Application Of Systems Biology Research Articles

Unveiling the Intricacies of Monoamine Oxidase-A (MAO-A) Inhibition in Colorectal Cancer: Computational Systems Biology, Expression Patterns, and the Anticancer Therapeutic Potential.

Colorectal cancer (CRC) remains a significant health burden globally, necessitating a deeper understanding of its molecular intricacies for effective therapeutic interventions. Elevated monoamine oxidase-A (MAO-A) expression has been consistently observed in CRC tissues, correlating with advanced disease stages and a poorer prognosis. This research explores the systems biology effects of MAO-A inhibition with small molecule inhibitor clorgyline regarding CRC. The applied systems biology approach starts with a chemocentric informatics approach to derive high-confidence hypotheses regarding the antiproliferative effects of MAO-A inhibitors and ends with experimental validation. Our computational results emphasized the anticancer effects of MAO-A inhibition and the chemogenomics similarities between clorgyline and structurally diverse groups of apoptosis inducers in addition to highlighting apoptotic, DNA-damage, and microRNAs in cancer pathways. Experimental validation results revealed that MAO inhibition results in antiproliferative antimigratory activities in addition to synergistic effects with doxorubicin. Moreover, the results demonstrated a putative role of MAO-A inhibition in commencing CRC cellular death by potentially mediating the induction of apoptosis.

Twin Scholarships of Glycomedicine and Precision Medicine in Times of Single-Cell Multiomics.

Systems biology and multiomics research expand the prospects of planetary health innovations. In this context, this mini-review unpacks the twin scholarships of glycomedicine and precision medicine in the current era of single-cell multiomics. A significant growth in glycan research has been observed over the past decade, unveiling and establishing co- and post-translational modifications as dynamic indicators of both pathological and physiological conditions. Systems biology technologies have enabled large-scale and high-throughput glycoprofiling and access to data-intensive biological repositories for global research. These advancements have established glycans as a pivotal third code of life, alongside nucleic acids and amino acids. However, challenges persist, particularly in the simultaneous analysis of the glycome and transcriptome in single cells owing to technical limitations. In addition, holistic views of the complex molecular interactions between glycomics and other omics types remain elusive. We underscore and call for a paradigm shift toward the exploration of integrative glycan platforms and analysis methods for single-cell multiomics research and precision medicine biomarker discovery. The integration of multiple datasets from various single-cell omics levels represents a crucial application of systems biology in understanding complex cellular processes and is essential for advancing the twin scholarships of glycomedicine and precision medicine.

Network pharmacology speaking to ethnopharmacology: new data on an ancient remedy

Network pharmacology as a “green approach”, predicting metabolite behaviours chemically and biologically and guid¬ing biological experimental design, is a new strategy aiming to uncover the mechanism of action of natural products as drug candidates. It provides a powerful way to identify novel mechanisms of natural products with potential thera¬peutic effects. This approach has emerged as a powerful tool to overcome the limitations of traditional methods, such as the ability to predict the adverse effects of a drug and the likelihood of failure during clinical trials, by applying systems biology principles to the field of pharmacology. This method combines the multi-omics dataset, computer modeling, and chemical biology so as to reveal pharmaceutical actions and guide drug discovery. Therefore, computer-aided drug design combined with network pharmacology can be viewed as a novel in silico screening ap¬proach to drug discovery, by utilising chemoinformatics, bioinformatics, structure biology, and chemical biology. This strategy includes target-based virtual screening - molecular docking, ligand similarity-based virtual screening, and inverse screening (Inver-dock), providing a powerful tool for target identification of drug candidates, multitarget dis¬covery, and natural bioactive product profiling. It can also be used for selectivity profiling of drugs, drug repositioning, safety profiling, and metabolism profiling prediction (ADMET).

Sarcopenia as a Risk Factor for Alzheimer's Disease: Genetic and Epigenetic Perspectives.

Sarcopenia, defined as the age-associated loss of muscle mass and increased fragility with age, is increasing worldwide. The condition often precedes the development of Alzheimer's disease, thereby decreasing the levels of mobility and physical activity in those affected. Indeed, the loss of muscle mass has, in some studies, been associated with an increased risk of Alzheimer's disease and other dementias. However, a detailed understanding of the interplay between both conditions is not available and needs to be thoroughly addressed. In the following review, we focus on several genes, specifically APOE, BDNF, ACE, FTO, and FNDC5, that have been associated with both conditions. We also discuss the epigenetic regulation of each of these genes along with non-coding RNAs (ncRNAs) that may have a role in the development of both the sarcopenic and Alzheimer's disease phenotypes. Finally, we assert that the application of systems biology will unravel the relationship between sarcopenia and Alzheimer's disease and believe that the prevention of muscle loss in older age will reduce the incidence of debilitating cognitive decline.

