Systems Biology Network Research Articles

Orthogonal DNA Self-Assembly-Based Expansion Microscopy Platform for Amplified, Multiplexed Biomarker Imaging.

Expansion microscopy (ExM) facilitates nanoscale imaging under conventional microscopes, but it frequently encounters challenges such as fluorescence losses, low signal-to-noise ratio (SNR), and limited detection throughput. To address these issues, a method of orthogonal DNA self-assembly-based ExM (o-DAExM) platform is developed, which employs hybridization chain reaction instead of conventional fluorescence labeling units, showcasing signal amplification efficacy, enhancement of SNR, and expandable multiplexing capability at any stage of the ExM process. In this work, o-DAExM has been applied to compare with immunofluorescence-based ExM for cellular cytoskeleton imaging, and the resolved nanoscale spatial distributions of cytoskeleton show outstanding performance and reliability of o-DAExM. Furthermore, the study demonstrates the utility of o-DAExM in accurately revealing exosome heterogeneous information and multiplexed analysis of protein targets in single cells, which provides infinite possibilities in super-resolution imaging of cells and other samples. Therefore, o-DAExM offers a straightforward expansion and signal labeling method, highlighting future prospects to study nanoscale structures and functional networks in biological systems.

The Impact of Centrality Measures in Protein–Protein Interaction Networks: Tools, Databases, Challenges and Future Directions

Analyzing protein–protein interaction (PPI) networks using machine learning and deep learning algorithms, alongside centrality measures, holds paramount importance in understanding complex biological systems. These advanced computational techniques enable the extraction of valuable insights from intricate network structures, shedding light on the functional relationships between proteins. By leveraging AI-driven approaches, researchers can uncover key regulatory mechanisms, identify critical nodes within the network and predict novel protein interactions with high accuracy. Ultimately, this integration of computational methodologies enhances our ability to comprehend the dynamic behavior of biological systems at a molecular level, paving the way for advancements in drug discovery, disease understanding and personalized medicine. This review paper starts by outlining various popular available PPI network databases and network centrality calculation tools. A thorough classification of various centrality measures has been identified. It primarily delves into the centrality-driven discoveries within PPI networks in biological systems and suggests using edge centrality measures and a hybrid version of node and edge centrality measures in machine learning algorithms and deep learning algorithms to predict hidden knowledge much more effectively.

Robustness of Turing models and gene regulatory networks with a sweet spot.

Traditional linear stability analysis based on matrix diagonalization is a computationally intensive process for high-dimensional systems of differential equations, posing substantial limitations for the exploration of Turing systems of pattern formation where an additional wave-number parameter needs to be investigated. In this paper, we introduce an efficient and intuitive technique that leverages Gershgorin's theorem to determine upper limits on regions of parameter space and the wave number beyond which Turing instabilities cannot occur. This method offers a streamlined avenue for exploring the phase diagrams of other complex multi-parametric models, such as those found in gene regulatory networks in systems biology. Due to its suitability for the asymptotic limit of infinitely large systems, it predicts the existence of a sweet spot in network size for maximal Jacobian stability.

Advancing Toxicity Predictions: A Review on in Vitro to in Vivo Extrapolation in Next-Generation Risk Assessment.

As a key step in next-generation risk assessment (NGRA), in vitro to in vivo extrapolation (IVIVE) aims to mobilize a mechanism-based understanding of toxicology to translate bioactive chemical concentrations obtained from in vitro assays to corresponding exposures likely to induce bioactivity in vivo. This conversion can be achieved via physiologically-based toxicokinetic (PBTK) models and machine learning (ML) algorithms. The last 5 years have witnessed a period of rapid development in IVIVE, with the number of IVIVE-related publications increasing annually. This Review aims to (1) provide a comprehensive overview of the origin of IVIVE and initiatives undertaken by multiple national agencies to promote its development; (2) compile and sort out IVIVE-related publications and perform a clustering analysis of their high-frequency keywords to capture key research hotspots; (3) comprehensively review PBTK and ML model-based IVIVE studies published in the last 5 years to understand the research directions and methodology developments; and (4) propose future perspectives for IVIVE from two aspects: expanding the scope of application and integrating new technologies. The former includes focusing on metabolite toxicity, conducting IVIVE studies on susceptible populations, advancing ML-based quantitative IVIVE models, and extending research to ecological effects. The latter includes combining systems biology, multiomics, and adverse outcome networks with IVIVE, aiming at a more microscopic, mechanistic, and comprehensive toxicity prediction. This Review highlights the important value of IVIVE in NGRA, with the goal of providing confidence for its routine use in chemical prioritization, hazard assessment, and regulatory decision making.

