Network Biology

The exchange of genetic information among coral reefs, through the transport of larvae, is critical to the function of Australia's Great Barrier Reef because it influences recruitment rates and resilience to disturbance. For many species the genetic composition is not homogeneous and is determined, in part, by the character of the complex dispersal pathways that connect the populations situated on each coral reef. One method of measuring these genetic connections is to examine the microsatellite composition of ...

Selforganizology (ISSN 2410-0080)

Volume 12, Number 3-4, 1 December 2025
http://www.iaees.org/publications/journals/selforganizology/articles/2025-12(3-4)/2025-12(3-4).asp

Articles

probTable: A Matlab calculator for lookuping probability tables
Selforganizology, 2025, 12(3-4): 21-29
WenJun Zhang
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A network analysis of adult education and higher education topics within the Erasmus+ Programme
Selforganizology, 2025, 12(3-4): 30-39
Gizem Engin, Emir Haliki
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Network Biology (ISSN 2220-8879; CODEN NBEICS)

Volume 16, Number 1, 1 March 2026
http://www.iaees.org/publications/journals/nb/articles/2026-16(1)/2026-16(1).asp

Articles
Production and optimisation of Polyhydroxyalkanoates (PHA) from Bacillus subtilis using response surface methodology
Network Biology, 2026, 16(1): 1-14
Pandya Trupti, Hajoori Murtaza
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Insights into the evolution of Wolbachia supergroup through the lens of genomic variation, phylogenetic and recombination analysis
Network Biology, 2026, 16(1): 15-30
Amresh Kumar Sharma, Anup Som
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Current oncology models attribute tumorigenesis to stochastic mutation accumulation, yet they fail to explain why regulatory failure predictably clusters in specific high-energy metabolic and structural pathways or why tumors exhibit stereotyped physical phenotypes including rigidity and metabolic reprogramming. This study reinterprets cancer not as a genetic accumulation error, but as a phase transition in control physics. By analyzing 20,531 genes across 11,069 patient samples in the PANCAN dataset [1], this analysis identifies a stability boundary at the critical damping ratio χ = γ/(2ω) ≈ 1, where γ is the dissipation rate and ω is the characteristic frequency. Control bandwidth S defines the admissible range over which feedback can act without inducing instability. This study reports the discovery of Substrate Capture, a bandwidth failure mode where regulatory capacity collapses when the energetic and temporal cost of correction exceeds substrate bandwidth. All results derive from empirical TCGA RNA-seq data; no simulated dynamics are used. In the high-energy structural regime (ω > 1000), regulation maintains adherence to the critical boundary (χ ≈ 1), providing stable bandwidth that enables adaptive control. However, in the low-energy signaling regime, bandwidth collapse triggers a phase transition through a three-stage diagnostic sequence (Warning, Confirmation, Collapse): the regulator decouples from the critical attractor and tracks the destabilized substrate, amplifying rather than correcting error. This failure is driven by high-output secretory and cytoskeletal genes (e.g., CELA3A, KRT1) that exhibit extreme overdamping (χ > 10). These findings demonstrate that chemotherapy resistance is a predictable physical consequence of bandwidth exhaustion in overdamped systems and propose state-dependent therapeutic targeting implemented via control guardrails: mobilize rigidity before suppression, increase damping in underdamped regimes, protect critical states. This framework shifts precision oncology from mutation-targeted therapy to state-targeted therapy, stratifying tumors by control-theoretic dynamics rather than genetic alterations alone.

The exchange of genetic information among coral reefs, through the transport of larvae, is critical to the function of Australia's Great Barrier Reef because it influences recruitment rates and resilience to disturbance. For many species the genetic composition is not homogeneous and is determined, in part, by the character of the complex dispersal pathways that connect the populations situated on each coral reef. One method of measuring these genetic connections is to examine the microsatellite composition of ...

