Systems Biology Studies Research Articles

A modular systems biological modelling framework studies cyclic nucleotide signaling in platelets

BackgroundThe cyclic nucleotides cAMP and cGMP inhibit platelet activation. Different platelet signaling modules work together. We develop here a modelling framework to integrate different signaling modules and apply it to platelets. ResultsWe introduce a novel standardized bilinear coupling mechanism allowing sub model debugging and standardization of coupling with optimal data driven modelling by methods from optimization. Besides cAMP signaling our model considers specific cGMP effects including external stimuli by drugs. Moreover, the output of the cGMP module serves as input for a modular model of VASP phosphorylation and for the activity of cAMP and cGMP pathways in platelets. Experimental data driven modeling allows us to design models with quantitative output. We use the condensed information about involved regulation and system responses for modeling drug effects and obtaining optimal experimental settings. Stepwise further validation of our model is given by direct experimental data. ConclusionsWe present a general framework for model integration using modules and their stimulus responses. We demonstrate it by a multi-modular model for platelet signaling focusing on cGMP and VASP phosphorylation. Moreover, this allows to estimate drug action on any of the inhibitory cyclic nucleotide pathways (cGMP, cAMP) and is supported by experimental data.

Investigation of Host-Microbe-Parasite Interactions in an In Vitro 3D Model of the Vertebrate Gut.

In vitro models of the gut-microbiome axis are in high demand. Conventionally, intestinal monolayers grown on Transwell setups are used to test the effects of commensals/pathogens on the barrier integrity, both under homeostatic and pathophysiological conditions. While such models remain valuable for deepening the understanding of host-microbe interactions, often, they lack key biological components that mediate this intricate crosstalk. Here, a 3D in vitro model of the vertebrate intestinal epithelium, interfaced with immune cells surviving in culture for over 3 weeks, is developed and applied to proof-of-concept studies of host-microbe interactions. More specifically, the establishment of stable host-microbe cocultures is described and functional and morphological changes in the intestinal barrier induced by the presence of commensal bacteria are shown. Finally, evidence is provided that the 3D vertebrate gut models can be used as platforms to test host-microbe-parasite interactions. Exposure of gut-immune-bacteria cocultures to helminth "excretory/secretory products" induces in vivo-like up-/down-regulation of certain cytokines. These findings support the robustness of the modular in vitro cell systems for investigating the dynamics of host-microbe crosstalk and pave the way toward new approaches for systems biology studies of pathogens that cannot be maintained in vitro, including parasitic helminths.

System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks.

Synthetic biology aims to apply engineering principles for the rational, systematical design and construction of biological systems displaying functions that do not exist in nature or even building a cell from scratch. Understanding how molecular entities interconnect, work, and evolve in an organism is pivotal to this aim. Here, we summarize and discuss some historical organizing principles identified in bacterial gene regulatory networks. We propose a new layer, the concilion, which is the group of structural genes and their local regulators responsible for a single function that, organized hierarchically, coordinate a response in a way reminiscent of the deliberation and negotiation that take place in a council. We then highlight the importance that the network structure has, and discuss that the natural decomposition approach has unveiled the system-level elements shaping a common functional architecture governing bacterial regulatory networks. We discuss the incompleteness of gene regulatory networks and the need for network inference and benchmarking standardization. We point out the importance that using the network structural properties showed to improve network inference. We discuss the advances and controversies regarding the consistency between reconstructions of regulatory networks and expression data. We then discuss some perspectives on the necessity of studying regulatory networks, considering the interactions’ strength distribution, the challenges to studying these interactions’ strength, and the corresponding effects on network structure and dynamics. Finally, we explore the ability of evolutionary systems biology studies to provide insights into how evolution shapes functional architecture despite the high evolutionary plasticity of regulatory networks.

