Large-scale Biological Networks Research Articles

Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks.

The study of neuron interactions and hardware implementations are crucial research directions in neuroscience, particularly in developing large-scale biological neural networks. The FitzHugh-Nagumo (FHN) model is a popular neuron model with highly biological plausibility, but its complexity makes it difficult to apply at scale. This paper presents a cost-saving and improved precision approximation algorithm for the digital implementation of the FHN model. By converting the computational data into floating-point numbers, the original multiplication calculations are replaced by adding the floating-point exponent part and fitting the mantissa part with piecewise linear. In the hardware implementation, shifters and adders are used, greatly reducing resource overhead. Implementing FHN neurons by this approximation calculations on FPGA reduces the normalized root mean square error (RMSE) to 3.5% of the state-of-the-art (SOTA) while maintaining a performance overhead ratio improvement of 1.09 times. Compared to implementations based on approximate multipliers, the proposed method achieves a 20% reduction in error at the cost of a 2.8% increase in overhead.This model gained additional biological properties compared to LIF while reducing the deployment scale by only 9%. Furthermore, the hardware implementation of nine coupled circular networks with eight nodes and directional diffusion was carried out to demonstrate the algorithm's effectiveness on neural networks. The error decreased to 60% compared to the single neuron of the SOTA. This hardware-friendly algorithm allows for the low-cost implementation of high-precision hardware simulation, providing a novel perspective for studying large-scale, biologically plausible neural networks.

Recent Progress in Development of Artificial Neuromorphic Devices Based on Emerging Materials

In the fourth industrial revolution, the efficient processing of huge amounts of data is important due to the development of artificial intelligence (AI), internet of things (IoT), and machine learning (ML). The conventional computing system, which is known as von Neumann architecture, has been facing bottleneck problems because of the physical separation of memory and central processing unit (CPU). Many researchers have interested to study on neuromorphic computing, inspired by the human brain, to solve the bottleneck problems. The development of artificial neuromorphic devices, such as neuron and synaptic devices, is important to successfully demonstrate a neuromorphic computing hardware. Various Si CMOS transistor-based circuits have been investigated to implement the behaviors of the biological neuron and synapse; however, they are not suitable for mimicking the large-scale biological neural networks because of Si CMOS transistor’s scalability and power consumption issues. In this report, we review the recent research progress in artificial neurons and synaptic devices based on emerging materials and discuss the future research direction of artificial neural networks.

Reconstructing gene regulatory networks of biological function using differential equations of multilayer perceptrons

BackgroundBuilding biological networks with a certain function is a challenge in systems biology. For the functionality of small (less than ten nodes) biological networks, most methods are implemented by exhausting all possible network topological spaces. This exhaustive approach is difficult to scale to large-scale biological networks. And regulatory relationships are complex and often nonlinear or non-monotonic, which makes inference using linear models challenging.ResultsIn this paper, we propose a multi-layer perceptron-based differential equation method, which operates by training a fully connected neural network (NN) to simulate the transcription rate of genes in traditional differential equations. We verify whether the regulatory network constructed by the NN method can continue to achieve the expected biological function by verifying the degree of overlap between the regulatory network discovered by NN and the regulatory network constructed by the Hill function. And we validate our approach by adapting to noise signals, regulator knockout, and constructing large-scale gene regulatory networks using link-knockout techniques. We apply a real dataset (the mesoderm inducer Xenopus Brachyury expression) to construct the core topology of the gene regulatory network and find that Xbra is only strongly expressed at moderate levels of activin signaling.ConclusionWe have demonstrated from the results that this method has the ability to identify the underlying network topology and functional mechanisms, and can also be applied to larger and more complex gene network topologies.

DWPPI: A Deep Learning Approach for Predicting Protein-Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network.

The prediction of protein–protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning–based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets: Arabidopsis thaliana (A. thaliana), mazie (Zea mays), and rice (Oryza sativa). The experimental results with the fivefold cross-validation technique demonstrated that DWPPI obtains great performance with the AUC (area under ROC curves) values of 0.9548, 0.9867, and 0.9213, respectively. To further verify the predictive capacity of DWPPI, we compared it with some different state-of-the-art machine learning classifiers. Moreover, case studies were performed with the AC149810.2_FGP003 protein. As a result, 14 of the top 20 PPI pairs identified by DWPPI with the highest scores were confirmed by the literature. These excellent results suggest that the DWPPI model can act as a promising tool for related plant molecular biology.

