision underlying the biological system. In this paper, the methods of phase plane analysis and bifurcation analysis are explored and demonstrated using an example from the literature, namely the synthetic genetic circuit proposed by Gardner et al. (2000) which involved two negative loops (from two mutually inhibiting genes). Figure 1 shows a schematic diagram of the synthetic genetic circuit constructed by Gardner et al. (2000). Particularly, a saddle-node bifurcation is used as a signal response curve to capture the bistability of the system. The notion of bistability is obscure to most novice researchers or biologists because it is difficult to understand the existence of two stable steady states and how to flip from one stable steady state to another and vice versa. Thus, the main purpose of this paper is to unlock the computational techniques (bifurcation analysis implemented in a software tool called XPPAUT) in mathematical modelling of bistability through a simple example from Gardner et al. (2000). In addition, time course simulations are provided to illustrate: 1) the notion of bistability where the existence of two stable steady states and we demonstrated that for two different initial conditions one of the genes is ‘ON’ and the other gene is ‘OFF’; 2) hysteresis behaviour where the saddle-node bifurcation points as two critical points in which to turn ‘ON’ one gene happens at a larger parameter value than to turn ‘OFF’ this gene (at a lower parameter value). The hysteresis behaviour is important for irreversible decision made by cell to commit to turn ‘ON’. In conclusion, the understanding of the computational techniques in modelling biological switch is important for elucidating genetic switch that has potential for gene therapy and can provide explanation for experimental findings of bistable systems.Publication Open Access Modelling calmodulin dependent calcium signalling involved with synaptic plasticity : A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Lincoln University(Lincoln University, 2018) Stevens-Bullmore, Hamish EdwardNeurotransmission of synapses is plastic in that they get modulated to increase or decrease conductivity (this is known as synaptic plasticity). Synaptic plasticity consists of two opposing forces: long term potentiation (LTP) which strengthens synapses; and long term depression (LTD) which weakens synapses. LTP and LTD are associated with memory formation and loss respectively. Synaptic plasticity is controlled at a molecular by Ca²⁺-mediated protein signalling. Here, Ca²⁺ binds the protein, calmodulin (CaM) which modulates synaptic plasticity in both directions. This is because Ca²⁺- bound CaM activates both LTD- and LTP- inducing proteins. Understanding how CaM responds to Ca²⁺ signalling, and how this translates into synaptic plasticity is therefore important to understand synaptic plasticity induction. In this thesis, CaM activation by Ca²⁺ and calmodulin binding to downstream proteins was mathematically modelled using differential equations. CaM was first modelled in isolation to determine its kinetic binding properties with Ca²⁺. By performing local and global sensitivity analyses of Ca²⁺ binding/unbinding parameters, distinct binding properties between the two Ca²⁺ binding lobes were found. The difference between the binding lobes was exacerbated as intracellular Ca²⁺ stimulation rose. A full model of the two opposing pathways of synaptic plasticity was then employed. Simulations were monitored, and global sensitivity analyses were performed to determine how Ca²⁺/CaM signalling occured at various Ca²⁺ signals. At elevated stimulations, the total CaM pool rapidly bound to its protein binding targets which regulate both LTP and LTD. This was followed by CaM getting redistributed from low affinity to high affinity binding targets. Specifically, CaM was redistributed away from LTD- inducing proteins to bind the high affinity LTP-inducing protein, calmodulin dependent kinase II (CaMKII). In this way, CaMKII acted as a dominant affecter and repressed activation of opposing CaM binding protein targets. This model thereby showed a novel form of CaM signalling by which the two opposing pathways can crosstalk indirectly. The model also investigated how cAMP is regulated by CaM. It was found that CaMKII can repress cAMP production by repressing CaM-regulated proteins which catalyse cAMP production. The model also found that at low Ca²⁺ stimulation levels typical of LTD- induction, CaM-signalling was unstable and is therefore unlikely to alone be sufficient to induce synaptic depression. Overall, this thesis showed how limiting levels of CaM may be a fundamental aspect of Ca²⁺ regulated signalling which allows crosstalk among proteins without requiring to interact directly. Understanding synaptic plasticity can help understand neurodegenerative disease and although the current study is focused on synaptic plasticity, understanding CaM regulation has implications in numerous other cell types.Publication Open Access Contouring and earthwork estimation for bordered strip irrigation(Lincoln College, University of Canterbury, 1977) Harrington, G. J.Computer programmes were developed for processing data from grid, direct, and random stadia field contouring systems. The three systems were evaluated for their use in providing contour plans for bordered strip irrigation