Rewiring Process Research Articles

A Random Boolean Network shifted toward a critical point

Abstract Random Boolean Networks (RBNs) model complex networks with numerous variables, serving as a tool for gene expression and genetic regulation modeling. RBNs exhibit phase transitions, contingent on node degrees. Given the significance of phase transitions in collective behaviors, the study explores the relationship between RBNs and actual living system networks, which also display critical behaviors. Notably, living systems exhibit such behaviors even beyond the predicted critical point in RBNs. This paper introduces a novel RBNs model incorporating a rewiring process for edge connections/disconnections. In contrast to prior studies, our model includes artificial genes occasionally adding self-loops and creating an instant and temporal lookup table. Consequently, our proposed model demonstrates the edge of chaos at higher node degrees. It serves as an abstract RBNs model generating noisy behaviors from internal agent processes without external parameter tuning.

Measuring the Metabolic State of Tissue-Resident Macrophages via SCENITH.

Functional reprograming of cells is linked to a process of metabolic rewiring that is adapted for such new functions or microenvironment. Macrophages are present in all tissues and exposed to different microenvironments throughout our body. Profiling energetic metabolism of tissue resident and other heterogeneous populations of macrophages in vitro and ex vivo is technologically very challenging. We have recently developed a method to functionally profile energetic metabolism with single-cell resolution, named SCENITH. This method can be performed rapidly ex vivo and does not require specialized equipment. In this book chapter, we will summarize the tissue processing, the procedure and methods, the analysis and example of results, and a series of frequently asked questions.

Coevolutionary dynamics of strategy and network structure with publicity mechanism

Understanding the emergence of cooperation on complex dynamic networks, especially how the dynamic changes of network topology interact with individual decision-makings, is an important interdisciplinary topic. This paper studies the coevolution of individual strategies and network structures by proposing a linking dynamics and introducing a publicity mechanism during the rewiring process of networks. Individuals can expand their popularity by spending some cost to publicize themselves, thereby increasing the likelihood of being connected in dynamic networks. It is found that the comparison between the publicity intensity of cooperation and defection determines which strategy is dominant in the evolutionary process. However, the existence of publicity costs will suppress the expansion of hype strategies such as defection. Excessive publicizing often fails to earn returns equivalent to huge costs. It is further revealed that in the process of dynamic adjustment of the network, the preference of network links, i.e. the combination of link breaking probabilities between different types of nodes, is an important factor that directly affects the evolution of cooperation.

ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks

Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training. However, parameter redundancy still hinders the efficiency of SNNs during training. In the human brain, the rewiring process of neural networks is highly dynamic, while synaptic connections maintain relatively sparse during brain development. Inspired by this, here we propose an efficient evolutionary structure learning (ESL) framework for SNNs, named ESL-SNNs, to implement the sparse SNN training from scratch. The pruning and regeneration of synaptic connections in SNNs evolve dynamically during learning, yet keep the structural sparsity at a certain level. As a result, the ESL-SNNs can search for optimal sparse connectivity by exploring all possible parameters across time. Our experiments show that the proposed ESL-SNNs framework is able to learn SNNs with sparse structures effectively while reducing the limited accuracy. The ESL-SNNs achieve merely 0.28% accuracy loss with 10% connection density on the DVS-Cifar10 dataset. Our work presents a brand-new approach for sparse training of SNNs from scratch with biologically plausible evolutionary mechanisms, closing the gap in the expressibility between sparse training and dense training. Hence, it has great potential for SNN lightweight training and inference with low power consumption and small memory usage.

Threshold Cascade Dynamics in Coevolving Networks.

We study the coevolutionary dynamics of network topology and social complex contagion using a threshold cascade model. Our coevolving threshold model incorporates two mechanisms: the threshold mechanism for the spreading of a minority state such as a new opinion, idea, or innovation and the network plasticity, implemented as the rewiring of links to cut the connections between nodes in different states. Using numerical simulations and a mean-field theoretical analysis, we demonstrate that the coevolutionary dynamics can significantly affect the cascade dynamics. The domain of parameters, i.e., the threshold and mean degree, for which global cascades occur shrinks with an increasing network plasticity, indicating that the rewiring process suppresses the onset of global cascades. We also found that during evolution, non-adopting nodes form denser connections, resulting in a wider degree distribution and a non-monotonous dependence of cascades sizes on plasticity.

