Hierarchical Modular Architecture Research Articles

Modelling somatic mutation accumulation and expansion in a long-lived tree with hierarchical modular architecture

In a long-lived organism with a modular architecture, such as trees, somatic mutations accumulate throughout the long lifespan and result in genetic mosaicism in each module within the same individual. In recent years, next-generation sequencing technology has provided a snapshot of such intra-organismal genetic variability. However, the dynamic processes underlying the accumulation and expansion of somatic mutations during the growth remain poorly understood. In this study, we constructed a model to describe these processes in a form that can be applied to a real tree. Given that the proliferation dynamics of meristematic cells vary across plant species, multiple possible processes for elongation and branching were comprehensively expressed in our model. Using published data from a poplar tree, we compared the prediction of the models with the observation and explained the cell lineage dynamics underlying somatic mutations accumulation that were not evident from the snapshot of the sequenced data. We showed that the somatic genetic drift during growth increases inter-meristem mosaicism, resulting in genetically distinct branches and less integrity within an individual tree. We also showed that the somatic genetic drift during branching leads to the mutation accumulation pattern that does not reflect the tree topology. Our modelling framework can help interpret and provide further insights into the empirical findings of genetic mosaicism in long-lived trees.

Hierarchical Modular Battery Equalizer With Open-Loop Control and Mitigated Recovery Effect

In this manuscript, an advanced battery equalizer with open-loop control is proposed. This equalizer is based on a two-layer hierarchical modular architecture. The top stringto- module (S2M) layer consists of a half-bridge inverter and a voltage multiplier (VM) rectifier, and the bottom cell-to-cell (C2C) layer is implemented by bidirectional buck-boost units. Without state-of-charge (SOC) estimation, the battery charge can be automatically transferred from high-voltage cell-modules/cells to low-voltage ones. Only a pair of symmetrical pulse width modulation (PWM) driving signals with fixed switching frequency and duty cycle are required.This reduces the control complexity remarkably. Meanwhile, the balancing current of each balancing path naturally attenuates with the convergence of cell-module/ cell voltages. This ensures a fast balancing of cell-module/cell with large voltage mismatch. The battery-recovery-effect induced balancing error is also effectively mitigated. Moreover, simple control facilitates a simultaneous module and cell voltage balancing in static, charging, and discharging conditions. The operation principles are analyzed in detail. An experimental platform with eight series-connected batteries is built and tested. The measured results well validate the theoretical analysis. Both cell and module voltages automatically converge with clearly mitigated recovery effect.

Intelligent multi-agent architecture for a supervisor of a water treatment plant

The rapid development of Information and Communication Technologies (ICT) and high-capacity hardware components make it necessary to achieve a strong integration of automatic systems based on new paradigms on intelligent distributed architectures, where require highly complex supervision and control tasks, due to the generated requirements of the new production systems, the high number of variables to control and the advancement of technologies, especially in industries where continuous processes have been established. In the present work, a distributed hierarchical modular architecture is proposed for a supervision system, based on multi-agent systems (MAS), oriented to the management of processes in the filtration stage of a water purification plant, using a methodology to the implementation of intelligent agents that allow to project, design, verify and validate the system. This methodology is fundamentally based on the use of the Unified Modeling Language (UML) for its projection and Petri nets (PN) for the simulation and validation of properties, which allows to guarantee the modularity, flexibility, and robustness of the proposed system. The architectures of the intelligent agents in the different programmable devices are modeled and simulated to achieve an adequate interaction and collaboration, allowing to reduce the conflicts that may be generated between them. The evaluation of the distributed architecture focuses on the fulfillment of the functional requirements and evaluation metrics, which, through the analysis of the properties of the Petri net, allows to determine the correct operation of the system and its dynamic behavior in the face of unforeseen situations at different levels of automation.