design. A computer method of calculating the earthwork volumes associated with bordered strip irrigation was developed which uses terrain data from the above surveying methods or any other convenient source. This method was compared with land grading to form plane or warped paddock surfaces onto which levees may be formed, thus creating bordered strips. With the aid of the bordered strip earthwork calculating programme, the effect of changes of bordered strip paddock layout and slope restraints was investigated. An attempt to correlate estimated earthworks with earthmoving machine times was made.Publication Open Access A nutrient dependant switch explains mutually exclusive existence of meiosis and mitosis initiation in budding yeast(Elsevier, 2014-01-21) Wannige, C; Kulasiri, Don; Samarasinghe, SandhyaNutrients from living environment are vital for the survival and growth of any organism. Budding yeast diploid cells decide to grow by mitosis type cell division or decide to create unique, stress resistant spores by meiosis type cell division depending on the available nutrient conditions. To gain a molecular systems level understanding of the nutrient dependant switching between meiosis and mitosis initiation in diploid cells of budding yeast, we develop a theoretical model based on ordinary differential equations (ODEs) including the mitosis initiator and its relations to budding yeast meiosis initiation network. Our model accurately and qualitatively predicts the experimentally revealed temporal variations of related proteins under different nutrient conditions as well as the diverse mutant studies related to meiosis and mitosis initiation. Using this model, we show how the meiosis and mitosis initiators form an all-or-none type bistable switch in response to available nutrient level (mainly nitrogen). The transitions to and from meiosis or mitosis initiation states occur via saddle node bifurcation. This bidirectional switch helps the optimal usage of available nutrients and explains the mutually exclusive existence of meiosis and mitosis pathways.Publication Open Access Remodelling circadian rhythm in Drosophila melanogaster: to investigate the role of a new clock component clockwork orange (CWO)(Lincoln University, 2015) Ranganathan, JeevabharathiThe ability of almost all organisms to change their behaviour on a daily basis is one of the remarkable features of life on earth. This phenomenon which is called circadian rhythm is observed in diverse organisms such as algae, fruit flies and humans and is a response arising due to the rotation of the earth around the axis resulting in an internal time-keeping system. Changes in myriad of biochemical and physiological processes take place in order for an organism to adapt for changes in physical environment. The period of this process is close to 24 hours in duration, hence the name “circadian rhythms”, from Latin circa diem meaning about a day. In the fruit fly Drosophila melanogaster, due to the increase in knowledge of genetics and molecular biology the molecular components such as genes and proteins involved in circadian rhythm and their roles are well understood. Due to the oscillatory properties of clock components they are an ideal candidate for mathematical models and many such models have been developed in the past. In this study, three new Drosophila circadian rhythm models were developed, each with three transcriptional regulatory feedback loops. Among which, two feedback loops (VRI/PDP1 and PER/TIM) are well known and have been included in earlier models. The main focus of this study is the newly discovered third feedback loops (CWO). The differences between the three models are defined by our conceptualization of three probable actions by which the newly discovered clock component CWO (Clockwork Orange) performs its dual role both as an activator and repressor of per, tim, vri, pdp1 genes, and cwo genes. We included existing in vitro understanding of molecular components and extended it to include probable molecular roles of the newly discovered clock component CWO. We based our hypothesis on discovered in vivo dynamics and by analysing the CWO protein sequence using basic bioinformatics servers. Detailed modelling in the form of probability based transcription factor binding and unbinding processes are used. All three models are expressed by a set of probability based mass action governed ordinary differential equations and the parameters were estimated using modelling tool COPASI. Due to the randomness and variation of different data sets generated for CWO activity by biologists, we made a choice to differ from a traditional approach in modelling, by not over-relaying on data generated from in vitro analysis. The reliance on wet-lab data was scaled down and we include them only to choose manageable mathematical inputs and validate a solved model. This approach gave us a relative degree of space to be innovative and permited us to test different hypothesis at conceptual level in three models. We proceeded to solve the models and validate the oscillations by testing with mutations. Outputs of our simulations will help broaden the research arguments in the field of cricadian biology. In particular our models hypothetically answers the molecular role of CWO protein.Publication Open Access Model-driven elucidation of the inherent capacity of Geobacter