Potentially linked to the basic physiology of stress response, Gulf War Illness (GWI) is a debilitating condition presenting with complex immune, endocrine and neurological symptoms. Here we interrogate the immune response to physiological stress by measuring 16 blood-borne immune markers at 8 time points before, during and after maximum exercise challenge in n = 12 GWI veterans and n = 11 healthy veteran controls deployed to the same theater. Immune markers were combined into functional sets and the dynamics of their joint expression described as classical rate equations. These empirical networks were further informed structurally by projection onto prior knowledge networks mined from the literature. Of the 49 literature-informed immune signaling interactions, 21 were found active in the combined exercise response data. However, only 4 signals were common to both subject groups while 7 were uniquely active in GWI and 10 uniquely active in healthy veterans. Feedforward mediation of IL-23 and IL-17 by IL-6 and IL-10 emerged as distinguishing control elements that were characteristically active in GWI versus healthy subjects. Simulated restructuring of the regulatory circuitry in GWI as a result of applying an IL-6 receptor antagonist in combination with either a Th1 (IL-2, IFNγ, and TNFα) or IL-23 receptor antagonist predicted a partial rescue of immune response elements previously associated with illness severity. Overall, results suggest that pharmacologically altering the topology of the immune response circuitry identified as active in GWI can inform on strategies that while not curative, may nonetheless deliver a reduction in symptom burden. A lasting and more complete remission in GWI may therefore require manipulation of a broader physiology, namely one that includes endocrine oversight of immune function.

Objective: Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. Results: Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/internetwork topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at .

We present here the first description of recorded sexual differences in flight behaviour and space use of lesser kestrel Falco naumanni. Lesser kestrel is a migratory, colonial, small falcon breeding mainly in holes and crevices in large historic buildings within towns and villages, or in abandoned farm houses across the countryside. Using accurate GPS data-loggers, we gathered data on the activities of lesser kestrels in the two of main colonies of lesser kestrels in Italy, i.e. Gravina in Puglia and Altamura (Apulia, Southern Italy) and the surrounding rural areas in a 20-days monitoring during the reproductive period. We tested for sex differences in space use (home range's circularity ratio) and flight attributes (5-minute flight length, instantaneous speed, distance from nest, flight altitude above ground level) of 9 monitored individuals (4 males and 5 females). We found significant sexual differences for all the observed traits. Our results demonstrate that female lesser kestrels during the monitoring period employed a lower amount of energy in local movements as measured by four flight attributes that resulted significantly different (and lower) than for males. Compact home ranges for females could represent a maximization of the benefit-cost ratio between prospected surface and distance from nest, i.e. the optimal trade-off between foraging requirements (explored surface) and costs in terms of time and energy (distance from nest). On the contrary, males showed a significantly different space use with very elongated home ranges and mean distance from nest almost three times as elevated as females' one. We argue that the detected sexual divergence was the product of their respective ways to optimize the relationship between resource acquisition and reproductive activity.

The theoretical aspects of the modeling have been discussed in part-1 of this paper. The part-II of the paper deals with the practical implementation of analog fuzzy logic based systems (FLS). The designing of a fuzzy logic based system has been automated by the development of a software tool, FACDEng, the Fuzzy Analog Controller Design Engineer. The designs proposed by FACDEng are based on easily available and inexpensive operational amplifiers and a few more passive components. The FACDEng does not need the FLS designer to be an expert in fuzzy logic or an analog circuit design expert. FACDEng also generates net lists of the designed modules. These net lists allow the designer to simulate the designs in OrCAD and verify the performance of designs in few minutes. The design time is considerably shortened, thus allowing the designer to focus on practical implementations and validation of the models. Further the tool has academic value as it can help instructor in classroom teaching of the fuzzy systems designs. The tool is expected to be made available to designers free of charge.