In-silico analysis and transformation of OsMYB48 transcription factor driven by CaMV35S promoter in model plant – Nicotiana tabacum L. conferring abiotic stress tolerance

ABSTRACT Global crop yield has been affected by a number of abiotic stresses. Heat, salinity, and drought stress are at the top of the list as serious environmental growth-limiting factors. To enhance crop productivity, molecular approaches have been used to determine the key regulators affecting stress-related phenomena. MYB transcription factors (TF) have been reported as one of the promising defensive proteins against the unfavorable conditions that plants must face. Different roles of MYB TFs have been suggested such as regulation of cellular growth and differentiation, hormonal signaling, mediating abiotic stress responses, etc. To gain significant insights, a comprehensive in-silico analysis of OsMYB TF was carried out in comparison with 21 dicot MYB TFs and 10 monocot MYB TFs. Their chromosomal location, gene structure, protein domain, and motifs were analyzed. The phylogenetic relationship was also studied, which resulted in the classification of proteins into four basic groups: groups A, B, C, and D. The protein motif analysis identified several conserved sequences responsible for cellular activities. The gene structure analysis suggested that proteins that were present in the same class, showed similar intron-exon structures. Promoter analysis revealed major cis-acting elements that were found to be responsible for hormonal signaling and initiating a response to abiotic stress and light-induced mechanisms. The transformation of OsMYB TF into tobacco was carried out using the Agrobacterium-mediated transformation method, to further analyze the expression level of a gene in different plant parts, under stress conditions. To summarize, the current studies shed light on the evolution and role of OsMYB TF in plants. Future investigations should focus on elucidating the functional roles of MYB transcription factors in abiotic stress tolerance through targeted genetic modification and CRISPR/Cas9-mediated genome editing. The application of omics approaches and systems biology will be indispensable in delineating the regulatory networks orchestrated by MYB TFs, facilitating the development of crop genotypes with enhanced resilience to environmental stressors. Rigorous field validation of these genetically engineered or edited crops is imperative to ascertain their utility in promoting sustainable agricultural practices.

BioKC: a collaborative platform for curation and annotation of molecular interactions.

Curation of biomedical knowledge into systems biology diagrammatic or computational models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever-increasing growth of domain literature. New findings demonstrating elaborate relationships between multiple molecules, pathways and cells have to be represented in a format suitable for systems biology applications. Importantly, curation should capture the complexity of molecular interactions in such a format together with annotations of the involved elements and support stable identifiers and versioning. This challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, community-based curation, an important source of curated knowledge, requires support in role management, reviewing features and versioning. Here, we present Biological Knowledge Curation (BioKC), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML). BioKC offers a graphical user interface for curation of complex molecular interactions and their annotation with stable identifiers and supporting sentences. With the support of collaborative curation and review, it allows to construct building blocks for systems biology diagrams and computational models. These building blocks can be published under stable identifiers and versioned and used as annotations, supporting knowledge building for modelling activities.

Non-Negative Universal Differential Equations With Applications in Systems Biology

Universal differential equations (UDEs) leverage the respective advantages of mechanistic models and artificial neural networks and combine them into one dynamic model. However, these hybrid models can suffer from unrealistic solutions, such as negative values for biochemical quantities. We present non-negative UDE (nUDEs), a constrained UDE variant that guarantees non-negative values. Furthermore, we explore regularisation techniques to improve generalisation and interpretability of UDEs.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Generalized Michaelis-Menten rate law with time-varying molecular concentrations.

The Michaelis-Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, systems biology, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium (quasi-steady state) much faster than the molecular concentrations change. Yet, this assumption is not always justified. Here, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations, termed the effective time-delay scheme (ETS), is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein-protein and protein-DNA interaction modeling, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover), which goes beyond the quasi-steady state assumption.