An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks

Spiking neural networks (SNNs) inspired by biological neurons enable a more realistic mimicry of the human brain. To realize SNNs similar to large-scale biological networks, neuron circuits with high area efficiency are essential. In this paper, we propose a compact leaky integrate-and-fire (LIF) neuron circuit with a long and tunable time constant, which consists of a capacitor and two pseudo resistors (PRs). The prototype chip was fabricated with TSMC 65 nm CMOS technology, and it occupies a die area of 1392 μm2. The fabricated LIF neuron has a power consumption of 6 μW and a leak time constant of up to 1.2 ms (the resistance of PR is up to 600 MΩ). In addition, the time constants are tunable by changing the bias voltage of PRs. Overall, this proposed neuron circuit facilitates the very-large-scale integration of adaptive SNNs, which is crucial for the implementation of bio-scale brain-inspired computing.

Network cartographs for interpretable visualizations

Networks offer an intuitive visual representation of complex systems. Important network characteristics can often be recognized by eye and, in turn, patterns that stand out visually often have a meaningful interpretation. In conventional network layout algorithms, however, the precise determinants of a node’s position within a layout are difficult to decipher and to control. Here we propose an approach for directly encoding arbitrary structural or functional network characteristics into node positions. We introduce a series of two- and three-dimensional layouts, benchmark their efficiency for model networks, and demonstrate their power for elucidating structure-to-function relationships in large-scale biological networks.

A heterogeneous parallel implementation of the Markov clustering algorithm for large-scale biological networks on distributed CPU–GPU clusters

A heterogeneous parallel implementation of the Markov clustering algorithm for large-scale biological networks on distributed CPU–GPU clusters

Biogc: A novel framework for biological network classification via machine learning

Biological network classification is an eminently challenging task in the domain of data mining since the networks contain complex structural information. Conventional biochemical experimental methods and the existing intelligent algorithms still suffer from some limitations such as immense experimental cost and inferior accuracy rate. To solve these problems, in this paper, we propose a novel framework for Biological graph classification named Biogc, which is specifically developed to predict the label of both small-scale and large-scale biological network data flexibly and efficiently. Our framework firstly presents a simplified graph kernel method to capture the structural information of each graph. Then, the obtained informative features are adopted to train different scale biological network data-oriented classifiers to construct the prediction model. Extensive experiments on five benchmark biological network datasets on graph classification task show that the proposed model Biogc outperforms the state-of-the-art methods with an accuracy rate of 98.90% on a larger dataset and 99.32% on a smaller dataset.

Community Detection in Large-Scale Bipartite Biological Networks.

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

ANCA: Alignment-Based Network Construction Algorithm.

Dynamic biological networks model changes in the network topology over time. However, often the topologies of these networks are not available at specific time points. Existing algorithms for studying dynamic networks often ignore this problem and focus only on the time points at which experimental data is available. In this paper, we develop a novel alignment based network construction algorithm, ANCA, that constructs the dynamic networks at the missing time points by exploiting the information from a reference dynamic network. Our experiments on synthetic and real networks demonstrate that ANCA predicts the missing target networks accurately, and scales to large-scale biological networks in practical time. Our analysis of an E. coli protein-protein interaction network shows that ANCA successfully identifies key temporal changes in the biological networks. Our analysis also suggests that by focusing on the topological differences in the network, our method can be used to find important genes and temporal functional changes in the biological networks.

Drug2ways: Reasoning over causal paths in biological networks for drug discovery.

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

Leverage Large-Scale Biological Networks to Decipher the Genetic Basis of Human Diseases Using Machine Learning.

A fundamental question in precision medicine is to quantitatively decode the genetic basis of complex human diseases, which will enable the development of predictive models of disease risks based on personal genome sequences. To account for the complex systems within different cellular contexts, large-scale regulatory networks are critical components to be integrated into the analysis. Based on the fast accumulation of multiomics and disease genetics data, advanced machine learning algorithms and efficient computational tools are becoming the driving force in predicting phenotypes from genotypes, identifying potential causal genetic variants, and revealing disease mechanisms. Here, we review the state-of-the-art methods for this topic and describe a computational pipeline that assembles a series of algorithms together to achieve improved disease genetics prediction through the delineation of regulatory circuitry step by step.

Cytomorphic Electronics With Memristors for Modeling Fundamental Genetic Circuits.