DNA copy number profiles and systems biology connect chromatin remodeling and DNA repair in high-risk neuroblastoma.

Neuroblastoma (NB) is a solid tumor that accounts for 15% of all pediatric oncological deaths, and much is due to the low response to therapy in relapsed tumors. High-risk NB may present deletions in chromosome 11q, which may be associated with other chromosomal alterations and a poor response to therapy, but this association is still poorly understood. Using a systems biology network approach, we studied three patients with high-risk NB with deleted 11q stage 4 to highlight the connections between treatment resistance and copy number alterations in distinct cases. We built different protein-protein interaction networks for each patient based on protein-coding genes mapped at the cytobands pre- and post-chemotherapy from distinct copy number alterations data. In the post-chemotherapy networks, we identified five common regulatory nodes corresponding to the gained region located in ch17q:BIRC5, BRCA1, PRKCA, SUMO2, andGPS1. A crosslink between DNA damage and chromatin remodeling proteins was also found - a connection still poorly understood in NB. We identified a potential connection between XPB gain and chemoresistance of NB. The findings help elucidate the molecular profiles of high-risk NB with 11q deletion in pre- and post-chemotherapy tumor samples, which may reflect unique profiles in poor response to treatment.

A New Strategy for the Old Challenge of Thalidomide: Systems Biology Prioritization of Potential Immunomodulatory Drug (IMiD)-Targeted Transcription Factors.

Several molecular mechanisms of thalidomide embryopathy (TE) have been investigated, from anti-angiogenesis to oxidative stress to cereblon binding. Recently, it was discovered that thalidomide and its analogs, named immunomodulatory drugs (IMiDs), induced the degradation of C2H2 transcription factors (TFs). This mechanism might impact the strict transcriptional regulation of the developing embryo. Hence, this study aims to evaluate the TFs altered by IMiDs, prioritizing the ones associated with embryogenesis through transcriptome and systems biology-allied analyses. This study comprises only the experimental data accessed through bioinformatics databases. First, proteins and genes reported in the literature as altered/affected by the IMiDs were annotated. A protein systems biology network was evaluated. TFs beta-catenin (CTNNB1) and SP1 play more central roles: beta-catenin is an essential protein in the network, while SP1 is a putative C2H2 candidate for IMiD-induced degradation. Separately, the differential expressions of the annotated genes were analyzed through 23 publicly available transcriptomes, presenting 8624 differentially expressed genes (2947 in two or more datasets). Seventeen C2H2 TFs were identified as related to embryonic development but not studied for IMiD exposure; these TFs are potential IMiDs degradation neosubstrates. This is the first study to suggest an integration of IMiD molecular mechanisms through C2H2 TF degradation.

Leveraging quantum computing for dynamic analyses of logical networks in systems biology

The dynamics of cellular mechanisms can be investigated through the analysis of networks. One of the simplest but most popular modeling strategies involves logic-based models. However, these models still face exponential growth in simulation complexity compared with a linear increase in nodes. We transfer this modeling approach to quantum computing and use the upcoming technique in the field to simulate the resulting networks. Leveraging logic modeling in quantum computing has many benefits, including complexity reduction and quantum algorithms for systems biology tasks. To showcase the applicability of our approach to systems biology tasks, we implemented a model of mammalian cortical development. Here, we applied a quantum algorithm to estimate the tendency of the model to reach particular stable conditions and further revert dynamics. Results from two actual quantum processing units and a noisy simulator are presented, and current technical challenges are discussed.

Methotrexate inhibition of SARS-CoV-2 entry, infection and inflammation revealed by bioinformatics approach and a hamster model.

Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 μM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms. Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals. We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

Systems biology network reveals the correlation between COX-2 expression and Ch 7q copy number alterations in Ch 11q-deleted pediatric neuroblastoma tumors.