Determining optimal granularity level of modular product with hierarchical clustering and modularity assessment

Modular product architecture is beneficial for the product maintenance, upgrade, components’ concurrent design through team work, and achieving the mass customization eventually. Recent researches tend to group elements into modules as a flat map, but this is inconsistent with the nested composition in product final assembly. Finding the hierarchical modular partition for elements of a product and obtaining its optimal granularity level are still necessary to ease the partition and combination for sub-design tasks. We use the design structure matrix (DSM) to represent the relationships among elements of a product. The hierarchical clustering functions such as pdist and linkage within MATLAB software are utilized to form the hierarchical dendrogram for DSM elements, and the modularity index Q is applied to assess the modular clustering results. The partitions are acquired by various distance threshold values in the vertical axis of the hierarchical dendrogram. The ‘cityblock’ and ‘average’ are found as preferred parameters for pdist and linkage functions, respectively, from loop test experiments. Combining the modularity formula in a research literature, a hierarchical modular architecture design methodology is proposed in this work to determine the optimal granularity level of a modular product and to provide solutions for designer selection. Case studies and comparisons in a benchmark problem and a concrete spraying machine illustrate the effectiveness and efficiency of the proposed method. It is characterized by being able to find stable modular partition results for both float and binary DSM elements and satisfying the recommended modular number rule simultaneously.

Information processing in the CNS: a supramolecular chemistry?

How does central nervous system process information? Current theories are based on two tenets: (a) information is transmitted by action potentials, the language by which neurons communicate with each other-and (b) homogeneous neuronal assemblies of cortical circuits operate on these neuronal messages where the operations are characterized by the intrinsic connectivity among neuronal populations. In this view, the size and time course of any spike is stereotypic and the information is restricted to the temporal sequence of the spikes; namely, the "neural code". However, an increasing amount of novel data point towards an alternative hypothesis: (a) the role of neural code in information processing is overemphasized. Instead of simply passing messages, action potentials play a role in dynamic coordination at multiple spatial and temporal scales, establishing network interactions across several levels of a hierarchical modular architecture, modulating and regulating the propagation of neuronal messages. (b) Information is processed at all levels of neuronal infrastructure from macromolecules to population dynamics. For example, intra-neuronal (changes in protein conformation, concentration and synthesis) and extra-neuronal factors (extracellular proteolysis, substrate patterning, myelin plasticity, microbes, metabolic status) can have a profound effect on neuronal computations. This means molecular message passing may have cognitive connotations. This essay introduces the concept of "supramolecular chemistry", involving the storage of information at the molecular level and its retrieval, transfer and processing at the supramolecular level, through transitory non-covalent molecular processes that are self-organized, self-assembled and dynamic. Finally, we note that the cortex comprises extremely heterogeneous cells, with distinct regional variations, macromolecular assembly, receptor repertoire and intrinsic microcircuitry. This suggests that every neuron (or group of neurons) embodies different molecular information that hands an operational effect on neuronal computation.

Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types.

The cerebral cortex exhibits neural activity even in the absence of external stimuli. This self-sustained activity is characterized by irregular firing of individual neurons and population oscillations with a broad frequency range. Questions that arise in this context, are: What are the mechanisms responsible for the existence of neuronal spiking activity in the cortex without external input? Do these mechanisms depend on the structural organization of the cortical connections? Do they depend on intrinsic characteristics of the cortical neurons? To approach the answers to these questions, we have used computer simulations of cortical network models. Our networks have hierarchical modular architecture and are composed of combinations of neuron models that reproduce the firing behavior of the five main cortical electrophysiological cell classes: regular spiking (RS), chattering (CH), intrinsically bursting (IB), low threshold spiking (LTS), and fast spiking (FS). The population of excitatory neurons is built of RS cells (always present) and either CH or IB cells. Inhibitory neurons belong to the same class, either LTS or FS. Long-lived self-sustained activity states in our network simulations display irregular single neuron firing and oscillatory activity similar to experimentally measured ones. The duration of self-sustained activity strongly depends on the initial conditions, suggesting a transient chaotic regime. Extensive analysis of the self-sustained activity states showed that their lifetime expectancy increases with the number of network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class. These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.