Generative abstraction of Markov population processes

Markov population models are a widespread formalism used to model the dynamics of complex systems, with applications in systems biology and many other fields. The associated Markov stochastic process in continuous time is often analyzed by simulation, which can be costly for large or stiff systems, particularly when a massive number of simulations has to be performed, e.g. in a multi-scale model. A strategy to reduce computational load is to abstract the population model, replacing it with a simpler stochastic model, faster to simulate. Here we pursue this idea, exploring and comparing state-of-the-art generative models, which are flexible enough to automatically learn distributions over entire trajectories, rather than single simulation steps, from observed realizations of the system. In particular, we compare a Generative Adversarial setting with a Score-based Diffusion approach and show how the latter outperforms the former both in terms of accuracy and stability at the cost of slightly higher simulation times. To improve the accuracy of abstract samples, we develop an active learning framework to enrich our dataset with observations whose expected satisfaction of a temporal requirement differs significantly from the abstract one. We experimentally show how the proposed abstractions are well suited to work on multi-scale and data-driven scenarios, meaning that we can infer a (black-box) dynamical model from a pool of real data.

SARS-CoV-2 hijacks p38β/MAPK11 to promote virus replication.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus disease 2019 (COVID-19) pandemic, drastically modifies infected cells to optimize virus replication. One such modification is the activation of the host p38 mitogen-activated protein kinase (MAPK) pathway, which plays a major role in inflammatory cytokine production, a hallmark of severe COVID-19. We previously demonstrated that inhibition of p38/MAPK activity in SARS-CoV-2-infected cells reduced both cytokine production and viral replication. Here, we combined quantitative genetic screening, genomics, proteomics, and phosphoproteomics to better understand mechanisms underlying the dependence of SARS-CoV-2 on the p38 pathway. We found that p38β is a critical host factor for SARS-CoV-2 replication in multiple relevant cell lines and that it functions at a step after viral mRNA expression. We identified putative host and viral p38β substrates in the context of SARS-CoV-2 infection and found that most host substrates have intrinsic antiviral activities. Taken together, this study reveals a unique proviral function for p38β and supports exploring p38β inhibitor development as a strategy toward creating a new class of COVID-19 therapies. IMPORTANCE SARS-CoV-2 is the causative agent of the COVID-19 pandemic that has claimed millions of lives since its emergence in 2019. SARS-CoV-2 infection of human cells requires the activity of several cellular pathways for successful replication. One such pathway, the p38 MAPK pathway, is required for virus replication and disease pathogenesis. Here, we applied systems biology approaches to understand how MAPK pathways benefit SARS-CoV-2 replication to inform the development of novel COVID-19 drug therapies.

Application of systems biology to identify pharmacological mechanisms of thrombotic microangiopathy evoked by combined activated prothrombin complex concentrate and emicizumab

Emicizumab is a bispecific monoclonal antibody that substitutes for the function of missing or deficient factor VIII (FVIII) in people with hemophilia A (PwHA). Long-term safety and efficacy of emicizumab have been demonstrated in several clinical trials. Nevertheless, in the first of these, three cases of thrombotic microangiopathy (TMA) occurred in PwHA treated with emicizumab receiving high doses of activated prothrombin complex concentrate (aPCC), a bypassing agent used for treating breakthrough bleeds when FVIII neutralizing antibodies (inhibitors) make FVIII replacement ineffective. The aim of the present work is to offer a method to elucidate the pathophysiological and pharmacological mechanisms involved in this treatment-induced TMA. Systems biology and machine learning-based Therapeutic Performance Mapping System is a validated in silico technology that allowed us to construct models of potential mechanisms behind induced TMA. Two drug combinations were modeled and assessed: emicizumab plus aPCC and emicizumab plus recombinant activated factor VII (another bypassing agent). Our models showed that both combinations were related to activation of the coagulation cascade. However, mechanisms involved mainly in platelet activation and possibly in complement activation were detected only for emicizumab plus aPCC, potentially explaining the occurrence of TMA only in this combination.

