Models Of Biological Networks Research Articles

DEVELOPMENT AND SIMULATION OF SPIKE NEURAL NETWORK ARCHITECTURE

A review of different models of spike neural networks has been conducted. A spike type neural network is developed in the article. The mathematical model of exchange of neurotransmitters between neurons is proposed. An algorithm for network operation and neuron interaction is proposed, as well as an approach to training the network based on the formation of new connections from dendrites and increasing the signal transmission coefficient. The model of neural network was created in Python using the developed algorithm and mathematical model. To speed up the calculations, they were paralleled using the Numba library. The library Matplotlib was used for visual modelling and plotting the number of neurotransmitters on neurons. Experimental studies of the developed model of biological type network were carried out. It was shown that the developed network after training responds faster to the signals that were applied to it in the process of training.

Logic-based modeling of biological networks with Netflux.

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux (https://github.com/saucermanlab/Netflux). Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays

Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.

Deep Learning-Driven Optimization Strategies for Teaching Decisions in Smart Classrooms

With the rapid advancement of information technology, smart classrooms have increasingly become a vital component of modern education. By integrating technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data, smart classrooms provide a smart, efficient educational setting for teachers and students. However, the challenge of fully utilizing these technologies to enhance teaching effectiveness in smart classrooms remains unresolved. Existing research has highlighted the significant potential of deep learning in optimizing teaching decisions. However, its application faces challenges, including insufficient integration of technologies and limited effectiveness in practical implementations. The main focus of this study encompasses three parts: firstly, a biological neural network model targeted at optimizing teaching decisions, which emulates the mechanisms of biological neural networks to efficiently optimize teaching decisions; secondly, an active consciousness teaching decision model within smart classroom settings, which merges deep learning with theories of active consciousness to support dynamic, intelligent teaching decisions; and thirdly, a biologically inspired teaching process coordination network in smart classrooms, designed to optimize and coordinate educational processes based on biological principles. Through these investigations, this study provides both theoretical and practical support for optimizing teaching decisions in smart classrooms, offering significant academic value and practical application prospects.

Comparative temporal dynamics of individuation and perceptual averaging using a biological neural network model

Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.

Construction of a Multicellular Communication Network Model for Cell Co-Culture Technology and Evaluation of Its Simulation Capability

Abstract Objective: Cell co-culture technology has been widely used to analyze the effects of drugs on cell proliferation and the expression of some proteins in cells, especially in the field of traditional Chinese medicine (TCM); however, the interactions between cells and the transmission of TCM effects between cells have not been adequately studied. Materials and Methods: Using data on gene transcription regulation, biological response, signal channel, and cell-specific expression protein, we built a network for cell types based on entity grammar. Through the correspondence and location information of signal molecules and receptors, type-specific networks of single cells were connected and a multicellular network of smooth muscle cells, neurons, and vascular endothelial cells was constructed. The mechanism of action of nimodipine was analyzed based on the multicellular communication network and its simulation capability was evaluated. Results: The outputs generated by the model developed in this study showed that nimodipine inhibited smooth muscle contraction, due to the overload of Ca2+ and the toxicity of excitatory amino acids, and protected neurons and vascular endothelial cells by supporting cell proliferation and inhibiting cell apoptosis. These results were consistent with the known mechanism of nimodipine action, thus confirming that the multicellular network can be used to study the transmission of drug effects among cells. Conclusions: This study lays a foundation for the analysis of the transmission of drug effects in multi-cells, tissues, organs, and other spatial scales through multicellular co-culture experiments, based on a multicellular communication network. In addition, it provides a biological network model for the analysis of TCM action mechanisms.

Classification of 2-node excitatory–inhibitory networks

We classify connected 2-node excitatory–inhibitory networks under various conditions. We assume that, as well as for connections, there are two distinct node-types, excitatory and inhibitory. In our classification we consider four different types of excitatory–inhibitory networks: restricted, partially restricted, unrestricted and completely unrestricted. For each type we give two different classifications. Using results on ODE-equivalence and minimality, we classify the ODE-classes and present a minimal representative for each ODE-class. We also classify all the networks with valence ≤2. These classifications are up to renumbering of nodes and the interchange of ‘excitatory’ and ‘inhibitory’ on nodes and arrows. These classifications constitute a first step towards analysing dynamics and bifurcations of excitatory–inhibitory networks. The results have potential applications to biological network models, especially neuronal networks, gene regulatory networks, and synthetic gene networks.

CEGAR-Based Approach for Solving Combinatorial Optimization Modulo Quantified Linear Arithmetics Problems

Bioinformatics has always been a prolific domain for generating complex satisfiability and optimization problems. For instance, the synthesis of multi-scale models of biological networks has recently been associated with the resolution of optimization problems mixing Boolean logic and universally quantified linear constraints (OPT+qLP), which can be benchmarked on real-world models. In this paper, we introduce a Counter-Example-Guided Abstraction Refinement (CEGAR) to solve such problems efficiently. Our CEGAR exploits monotone properties inherent to linear optimization in order to generalize counter-examples of Boolean relaxations. We implemented our approach by extending Answer Set Programming (ASP) solver Clingo with a quantified linear constraints propagator. Our prototype enables exploiting independence of sub-formulas to further exploit the generalization of counter-examples. We evaluate the impact of refinement and partitioning on two sets of OPT+qLP problems inspired by system biology. Additionally, we conducted a comparison with the state-of-the-art ASP solver Clingo[lpx] that handles non-quantified linear constraints, showing the advantage of our CEGAR approach for solving large problems.