Tumor-associated inflammation and chromosomal aberrations can play crucial roles in cancer development and progression. In neuroblastoma (NB), the enzyme cyclooxygenase-2 (COX-2) is associated with copy number alterations on the long arm of chromosome 11 (Ch 11q), defining an aggressive disease subset. This retrospective study included formalin-fixed paraffin-embedded tumor samples collected from nine patients during diagnosis at the pediatric Pequeno Principe Hospital, Curitiba, PR, Brazil, and post-chemotherapy (CT). COX-2 expression was evaluated using immunohistochemistry and correlated with the genome profile of paired pre- and post-CT samples, determined by array comparative genomic hybridization. A systems biology approach elucidated the PTGS2 network interaction. The results showed positive correlations between pre-CT Ch 7q gain and COX-2 expression (ρ = 0.825; p-value = 0.006) and negative correlations between Ch 7q gain and Ch 11q deletion (ρ = -0.919; p-value = 0.0005). Three samples showed Ch 11q deletion and Ch 7q gain. Network analysis identified a direct connection between CAV-1 (Ch 7q) and COX-2 in NB tumors and highlighted the connection between amplified genes in Ch 7q and deleted ones in 11q. The identification of hub-bottleneck-switch genes provides new biological insights into this connection between NB, tumorigenesis, and inflammation.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Weakly activated core neuroinflammation pathways were identified as a central signaling mechanism contributing to the chronic neurodegeneration in Alzheimer's disease.

ObjectivesNeuroinflammation signaling has been identified as an important hallmark of Alzheimer’s disease (AD) in addition to amyloid β plaques (Aβ) and neurofibrillary tangles (NFTs). However, the molecular mechanisms and biological processes of neuroinflammation remain unclear and have not well delineated using transcriptomics data available. Our objectives are to uncover the core neuroinflammation signaling pathways in AD using integrative network analysis on the transcriptomics data.Materials and methodsFrom a novel perspective, i.e., investigating weakly activated molecular signals (rather than the strongly activated molecular signals), we developed integrative and systems biology network analysis to uncover potential core neuroinflammation signaling targets and pathways in AD using the two large-scale transcriptomics datasets, i.e., Mayo Clinic (77 controls and 81 AD samples) and ROSMAP (97 controls and 260 AD samples).ResultsOur analysis identified interesting core neuroinflammation signaling pathways, which are not systematically reported in the previous studies of AD. Specifically, we identified 7 categories of signaling pathways implicated on AD and related to virus infection: immune response, x-core signaling, apoptosis, lipid dysfunctional, biosynthesis and metabolism, and mineral absorption signaling pathways. More interestingly, most of the genes in the virus infection, immune response, and x-core signaling pathways are associated with inflammation molecular functions. The x-core signaling pathways were defined as a group of 9 signaling proteins: MAPK, Rap1, NF-kappa B, HIF-1, PI3K-Akt, Wnt, TGF-beta, Hippo, and TNF, which indicated the core neuroinflammation signaling pathways responding to the low-level and weakly activated inflammation and hypoxia and leading to the chronic neurodegeneration. It is interesting to investigate the detailed signaling cascades of these weakly activated neuroinflammation signaling pathways causing neurodegeneration in a chronic process, and consequently uncover novel therapeutic targets for effective AD treatment and prevention.ConclusionsThe potential core neuroinflammation and associated signaling targets and pathways were identified using integrative network analysis on two large-scale transcriptomics datasets of AD.

Prioritization of Candidate Biomarkers for Degenerative Aortic Stenosis through a Systems Biology-Based In-Silico Approach.

Degenerative aortic stenosis is the most common valve disease in the elderly and is usually confirmed at an advanced stage when the only treatment is surgery. This work is focused on the study of previously defined biomarkers through systems biology and artificial neuronal networks to understand their potential role within aortic stenosis. The goal was generating a molecular panel of biomarkers to ensure an accurate diagnosis, risk stratification, and follow-up of aortic stenosis patients. We used in silico studies to combine and re-analyze the results of our previous studies and, with information from multiple databases, established a mathematical model. After this, we prioritized two proteins related to endoplasmic reticulum stress, thrombospondin-1 and endoplasmin, which have not been previously validated as markers for aortic stenosis, and analyzed them in a cell model and in plasma from human subjects. Large-scale bioinformatics tools allow us to extract the most significant results after using high throughput analytical techniques. Our results could help to prevent the development of aortic stenosis and open the possibility of a future strategy based on more specific therapies.

Effective connectivity determines the critical dynamics of biochemical networks.