Cytomorphic engineering attempts to study the cellular behavior of biological systems using electronics. As such, it can be considered analogous to the study of neurobiological concepts for neuromorphic engineering applications. To date, digital and analog translinear electronics have commonly been used in the design of cytomorphic circuits; Such circuits could greatly benefit from lowering the area of the digital memory via memristive circuits. In this article, we propose a novel approach that utilizes the Boltzmann-exponential stochastic transport of ionic species through insulators to naturally model the nonlinear and stochastic behavior of biochemical reactions. We first show that two-terminal memristive devices can capture the non-linear and stochastic behavior of biochemical reactions. Then, we present the design of several building blocks based on analog memristive circuits that inherently model the biophysical mechanisms of gene expression. The circuits model induction by small molecules, activation and repression by transcription factors, biological promoters, cooperative binding, and transcriptional and translational regulation of gene expression. Finally, we utilize the building blocks to form complex mixed-signal networks that can simulate the delay-induced oscillator and the p53-mdm2 interaction in the cancer signaling pathway. Our approach can provide a fast and simple emulative framework for studying genetic circuits and arbitrary large-scale biological networks in systems and synthetic biology. Some challenges may be that memristive devices with frequent learning and programming do not have the same longevity as traditional transistor-based electron-transport devices, and operate with significantly slower time constants, which can limit emulation speed.

Reconciling periodic rhythms of large-scale biological networks by optimal control.

Periodic rhythms are ubiquitous phenomena that illuminate the underlying mechanism of cyclic activities in biological systems, which can be represented by cyclic attractors of the related biological network. Disorders of periodic rhythms are detrimental to the natural behaviours of living organisms. Previous studies have shown that the state transition from one to another attractor can be accomplished by regulating external signals. However, most of these studies until now have mainly focused on point attractors while ignoring cyclic ones. The aim of this study is to investigate an approach for reconciling abnormal periodic rhythms, such as diminished circadian amplitude and phase delay, to the regular rhythms of complex biological networks. For this purpose, we formulate and solve a mixed-integer nonlinear dynamic optimization problem simultaneously to identify regulation variables and to determine optimal control strategies for state transition and adjustment of periodic rhythms. Numerical experiments are implemented in three examples including a chaotic system, a mammalian circadian rhythm system and a gastric cancer gene regulatory network. The results show that regulating a small number of biochemical molecules in the network is sufficient to successfully drive the system to the target cyclic attractor by implementing an optimal control strategy.

Structural controllability of dynamic transcriptional regulatory networks for Saccharomyces cerevisiae

Biological networks can dynamically change their structures to adapt to various biotic and abiotic stimuli. However, state-of-the-art studies on large-scale biological networks mainly focus on static networks. Here, we applied the sophisticated structural controllability tool to analyze one static and five dynamic transcriptional regulatory networks (TRNs) for Saccharomyces cerevisiae (S. cerevisiae), the five dynamic networks include two endogenous and three exogenous ones. Structural controllability allows us to classify nodes into different categories according to their control roles, and we clarify their topological features. Our results reveal that biological networks are rather difficult to be fully controlled. However, the dynamic networks are relatively easier to be controlled than the static one, and one needs relatively more external inputs to control the exogenous networks than the endogenous ones. Moreover, we found that nodes consisting of the single input module (SIM) and multiple input module (MIM) tend to be critical driver nodes, and those consisting of the feed-forward loop (FFL) in dynamic networks are prone to be critical drivers in comparison with those in the static network. Furthermore, GO enrichment analysis reveals that driver nodes but not critical driver nodes contribute to functional differences between the static and dynamic networks. Critical drivers, indispensable and neutral nodes are all enriched with transcriptional factors (TFs). On the contrary, essential genes tend to be enriched as driver nodes and dispensable nodes. The obtained results have certain robustness against perturbations. We declare that control roles of TFs and essential genes facilitate the identification of them via structural controllability theory. Our investigations provide some insights into real-world control of biological networks.

A network-based pathway-extending approach using DNA methylation and gene expression data to identify altered pathways

Pathway analysis allows us to gain insights into a comprehensive understanding of the molecular mechanisms underlying cancers. Currently, high-throughput multi-omics data and various types of large-scale biological networks enable us to identify cancer-related pathways by comprehensively analyzing these data. Combining information from multidimensional data, pathway databases and interaction networks is a promising strategy to identify cancer-related pathways. Here we present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to extend original pathways. The results show that the extension of original pathways can provide a basis for discovering new components of the original pathway and understanding the crosstalk between pathways in a large-scale biological network. By inputting the gene lists of the extended pathways into the classical gene set analysis (ORA and FCS), we effectively identified the altered pathways which are correlated well with the corresponding cancer. The method is evaluated on three datasets retrieved from TCGA (BRCA, LUAD and COAD). The results show that the integration of DNA methylation and gene expression data through a network of known gene interactions is effective in identifying altered pathways.