Reconfiguration of Nematic Disclinations in Plane-Parallel Confinements

We study numerically the reconfiguration process of colliding m=1/2 strength disclinations in an achiral nematic liquid crystal (NLC). A Landau–de Gennes approach in terms of tensor nematic-order parameters is used. Initially, different pairs m1,m2 of parallel wedge disclination lines connecting opposite substrates confining the NLC in a plane-parallel cell of a thickness h are imposed: {1/2,1/2}, {−1/2,−1/2} and {−1/2,1/2}. The collisions are imposed by the relative rotation of the azimuthal angle θ of the substrates that strongly pin the defect end points. Pairs {1/2,1/2} and {−1/2,−1/2} “rewire” at the critical angle θc1=3π4 in all cases studied. On the other hand, two qualitatively different scenarios are observed for {−1/2,1/2}. In the thinner film regime h<hc, the disclinations rewire at θc2=5π4. The rewiring process is mediated by an additional chargeless loop nucleated in the middle of the cell. In the regime h>hc, the colliding disclinations at θc2 reconfigure into boojum-like twist disclinations.

Abstract 1746: Broad-spectrum kinome profiling identifies CDK6 upregulation as a driver of lenvatinib resistance in hepatocellular carcinoma

Abstract Lenvatinib is the FDA-approved targeted drug for advanced hepatocellular carcinoma (HCC), but the efficacy is modest due to drug resistance. Tumor kinome re-wiring governs drug resistance in resistant cancer cells, which is an obstacle for efficient cancer therapy. Therefore, identification of the kinases critical for this rewiring process in HCC is crucial. Upon mass spectrometry analysis using a lysine-targeted sulfonyl fluoride probe named XO44, CDK6 was identified to be the most upregulated kinase in lenvatinib-resistant HCC cell lines. Lentiviral-based overexpression and knockdown approaches were performed to characterize functional roles of CDK6 in regulation of cancer stemness and lenvatinib resistance. The upregulation of CDK6 in lenvatinib-resistant HCC cells is found to be mediated by the ERK/YAP signaling cascade. CDK6 is crucial in the regulation of acquired lenvatinib resistance via augmentation of cancer stemness and the overexpression of CDK6 led to poorer patient survival. We identified a novel noncanonical pathway of CDK6 in which it binds and regulates the activity of GSK3β, leading to activation of the Wnt/β-catenin signaling pathway. CDK6 inhibition by CDK4/6 inhibitor palbociclib or CDK6 degradation by proteolysis targeting chimeras (PROTACs) is highly synergistic with lenvatinib in vitro. Interestingly, palbociclib not only exerts the maximal growth suppressive effect with lenvatinib in lenvatinib-resistant HCC xenografts and Trp53KO/MYCOE-induced HCC models but also reshapes the tumor immune microenvironment. CDK6 blockade impedes the lenvatinib-induced activation of the ERK/YAP/CDK6/β-catenin signaling cascade. Targeting CDK6 with palbociclib in combination with lenvatinib may be a novel therapeutic strategy against HCC. Citation Format: Carmen Oi Leung, Yang Yang, Karl Kam So, Qian Zhao, Terence Kin Lee. Broad-spectrum kinome profiling identifies CDK6 upregulation as a driver of lenvatinib resistance in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1746.

Divergent Metabolomic Signatures of TGFβ2 and TNFα in the Induction of Retinal Epithelial-Mesenchymal Transition.