Knowledge of the molecular interactions of human proteins within tissues is important for identifying their tissue-specific roles and for shedding light on tissue phenotypes. However, many protein-protein interactions (PPIs) have no tissue-contexts. The TissueNet database bridges this gap by associating experimentally-identified PPIs with human tissues that were shown to express both pair-mates. Users can select a protein and a tissue, and obtain a network view of the query protein and its tissue-associated PPIs. TissueNet v.2 is an updated version of the TissueNet database previously featured in NAR. It includes over 40 human tissues profiled via RNA-sequencing or protein-based assays. Users can select their preferred expression data source and interactively set the expression threshold for determining tissue-association. The output of TissueNet v.2 emphasizes qualitative and quantitative features of query proteins and their PPIs. The tissue-specificity view highlights tissue-speci...

The sole purpose of Test Case Prioritization technique is to schedule test cases for Regression Testing in a certain order that increases the effectiveness of the testing process while achieving some performance goal. It ensures that the most beneficial test cases are executed first. A number of techniques have been proposed for Test Case Prioritization. One of the techniques prioritizes test cases based on Fault Dependency. This technique helps the developers to start debugging on the faults that cause other fault sto appear later. The only limitation of this approach is that the fault severity is considered uniform, which in practical world may often vary. So, this paper aims to resolve it by considering fault severity along with fault dependency and thus further improving the performance of the regressiontesting process.

Background Early-stage invasive ductal carcinoma displays high survival rates due to early detection and treatments. However, there is still a chance of relapse of 3-15% after treatment. The aim of this study was to uncover the distinctive transcriptomic characteristics and monitoring prognosis potential of peritumoral tissue in early-stage cases. Methods RNA was isolated from tumoral, peritumoral, and non-tumoral breast tissue from surgical resection of 10 luminal early-stage invasive ductal carcinoma patients. Transcriptome expression profiling for differentially expressed genes (DEGs) identification was carried out through microarray analysis. Gene Ontology and KEGG pathways enrichment analysis were explored for functional characterization of identified DEGs. Protein-Protein Interactions (PPI) networks analysis was performed to identify hub nodes of peritumoral tissue alterations and correlated with Overall Survival and Relapse Free Survival. DEGs closely related with cell migration, extracellular matrix organization, and cell cycle were upregulated in peritumoral tissue compared to non-tumoral. Analyzing PPI networks, we observed that the proximity to tumor leads to the alteration of gene modules involved in cell proliferation and differentiation signaling pathways. In fact, in the peritumoral area were identified the top ten upregulated hub nodes including CDK1, ESR1, NOP58, PCNA, EZH2, PPP1CA, BUB1, TGFBR1, CXCR4, and CCND1. A signature performed by four of these hub nodes (CDK1, PCNA, EZH2, and BUB1) was associated with relapse events in untreated luminal breast cancer patients. In conclusion, our study characterizes in depth breast peritumoral tissue providing clues on the changes that tumor signaling could cause in patients with early-stage breast cancer. We propose that the use of a four gene signature could help to predict local relapse. Overall, our results highlight the value of peritumoral tissue as a potential source of new biomarkers for early detection of relapse and improvement in invasive ductal carcinoma patient's prognosis.

Motivation Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking. Results Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82–0.9). Next, we a...

Traditional Chinese Medicines (TCM) are known for their curative effects on hypertension through a holistic approach. The molecular mechanisms of the formulation comprising Polygonum multiflorum, Rehmannia glutinosa, Senna obtusifolia and Crataegus, used by Chinese practitioners in ameliorating hypertension, however remain a mystery. This initial study is thus aimed at unveiling the molecular mechanisms of this TCM formulation in treating hypertension. The methanolic extract compounds of the decoction were identified through Liquid chromatography mass spectrometry-mass spectrometry (LC-MS/MS). Oral bioavailability and drug likeness were then measured to filter out identified compounds. Several databases, such as the SwissTargetPrediction, STRING, OMIM and KEGG, were used to retrieve information on the predicted targets for the purpose of developing a network using Cytoscape Version 3.8. Enrichment analysis was then performed to elucidate the mechanisms of the decoction in hypertensi...