sulfurreducens for electricity generation(BioMed Central, 2013) Mao, L; Verwoerd, WBackground: G. sulfurreducens is one of the commonest microbes used in microbial fuel cells (MFCs) for organic-to-electricity biotransformation. In MFCs based on this microorganism, electrons can be conveyed to the anode via three ways: 1) direct electron transfer (DET) mode, in which electrons of reduced c-type cytochromes in the microbial outer membrane are directly oxidized by the anode; 2) mediated electron transfer (MET) mode, in which the reducing potential available from cell metabolism in the form of NADH is targeted as an electron source for electricity generation with the aid of exogenous mediators; and 3) a putative mixed operation mode involving both electron transfer mechanisms described above (DET and MET). However, the potential of G. sulfurreducens for current output in these three operation modes and the metabolic mechanisms underlying the extraction of the reducing equivalents are still unknown.Results: In this study, we performed flux balance analysis (FBA) of the genome-scale metabolic network to compute the fundamental metabolic potential of G. sulfurreducens for current output that is compatible with reaction stoichiometry, given a realistic nutrient uptake rate. We also developed a method, flux variability analysis with target flux minimization (FATMIN) to eliminate futile NADH cycles. Our study elucidates the possible metabolic strategies to sustain the NADH for current production under the MET and Mixed modes. The results showed that G. sulfurreducens had a potential to output current at up to 3.710 A/gDW for DET mode, 2.711 A/gDW for MET mode and 3.272 A/gDW for a putative mixed MET and DET mode. Compared with DET, which relies on only one contributing reaction, MET and Mixed mode were more resilient with ten and four reactions respectively for high current production.Conclusions: The DET mode can achieve a higher maximum limit of the current output than the MET mode, but the MET has an advantage of higher power output and more flexible metabolic choices to sustain the electric current. The MET and DET modes compete with each other for the metabolic resource for the electricity generation. © 2013 Mao and Verwoerd; licensee BioMed Central Ltd.Publication Open Access Mathematical modelling of the core regulatory feedback mechanisms of p53 protein that decide cell fate(Lincoln University, 2014) Chong, Ket HingCells defence against stresses that can cause DNA damage (single-strand breaks, double-strand breaks) is crucial in safeguarding the integrity of the genome and the survival of the organism as a whole. One of the genes that plays a pivotal role in maintaining the stability of the genome in humans is p53, which encodes its product p53 protein. The regulation of p53 activation is extremely complex, and molecular cell biology has gathered parts and pieces of the whole pathway. Mental intuition of this complex regulation is challenging; therefore, it requires a different method to quantitatively model and analyse to enhance the current understanding. This thesis has attempted to create two quantitative models of the mechanisms that regulate p53 basal levels and its appropriate activation as a stress response in deciding cell fate by either cell cycle arrest (to stop proliferation of DNA-damaged cells) or apoptosis (programmed cell death) to eliminate damaged cells. In the first part of the research, a modified and improved model from Sun et al. (2011) deterministic model is proposed to explain the p53 basal dynamics and its response to stress due to DNA double-strand breaks. This model in the form of delay differential equations incorporates the most recently found molecular interactions and hypothesis: the core regulators consist of ATM, Mdm2, MdmX, Wip1 and p53. ATM as a stress transducer, amplifies the stress signal and activates p53 and inhibits its regulators Mdm2 and MdmX. The network structure consists of two positive feedback loops (p53 auto-regulation and ATM auto-activation), three negative feedback loops (Mdm2, MdmX and Wip1) and the interplay of p53, Mdm2 and MdmX that have successfully captured the basal dynamics (spontaneous pulses under non-stressed conditions) and stress response (repeated pulses or oscillations under stressed conditions). The model simulation results show that p53 spontaneous pulses are due to intrinsic DNA damage involving low number of DNA double-strand breaks; and p53 auto-regulation is an important positive feedback contributing to a threshold activation of p53 in generating pulses whether spontaneous or repeated. It also shows that p53 dynamics are excitable, in that once initiated, it completes the pulse even if stress signal is inhibited. Bifurcation analysis revealed a spectrum of p53 behaviour under stressed and non-stressed conditions and characterised p53 dynamics as Type II excitability (oscillations arises from non-zero frequency). Most importantly, we reveal some novel findings on the mechanism of threshold activation of p53 pulsatile and oscillatory dynamics that are crucial for its physiological function as a transcription factor and guardian of the genome. The second model is an extension of the first model by incorporating the apoptosis initiation module structure from Zhang et al. (2009a) with modified parameter values for modelling the core regulatory