NetGestalt: integrating multidimensional omics data over biological networks

NetGestalt: integrating multidimensional omics data over biological networks

A Hierarchical Modular Architecture for Embodied Cognition

Cognition can appear complex owing to the fact that the brain is capable of an enormous repertoire of behaviors. However, this complexity can be greatly reduced when constraints of time and space are taken into account. The brain is constrained by the body to limit its goal-directed behaviors to just a few independent tasks over the scale of 1-2 min, and can pursue only a very small number of independent agendas. These limitations have been characterized from a number of different vantage points such as attention, working memory and dual task performance. It may be possible that the disparate perspectives of all these methodologies can be unified if behaviors can be seen as modular and hierarchically organized. From this vantage point, cognition can be seen as having a central problem of scheduling behaviors to achieve short-term goals. Thus dual-task paradigms can be seen as studying the concurrent management of simultaneous, competing agendas. Attention can be seen as focusing on the decision as to whether to interrupt the current agenda or persevere. Working memory can be seen as the bookkeeping necessary to manage the state of the current active agenda items.

Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing.

Cognitive memory network

A resistive memory network that has no crossover wiring is proposed to overcome the hardware limitations to size and functional complexity that is associated with conventional analogue neural networks. The proposed memory network is based on simple network cells that are arranged in a hierarchical modular architecture. Cognitive functionality of this network is demonstrated by an example of character recognition. The network is trained by an evolutionary process to completely recognise characters deformed by random noise, rotation, scaling and shifting

Extended CADLIVE: a novel graphical notation for design of biochemical network maps and computational pathway analysis

Biochemical network maps are helpful for understanding the mechanism of how a collection of biochemical reactions generate particular functions within a cell. We developed a new and computationally feasible notation that enables drawing a wide resolution map from the domain-level reactions to phenomenological events and implemented it as the extended GUI network constructor of CADLIVE (Computer-Aided Design of LIVing systEms). The new notation presents ‘Domain expansion’ for proteins and RNAs, ‘Virtual reaction and nodes’ that are responsible for illustrating domain-based interaction and ‘InnerLink’ that links real complex nodes to virtual nodes to illustrate the exact components of the real complex. A modular box is also presented that packs related reactions as a module or a subnetwork, which gives CADLIVE a capability to draw biochemical maps in a hierarchical modular architecture. Furthermore, we developed a pathway search module for virtual knockout mutants as a built-in application of CADLIVE. This module analyzes gene function in the same way as molecular genetics, which simulates a change in mutant phenotypes or confirms the validity of the network map. The extended CADLIVE with the newly proposed notation is demonstrated to be feasible for computational simulation and analysis.

Deciphering modular and dynamic behaviors of transcriptional networks

The coordinated and dynamic modulation or interaction of genes or proteins acts as an important mechanism used by a cell in functional regulation. Recent studies have shown that many transcriptional networks exhibit a scale-free topology and hierarchical modular architecture. It has also been shown that transcriptional networks or pathways are dynamic and behave only in certain ways and controlled manners in response to disease development, changing cellular conditions, and different environmental factors. Moreover, evolutionarily conserved and divergent transcriptional modules underline fundamental and species-specific molecular mechanisms controlling disease development or cellular phenotypes. Various computational algorithms have been developed to explore transcriptional networks and modules from gene expression data. In silico studies have also been made to mimic the dynamic behavior of regulatory networks, analyzing how disease or cellular phenotypes arise from the connectivity or networks of genes and their products. Here, we review the recent development in computational biology research on deciphering modular and dynamic behaviors of transcriptional networks, highlighting important findings. We also demonstrate how these computational algorithms can be applied in systems biology studies as on disease, stem cells, and drug discovery.

A hierarchical modular architecture for musical instrument classification

This paper proposes a classification system based on a hierarchical modular architecture. Such an architecture is designed to address high-dimensional classification problems, that are characterized by a high number of input features and a large number of classes. The hierarchical approach decomposes a many-class problem into a hierarchy of few-class sub-problems, while the {modular approach} allows efficient treatment of high-dimensional input spaces. Specifically, each classifier in the hierarchy has a modular structure. Each module is implemented as a neuro-fuzzy network that operates on a distinct region of the input space. As an application, the proposed architecture is employed to solve the problem of classifying musical instruments from the sound they produce.

Modular analysis of the transcriptional regulatory network of E. coli

The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. Feed-forward motifs tend to be embedded within modules, whereas bi-fan motifs tend to link modules, supporting the notion of a hierarchical network with defined functional modules.