Evidence of multiple genome duplication events in Mytilus evolution

BackgroundMolluscs remain one significantly under-represented taxa amongst available genomic resources, despite being the second-largest animal phylum and the recent advances in genomes sequencing technologies and genome assembly techniques. With the present work, we want to contribute to the growing efforts by filling this gap, presenting a new high-quality reference genome for Mytilus edulis and investigating the evolutionary history within the Mytilidae family, in relation to other species in the class Bivalvia.ResultsHere we present, for the first time, the discovery of multiple whole genome duplication events in the Mytilidae family and, more generally, in the class Bivalvia. In addition, the calculation of evolution rates for three species of the Mytilinae subfamily sheds new light onto the taxa evolution and highlights key orthologs of interest for the study of Mytilus species divergences.ConclusionsThe reference genome presented here will enable the correct identification of molecular markers for evolutionary, population genetics, and conservation studies. Mytilidae have the capability to become a model shellfish for climate change adaptation using genome-enabled systems biology and multi-disciplinary studies of interactions between abiotic stressors, pathogen attacks, and aquaculture practises.

Interactome of SARS-CoV-2 Modulated Host Proteins With Computationally Predicted PPIs: Insights From Translational Systems Biology Studies

Accelerated efforts to identify intervention strategies for the COVID-19 pandemic caused by SARS-CoV-2 need to be supported by deeper investigations into host invasion and response mechanisms. We constructed the neighborhood interactome network of the 332 human proteins targeted by SARS-CoV-2 proteins, augmenting it with 1,941 novel human protein-protein interactions predicted using our High-precision Protein-Protein Interaction Prediction (HiPPIP) model. Novel interactors, and the interactome as a whole, showed significant enrichment for genes differentially expressed in SARS-CoV-2-infected A549 and Calu-3 cells, postmortem lung samples of COVID-19 patients and blood samples of COVID-19 patients with severe clinical outcomes. The PPIs connected host proteins to COVID-19 blood biomarkers, ACE2 (SARS-CoV-2 entry receptor), genes differentiating SARS-CoV-2 infection from other respiratory virus infections, and SARS-CoV-targeted host proteins. Novel PPIs facilitated identification of the cilium organization functional module; we deduced the potential antiviral role of an interaction between the virus-targeted NUP98 and the cilia-associated CHMP5. Functional enrichment analyses revealed promyelocytic leukaemia bodies, midbody, cell cycle checkpoints and tristetraprolin pathway as potential viral targets. Network proximity of diabetes and hypertension associated genes to host proteins indicated a mechanistic basis for these co-morbidities in critically ill/non-surviving patients. Twenty-four drugs were identified using comparative transcriptome analysis, which include those undergoing COVID-19 clinical trials, showing broad-spectrum antiviral properties or proven activity against SARS-CoV-2 or SARS-CoV/MERS-CoV in cell-based assays. The interactome is available on a webserver at http://severus.dbmi.pitt.edu/corona/.

DeepBtoD: Improved RNA-binding proteins prediction via integrated deep learning.

RNA-binding proteins (RBPs) have crucial roles in various cellular processes such as alternative splicing and gene regulation. Therefore, the analysis and identification of RBPs is an essential issue. However, although many computational methods have been developed for predicting RBPs, a few studies simultaneously consider local and global information from the perspective of the RNA sequence. Facing this challenge, we present a novel method called DeepBtoD, which predicts RBPs directly from RNA sequences. First, a [Formula: see text]-BtoD encoding is designed, which takes into account the composition of [Formula: see text]-nucleotides and their relative positions and forms a local module. Second, we designed a multi-scale convolutional module embedded with a self-attentive mechanism, the ms-focusCNN, which is used to further learn more effective, diverse, and discriminative high-level features. Finally, global information is considered to supplement local modules with ensemble learning to predict whether the target RNA binds to RBPs. Our preliminary 24 independent test datasets show that our proposed method can classify RBPs with the area under the curve of 0.933. Remarkably, DeepBtoD shows competitive results across seven state-of-the-art methods, suggesting that RBPs can be highly recognized by integrating local [Formula: see text]-BtoD and global information only from RNA sequences. Hence, our integrative method may be useful to improve the power of RBPs prediction, which might be particularly useful for modeling protein-nucleic acid interactions in systems biology studies. Our DeepBtoD server can be accessed at http://175.27.228.227/DeepBtoD/.