Preponderance of generalized chain functions in reconstructed Boolean models of biological networks

Boolean networks (BNs) have been extensively used to model gene regulatory networks (GRNs). The dynamics of BNs depend on the network architecture and regulatory logic rules (Boolean functions (BFs)) associated with nodes. Nested canalyzing functions (NCFs) have been shown to be enriched among the BFs in the large-scale studies of reconstructed Boolean models. The central question we address here is whether that enrichment is due to certain sub-types of NCFs. We build on one sub-type of NCFs, the chain functions (or chain-0 functions) proposed by Gat-Viks and Shamir. First, we propose two other sub-types of NCFs, namely, the class of chain-1 functions and generalized chain functions, the union of the chain-0 and chain-1 types. Next, we find that the fraction of NCFs that are chain-0 (also holds for chain-1) functions decreases exponentially with the number of inputs. We provide analytical treatment for this and other observations on BFs. Then, by analyzing three different datasets of reconstructed Boolean models we find that generalized chain functions are significantly enriched within the NCFs. Lastly we illustrate that upon imposing the constraints of generalized chain functions on three different GRNs we are able to obtain biologically viable Boolean models.

Optimizing Brain-Computer Interfaces through Spiking Neural Networks and Memristors

Brain-computer interfaces (BCIs) have emerged as a transformative conduit bridging the human brain's intricate realms and computing systems' capabilities. However, numerous challenges remain in improving BCI accuracy, efficiency, and adaptability. This paper investigates the integration of spiking neural networks (SNNs) and memristors to optimize BCI performance. SNNs offer exceptional potential to enhance BCI accuracy through biomimetic modeling of biological neural networks. By emulating the brain's spatio-temporal signaling patterns, SNNs may significantly improve neural decoding precision. Meanwhile, memristors can simulate synaptic plasticity and potentially enable real-time adaptive learning in BCIs. Preliminary studies demonstrate substantially improved signal processing, feature extraction, and classification capabilities when using SNNs and memristors in BCIs. This neuroinspired integration offers a compelling vision for personalized BCIs that continuously adapt to individual users. However, realizing the full potential relies on addressing lingering technical hurdles as well as emerging ethical considerations around user autonomy, privacy, responsibility, and access. Ultimately, interdisciplinary collaboration remains imperative to harness the promising trajectory of optimized BCIs while navigating the multifaceted challenges ahead.

The local topology of dynamical network models for biology

Abstract The search for motifs—recurrent patterns in network topology—has led to the identification of universal classes of complex systems across diverse fields and has served as a quantitative tool to reveal common properties in both evolved and designed networks. In this study, we investigate the presence and significance of network superfamilies—previously identified through the census of triadic motifs—in the largest data set of dynamic, biological network models. We present triad significance profiles of 71 existing biological network models, all experimentally inspired. The generated data are treated in an unbiased manner and consistently clustered into two classes using several unsupervised techniques. The more prevalent class does exhibit a strong correlation with the superfamily of sensory transmission networks, which are characterized by the feedforward loop motif commonly found in signal-processing systems. Surprisingly, the remaining class shows a better correlation with the superfamily of word-adjacency networks. To better understand this, the results are analysed for varying network size thresholds, and their connection to the effect of model building activity is examined. It is highlighted that the more the model focuses on smaller portions of the regulatory network, as a result of the coarse-graining of the boundary dynamics and the peripheral regions of the network, the more its topology starts resembling that of ‘sentences’ of word-adjacency networks.

SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction.

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.

Predicting circRNA-miRNA interactions utilizing transformer-based RNA sequential learning and high-order proximity preserved embedding

A key regulatory mechanism involves circular RNA (circRNA) acting as a sponge to modulate microRNA (miRNA), and thus, studying their interaction has significant medical implications. In this field, there are currently two pressing issues that remain unresolved. Firstly, due to the scarcity of verified interactions, we require a minimal amount of samples for training. Secondly, the current models lack interpretability. Therefore, we propose SPBCMI, a method that combines sequence features extracted using the Bidirectional Encoder Representations from Transformer (BERT) model and structural features of biological molecule networks extracted through graph embedding to train a GBDT (Gradient-boosted decision trees) classifier for prediction. Our method yielded an AUC of 0.9143, which is currently the best for this problem. Furthermore, in the case study, SPBCMI accurately predicted 7 out of 10 circRNA-miRNA interactions. These results show that our method provides an innovative and high-performing approach to understanding the interaction between circRNA and miRNA.

How children learn to understand language meanings: a neural model of adult-child multimodal interactions in real-time.

