Systems biology approaches to the computational modelling of trypanothione metabolism in Trypanosoma brucei

This work presents an advanced modelling procedure, which applies both structural modelling and kinetic modelling approaches to the trypanothione metabolic network in the bloodstream form of Trypanosoma brucei, the parasite responsible for African Sleeping sickness. Trypanothione has previously been identified as an essential compound for parasitic protozoa, however the underlying metabolic processes are poorly understood. Structural modelling allows the study of the network metabolism in the absence of sufficient quantitative information of target enzymes. Using this approach we examine the essential features associated with the control and regulation of intracellular trypanothione level. The first detailed kinetic model of the trypanothione metabolic network is developed, based on a critical review of the relevant scientific papers. Kinetic modelling of the network focuses on understanding the effect of anti-trypanosomal drug DFMO and examining other enzymes as potential targets for anti-trypanosomal chemotherapy. We also consider the inverse problem of parameter estimation when the system is defined with non-linear differential equations. The performance of a recently developed population-based PSwarm algorithm that has not yet been widely applied to biological problems is investigated and the problem of parameter estimation under conditions such as experimental noise and lack of information content is illustrated using the ERK signalling pathway. We propose a novel multi-objective optimization algorithm (MoPSwarm) for the validation of perturbation-based models of biological systems, and perform a comparative study to determine the factors crucial to the performance of the algorithm. By simultaneously taking several, possibly conflicting aspects into account, the problem of parameter estimation arising from non-informative experimental measurements can be successfully overcome. The reliability and efficiency of MoPSwarm is also tested using the ERK signalling pathway and demonstrated in model validation of the polyamine biosynthetic pathway of the trypanothione network. It is frequently a problem that models of biological systems are based on a relatively small amount of experimental information and that extensive in vivo observations are rarely available. To address this problem, we propose a new and generic methodological framework guided by the principles of Systems Biology. The proposed methodology integrates concepts from mathematical modelling and system identification to enable physical insights about the system to be accounted for in the modelling procedure. The framework takes advantage of module-based representation and employs PSwarm and our proposed multi-objective optimization algorithm as the core of this framework. The methodological framework is employed in the study of the trypanothione metabolic network, specifically, the validation of the model of the polyamine biosynthetic pathway. Good agreements with several existing data sets are obtained and new predictions about enzyme kinetics and regulatory mechanisms are generated, which could be tested by in vivo approaches

A Survey of Neural Trees

Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the two worlds, a variety of approaches are proposed to integrate NNs and DTs explicitly or implicitly. In this survey, these approaches are organized in a school which we term as neural trees (NTs). This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability. We first propose a thorough taxonomy of NTs that expresses the gradual integration and co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their interpretability and performance, and suggest possible solutions to the remaining challenges. Finally, this survey concludes with a discussion about other considerations like conditional computation and promising directions towards this field. A list of papers reviewed in this survey, along with their corresponding codes, is available at: https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl

Energy and Route Optimization of Moving Devices

This thesis highlights our efforts in energy and route optimization of moving devices. We have focused on three categories of such devices; industrial robots in a multi-robot environment, generic vehicles in a vehicle routing problem (VRP) context, automatedguided vehicles (AGVs) in a large-scale flexible manufacturing system (FMS). In the first category, the aim is to develop a non-intrusive energy optimization technique, based on a given set of paths and sequences of operations, such that the original cycle time is not exceeded. We develop an optimization procedure based on a mathematical programming model that aims to minimize the energy consumption and peak power. Our technique has several advantages. It is non-intrusive, i.e. it requires limited changes in the robot program and can be implemented easily. Moreover,it is model-free, in the sense that no particular, and perhaps secret, parameter or dynamic model is required. Furthermore, the optimization can be done offline, within seconds using a generic solver. Through careful experiments, we have shown that it is possible to reduce energy and peak-power up to about 30% and 50% respectively. The second category of moving devices comprises of generic vehicles in a VRP context. We have developed a hybrid optimization approach that integrates a distributed algorithm based on a gossip protocol with a column generation (CG) algorithm, which manages to solve the tested problems faster than the CG algorithm alone. The algorithm is developed for a VRP variation including time windows (VRPTW), which is meant to model the task of scheduling and routing of caregivers in the context of home healthcare routing and scheduling problems (HHRSPs). Moreover,the developed algorithm can easily be parallelized to further increase its efficiency. The last category deals with AGVs. The choice of AGVs was not arbitrary; by design, we decided to transfer our knowledge of energy optimization and routing algorithms to a class of moving devices in which both techniques are of interest. Initially, we improve an existing method of conflict-free AGV scheduling and routing, such that the new algorithm can manage larger problems. A heuristic version of the algorithm manages to solve the problem instances in a reasonable amount of time. Later, we develop strategies to reduce the energy consumption. The study is carried out using an AGV system installed at Volvo Cars. The results are promising; (1)the algorithm reduces performance measures such as makespan up to 50%, while reducing the total travelled distance of the vehicles about 14%, leading to an energy saving of roughly 14%, compared to the results obtained from the original traffic controller. (2) It is possible to reduce the cruise velocities such that more energy is saved, up to 20%, while the new makespan remains better than the original one

Graded Decompositional Semantic Prediction

Compared to traditional approaches, decompositional semantic labeling (DSL) is compelling but introduces complexities for data collection, quality assessment, and modeling. To shed light on these issues and lower barriers to the adoption of DSL or related approaches I bring existing models and novel variations into a shared, familiar framework, facilitating empirical investigation

Re-identification and semantic retrieval of pedestrians in video surveillance scenarios

Person re-identification consists of recognizing individuals across different sensors of a camera network. Whereas clothing appearance cues are widely used, other modalities could be exploited as additional information sources, like anthropometric measures and gait. In this work we investigate whether the re-identification accuracy of clothing appearance descriptors can be improved by fusing them with anthropometric measures extracted from depth data, using RGB-Dsensors, in unconstrained settings. We also propose a dissimilaritybased framework for building and fusing multi-modal descriptors of pedestrian images for re-identification tasks, as an alternative to the widely used score-level fusion. The experimental evaluation is carried out on two data sets including RGB-D data, one of which is a novel, publicly available data set that we acquired using Kinect sensors. In this dissertation we also consider a related task, named semantic retrieval of pedestrians in video surveillance scenarios, which consists of searching images of individuals using a textual description of clothing appearance as a query, given by a Boolean combination of predefined attributes. This can be useful in applications like forensic video analysis, where the query can be obtained froma eyewitness report. We propose a general method for implementing semantic retrieval as an extension of a given re-identification system that uses any multiple part-multiple component appearance descriptor. Additionally, we investigate on deep learning techniques to improve both the accuracy of attribute detectors and generalization capabilities. Finally, we experimentally evaluate our methods on several benchmark datasets originally built for re-identification task