Living systems comprise interacting biochemical components in very large networks. Given their high connectivity, biochemical dynamics are surprisingly not chaotic but quite robust to perturbations—a feature C.H. Waddington named canalization. Because organisms are also flexible enough to evolve, they arguably operate in a critical dynamical regime between order and chaos. The established theory of criticality is based on networks of interacting automata where Boolean truth values model presence/absence of biochemical molecules. The dynamical regime is predicted using network connectivity and node bias (to be on/off) as tuning parameters. Revising this to account for canalization leads to a significant improvement in dynamical regime prediction. The revision is based on effective connectivity, a measure of dynamical redundancy that buffers automata response to some inputs. In both random and experimentally validated systems biology networks, reducing effective connectivity makes living systems operate in stable or critical regimes even though the structure of their biochemical interaction networks predicts them to be chaotic. This suggests that dynamical redundancy may be naturally selected to maintain living systems near critical dynamics, providing both robustness and evolvability. By identifying how dynamics propagates preferably via effective pathways, our approach helps to identify precise ways to design and control network models of biochemical regulation and signalling.

Mass-produced Spirulina-mediated altered responses in ARPE-19 and HaCaT cells for biomedical applications

Spirulina has been highlighted for potential health benefits based on its therapeutic functions. In the present study, the physicochemical characteristics and effects of Spirulina on ARPE-19 and HaCaT cells were monitored after mass-production, compared with conventional product. Gene ontology (GO) was analyzed using systems biology network that accounted for the function of Spirulina in human health. Spirulina powder had a smooth surface with adsorbing particles and contained sodium, magnesium, and potassium. Spirulina powder extracts in 0.1 × PBS, 0.1 M HEPES, 0.1 M NaCl, ethanol, isopropyl alcohol, and acetonitrile showed broad absorbance peaks at 300–450 and 500–700 nm. In ARPE-19 and HaCaT cells, Spirulina powder in PBS displayed proliferation at 2–20 mg/mL and 2 mg/mL, respectively. Cell viability levels of HaCaT cells were almost higher than 90% at 0.1–20 mg/mL. Despite cell susceptibility, phycocyanin, which is a component of Spirulina, helped maintain consistency of cell responses with Spirulina. Spirulina functions were explained by GO-terms, mainly binding, cellular process, and cell in the network, in terms of anti-oxidant activity. Mass-produced Spirulina powder can be developed as a high-value nutraceutical for therapeutic applications in clinics. This protocol can be extended to other materials for biomedical applications.

"What Have We Learned about the Consequences of the COVID-19 Pandemic?" - International Scientific Symposium Organized in Tuzla in Bosnia-Herzegovina.

The evolution of immunology enabled the study of role of innate and adaptive in systems biology network of immunosenescence and inflammaging. Due to global reduction in birth rates and reduced mortality, in year 2025 there will be about 1.2 billion of people over age of sixty, worldwide. The notion that the real age is not chronological, but the biological one led to the concept of bioage, defining the biologic reactivity and resilience, including the immune competence of an individual. A competent immune network, systemic and mucosal is intrinsic to resilience and homeostasis of the human holobiont as the unit of evolution. In elderly, the immunosenescence could be associated with higher levels of proinflammatory mediators (such as IL-6), frialty and mortality. Proi-inflammatory state in elderly is denoted as inflammaging, characterized with low-grade (sterile) inflammation, as a physiologic response to life-long antigenic stimuli. When under control, inflammaging could be regarded as an efficient defense mechanism, oposed and regulated by anti-inflammatory pathways and molecules. Immunosensecence. The emerging concepts of immunobiography and trained immunity speak in favour that the immunological experience during the life would shape the ability of each individual to respond to various stimuli, strongly influencing the elements of innate and adaptive immunity, including macrophages and innate lymphoid cells. Older age is one of the main risk factors for the severe clinical picture and adverse outcome of COVID-19 infection, due to immunosenscence and chronic low-grade inflammation (inflammaging), both characterizing the immune reactioin in elderly. The senescent immune system, along with the advanced process of inflammaging is prone to react with uncontrolled activation of innate immune response that leads to cytokine release syndrome, tissue damage and adverse outcome of infection. Further research is aimed to nutritional and pharmacologic (immunomodulatory) interventions to influence the process of bioaging and immunosenscence, and to modulate the reaction of elderly to infection, including the COVID-19.

Targeted and systemic insights into the crosstalk between DNA-dependent protein kinase catalytic subunit and receptors of estrogen, progesterone and epidermal growth factor in the context of cancer.