This article describes a biological neural network model that can be used to explain how children learn to understand language meanings about the perceptual and affective events that they consciously experience. This kind of learning often occurs when a child interacts with an adult teacher to learn language meanings about events that they experience together. Multiple types of self-organizing brain processes are involved in learning language meanings, including processes that control conscious visual perception, joint attention, object learning and conscious recognition, cognitive working memory, cognitive planning, emotion, cognitive-emotional interactions, volition, and goal-oriented actions. The article shows how all of these brain processes interact to enable the learning of language meanings to occur. The article also contrasts these human capabilities with AI models such as ChatGPT. The current model is called the ChatSOME model, where SOME abbreviates Self-Organizing MEaning.

Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models.

Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca2+/calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling. KEY POINTS: Protein interaction databases are used to recover signalling interactions from previously developed network models. The five protein interaction databases benchmarked recovered well-conserved pathways, but did poorly at recovering tissue-specific pathways and transcriptional regulation, indicating the importance of manual curation. We identify new signalling interactions not previously used in the network models, including a role for Ca2+/calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy.

Tobacco Alkaloid Assessment in a DSS-Induced Colitis Mouse Model with a Fully Humanized Immune System

Inflammatory bowel disease (IBD) refers to chronic intestinal immune-mediated diseases including two main disease manifestations: ulcerative colitis (UC) and Crohn's disease (CD). Epidemiological, clinical, and preclinical evidence has highlighted the potential anti-inflammatory properties of naturally occurring alkaloids. In the present study, we investigated the potential anti-inflammatory activities of the tobacco alkaloids nicotine and anatabine in a dextran sulfate sodium (DSS)-induced UC mouse model with a fully humanized immune system. Our results show that nicotine significantly reduced all acute colitis symptoms and improved colitis-specific endpoints, including histopathologically assessed colon inflammation, tissue damage, and mononuclear cell infiltration. The tobacco alkaloid anatabine showed similar effectiveness trends, although they were generally weaker or not significant. Gene expression analysis in the context of biological network models of IBD further pinpointed a possible mechanism by which nicotine attenuated DSS-induced colitis in humanized mice. The current study enables further investigation of possible molecular mechanisms by which tobacco alkaloids attenuate UC symptoms.

Time-Reduced Model for Multilayer Spiking Neural Networks

Spiking neural networks (SNNs) is a type of biological neural network model, which is more biologically plausible and computationally powerful than traditional artificial neural networks (ANNs). SNNs can achieve the same goals as ANNs, and can build a large-scale network structure (i.e. deep spiking neural network) to accomplish complex tasks. However, training deep spiking neural network is difficult due to the non-differentiable nature of spike events, and it requires much computation time during the training period. In this paper, a time-reduced model adopting two methods is presented for reducing the computation time of a deep spiking neural network (i.e. approximating the spike response function by the piecewise linear method, and choosing the suitable number of sub-synapses). The experimental results show that the methods of piecewise linear approximation and choosing the suitable number of sub-synapses is effective. This method can not only reduce the training time but also simplify the network structure. With the piecewise linear approximation method, the half of computation time of the original model can be reduced by at least. With the rule of choosing the number of sub-synapses, the computation time of less than one-tenth of the original model can be reduced for XOR and Iris tasks.

VR-Based Visualization for Biological Networks

The current revolution in technology forces most daily life systems to generate large-scale data. Understanding such data is not an easy task due to its size and complexity. Therefore, data visualization has played a key role in understanding data more clearly. Moreover, most of the available approaches for data visualization are considered traditional that deal with data in a 2D way. Furthermore, one of the most modern methods for data visualization is converting data into network models that include nodes and edges. This kind of visualization has been proven as an efficient method of analyzing large-scale data. On the other hand, with the advent of virtual reality technology and the metaverse concept, data can be visualized and analyzed in an interactive and 3D way. However, there is a lack in the literature providing enough information on how to generate such 3D models. This thesis comes to investigate the integration of virtual reality technology and complex networks, and how this integration can generate interactive data visualization models. To this end, a 3D VR-based interactive biological network model is designed and implemented to be adoptable by the metaverse platform. The biological network represents the gene-gene interactions of the sex chromosomes. The developed model enables analysts to dive into data and interact with its objects, which lead to a more professional, deep, and accurate analysis. The model proved its efficiency in terms of usability when tested by experts.

Persistence in a large network of sparsely interacting neurons.

This article presents a biological neural network model driven by inhomogeneous Poisson processes accounting for the intrinsic randomness of synapses. The main novelty is the introduction of sparse interactions: each firing neuron triggers an instantaneous increase in electric potential to a fixed number of randomly chosen neurons. We prove that, as the number of neurons approaches infinity, the finite network converges to a nonlinear mean-field process characterised by a jump-type stochastic differential equation. We show that this process displays a phase transition: the activity of a typical neuron in the infinite network either rapidly dies out, or persists forever, depending on the global parameters describing the intensity of interconnection. This provides a way to understand the emergence of persistent activity triggered by weak input signals in large neural networks.

Retracted: Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method

Retracted: Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method