A systems biology study unveils the association between a melatonin biosynthesis gene, O-methyl transferase 1 (OMT1) and wheat (Triticum aestivum L.) combined drought and salinity stress tolerance.

Enhanced levels of endogenous melatonin in the root of wheat, mainly through the OMT1 gene, augment the antioxidant system, reestablish redox homeostasis and are associated with combined stress tolerance. A systems biology approach, including a collection of computational analyses and experimental assays, led us to uncover some aspects of a poorly understood phenomenon, namely wheat (Triticum aestivum L.) combined drought and salinity stress tolerance. Accordingly, a cross-study comparison of stress experiments was performed via a meta-analysis of Expressed Sequence Tags (ESTs) data from wheat roots to uncover the overlapping gene network of drought and salinity stresses. Identified differentially expressed genes were functionally annotated by gene ontology enrichment analysis and gene network analysis. Among those genes, O-methyl transferase 1 (OMT1) was highlighted as a more important (hub) gene in the dual-stress response gene network. Afterwards, the potential roles of OMT1 in mediating physiochemical indicators of stress tolerance were investigated in two wheat genotypes differing in abiotic stress tolerance. Regression analysis and correspondence analysis (CA) confirmed that the expression profiles of the OMT1 gene and variations in melatonin content, antioxidant enzyme activities, proline accumulation, H2O2 and malondialdehyde (MDA) contents are significantly associated with combined stress tolerance. These results reveal that the OMT1 gene may contribute to wheat combined drought and salinity stress tolerance through augmenting the antioxidant system and re-establishing redox homeostasis, probably via the regulation of melatonin biosynthesis as a master regulator molecule. Our findings provide new insights into the roles of melatonin in wheat combined drought and salinity stress tolerance and suggest a novel plausible regulatory node through the OMT1 gene to improve multiple-stress tolerant crops.

Genome-wide evaluation of transcriptomic responses of human tissues to smoke: A systems biology study.

The harmful compounds in various sources of smoke threaten human health. So far, many studies have investigated the effects of compounds of smoke on transcriptome changes in different human tissues. However, no study has been conducted on the effects of these compounds on transcriptome changes in different human tissues simultaneously. Hence, the present study was conducted to identify smoke-related genes (SRGs) and their response mechanisms to smoke in various human cells and tissues using systems biology based methods. A total of 6,484 SRGs were identified in the studied tissues, among which 4,095 SRGs were up-regulated and 2,389 SRGs were down-regulated. Totally, 459 SRGs were smoke-related transcription factors (SRTFs). Gene regulatory network analysis showed that the studied cells and tissues have different gene regulation and responses to compounds of smoke. The comparison of different tissues revealed no common SRG among the all studied tissues. However, the CYP1B1 gene was common among seven cells and tissues, and had the same expression trend. Network analysis showed that the CYP1B1 is a hub gene among SRGs in various cells and tissues. To the best of our knowledge, for the first time, our results showed that compounds of smoke induce and increase the expression of CYP1B1 key gene in all target and non-target tissues of human. Moreover, despite the specific characteristics of CYP1B1 gene and its identical expression trend in target and non-target tissues, it can be used as a biomarker for diagnosis and prognosis.

Early pathways, biomarkers, and four distinct molecular subclasses of preeclampsia: The intersection of clinical, pathological, and high-dimensional biology studies