mechanism of p53 protein that activates apoptotic switch in response to high DNA double-strand breaks. The apoptosis initiation module includes Puma, Bcl2 and Bax. p53 activates the transcription of Puma (BH3-only protein that is pro-apoptotic) as a trigger of apoptosis that inhibits Bcl2 protein (pro-survival) and directly activates Bax. Activation of Bax was assumed to be an indicator of apoptosis initiation. The constructed model demonstrated how molecular interactions and stress signal amplification from ATM auto-activation in the p53 network control cell life and death decisions. Particularly, the model simulation results are qualitatively consistent with the experimental findings of an all-or-none activation of apoptosis and predicted overexpression of Bcl2 as a factor in causing the malfunction of the apoptotic switch. This model presents a simplified yet plausible model for molecular mechanism that regulates p53 activation of the apoptotic switch. The model gives insight into the design principles underlying p53 regulation of apoptosis. In summary, the two models presented in this thesis have proposed plausible design principles of p53 basal dynamics and DNA damage response, and activation of apoptotic switch. These models provide novel theoretical insights into p53 regulation.Publication Open Access Minimal cut sets and the use of failure modes in metabolic networks(MDPI, 2012) Clark, ST; Verwoerd, WA minimal cut set is a minimal set of reactions whose inactivation would guarantee a failure in a certain network function or functions. Minimal cut sets (MCSs) were initially developed from the metabolic pathway analysis method (MPA) of elementary modes (EMs); they provide a way of identifying target genes for eliminating a certain objective function from a holistic perspective that takes into account the structure of the whole metabolic network. The concept of MCSs is fairly new and still being explored and developed; the initial concept has developed into a generalized form and its similarity to other network characterizations are discussed. MCSs can be used in conjunction with other constraints-based methods to get a better understanding of the capability of metabolic networks and the interrelationship between metabolites and enzymes/genes. The concept could play an important role in systems biology by contributing to fields such as metabolic and genetic engineering where it could assist in finding ways of producing industrially relevant compounds from renewable resources, not only for economical, but also for sustainability reasons.Publication Open Access Modelling bistable systems: a new model for meiosis initiation in Saccharomyces cerevisiae and an investigation of the bistable switch of meiotic-mitotic initiation(Lincoln University, 2014) Wannige, Champi ThusangiLiving cells process diverse information received from the environment and make important decisions to undergo cellular processes such as differentiation, cell growth, division and apoptosis. The complex information processing in cells is carried out by a network of biochemical switches and oscillators similar to those seen in complex electrical circuits. The main objective of systems biology is to acquire a thorough understanding of how the functions and behaviour emerge from the complex networks of interactions and components in biological systems. In this thesis, using the systems biology approaches of mathematical modelling, dynamical system analysis and computational simulations, we investigate one example of a gene regulatory system that exhibits bistable switching behaviour to gain deeper insight into its dynamic aspects. In the first section of this thesis, we develop a mathematical model for meiosis initiation of the budding yeast, Saccharomyces cerevisiae, incorporating the main mitosis initiator and its relationships to the meiosis initiation network. Using this mathematical model, we study meiosis and mitosis initiation dynamics under different extra-cellular nutrient levels. Our ultimate goal is to explore the experimentally-observed initial stage meiotic-mitotic switching behaviour in budding yeast under different nutrient conditions, which has not yet been explained at a gene expression level. We extend an available Boolean model of meiosis initiation, which is more biologically sound with stronger experimental validity than other models, and include all recent findings. We develop the model in an ODE framework that enables us to perform phase plane and bifurcation analyses to obtain deeper insights into the behaviour of the system. Our model accurately and qualitatively predicts the experimentally-revealed temporal variations in the related proteins under different nutrient conditions as well as diverse mutant studies related to meiosis and mitosis initiation. Further, the model explains the organism-level experimental outputs of mutation studies at the gene expression level. Using model simulations, we clarify the reasoning behind the conflicting experimental observations from two mutation studies carried out using different procedures.Experimental evidence shows that budding yeast cells can choose between meiosis and mitosis initiation alternatively depending on the available nutrient conditions at the early stage of these two cell division initiation processes. In the second section of this thesis, we use the