Sex specific molecular networks and key drivers of Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is a progressive and age-associated neurodegenerative disorder that affects women disproportionally. However, the underlying mechanisms are poorly characterized. Moreover, while the interplay between sex and ApoE genotype in AD has been investigated, multi-omics studies to understand this interaction are limited. Therefore, we applied systems biology approaches to investigate sex-specific molecular networks of AD.MethodsWe integrated large-scale human postmortem brain transcriptomic data of AD from two cohorts (MSBB and ROSMAP) via multiscale network analysis and identified key drivers with sexually dimorphic expression patterns and/or different responses to APOE genotypes between sexes. The expression patterns and functional relevance of the top sex-specific network driver of AD were further investigated using postmortem human brain samples and gene perturbation experiments in AD mouse models.ResultsGene expression changes in AD versus control were identified for each sex. Gene co-expression networks were constructed for each sex to identify AD-associated co-expressed gene modules shared by males and females or specific to each sex. Key network regulators were further identified as potential drivers of sex differences in AD development. LRP10 was identified as a top driver of the sex differences in AD pathogenesis and manifestation. Changes of LRP10 expression at the mRNA and protein levels were further validated in human AD brain samples. Gene perturbation experiments in EFAD mouse models demonstrated that LRP10 differentially affected cognitive function and AD pathology in sex- and APOE genotype-specific manners. A comprehensive mapping of brain cells in LRP10 over-expressed (OE) female E4FAD mice suggested neurons and microglia as the most affected cell populations. The female-specific targets of LRP10 identified from the single cell RNA-sequencing (scRNA-seq) data of the LRP10 OE E4FAD mouse brains were significantly enriched in the LRP10-centered subnetworks in female AD subjects, validating LRP10 as a key network regulator of AD in females. Eight LRP10 binding partners were identified by the yeast two-hybrid system screening, and LRP10 over-expression reduced the association of LRP10 with one binding partner CD34.ConclusionsThese findings provide insights into key mechanisms mediating sex differences in AD pathogenesis and will facilitate the development of sex- and APOE genotype-specific therapies for AD.

Application and development of systems biology in computer-aided drug design

With the advances in medicine, people have deeply understood the complex pathogenesis of diseases. Revealing the mechanism of action and therapeutic effect of drugs from an overall perspective has become the top priority of drug design. However, the traditional drug design methods cannot meet the current needs. In recent years, with the rapid development of systems biology, a variety of new technologies including metabolomics, genomics, and proteomics have been used in drug research and development. As a bridge between traditional pharmaceutical theory and modern science, computer-aided drug design(CADD) can shorten the drug development cycle and improve the success rate of drug design. The application of systems biology and CADD provides a methodological basis and direction for revealing the mechanism and action of drugs from an overall perspective. This paper introduces the research and application of systems biology in CADD from different perspectives and proposes the development direction, providing reference for promoting the application.

Big data can help prepare nurses and improve patient outcomes by improving quality, safety, and outcomes

Abstract Objectives Big data has revolutionized nursing and health care and raised concerns. This research aims to help nurses understand big data sets to provide better patient care. Methods This study used big data in nursing to improve patient care. Big data in nursing has sparked a global revolution and raised concerns, but few studies have focused on helping nurses understand big data to provide the best patient care. This systematic review was conducted based on PRISMA guidelines. PubMed, MEDLINE, CINAHL, Google Scholar, and ResearchGate were used for 2010–2020 studies. Results The most common use of big data in nursing was investigated in eight papers between 2015 and 2018. All research showed improvements in patient outcomes and healthcare delivery when big data was used in the medical-surgical, emergency department, critical care unit, community, systems biology, and leadership applications. Big data is not taught to nurses. Conclusions Big data applications in nursing and health care improve early intervention and decision-making. Big data provides a comprehensive view of a patient’s status and social determinants of health, allowing treatment using all metaparadigms and avoiding a singular focus. Big data can help prepare nurses and improve patient outcomes by improving quality, safety, and outcomes.

Venom biotechnology: casting light on nature's deadliest weapons using synthetic biology.

Venoms are complex chemical arsenals that have evolved independently many times in the animal kingdom. Venoms have attracted the interest of researchers because they are an important innovation that has contributed greatly to the evolutionary success of many animals, and their medical relevance offers significant potential for drug discovery. During the last decade, venom research has been revolutionized by the application of systems biology, giving rise to a novel field known as venomics. More recently, biotechnology has also made an increasing impact in this field. Its methods provide the means to disentangle and study venom systems across all levels of biological organization and, given their tremendous impact on the life sciences, these pivotal tools greatly facilitate the coherent understanding of venom system organization, development, biochemistry, and therapeutic activity. Even so, we lack a comprehensive overview of major advances achieved by applying biotechnology to venom systems. This review therefore considers the methods, insights, and potential future developments of biotechnological applications in the field of venom research. We follow the levels of biological organization and structure, starting with the methods used to study the genomic blueprint and genetic machinery of venoms, followed gene products and their functional phenotypes. We argue that biotechnology can answer some of the most urgent questions in venom research, particularly when multiple approaches are combined together, and with other venomics technologies.

Deciphering the recent trends in pesticide bioremediation using genome editing and multi-omics approaches: a review.