Epithelial-mesenchymal transition (EMT) is a dedifferentiation program in which polarized, differentiated epithelial cells lose their cell-cell adhesions and transform into matrix-producing mesenchymal cells. EMT of retinal pigment epithelial (RPE) cells plays a crucial role in many retinal diseases, including age-related macular degeneration, proliferative vitreoretinopathy, and diabetic retinopathy. This dynamic process requires complex metabolic reprogramming to accommodate the demands of this dramatic cellular transformation. Both transforming growth factor-beta 2 (TGFβ2) and tumor necrosis factor-alpha (TNFα) have the capacity to induce EMT in RPE cells; however, little is known about their impact on the RPE metabolome. Untargeted metabolomics using high-resolution mass spectrometry was performed to reveal the metabolomic signatures of cellular and secreted metabolites of primary human fetal RPE cells treated with either TGFβ2 or TNFα for 5 days. A total of 638 metabolites were detected in both samples; 188 were annotated as primary metabolites. Metabolomics profiling showed distinct metabolomic signatures associated with TGFβ2 and TNFα treatment. Enrichment pathway network analysis revealed alterations in the pentose phosphate pathway, galactose metabolism, nucleotide and pyrimidine metabolism, purine metabolism, and arginine and proline metabolism in TNFα-treated cells compared to untreated control cells, whereas TGFβ2 treatment induced perturbations in fatty acid biosynthesis metabolism, the linoleic acid pathway, and the Notch signaling pathway. These results provide a broad metabolic understanding of the bioenergetic rewiring processes governing TGFβ2- and TNFα-dependent induction of EMT. Elucidating the contributions of TGFβ2 and TNFα and their mechanistic differences in promoting EMT of RPE will enable the identification of novel biomarkers for diagnosis, management, and tailored drug development for retinal fibrotic diseases.

Neural rewiring from peripheral to central

Peripheral nerve injury and reconstruction would lead to alteration of neural pathways. This is regarded as rewiring peripheral nerves, which could also be a trigger for the corresponding neural rewiring process in the brain. Brain plasticity subsequent to peripheral nerve reconstruction plays an important role in the functional recovery of limbs, which has attracted increasing concerns. The present study aimed to overview recent progress in neuroregeneration-related brain plasticity. Nerve transfer is a special technique of nerve reconstruction that usually leads to substantial peripheral neural rewiring and cortical reorganization. Nerve transfer-related shifting of motor representation was particularly discussed. We also emphasized rehabilitation strategies based on the current peripheral-central rewiring theory. Specific strategies based on neural plasticity were proposed for corresponding recovery stages.

It is a small world, or is it? A look into two decades of tourism system

By adopting network science and Leiper (1979)’s tourism system conceptualization and by utilizing the UNWTO’s outbound tourism database, a portray of Global Tourism System (GTS) was constructed for the years between 1995 and 2018. GTS was then studied by using the measures of degree distribution, shortest geodesic distance, transitivity, and modularity analysis. Our results showed that GTS is a dense system (≈30%) which becomes denser with an average increase of 0.8% per year. Also while the system is on track to becoming resilient to economic crises, it is becoming vulnerable to other perturbations such as health crises. Furthermore, although GTS’s degree centrality is on the rise, its degree centralization is on a continuous decline. This continuous decline coupled with GTS’s disassortative nature are the indications of system’s decentralization. The findings of this study support the proposed hypothesis that GTS is best represented with Watts–Strogatz small-world network model. Nevertheless, the system’s in-degree and out-degree centrality measures follow two separate evolution regimes. From an out-degree perspective, we witness GTS’s transformation from a broad-scale small-world network to a single-scale one, while from an in-degree perspective, the system strictly follows a broad-scale small-world network. This means that although the small-world network is the best fitting model to GTS, the degree-preserved rewiring process is widely violated and therefore should be considered when modeling GTS. The disassortative nature of GTS and the lack of degree-preserved rewiring process have resulted in an obscure modularity structure which in turn generates a dynamic alternating pattern. Therefore, while, GTS demonstrates a static behavior with a fixed number of modules at all times, it has a dynamic destination membership in modules and destinations’ memberships constantly shift between modules.

Maximizing synchronizability of networks with community structure based on node similarity.