PLOS COmPutatiOnaL BiOLOgy ENQUIRE reconstructs and expands context-specific co-occurrence networks from literature one hand, manual literature search may result in an incomplete interaction network due to the difficulty in annotating all the relevant findings in an increasingly growing corpus of publications; on the other hand, established databases of molecular interactions omit the context in which the interaction has been observed. Here, we present ENQUIRE (Expanding Networks by Querying Unexpectedly InterRelated Entities), a software that offers a time-and resource-efficient alternative to manual literature search and database mining. ENQUIRE automatically parses biomedical articles from PubMed, a database of biomedical studies, starting from an initial set of relevant publications chosen by the user. It then extracts literature co-occurrences between genes and associated biomedical concepts-such as pathology and cell typethus creating a network of co-occurring genes and concepts. It can also use genes and concepts that are central in the reconstructed network of co-occurrences to search for new PubMed articles and expand the original input corpus and, in turn, the network. Moreover, ENQUIRE-generated co-occurrence networks can complement previously annotated interactions by highlighting those that are relevant to the case study of interest, including previously undescribed ones. ENQUIRE supports biomedical researchers by making literature annotation easy, boosting hypothesis formulation, and facilitating the identification of targets for laboratory experiments.

Tuberculosis remains a major global health challenge worldwide, causing more than a million deaths annually. To determine newer methods for detecting and combating the disease, it is necessary to characterise global host responses to infection. Several high throughput omics studies have provided a rich resource including a list of several genes differentially regulated in tuberculosis. An integrated analysis of these studies is necessary to identify a unified response to the infection. Such data integration is met with several challenges owing to platform dependency, patient heterogeneity, and variability in the extent of infection, resulting in little overlap among different datasets. Network-based approaches offer newer alternatives to integrate and compare diverse data. In this study, we describe a meta-analysis of host's whole blood transcriptomic profiles that were integrated into a genome-scale protein-protein interaction network to generate response networks in active tub...

➢ A multi-gene host biomarker signature is identified for detecting pulmonary tuberculosis from patient blood samples ➢ The signature discriminates TB from HIV and latent-TB and can serve as an adjuvant tool in confirming TB diagnosis Host factors that are altered significantly due to tuberculosis are investigated, with an aim to identify a biomarker panel. A network approach provides a genome-wide view of the molecular interactions, analogous to a road network of a city. By comparing networks between healthy and TB samples, we identify the set of variations in a systematic fashion, analogous to identifying all major variations in the traffic flow in a city between two time points. We then apply a series of filters to identify the most discriminating genes among them. The 10-gene signature is seen to be characteristic of TB.

In this work, DNA (de-oxy ribonucleic acid) sequences are transformed to binary sequences to explore the dissimilarities between different species. A mathematical approach has been reported and implemented to establish phylogenetic networks by using the concept of hamming distances on binary sequences to explore the evolutionary history of the species. A program is also coded in python language for the conversion of DNA sequences as well as to find the hamming distances among them.

There is an emerging trend in post-genome biology to study the collection of thousands of protein interaction pairs (protein interactome) derived from high-throughput experiments. However, high-throughput protein interactome data, especially when derived from the Yeast 2-Hybrid (Y2H) method, have been generally believed to be irreproducible and unreliable, with an estimated high "noise ratio" of more than 50%. In this work, we performed a comprehensive study on approximately 70,000 protein interactions derived from a systematic yeast 2-hybrid (SY2H) method. We performed a comprehensive analysis of biases, reproducibility, statistical significance, and biologically significant patterns in this data set. Surprisingly, we found these protein interactions have a much higher quality. The data represented a comprehensive survey of the entire human proteome with no chromosomal location bias. The reproducibility rate of interactions among replicated searches was quite good, i.e., at 78.5%. The false positive rate, 5.5e-5, was two orders of magnitude better than that reported elsewhere. We further developed several statistical measures and concluded that a protein interaction only needs to appear in two different SY2H searches to become significant. We also developed techniques to show supporting evidence that "promiscuous" protein interactions were not random noises; instead, they could be "network hubs" of the cell signaling network. We also attributed the low noise in our data to the adoption of standard control in the experimental data generation process.