constructed model to understand this initial phase meiotic-mitotic switching seen in budding yeast cells. In this section, we use dynamical system analysis tools such as phase space analysis, nullcline analysis and bifurcation analysis to explore the model's dynamics under varying nutrient conditions. Using these approaches, we show how meiosis and mitosis initiators form an all-or-none type of bistable switch in response to the available nutrient levels (mainly nitrogen). The transitions to and from the meiosis or mitosis states occur via saddle node bifurcations. This reversible switch helps the optimum usage of available nutrients and explains the mutually exclusive existence of the meiosis and mitosis pathways. Similarly as seen in experiments, temporal analysis of the switch shows that budding yeast cells can transit to mitosis from meiosis initiation throughout all the initial hours of meiosis initiation.Cellular systems behave robustly against external and internal perturbations. In the third section of this thesis, we investigate the robustness and probable effects on the meiotic-mitotic switch by perturbation of the parameters in the meiosis network. To identify the most sensitive set of parameters, we employ both local and global sensitivity analyses. Based on the global sensitivity analysis results, we find a common group of parameters sensitive in all the main states of the switch. After formulating a mathematical definition for robustness, we perturb this common sensitive group of parameters and study the robustness of meiosis and mitosis initiation against the perturbations at the gene expression level. To examine the effects on the meiotic-mitotic switch by perturbations of the parameters, we perturb the common parameter group and create parameter sets using Latin hypercube sampling. Using these sample parameter sets, we investigate the effects of the most sensitive parameters on the transition from meiosis to mitosis when the nutrient level is varied from a low value to a high value. The robustness analysis carried out at the gene expression level identifies that the state corresponding to the transitions between meiosis and mitosis is less robust against perturbations than the other states. We observe three different types of switches when the sensitive parameters are perturbed. We categorise them as normal bistable, memory-less and abnormal switches. The robustness of these switches is implied from the observation that the main function of the transition from meiosis to mitosis initiation, when nutrients are increased, is maintained despite perturbation of the most sensitive parameters in all three switch types.Publication Open Access Fuzzy representation and aggregation of fuzzy cognitive maps(Modelling and Simulation Society of Australia and New Zealand, 2013-12) Obiedat, M; Samarasinghe, Sandhya; Piantadosi, J; Anderssen, RS; Boland, JTypically, complex systems such as socio-ecological systems are ambiguous and ill-defined due to human-environment interactions. These systems could be participatory systems which involve many participants with different levels of knowledge and experience. The various perceptions of the participants may need to be combined to get a comprehensive understanding and useful knowledge of the system. Modelling these systems involves a high level of uncertainty and soft computing approaches based on the concept of fuzzy logic offer a way to deal with such uncertainty. Fuzzy cognitive map (FCM) incorporates fuzzy logic and has proven its efficiency in modelling and extracting knowledge from various qualitative complex systems. However, the literature shows a lack of appropriate ways to incorporate imprecise human perception in fuzzy form in FCM representation and to deal with these fuzzy values in aggregation of multiple FCMs into a group FCM. The aim of this paper is to provide adequate methods for both representation and aggregation of fuzzy values in FCMs. For FCM representaion, this paper utilizes a 2-tuple fuzzy linguistic representation model Herrera and Martinez (2000a) to represent the FCM connection values in a fuzzy way. This model can represent and deal with linguistic and numeric fuzzy values without any loss of information, and it keeps the consistency of these values throughout any subsequent computational processes. For FCM aggregation, which is the first step, this paper proposes a fuzzy method to combine linguistic and numeric fuzzy values at the same time. In the second step, it proposes a new calculation method to assess the different levels of knowledge of FCM designers (FCMs’ credibility weights). These credibility weights of FCMs are then used in the proposed fuzzy aggregation method for a better representation of contrasts between participants resulting from their varied experiences and preferences. For the first step, the 2-tuple fuzzy model is used to represent the FCM connection values during the aggregation process, and therefore the connection values of the group FCM resulting from the aggregation process will be fuzzy values. For the second step, this paper utilizes the Consensus Centrality Measure (CCM) proposed in Obiedat et al. (2011) to calculate a credibility weight for each FCM.Publication Open Access Modeling rapid stomatal closure with synchronous Boolean network approach(Modelling and Simulation Society of Australia and New