In reality, numerous networks have a community structure characterized by dense intra-community connections and sparse inter-community connections. In this article, strategies are proposed to enhance synchronizability of such networks by rewiring a certain number of inter-community links, where the research scope is complete synchronization on undirected and diffusively coupled dynamic networks. First, we explore the effect of adding links between unconnected nodes with different similarity levels on network synchronizability and find that preferentially adding links between nodes with lower similarity can improve network synchronizability more than that with higher similarity, where node similarity is measured by our improved Asymmetric Katz (AKatz) and Asymmetric Leicht-Holme-Newman (ALHNII) methods from the perspective of link prediction. Additional simulations demonstrate that the node similarity-based link-addition strategy is more effective in enhancing network synchronizability than the node centrality-based methods. Furthermore, we apply the node similarity-based link-addition or deletion strategy as the valid criteria to the rewiring process of inter-community links and then propose a Node Similarity-Based Rewiring Optimization (NSBRO) algorithm, where the optimization process is realized by a modified simulated annealing technique. Simulations show that our proposed method performs better in optimizing synchronization of such networks compared with other centrality-based heuristic methods. Finally, simulations on the Rössler system indicate that the network structure optimized by the NSBRO algorithm also leads to better synchronizability of coupled oscillators.

Adaptive multilayer networks resolve the cooperation dilemma induced by breaking the symmetry between interaction and learning

We study the coupled dynamics between strategy updating and partner switching on adaptive multilayer networks whose structure is split into an interaction layer for accumulating payoff and a learning layer for updating strategy. Two different types of adaptive multilayer network dynamics are modeled to study the evolution of cooperation. First, the selected individual either varies his strategy updating environment or switches his interaction partners during the partner switching process. It is proved that an increasing ratio of interaction network reconfiguration facilitates the coevolution of cooperation, indicating that interaction network acts a profound role in promoting the coevolution cooperation. Furthermore, we consider a type that the selected player simultaneously updates his strategy updating network and interaction network during the rewiring process. For a low frequent partner switching process, it is found that the evolution of cooperation is hindered whenever the two layers do not coincide. However, when the frequency of partner switching process increases, breaking the symmetry between interaction network and learning network resolves the social dilemma and enhances the evolution of cooperation. Moreover, a comparison between two adaptive multilayer network dynamics shows that the former type that only permits one layer to evolve every step on the adaptive multilayer networks suppresses the evolution of cooperation.

Salinity shapes the stress responses and energy reserves of marine polychaetes exposed to warming: From molecular to functional phenotypes

Estuarine systems are critical transition zones influenced by sea, land and freshwater. An array of human activities impacts these areas leading to multiple-stressor interactions. Temperature and salinity are among the most relevant drivers in estuaries, shaping species growth, reproduction and distribution. However, few studies provide an overview of cellular rewiring processes under multiple-stressor environments. Here, we tested how salinity could shape the response of ragworms Hediste diversicolor, an important bioindicator and commercial species, to elevated temperature. We exposed polychaetes to three temperatures for a month, simulating control, ocean warming and heatwave conditions (24, 27 and 30 °C, respectively) combined with two salinities (20 and 30). We quantified whole-organism performance (wet weight gain and survival), along with cellular stress response (CSR) and energy reserves of worms after 14 and 28 days of exposure. Significant three-way interactions between temperature, salinity and exposure time show the non-linearity of molecular responses. Worms at a salinity of 20 were more sensitive to warming than worms exposed to a salinity of 30. The combination of high temperature and low salinity can act synergistically to induce oxidative stress and macromolecular damage in worm tissues. This finding was supported by an induction of the CSR, with a concomitant decrease of energy reserves, pointing towards a metabolic compensation strategy. However, under a higher salinity (30), the need for a CSR upon thermal challenge was reduced and energy content increased with temperature, which suggests that environmental conditions were within the optimum range. Heatwaves striking low-salinity areas of estuaries can therefore negatively impact the cellular physiology of H. diversicolor, with greater metabolic costs. However, extreme stress levels were not reached as worms incremented wet weight and survival was high under all conditions tested. Our findings are important for the optimization of ragworm aquaculture and adaptive conservation strategies of estuarine systems.

