Engineering stress resilient plants using gene regulatory network rewiring

In spite of advances in food production brought on by the Green Revolution, the challenge of providing access to nutritious, safe food that has been grown sustainably is considerable. One such barrier to food security is biotic stress - infection with pathogens such as bacteria, fungi and oomycetes impact negatively on plant growth and survival. Synthetic biology, an interdisciplinary field combining biology, engineering and mathematics, is a promising tool for understanding and developing stress tolerant plants. The response of the model plant Arabidopsis thaliana to biotic and abiotic stresses involves the transcriptional reprogramming of thousands of genes. Among these differentially expressed genes are transcription factors, which form complex causal networks specific to the stress in question. This thesis focuses on network rewiring as a tool for enhancing the Arabidopsis response to stress, in particular to Botrytis cinerea infection. This is a model system for studying plant-necrotrophic pathogen interactions and as such, a large amount of data are available, including a high-resolution transcriptomic time series of Arabidopsis during B. cinerea infection. This was used to construct gene regulatory networks with hundreds of transcription factors that are differentially expressed, in order to obtain a systems view of the effects of infection and the relationships between these regulators. Rewiring was applied to subnetworks of the original network using two different methodologies: control engineering, and Gaussian process dynamical systems. The former focuses on eliminating the effects of perturbation on a single node in a small 9-gene network, and requires detailed parameterisation of biological processes such as mRNA degradation and transcription rates. The latter provides a general modelling framework for optimising the overall expression of genes in a larger 70 gene subnetwork that eschews parameterisation or definition of a precise function for modelling relationships between genes. The process of generating stably transformed and rewired Arabidopsis is long and requires growing hundreds of plants for each construct. In order to test the hypotheses generated by such computational tools quickly and on a large scale, Arabidopsis protoplasts treated with chitin were trialled as a model system for studying plant defence responses to B. cinerea. RNAseq analysis of protoplasts was used to determine the similarities and differences between the defence responses triggered in protoplasts and in Arabidopsis plants. Both protoplasts and plants were also rewired, and gene expression measurements used to understand the effects of this genetic engineering on the defence response of each

Fundamental boolean network modelling for genetic regulatory pathways : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

A Boolean model is a switch-like behaviour model of which it is easy to ignore any effects at the intermediate levels. Boolean modelling has been applied in many areas, including mammalian cell cycle networks. However, little effort has been put into the consideration of activation, inhibition and protein decay networks to designate the direct roles of a gene or a synthesised protein, as an activator or inhibitor of a target gene. Hence, we proposed to split the conventional Boolean functions at the subfunction level into activation and inhibition domains, taking into account the effectiveness of protein decay. As a consequence, two novel data-driven Boolean models for genetic regulatory pathways, namely the fundamental Boolean model (FBM) and the temporal fundamental Boolean model (TFBM), have been proposed to draw insights into gene activation, inhibition, and protein decay. The novel Boolean models could reveal significant trajectories in genes and provide a new direction on Boolean modelling research. The proposed novel Boolean models are fine-grained. A novel network inference methodology named Orchard cube technology has been proposed to infer the related networks, namely fundamental Boolean networks (FBNs) and temporal fundamental Boolean networks (TFBNs) based on FBM and TFBM respectively. As a primary result of this study, an R package, called FBNNet, has been developed based on the proposed methodology and has been used to demonstrate the FBNs and TFBNs for mammalian cell cycle pathways and acute childhood leukaemia pathways respectively. Our experimental results show that the proposed FBM and TFBM could be used to explicitly reconstruct the internal networks of mammalian cell cycles and acute childhood leukaemia. Especially during the study, we produced the fundamental Boolean networks on the childhood acute lymphoblastic leukaemia gene expression data, which were produced in clinical settings. The pathways may be useful for pharmaceutical agents to identify any side effects when applying GC induced apoptosis on children

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page

Bioimage Data Analysis Workflows ‒ Advanced Components and Methods

This open access textbook aims at providing detailed explanations on how to design and construct image analysis workflows to successfully conduct bioimage analysis. Addressing the main challenges in image data analysis, where acquisition by powerful imaging devices results in very large amounts of collected image data, the book discusses techniques relying on batch and GPU programming, as well as on powerful deep learning-based algorithms. In addition, downstream data processing techniques are introduced, such as Python libraries for data organization, plotting, and visualizations. Finally, by studying the way individual unique ideas are implemented in the workflows, readers are carefully guided through how the parameters driving biological systems are revealed by analyzing image data. These studies include segmentation of plant tissue epidermis, analysis of the spatial pattern of the eye development in fruit flies, and the analysis of collective cell migration dynamics. The presented content extends the Bioimage Data Analysis Workflows textbook (Miura, Sladoje, 2020), published in this same series, with new contributions and advanced material, while preserving the well-appreciated pedagogical approach adopted and promoted during the training schools for bioimage analysis organized within NEUBIAS – the Network of European Bioimage Analysts. This textbook is intended for advanced students in various fields of the life sciences and biomedicine, as well as staff scientists and faculty members who conduct regular quantitative analyses of microscopy images

Digital control networks for virtual creatures

Robot control systems evolved with genetic algorithms traditionally take the form of floating-point neural network models. This thesis proposes that digital control systems, such as quantised neural networks and logical networks, may also be used for the task of robot control. The inspiration for this is the observation that the dynamics of discrete networks may contain cyclic attractors which generate rhythmic behaviour, and that rhythmic behaviour underlies the central pattern generators which drive lowlevel motor activity in the biological world. To investigate this a series of experiments were carried out in a simulated physically realistic 3D world. The performance of evolved controllers was evaluated on two well known control tasks—pole balancing, and locomotion of evolved morphologies. The performance of evolved digital controllers was compared to evolved floating-point neural networks. The results show that the digital implementations are competitive with floating-point designs on both of the benchmark problems. In addition, the first reported evolution from scratch of a biped walker is presented, demonstrating that when all parameters are left open to evolutionary optimisation complex behaviour can result from simple components

Bioimage Data Analysis Workflows ‒ Advanced Components and Methods

This open access textbook aims at providing detailed explanations on how to design and construct image analysis workflows to successfully conduct bioimage analysis. Addressing the main challenges in image data analysis, where acquisition by powerful imaging devices results in very large amounts of collected image data, the book discusses techniques relying on batch and GPU programming, as well as on powerful deep learning-based algorithms. In addition, downstream data processing techniques are introduced, such as Python libraries for data organization, plotting, and visualizations. Finally, by studying the way individual unique ideas are implemented in the workflows, readers are carefully guided through how the parameters driving biological systems are revealed by analyzing image data. These studies include segmentation of plant tissue epidermis, analysis of the spatial pattern of the eye development in fruit flies, and the analysis of collective cell migration dynamics. The presented content extends the Bioimage Data Analysis Workflows textbook (Miura, Sladoje, 2020), published in this same series, with new contributions and advanced material, while preserving the well-appreciated pedagogical approach adopted and promoted during the training schools for bioimage analysis organized within NEUBIAS – the Network of European Bioimage Analysts. This textbook is intended for advanced students in various fields of the life sciences and biomedicine, as well as staff scientists and faculty members who conduct regular quantitative analyses of microscopy images

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field