Preeclampsia is a syndromic disease of the mother, fetus, and placenta. The main limitation in early and accurate diagnosis of preeclampsia is rooted in the heterogeneity of this syndrome as reflected by diverse molecular pathways, symptoms, and clinical outcomes. Gaps in our knowledge preclude successful early diagnosis, personalized treatment, and prevention. The advent of “omics” technologies and systems biology approaches addresses this problem by identifying the molecular pathways associated with the underlying mechanisms and clinical phenotypes of preeclampsia. Here, we provide a brief overview on how the field has progressed, focusing on studies utilizing state-of-the-art transcriptomics and proteomics methods. Moreover, we summarize our systems biology studies involving maternal blood proteomics and placental transcriptomics, which identified early maternal and placental disease pathways and showed that their interaction influences the clinical presentation of preeclampsia. We also present an analysis of maternal blood proteomics data which revealed distinct molecular subclasses of preeclampsia and their molecular mechanisms. Maternal and placental disease pathways behind these subclasses are similar to those recently reported in studies on the placental transcriptome. These findings may promote the development of novel diagnostic tools for the distinct subtypes of preeclampsia syndrome, enabling early detection and personalized follow-up and tailored care of patients.

Microbial metabolites: cause or consequence in gastrointestinal disease?

Systems biology studies have established that changes in gastrointestinal microbiome composition and function can adversely impact host physiology. Notable diseases synonymously associated with dysbiosis include inflammatory bowel diseases, cancer, metabolic disorders, and opportunistic and recurrent pathogen infections. However, there is a scarcity of mechanistic data that advances our understanding of taxonomic correlations with pathophysiological host-microbiome interactions. Generally, to survive a hostile gut environment, microbes are highly metabolically active and produce trans-kingdom signaling molecules to interact with competing microorganisms and the host. These specialized metabolites likely play important homeostatic roles, and identifying disease-specific taxa and their effector pathways can provide better strategies for diagnosis, treatment, and prevention, as well as the discovery of innovative therapeutics. The signaling role of microbial biotransformation products such as bile acids, short-chain fatty acids, polysaccharides, and dietary tryptophan is increasingly recognized, but little is known about the identity and function of metabolites that are synthesized by microbial biosynthetic gene clusters, including ribosomally synthesized and posttranslationally modified peptides (RiPPs), nonribosomal peptides (NRPs), polyketides (PKs), PK-NRP hybrids, and terpenes. Here we consider how bioactive natural products directly encoded by the human microbiome can contribute to the pathophysiology of gastrointestinal disease, cancer, autoimmune, antimicrobial-resistant bacterial and viral infections (including COVID-19). We also present strategies used to discover these compounds and the biological activities they exhibit, with consideration of therapeutic interventions that could emerge from understanding molecular causation in gut microbiome research.

Surrogate markers of gut dysfunction are related to heart failure severity and outcome–from the BIOSTAT-CHF consortium

The contribution of gut dysfunction to heart failure (HF) pathophysiology is not routinely assessed. We sought to investigate whether biomarkers of gut dysfunction would be useful in assessment of HF (eg, severity, adverse outcomes) and risk stratification. A panel of gut-related biomarkers including metabolites of the choline/carnitine- pathway (acetyl-L-carnitine, betaine, choline, γ-butyrobetaine, L-carnitine and trimethylamine-N-oxide [TMAO]) and the gut peptide, Trefoil factor-3 (TFF-3), were investigated in 1,783 patients with worsening HF enrolled in the systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) cohort and associations with HF severity and outcomes, and use in risk stratification were assessed. Metabolites of the carnitine-TMAO pathway (acetyl-L-carnitine, γ-butyrobetaine, L-carnitine, and TMAO) and TFF-3 were associated with the composite outcome of HF hospitalization or all-cause mortality at 3 years (hazards ratio [HR] 2.04-2.93 [95% confidence interval {CI} 1.30-4.71] P≤ .002). Combining the carnitine-TMAO metabolites with TFF-3, as a gut dysfunction panel, showed a graded association; a greater number of elevated markers was associated with higher New York Heart Association class (P< .001), higher plasma concentrations of B-type natriuretic peptide (P< .001), and worse outcome (HR 1.90-4.58 [95% CI 1.19-6.74] P≤ 0.008). Addition of gut dysfunction biomarkers to the contemporary BIOSTAT HF risk model also improved prediction for the aforementioned composite outcome (C-statistics P≤ .011, NRI 13.5-21.1 [95% CI 2.7-31.9] P≤ .014). A panel of biomarkers of gut dysfunction showed graded association with severity of HF and adverse outcomes. Biomarkers as surrogate markers are potentially useful for assessment of gut dysfunction to HF pathophysiology and in risk stratification.