Regulation of UNC-40/DCC and UNC-6/Netrin by DAF-16 promotes functional rewiring of the injured axon.

The adult nervous system has a limited capacity to regenerate after accidental damage. Post-injury functional restoration requires proper targeting of the injured axon to its postsynaptic cell. Although the initial response to axonal injury has been studied in great detail, it is rather unclear what controls the re-establishment of a functional connection. Using the posterior lateral microtubule neuron in Caenorhabditis elegans, we found that after axotomy, the regrowth from the proximal stump towards the ventral side and accumulation of presynaptic machinery along the ventral nerve cord correlated to the functional recovery. We found that the loss of insulin receptor DAF-2 promoted 'ventral targeting' in a DAF-16-dependent manner. We further showed that coordinated activities of DAF-16 in neuron and muscle promoted 'ventral targeting'. In response to axotomy, expression of the Netrin receptor UNC-40 was upregulated in the injured neuron in a DAF-16-dependent manner. In contrast, the DAF-2-DAF-16 axis contributed to the age-related decline in Netrin expression in muscle. Therefore, our study revealed an important role for insulin signaling in regulating the axon guidance molecules during the functional rewiring process.

Tuning the clustering coefficient of generalized circulant networks

Apart from the role the clustering coefficient plays in the definition of the small-world phenomena, it also has great relevance for practical problems involving networked dynamical systems. To study the impact of the clustering coefficient on dynamical processes taking place on networks, some authors have focused on the construction of graphs with tunable clustering coefficients. These constructions are usually realized through a stochastic process, either by growing a network through the preferential attachment procedure, or by applying a random rewiring process. In contrast, we consider here several families of static graphs whose clustering coefficients can be determined explicitly. The basis for these families is formed by the k-regular graphs on N nodes, that belong to the family of so-called circulant graphs denoted by CN,k. We show that the expression for the clustering coefficient of CN,k reported in literature, only holds for sufficiently large N. Next, we consider three generalizations of the circulant graphs, either by adding some pendant links to CN,k, or by connecting, in two different ways, an additional node to some nodes of CN,k. For all three generalizations, we derive explicit expressions for the clustering coefficient. Finally, we construct a family of pairs of generalized circulant graphs, with the same number of nodes and links, but with different clustering coefficients.

The LRR-TM protein PAN-1 interacts with MYRF to promote its nuclear translocation in synaptic remodeling.

Neural circuits develop through a plastic phase orchestrated by genetic programs and environmental signals. We have identified a leucine-rich-repeat domain transmembrane protein PAN-1 as a factor required for synaptic rewiring in C. elegans. PAN-1 localizes on cell membrane and binds with MYRF, a membrane-bound transcription factor indispensable for promoting synaptic rewiring. Full-length MYRF was known to undergo self-cleavage on ER membrane and release its transcriptional N-terminal fragment in cultured cells. We surprisingly find that MYRF trafficking to cell membrane before cleavage is pivotal for C. elegans development and the timing of N-MYRF release coincides with the onset of synaptic rewiring. On cell membrane PAN-1 and MYRF interact with each other via their extracellular regions. Loss of PAN-1 abolishes MYRF cell membrane localization, consequently blocking myrf-dependent neuronal rewiring process. Thus, through interactions with a cooperating factor on the cell membrane, MYRF may link cell surface activities to transcriptional cascades required for development.

The Glioblastoma Microenvironment: Morphology, Metabolism, and Molecular Signature of Glial Dynamics to Discover Metabolic Rewiring Sequence.