Zealand, 2013-12) Waidyarathne, KP; Samarasinghe, Sandhya; Piantadosi, J; Anderssen, RS; Boland, JThe phytohormone Abscisic acid (ABA) is an endogenous messenger in plant abiotic stress responses. Drought stress increases the level of ABA triggering the fastest adaptive physiological response of plants- closure of stomata (guard cells). Understanding gene/protein expression involved in stomatal closure has great importance to genetic modification of plants to survive in severe climatic conditions. However, systems level information that defines the communication pattern of the related network of cellular molecules is not yet known. This study integrates fragmentary information collected from literature to define the dynamics of ABA signaling in rapid closure of stomata through a synchronous Boolean model. Stomatal closure in broad terms is a combined result of organic and inorganic ion regulation to release water from the guard cells through osmosis, and rearrangement of Actin to facilitate resulting stomatal movement. Our network consists of 57 nodes and their interaction dynamics defined in accordance with past experimental results to regulate stomatal closure. Perturbation analysis was conducted to identify the essential elements crucial for the above mentioned global functions of pumping out water and stomatal movement. It revealed that destruction of ABA receptor complex (PYR/PYL, PP2C and SnRK2 proteins) made stomata insensitive to closure as a result of disruption of signal transmission to downstream regulators. It was identified that plasma membrane outward ion channels GORK and SLAC1 are crucial for pumping out water by reducing the osmotic load inside the guard cell which facilitates osmosis. Inhibition of MAPK kinases and cytosolic alkalization, as being important regulators of SLAC1, and membrane depolarization, important for GORK, showed drastic effect on the stomatal closure. In contrast, overexpression of plasma membrane H⁺-pumping and potassium-in channels inhibit stomatal closure by enhancing the osmotic concentration and there by attracting water inside. Loss of function of Actin rearrangement resulted in a loss of stomatal closure as structural rearrangement of guard cell are necessary to facilitate the cell shrinkage. Disruption of Reactive Oxygen Species or their regulators (RBOH, PA, PLD or RCN1), SCAB1 protein and overexpression of AtRAC1 showed drastic effects on structural rearrangements. Perturbation analysis revealed that the number of elements crucial to stomatal closure comprises about 30% of the network; and thus stomatal closure is robust against perturbation in the other 70% of network elements. These results are in agreement with experimental findings and indicate potential redundancy with respect to stomatal closure.Publication Open Access Gene expression based computer aided diagnostic system for breast cancer: a novel biological filter for biomarker detection(Modelling and Simulation Society of Australia and New Zealand, 2013-12) Al yousef, A; Samarasinghe, Sandhya; Kulasiri, Don; Piantadosi, J; Anderssen, RS; Boland, JCancer is a complex disease because it makes complex cellular changes. Therefore, microarrays have become a powerful way to analyse cancer and identify what changes are produced within a cell. Through DNA microarrays, it has become possible to look at the expression of thousands of genes in one sample and this is called gene expression profiling. Gene expression profiling is important to capture a set of expressed genes that determines a cell phenotype. However, analysing microarray data is challenged by the high-dimensionality of the data compared with the number of samples. The aim of this study was to enhance the diagnostic accuracy of Breast Cancer Computer Aided Diagnostic Systems (CADs) that use gene expression profiling of peripheral blood cells, by introducing a novel feature selection method called Bi-biological filter that was further refined by Best First Search with Support Vector Machines SVM (BFS-SVM) to select a small set of the most effective genes predictive of breast cancer. From each patient’s gene expression profiles, a gene co-expression network was built and divided into functional groups or clusters using Topological Overlap Matrix (TOM) and Spectral Clustering (SC) in the design of the Bi-Biological filter to obtain the preliminary set of gene markers. BFSSVM was used to further filter a smaller set of best gene markers, and Artificial Neural Networks (ANN), SVM and Linear Discriminant Analysis (LDA) were used to assess their classification performance. The study used 121 samples – 67 malignant and 54 benign cases as input to for the system. The Bi-biological filter selected 415 genes as mRNA biomarkers and BFS-SVM was able to select just 13 out of 415 genes for classification of breast cancer. ANN was found to be the superior classifier with 93.4% classification accuracy which was a 14% improvement over the past best CAD system developed by Aaroe et al. (2010)Publication Open Access Computational framework for early detection of breast cancer(Lincoln University, 2013) Al Yousef, A.Breast Cancer is the second leading cause of death after lung cancer in women all over the world whose lives could be saved by an early detection. This could be achieved by improving the diagnostic accuracy of the present Computer Aided Diagnosis systems (CAD) for breast cancer, which use both clinical and biological data. As a means of achieving this goal, the thesis focussed on examining and evaluating both clinical and biological data used in the present Breast Cancer CAD systems. Results were then applied for early detection of breast cancer in women from a low income country, Jordan, where breast cancer incidence (32%), ranks among the highest in the world. In the first part of the study, the clinical part, we identified a new mass feature related to mass shape, called Central Regularity Degree (CRD) from Ultrasound images, which was then used along with five other powerful mass features: one geometric feature: Depth-Width ratio (DW); two morphological features: shape and margin; blood flow and age, in the classification with four different classifiers: Artificial Neural Networks (ANN), K Nearest Neighbour (KNN), Nearest Centroid (NC) and Linear Discriminant Analysis (LDA). ANN gave the best performance with an improved accuracy of classification, from 81.8% to 95.5% after adding CRD. The overall improvement of the diagnostic accuracy of the CAD, after adding CRD was 14%, which was a significant improvement. The second focus of the study was centred on biological data. The aim was to enhance the diagnostic accuracy of CADs that use gene expression profiling of peripheral blood cells, by introducing a novel feature selection method called Bi-biological filter and Best First Search with SVM (BFS-SVM). The bi-biological filter contained two biological filters; the first one to find the shared biomarkers between two cancer subsets and the second one to eliminate the healthy biomarkers from the shared ones. The study evaluated the diagnostic accuracy of three classifiers; Artificial Neural Network (ANN), SVM and Linear Discriminant Analysis (LDA)with 5-fold out cross validation. The study used 121 samples – 67 malignant and 54 benign cases as input for the system. The Bi-biological filter selected 415 genes as mRNA biomarkers and BFS-SVM was able to select 13 out of 415 genes for classification of breast cancer. ANN was found to be the superior classifier with 93.2% classification accuracy which was a 14% improvement over the original study (Aaroe et al. 2010). The third focus of the study was on female patients in Jordan, a low income country with a high rank in breast cancer incidence. We used Bi-Biological filter and BFS-SVM wrapper to analyse 56 blood serum samples to detect circulating breast cancer miRNA biomarkers in Jordanian women and use them to improve the diagnostic accuracy of circulating miRNA based breast cancer CADs. The Bi-biological filter selected 74 miRNAs as breast cancer biomarkers. And 7 out of 74 were selected by BFS-SVM for breast cancer classification. SVM was found to be the superior classifier with 98.2% classification accuracy which was a 12% improvement compared with Schrauder et al. (2012) and %7 compared with Hu et al. (2012).Publication Open Access Applying differential evolution to a whole-farm model to assist optimal strategic decision making(Modelling and Simulation Society of Australia and New Zealand, 2005-12) Neal, Marie J.; Wastney, M.; Levy, G.; Drynan, R.; Fulkerson, W.; Post, Elizabeth; Thorrold, B.; Palliser, C.; Beukes, P.; Folkers, C.A whole-farm model of a dairy farm was optimised to assist the strategic decision making given the uncertain environment. Decisions under consideration included the stocking rate, the calving pattern (defined by calving and dry-off dates) and the use of supplementary feed. These individual decisions are considered as the choice of a farm system in this paper. The farmer’s decision making problem is simplified by assuming a perfect labour market, and so the farmer will be primarily concerned with the return on equity. Farm systems are compared with stochastic dominance or the Sharpe ratio, depending on whether there is a perfect capital market and returns are normally distributed.Publication Open Access On-line detection of mastitis in dairy herds using artificial neural networks(Modelling and Simulation Society of Australia and New Zealand, 2005-12) Wang, E; Samarasinghe, SandhyaMastitis, one of the most significant diseases in dairy herds, is a highly complex sequence of events with various biological causes and associated physiological and behavioral effects that occur as bacterial infection progresses. The aim of the research is to develop a model for online detection of mastitis for robotic milking stations.Publication Open Access A hierarchical systems modelling approach based on neural networks for forecasting global waste generation: a case study of Chile(Modelling and Simulation Society of Australia and New Zealand, 2005-12) Samarasinghe, Sandhya; Ordonez Ponce, EIn this-first every study for Chile, a neural network based hierarchical modelling approach is proposed for forecasting domestic waste generation for the whole country. Over 30 global variables from the 342 communes (municipalities) in the country were analysed extensively using statistical tools that led to 5 significant explanatory variables: population, percentage of urban population, years of education, number of libraries and number of indigents. The five explanatory variables were used to develop a feedforward neural network for predicting volume of global waste generation for a particular year (2002 in this