DNA-dependent protein kinase catalytic subunit (DNA-PKcs) has emerged as a regulator of carcinogenesis. Increased expression of DNA-PKcs correlates with metastatic cancers. Here we review recently reported crosstalk of DNA-PKcs with estrogen (ER), progesterone (PR) and epidermal growth factor (EGFR) receptors. The reports show an extensive network of functional and direct interactions. Targeted studies focused on specific molecular mechanisms, and a systems biology network analysis shows unbiasedly engagement of various cellular functions. Feedforward regulation between expression and activities of DNA-PKcs and ER, DNA-PKcs-dependent phosphorylation of PR and an impact on PR-dependent transcription, and DNA-PKcs-promoted EGFR-dependent aggressiveness and metastases are examples of the results of targeted studies. Systems biology approach extracted many more genes and proteins engaged by DNA-PKcs in interaction with ER, PR, and EGFR. Examples are such regulators and predictors of breast tumorigenesis as BRCA1, TP53, and 18 genes of the MammaPrint signature. Reviewed here data suggest that the diagnostic value of DNA-PKcs in the context of ER, PR and EGFR signaling is defined by a network signature rather than by single markers. This review summarizes mechanisms of DNA-PKcs interaction with ER, PR, and EGFR, highlights tumor suppressors and oncogenes engaged by DNA-PKcs, and emphasizes the importance of diagnostic network-based signatures.

Exact Boolean Abstraction of Linear Equation Systems

We study the problem of how to compute the boolean abstraction of the solution set of a linear equation system over the positive reals. We call a linear equation system ϕ exact for the boolean abstraction if the abstract interpretation of ϕ over the structure of booleans is equal to the boolean abstraction of the solution set of ϕ over the positive reals. Abstract interpretation over the booleans is thus complete for the boolean abstraction when restricted to exact linear equation systems, while it is not complete more generally. We present a new rewriting algorithm that makes linear equation systems exact for the boolean abstraction while preserving the solutions over the positive reals. The rewriting algorithm is based on the elementary modes of the linear equation system. The computation of the elementary modes may require exponential time in the worst case, but is often feasible in practice with freely available tools. For exact linear equation systems, we can compute the boolean abstraction by finite domain constraint programming. This yields a solution of the initial problem that is often feasible in practice. Our exact rewriting algorithm has two further applications. Firstly, it can be used to compute the sign abstraction of linear equation systems over the reals, as needed for analyzing function programs with linear arithmetics. Secondly, it can be applied to compute the difference abstraction of a linear equation system as used in change prediction algorithms for flux networks in systems biology.

Convolutional neural network in proteomics and metabolomics for determination of comorbidity between cancer and schizophrenia

The association between cancer risk and schizophrenia is widely debated. Despite many epidemiological studies, there is still no strong evidence regarding the molecular basis for the comorbidity between these two pathological conditions. The vast majority of assays have been performed using clinical records of schizophrenic patients or those undergoing cancer treatment and monitored for sufficient time to find shared features between the considered conditions. We performed mass spectrometry-based proteomic and metabolomic investigations of patients with different cancer phenotypes (breast, ovarian, renal, and prostate) and patients with schizophrenia. The resulting vast quantity of proteomic and metabolomic data were then processed using systems biology and one-dimensional (1D) convolutional neural network (1DCNN) machine learning approaches. Traditional systematic approaches permit the segregation of schizophrenia and cancer phenotypes on the level of biological processes, while 1DCNN recognized “signatures” that could segregate distinct cancer phenotypes and schizophrenia at the comorbidity level. The designed network efficiently discriminated unrelated pathologies with a model accuracy of 0.90 and different subtypes of oncophenotypes with an accuracy of 0.94. The proposed strategy integrates systematic analysis of identified compounds and application of 1DCNN model for unidentified ones to reveal the similarity between distinct phenotypes.

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence.

Simple SummaryIn recent years both research areas of next-generation sequencing and artificial intelligence have grown remarkably. Their intersection simultaneously gave rise to a panacea of different algorithms and applications. This article delineates tailored machine learning and systems biology approaches and combinations thereof that tackle the various challenges that arise in the face of big data. Moreover, it provides an overview of the numerous applications of artificial intelligence aiding the analysis and interpretation of next-generation sequencing data.The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.

Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis.

In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.