Pesticide pollution in recent times has emerged as a grave environmental problem contaminating both aquatic and terrestrial ecosystems owing to their widespread use. Bioremediation using gene editing and system biology could be developed as an eco-friendly and proficient tool to remediate pesticide-contaminated sites due to its advantages and greater public acceptance over the physical and chemical methods. However, it is indispensable to understand the different aspects associated with microbial metabolism and their physiology for efficient pesticide remediation. Therefore, this review paper analyses the different gene editing tools and multi-omics methods in microbes to produce relevant evidence regarding genes, proteins and metabolites associated with pesticide remediation and the approaches to contend against pesticide-induced stress. We systematically discussed and analyzed the recent reports (2015-2022) on multi-omics methods for pesticide degradation to elucidate the mechanisms and the recent advances associated with the behaviour of microbes under diverse environmental conditions. This study envisages that CRISPR-Cas, ZFN and TALEN as gene editing tools utilizing Pseudomonas, Escherichia coli and Achromobacter sp. can be employed for remediation of chlorpyrifos, parathion-methyl, carbaryl, triphenyltin and triazophos by creating gRNA for expressing specific genes for the bioremediation. Similarly, systems biology accompanying multi-omics tactics revealed that microbial strains from Paenibacillus, Pseudomonas putida, Burkholderia cenocepacia, Rhodococcus sp. and Pencillium oxalicum are capable of degrading deltamethrin, p-nitrophenol, chlorimuron-ethyl and nicosulfuron. This review lends notable insights into the research gaps and provides potential solutions for pesticide remediation by using different microbe-assisted technologies. The inferences drawn from the current study will help researchers, ecologists, and decision-makers gain comprehensive knowledge of value and application of systems biology and gene editing in bioremediation assessments.

Systems biology and real-world data as drivers of change in drug research and development

In recent years, systems biology is not only being applied in fundamental science but also in drug development and healthcare. The application of real-world data in clinical research generates a large volume of data and information that is difficult to handle without a proper tool to process, decode, and interpret the data. That is where systems biology comes into play. This article discusses the idea of applying systems biology in clinical research and how it may play an important role in facilitating the process of drug development.

Mapping out the gut microbiota-dependent trimethylamine N-oxide super pathway for systems biology applications

The metabolic axis linking the gut microbiome and heart is increasingly being researched in the context of cardiovascular health. The gut microbiota-derived trimethylamine/trimethylamine N-oxide (TMA/TMAO) pathway is responsible along this axis for the bioconversion of dietary precursors into TMA/TMAO and has been implicated in the progression of heart failure and dysbiosis through a positive-feedback interaction. Systems biology approaches in the context of researching this interaction offer an additional dimension for deepening the understanding of metabolism along the gut-heart axis. For instance, genome-scale metabolic models allow to study the functional role of pathways of interest in the context of an entire cellular or even whole-body metabolic network. In this mini review, we provide an overview of the latest findings on the TMA/TMAO super pathway and summarize the current state of knowledge in a curated pathway map on the community platform WikiPathways. The pathway map can serve both as a starting point for continual curation by the community as well as a resource for systems biology modeling studies. This has many applications, including addressing remaining gaps in our understanding of the gut-heart axis. We discuss how the curated pathway can inform a further curation and implementation of the pathway in existing whole-body metabolic models, which will allow researchers to computationally simulate this pathway to further understand its role in cardiovascular metabolism.

A General Hybrid Modeling Framework for Systems Biology Applications: Combining Mechanistic Knowledge with Deep Neural Networks under the SBML Standard

In this paper, a computational framework is proposed that merges mechanistic modeling with deep neural networks obeying the Systems Biology Markup Language (SBML) standard. Over the last 20 years, the systems biology community has developed a large number of mechanistic models that are currently stored in public databases in SBML. With the proposed framework, existing SBML models may be redesigned into hybrid systems through the incorporation of deep neural networks into the model core, using a freely available python tool. The so-formed hybrid mechanistic/neural network models are trained with a deep learning algorithm based on the adaptive moment estimation method (ADAM), stochastic regularization and semidirect sensitivity equations. The trained hybrid models are encoded in SBML and uploaded in model databases, where they may be further analyzed as regular SBML models. This approach is illustrated with three well-known case studies: the Escherichia coli threonine synthesis model, the P58IPK signal transduction model, and the Yeast glycolytic oscillations model. The proposed framework is expected to greatly facilitate the widespread use of hybrid modeling techniques for systems biology applications.