case) in Chile and assessing the contribution of variables. The model had validation R² of 0.82.Publication Open Access A new method for identifying the central nodes in fuzzy cognitive maps using consensus centrality measure(Modelling and Simulation Society of Australia and New Zealand, 2011-12) Obeidat, M; Samarasinghe, SandhyaThe Fuzzy Cognitive Map (FCM) provides a robust model for knowledge representation. FCM is a fuzzy signed weighted directed graph that depicts the knowledge of the domain as nodes representing the factors of the domain and arcs representing the connections among these factors. The centrality of a node in FCM, also called the importance of a node in this paper, is considered the most important index of all the graph theory indices applying to FCM which helps decision makers in analysing their FCM models. By finding the centrality values of the nodes in FCM, the important (central) nodes, which are the focal point for decision makers, are determined. The highest centrality value of a node in FCM is the most important one. Little research has addressed the centrality of the nodes in an FCM using only the degree centrality measure. The degree centrality measure only accounts for the direct connections of the node. Although the degree centrality index is considered an important measure in determining the centrality of a node in an FCM, it is not sufficient and has significant shortcomings; it ignores the importance of the indirect connections, the role of the node’s position and flow of information through that node, i.e., how a node is close to other nodes and how the node contributes to the flow of information (communication control) through that node. In the literature, there are other centrality measures that can handle direct and indirect connections to determine the central nodes in a directed graph. This paper presents a new method for identifying the central nodes in an FCM. In order to achieve that, we provide, in addition to the degree measure, new important measures to overcome the above drawbacks. These new centrality measures are: betweenness and closeness measures. In this paper, we calculate and normalize the three centrality measures values for each node in the FCM. These values are then transformed into linguistic terms using 2-tuple fuzzy linguistic representation model. We use the 2-tuple model because it describes the granularity of uncertainty of the fuzzy sets and avoids the loss of information resulted from the imprecision and normalization of the measures. The calculated centrality measures values for each node in the FCM are then aggregated using a 2-tuple fuzzy fusion approach to obtain consensus centrality measure. The resulting aggregated values are then ranked in descending order to identify the most central nodes in the FCM, and this would improve the decision-making and help in simplify the FCM by removing the least important nodes from it. Finally, a list of future works related to this paper is suggested.Publication Open Access A fuzzy SMART based dynamic decision making system: a voltage control case study(Modelling and Simulation Society of Australia and New Zealand, 2005-12) Lin, M; Samarasinghe, Sandhya; Kulasiri, DonMost real-life decisions involve Dynamic Decision Making (DDM) that is characterised by the need to make multiple and interdependent decisions in an environment that changes as a function of the decision maker’s actions, environmental events, or both. Some examples can be found in management of transportation networks and in controlling of power systems. To assist humans in these difficult decision making scenarios, computer-based decision making systems have been developed. Most of them rely on dynamic programming algorithms. Unfortunately, heavy computational burden makes them not suitable for application to large systems and precludes finding a solution in a limited time which is an important determinant of the performance. This paper describes the development of a fuzzy Simple Multi-Attribute Rating Technique (SMART) based dynamic decision making system which incorporates the merits of human decision making mechanisms and operational research methods to find an optimal solution taking the least amount of time in a dynamic environment. To illustrate the proposed framework, we apply it to a practical voltage control problem in abnormal scenarios in power systems. The proposed system (see Figure 1) includes three components: a ‘voltage monitor’ to monitor abnormal voltage profiles based on a power flow algorithm, an ‘evaluator’ to evaluate the effectiveness of candidate control actions based on a SMART algorithm, and a ‘decision maker’ to search an optimal voltage control schedule based on a fuzzy linear programming algorithm. We present the test results on a benchmark 9-bus test power system under a dynamic scenario caused by load demand variations. The results show that the proposed approach can quickly find optimal decisions for maintaining an acceptable voltage profile in a dynamically changing power transmission environment. Furthermore, it can reduce the number of unnecessary control actions in comparison to a traditional sensitivity based method. The proposed framework can be easily applied to other similar dynamic decision making problems, such as ordering system in industry.
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