Kinetics of Severity Biomarkers and Immunological Features of Methylprednisolone Therapy for Severe COVID-19 Patients.

In contrast to dexamethasone, the clinical efficacy of methylprednisolone (MP) remains controversial, and a systems biology study on its mechanism is lacking. In this study, a total of 38 severe COVID-19 patients were included. The demographics, clinical characteristics, and severity biomarkers including C-reactive protein (CRP), d-dimer, albumin, and Krebs von den Lungen 6 of patients receiving MP (n=26, 40 mg or 80 mg daily for 3-5 days) and supportive therapy (n=12) were compared. Longitudinal measurements of 92 cytokines in MP group from admission to over six months after discharge were performed by multiplex Proximity Extension Assay. The results showed that demographics, baseline clinical characteristics were similar in MP and non-MP groups. No death occurred and the hospital stays between the two groups were similar. Kinetics studies showed that MP was not better than supportive therapy at improving the four severity biomarkers. Cytokines in MP group were characterized by five clusters according to their baseline levels and responses to MP. The immunological feature of severe COVID-19 could be defined by the “core signature” cytokines in cluster 2: MCP-3, IL-6, IFN-γ, and CXCL10, which strongly correlated with each other and CRP, and are involved in cytokine release storm. The “core signature” cytokines were significantly upregulated at baseline and remained markedly elevated after MP treatment. Our work showed a short course of MP therapy could not rapidly improve the immune disorders among severe COVID-19 patients or clinical outcomes, also confirmed “core signature” cytokines, as severity biomarkers similar to CRP, could be applied to evaluate clinical treatment effect.

A Motif-Based Network Analysis of Regulatory Patterns in Doxorubicin Effects on Treating Breast Cancer, a Systems Biology Study.

Background:Breast cancer is the most common malignancy worldwide. Doxorubicin is an anthracycline used to treat breast cancer as the first treatment choice. Nevertheless, the molecular mechanisms underlying the response to Doxorubicin and its side effects are not comprehensively understood so far. We used systems biology and bioinformatics methods to identify essential genes and molecular mechanisms behind the body response to Doxorubicin and its side effects in breast cancer patients.Methods:Omics data were extracted and analyzed to construct the protein-protein interaction and gene regulatory networks. Network analysis was performed to identify hubs, bottlenecks, clusters, and regulatory motifs to evaluate crucial genes and molecular mechanisms behind the body response to Doxorubicin and its side effects.Results:Analyzing the constructed PPI and gene-TF-miRNA regulatory network showed that MCM3, MCM10, and TP53 are key hub-bottlenecks and seed proteins. Enrichment analysis also revealed cell cycle, TP53 signaling, Forkhead box O (FoxO) signaling, and viral carcinogenesis as essential pathways in response to this drug. Besides, SNARE interactions in vesicular transport and neurotrophin signaling were identified as pathways related to the side effects of Doxorubicin. The apoptosis induction, DNA repair, invasion inhibition, metastasis, and DNA replication are suggested as critical molecular mechanisms underlying Doxorubicin anti-cancer effect. SNARE interactions in vesicular transport and neurotrophin signaling and FoxO signaling pathways in glucose metabolism are probably the mechanisms responsible for side effects of Doxorubicin.Conclusion:Following our model validation using the existing experimental data, we recommend our other newly predicted biomarkers and pathways as possible molecular mechanisms and side effects underlying the response to Doxorubicin in breast cancer requiring further investigations.