Characterizing building blocks of resource constrained biological networks

BackgroundIdentification of motifs–recurrent and statistically significant patterns–in biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over- or under-estimates), and thus, wrong biological interpretations.ResultsIn this paper, we develop a novel motif counting algorithm, Partially Overlapping MOtif Counting (POMOC), that considers capacity levels for all interactions in counting motifs.ConclusionsOur experiments on real and synthetic networks demonstrate that motif count using the POMOC method significantly differs from the existing motif counting approaches, and our method extends to large-scale biological networks in practical time. Our results also show that our method makes it possible to characterize the impact of different stress factors on cell’s organization of network. In this regard, analysis of a S. cerevisiae transcriptional regulatory network using our method shows that oxidative stress is more disruptive to organization and abundance of motifs in this network than mutations of individual genes. Our analysis also suggests that by focusing on the edges that lead to variation in motif counts, our method can be used to find important genes, and to reveal subtle topological and functional differences of the biological networks under different cell states.

Identification of regulatory variables for state transition of biological networks

Attractors represent steady states of biological networks. Recent studies have shown that regulatory variables can be used to steer a network state transition from an undesired attractor, such as a cancerous state, to a desired healthy one. Therefore, it is important to identify the regulatory variables and determine their time-dependent profile for state transition of a given network. However, this is a challenging task since regulatory variables have to be identified among numerous candidates in a large-scale biological network. In this study, we developed a new method for identifying regulatory variables in large-scale biological networks for the purpose of state transition. As a result, a set of optimal regulatory variables can be determined based on formulating and solving a mixed-integer nonlinear dynamic optimization problem. A relaxation scheme is used to overcome the difficulties in solving this complex problem containing a large number of binary variables. The solution to this problem simultaneously identifies the optimal regulatory variables, provides strength of regulatory interactions, and obtains the minimal control time to realize the required state transition. In addition, by adjusting the objective function, various combinations of the strength of regulatory interactions and the transition time can be achieved according to the requirement for disease therapy. Results of three case studies (a myeloid differentiation regulatory network, a cancer gene regulatory network, and a T-LGL signaling network) demonstrate the efficacy of the proposed approach. Therefore, this study establishes an appropriate framework for identifying the regulatory variables for state transition of complex biological networks.

Robustness of a Reconstituted Escherichia coli Protein Translation System Analyzed by Computational Modeling.

Robustness against environmental changes is one of the major features of biological systems, but its origin is not well understood. We recently constructed a large-scale computational model of an Escherichia coli-based reconstituted in vitro translation system that enumerates all protein synthesis processes in detail. Our model synthesizes a formyl-Met-Gly-Gly tripeptide (MGG peptide) from 27 initial molecular components through 968 biochemical reactions. Among the 968 kinetic parameters, 483 are nonzero parameters, and the simulator was used to determine how perturbations of 483 individual reactions affect the complex reaction network. We found that even when the kinetic parameter was changed from 100- to 0.01-fold, 94% of the changes hardly affected the two indicators of reaction dynamics in MGG peptide synthesis, which represent the yield of the MGG peptide and the initial lag-time of the peptide synthesis. Moreover, none of the indicators increased proportionally to these changes: e.g., a 100-fold increase in the kinetic parameter increased the yield by only 2.2-fold at most, indicating the insensitivity of the reaction network to perturbation. Robustness and insensitivity are likely to be a common feature of large-scale biological reaction networks.

A Digitally Programmable Cytomorphic Chip for Simulation of Arbitrary Biochemical Reaction Networks.

Prior work has shown that compact analog circuits can faithfully represent and model fundamental biomolecular circuits via efficient log-domain cytomorphic transistor equivalents. Such circuits have emphasized basis functions that are dominant in genetic transcription and translation networks and deoxyribonucleic acid (DNA)-protein binding. Here, we report a system featuring digitally programmable 0.35 μm BiCMOS analog cytomorphic chips that enable arbitrary biochemical reaction networks to be exactly represented thus enabling compact and easy composition of protein networks as well. Since all biomolecular networks can be represented as chemical reaction networks, our protein networks also include the former genetic network circuits as a special case. The cytomorphic analog protein circuits use one fundamental association-dissociation-degradation building-block circuit that can be configured digitally to exactly represent any zeroth-, first-, and second-order reaction including loading, dynamics, nonlinearity, and interactions with other building-block circuits. To address a divergence issue caused by random variations in chip fabrication processes, we propose a unique way of performing computation based on total variables and conservation laws, which we instantiate at both the circuit and network levels. Thus, scalable systems that operate with finite error over infinite time can be built. We show how the building-block circuits can be composed to form various network topologies, such as cascade, fan-out, fan-in, loop, dimerization, or arbitrary networks using total variables. We demonstrate results from a system that combines interacting cytomorphic chips to simulate a cancer pathway and a glycolysis pathway. Both simulations are consistent with conventional software simulations. Our highly parallel digitally programmable analog cytomorphic systems can lead to a useful design, analysis, and simulation tool for studying arbitrary large-scale biological networks in systems and synthetic biology.