Different functional states determine glioblastoma (GBM) heterogeneity. Brain cancer cells coexist with the glial cells in a functional syncytium based on a continuous metabolic rewiring. However, standard glioma therapies do not account for the effects of the glial cells within the tumor microenvironment. This may be a possible reason for the lack of improvements in patients with high-grade gliomas therapies. Cell metabolism and bioenergetic fitness depend on the availability of nutrients and interactions in the microenvironment. It is strictly related to the cell location in the tumor mass, proximity to blood vessels, biochemical gradients, and tumor evolution, underlying the influence of the context and the timeline in anti-tumor therapeutic approaches. Besides the cancer metabolic strategies, here we review the modifications found in the GBM-associated glia, focusing on morphological, molecular, and metabolic features. We propose to analyze the GBM metabolic rewiring processes from a systems biology perspective. We aim at defining the crosstalk between GBM and the glial cells as modules. The complex networking may be expressed by metabolic modules corresponding to the GBM growth and spreading phases. Variation in the oxidative phosphorylation (OXPHOS) rate and regulation appears to be the most important part of the metabolic and functional heterogeneity, correlating with glycolysis and response to hypoxia. Integrated metabolic modules along with molecular and morphological features could allow the identification of key factors for controlling the GBM-stroma metabolism in multi-targeted, time-dependent therapies.

Cell-specific network analysis of human folliculogenesis reveals network rewiring in antral stage oocytes.

Although previous studies have explored the gene expression profiles of human oocytes and granulosa cells by single‐cell RNA sequencing (scRNA‐seq), the dynamic regulatory network at a single‐cell resolution during folliculogenesis remains largely unknown. We identified 10 functional modules by WGCNA, four of which were significantly correlated with primary/antral oocyte and antral/pre‐ovulatory granulosa cells. Functional enrichment analysis showed that the brown module, which was correlated with antral oocyte, was enriched in oocyte differentiation, and two core subnetworks identified by MCODE were involved in cell cycle (blue subnetwork) and oogenesis (red subnetwork). The cell‐specific network (CSN) analysis demonstrated a distinct gene network structure associated with the antral follicular stage, which was notably different from other developmental stages. To our knowledge, this is the first study to explore gene functions during folliculogenesis at single‐cell network level. We uncovered two potential gene subnetworks, which may play an important role in oocyte function beginning at the antral stage, and further established their rewiring process at intra‐network/whole transcriptome level. The findings provide crucial insights from a novel network perspective to be further explored in functional mechanistic studies.

The influence of uncertainties on optimization of vaccinations on a network of animal movements

In this article, multiobjective optimization of vaccinations is studied using graph-based modelling and simulations of the spreading of the disease. Real-life dataset of animal movements between farms and pastures in the Piedmont region of Italy is used, from which a dynamic network of contacts is reconstructed. Evolutionary multiobjective optimization of vaccinations is compared with vaccination strategies based on degrees or strengths of graph nodes, number of animals in the farms as well as with the ring vaccination strategy. In the article, the influence of uncertainties represented by the lack of knowledge of initial disease cases and the change of the contacts network by a rewiring process on the vaccination optimization is studied. Results of experiments show that evolutionary optimization of vaccinations can outperform vaccination strategies when enough information is provided. When many disease cases remain unknown or when the changes in the contacts network are large, the performance of the optimization algorithm is adversely affected. Obtained results motivate further research on modelling changes in animal movement patterns, as well as hybrid methods combining evolutionary optimization with vaccination strategies.

Metabolic rewiring in melanoma drug-resistant cells.

Several evidences indicate that melanoma, one of the deadliest types of cancer, presents the ability to transiently shift its phenotype under treatment or microenvironmental pressure to an invasive and treatment-resistant phenotype, which is characterized by cells with slow division cycle (also called slow-cycling cells) and high-OXPHOS metabolism. Many cellular marks have been proposed to track this phenotype, such as the expression levels of the master regulator of melanocyte differentiation (MITF) and the epigenetic factor JARID1B. It seems that the slow-cycling phenotype does not necessarily present a single gene expression signature. However, many lines of evidence lead to a common metabolic rewiring process in resistant cells that activates mitochondrial metabolism and changes the mitochondrial network morphology. Here, we propose that mitochondria-targeted drugs could increase not only the efficiency of target therapy, bypassing the dynamics between fast-cycling and slow-cycling, but also the sensitivity to immunotherapy by modulation of the melanoma microenvironment.