Rapid testing of candidate oncogenes and tumour suppressor genes in signal transduction and neoplastic transformation

The COSMIC database (version 94) lists 576 genes in the Cancer Gene Census which have a defined function as drivers of malignancy (oncogenes) or as tumour suppressors (Tier 1). In addition, there are 147 genes with similar functions, but which are less well characterised (Tier 2). Furthermore, next-generation sequencing projects in the context of precision oncology activities are constantly discovering new ones. Since cancer genes differ from their wild-type precursors in numerous molecular and biochemical properties and exert significant differential effects on downstream processes, simple assays that can uncover oncogenic or anti-oncogenic functionality are desirable and may precede more sophisticated analyses. We describe simple functional assays for PTPN11 (protein-tyrosine phosphatase, non-receptor-type 11)/SHP2 mutants, which are typically found in RASopathies and exhibit potential oncogenic activity. We have also designed a functional test for lysyl oxidase (LOX), a prototypical class II tumour suppressor gene whose loss of function may contribute to neoplastic transformation by RAS oncogenes. Moreover, we applied this test to analyse three co-regulated, RAS-responsive genes for transformation-suppressive activity. The integration of these tests into systems biology studies will contribute to a better understanding of cellular networks in cancer.

Genome-scale Metabolic Model Guided Subtyping Lung Cancer towards Personalized Diagnosis

Mathematical modeling of biological systems are useful in (i) gaining better understanding about the physiological processes in an organism, (ii) simulating alternative scenarios, (iii) finding targets for improved performance within metabolic engineering context (iv) performing several functional analyses, e.g. identify drug targets (v) process scheduling within the context of industrial biotechnology etc. Increasing the predictive capability of these models is of common interest within systems biology studies which allows identification of more effective and personalized treatment strategies for complex metabolic diseases such as cancer by investigation of disease metabolism and providing correct subtyping and staging. By transforming gene-level information to flux/metabolite level information, current disease state can be analyzed and diagnosis of cancer subtype can be performed using a less invasive methods.In this study, subtyping and staging of lung cancer, that is one of the main causes of cancer related deaths, was performed by integrating publicly available RNAseq data of normal, lung adenoma and adenocarcinomas and lung squamous cell neoplasms to human genome scale metabolic model and classification of obtained flux distributions using linear support vector machine (SVM) classifications. Differential flux analysis and pathway enrichment methods showed that model adequately represented tumour metabolism. SVM classification accuracies were calculated as more than 99% for normal and cancer cells and 94% for adenomas and adenocarcinomas and squamous cell neoplasms, indicating high predictive capability of flux distributions.

A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature.

A biological pathway or regulatory network is a collection of molecular regulators which can activate the changes in cellular processes leading to an assembly of new molecules by series of actions among the molecules. There are three important pathways in system biology studies namely signaling pathways, metabolic pathways, and genetic pathways (or) gene regulatory networks. Recently, biological pathway construction from scientific literature is given much attention as the scientific literature contains a rich set of linguistic features to extract biological associations between genes and proteins. These associations can be united to construct biological networks. Here, we present a brief overview about various biological pathways, biomedical text resources/corpora for network construction and state-of-the-art existing methods for network construction followed by our hybridtext mining protocol for extracting pathways and regulatory networks from biomedical literature.

The Yin-Yang Dynamics in Cardiovascular Pharmacogenomics and Personalized Medicine.

Studies of genetic variants and systems biology have indicated that Yin-Yang dynamics are especially meaningful for cardiovascular pharmacogenomics and personalized therapeutic strategies. The comprehensive concepts of Yin-Yang can be used to characterize the dynamical factors in the adaptive microenvironments of the complex cardiovascular systems. The Yin-Yang imbalances in the complex adaptive systems (CAS) at different levels and stages are essential for cardiovascular diseases (CVDs), including atherosclerosis, hypertension, and heart failure (HF). At the molecular and cellular levels, Yin-Yang interconnections have been considered critical for genetic variants and various pathways, mitophagy, cell death, and cholesterol homeostasis. The significance of the adaptive and spatiotemporal factors in the nonlinear Yin-Yang interactions has been identified in different pathophysiological processes such as fibrosis. The Yin-Yang dynamical balances between proinflammatory and anti-inflammatory cytokines have vital roles in the complex reactions to stress and impairments to the heart. Procoagulant and anticoagulant lipids and lipoproteins in plasma have the Yin-Yang roles that increase or decrease thrombin productions and thrombosis. At the systems level, the Yin-Yang type of relationships has been suggested between atrial fibrillation (AF), diastolic dysfunction (DD), and HF. Based on such perceptions, systemic and personalized cardiovascular profiles can be constructed by embracing the features of CAS, especially the microenvironments and the adaptative pathophysiological stages. These features can be integrated into the comprehensive Yin-Yang dynamics framework to identify more accurate biomarkers for better prevention and treatments. The goal of reestablishing ubiquitous Yin-Yang dynamical balances may become the central theme for personalized and systems medicine for cardiovascular diseases.

HUNTER: Sensitive Automated Characterization of Proteolytic Systems by N Termini Enrichment from Microscale Specimen.

Proteolysis occurs at low frequency in the cellular environment. Protein N termini reveal essential mechanisms associated with cellular functions, and are useful indicators to track dysfunctional regulation of proteins and pathways in diseases. N terminomics has so far relied on labor-intensive methods, which require relatively large starting sample amounts rendering it ill-suited for high-throughput systems biology studies. Here, we describe protocols for the first scalable and automatable method for sensitive enrichment and identification of N termini from minute samples.

Clinico-pathological correlation: Teaching aspects, avenues, and advances.

Introduction: Explosion in scientific knowledge of our understanding of diseases, diagnostics, and targeted therapeutics all possible & advantageously with Clinico-pathological correlation (CPC). A narrative of evolution from the past to the present & the novelty in the future is exciting, with pragmatic presentation of important recent developments & interesting research directions, for ‘evolving excellence’.Methods: Analysis of the guidance was produced by leading medical education institutions and an in-depth search on Google Scholar, Medline, and PubMed for CPC. We thoughtfully formulated ways of incorporation of CPC for teaching and training of medical students at different levels. We put available policies, evidence, and personal experience gleaned in proper perspective. Results: The four HPs (i) Historical perspective… Tracing the progress, the important developments extending the physician’s range are clinical correlation with the outgivings of chemical analysis and biological experimentation. (ii) Holistic professionalism: Integrating Basic and Clinical Sciences. Knowledge for practice and knowledge applications in practice are two pillars of medical education. Learning opportunities provisioning should enable linking of theory and practice. (iii) The happening perfect: Clinic-Pathological Conference. Problem-based learning is highly effective. A clinical case presentation with deliberation on all aspects is useful for all. The denouement should include research developments and desired research directions. (iv) Heightened professionalism: Progress features promise exciting future. Chemistry of life and disease has been evolving manifold, opening new vistas. Microbiome research results for clinically actionable microbe-host relationships identification are in foresight. Genomic and proteomic approach to human disease is leading to new understanding of pathogenesis for clinical strategies. Systems Biology studies living organisms with their cellular or molecular network components holistically to predict response to perturbations. ‘Systems pathobiology’ is energising avenues for early diagnosis and advancing clinical applications for halting progress for better medical practice. Conclusions: Teaching CPC for all round understanding will lead to all knowledgeable proficient medical practitioners.

Can Systems Biology Advance Clinical Precision Oncology?

Simple SummaryOmics technologies and targeted drugs are revolutionizing the clinical oncology landscape, portending the promise of precision oncology. However, our abilities to define the best treatment for the individual tumor, based on its molecular characterization, are still limited. Systems biology, by studying the collective behavior of the different types of molecules involved in a biological process, allows us to reconstruct the complex behavior of biological systems and to compute the system’s response to perturbations, such as targeted therapies. This helps to dissect drug resistance phenomena, as well as to establish the best drug combinations for a specific tumor. Patient-specific biomarkers can be built from dynamical models of signaling networks and can have a greater prognostic value than conventional biomarkers. In this paper, we review current systems biology methods to highlight how they can contribute to advance clinical and translational research in precision oncology.Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.