Making PBPK models more reproducible in practice.

Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields.

Intratumoral Heterogeneity and Metabolic Cross-Feeding in a Three-Dimensional Breast Cancer Culture: An In Silico Perspective.

Today, the intratumoral composition is a relevant factor associated with the progression and aggression of cancer. Although it suggests a metabolic interdependence among the subpopulations inside the tumor, a detailed map of how this interdependence contributes to the malignant phenotype is still lacking. To address this issue, we developed a systems biology approach integrating single-cell RNASeq and genome-scale metabolic reconstruction to map the metabolic cross-feeding among the subpopulations previously identified in the spheroids of MCF7 breast cancer. By calibrating our model with expression profiles and the experimental growth rate, we concluded that the reverse Warburg effect emerges as a mechanism to optimize community growth. Furthermore, through an in silico analysis, we identified lactate, alpha-ketoglutarate, and some amino acids as key metabolites whose disponibility alters the growth rate of the spheroid. Altogether, this work provides a strategy for assessing how space and intratumoral heterogeneity influence the metabolic robustness of cancer, issues suggesting that computational strategies should move toward the design of optimized treatments.

Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises.

BACKGROUND: The transition from explanative modeling of fitted data to the predictive modeling of unseen data for systems biology endeavors necessitates the effective recovery of reaction parameters. Yet, the relative efficacy of optimization algorithms in doing so remains under-studied, as to the specific reaction kinetics and the effect of measurement noises. To this end, we simulate the reactions of an artificial pathway using 4 kinetic formulations: generalized mass action (GMA), Michaelis-Menten, linear-logarithmic, and convenience kinetics. We then compare the effectiveness of 5 evolutionary algorithms (CMAES, DE, SRES, ISRES, G3PCX) for objective function optimization in kinetic parameter hyperspace to determine the corresponding estimated parameters. RESULTS: We quickly dropped the DE algorithm due to its poor performance. Baring measurement noise, we find the CMAES algorithm to only require a fraction of the computational cost incurred by other EAs for both GMA and linear-logarithmic kinetics yet performing as well by other criteria. However, with increasing noise, SRES and ISRES perform more reliably for GMA kinetics, but at considerably higher computational cost. Conversely, G3PCX is among the most efficacious for estimating Michaelis-Menten parameters regardless of noise, while achieving numerous folds saving in computational cost. Cost aside, we find SRES to be versatilely applicable across GMA, Michaelis-Menten, and linear-logarithmic kinetics, with good resilience to noise. Nonetheless, we could not identify the parameters of convenience kinetics using any algorithm. CONCLUSIONS: Altogether, we identify a protocol for predicting reaction parameters under marked measurement noise, as a step towards predictive modeling for systems biology endeavors.

RNA-Binding Proteins as Novel Effectors in Osteoblasts and Osteoclasts: A Systems Biology Approach to Dissect the Transcriptional Landscape.

Bone health is ensured by the coordinated action of two types of cells-the osteoblasts that build up bone structure and the osteoclasts that resorb the bone. The loss of balance in their action results in pathological conditions such as osteoporosis. Central to this study is a class of RNA-binding proteins (RBPs) that regulates the biogenesis of miRNAs. In turn, miRNAs represent a critical level of regulation of gene expression and thus control multiple cellular and biological processes. The impact of miRNAs on the pathobiology of various multifactorial diseases, including osteoporosis, has been demonstrated. However, the role of RBPs in bone remodeling is yet to be elucidated. The aim of this study is to dissect the transcriptional landscape of genes encoding the compendium of 180 RBPs in bone cells. We developed and applied a multi-modular integrative analysis algorithm. The core methodology is gene expression analysis using the GENEVESTIGATOR platform, which is a database and analysis tool for manually curated and publicly available transcriptomic data sets, and gene network reconstruction using the Ingenuity Pathway Analysis platform. In this work, comparative insights into gene expression patterns of RBPs in osteoblasts and osteoclasts were obtained, resulting in the identification of 24 differentially expressed genes. Furthermore, the regulation patterns upon different treatment conditions revealed 20 genes as being significantly up- or down-regulated. Next, novel gene-gene associations were dissected and gene networks were reconstructed. Additively, a set of osteoblast- and osteoclast-specific gene signatures were identified. The consolidation of data and information gained from each individual analytical module allowed nominating novel promising candidate genes encoding RBPs in osteoblasts and osteoclasts and will significantly enhance the understanding of potential regulatory mechanisms directing intracellular processes in the course of (patho)physiological bone turnover.

Systems Biology Methods via Genome-Wide RNA Sequences to Investigate Pathogenic Mechanisms for Identifying Biomarkers and Constructing a DNN-Based Drug-Target Interaction Model to Predict Potential Molecular Drugs for Treating Atopic Dermatitis.

This study aimed to construct genome-wide genetic and epigenetic networks (GWGENs) of atopic dermatitis (AD) and healthy controls through systems biology methods based on genome-wide microarray data. Subsequently, the core GWGENs of AD and healthy controls were extracted from their real GWGENs by the principal network projection (PNP) method for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Then, we identified the abnormal signaling pathways by comparing the core signaling pathways of AD and healthy controls to investigate the pathogenesis of AD. Then, IL-1ß, GATA3, Akt, and NF-κB were selected as biomarkers for their important roles in the abnormal regulation of downstream genes, leading to cellular dysfunctions in AD patients. Next, a deep neural network (DNN)-based drug-target interaction (DTI) model was pre-trained on DTI databases to predict molecular drugs that interact with these biomarkers. Finally, we screened the candidate molecular drugs based on drug toxicity, sensitivity, and regulatory ability as drug design specifications to select potential molecular drugs for these biomarkers to treat AD, including metformin, allantoin, and U-0126, which have shown potential for therapeutic treatment by regulating abnormal immune responses and restoring the pathogenic signaling pathways of AD.

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood.

Cytokines operate in concert to maintain immune homeostasis and coordinate immune responses. In cases of ER+ breast cancer, peripheral immune cells exhibit altered responses to several cytokines, and these alterations are correlated strongly with patient outcomes. To develop a systems-level understanding of this dysregulation, we measured a panel of cytokine responses and receptor abundances in the peripheral blood of healthy controls and ER+ breast cancer patients across immune cell types. Using tensor factorization to model this multidimensional data, we found that breast cancer patients exhibited widespread alterations in response, including drastically reduced response to IL-10 and heightened basal levels of pSmad2/3 and pSTAT4. ER+ patients also featured upregulation of PD-L1, IL6Rα, and IL2Rα, among other receptors. Despite this, alterations in response to cytokines were not explained by changes in receptor abundances. Thus, tensor factorization helped to reveal a coordinated reprogramming of the immune system that was consistent across our cohort.

System biology-based assessment of the molecular mechanism of IMPHY000797 in Parkinson's disease: a network pharmacology and in-silico evaluation.

IMPHY000797 derivatives have been well known for their efficacy in various diseases. Moreover, IMPHY000797 derivatives have been found to modulate such genes involved in multiple neurological disorders. Hence, this study seeks to identify such genes and the probable molecular mechanism that could be involved in the pathogenesis of Parkinson's disease. The study utilized various biological tools such as DisGeNET, STRING, Swiss target predictor, Cytoscape, AutoDock 4.2, Schrodinger suite, ClueGo, and GUSAR. All the reported genes were obtained using DisGeNET, and further, the common genes were incorporated into the STRING to get the KEGG pathway, and all the data was converted to a protein/pathway network via Cytoscape. The clustering of the genes was performed for the gene-enriched data using two-sided hypergeometrics (p-value). The binding affinity of the IMPHY000797 was verified with the highest regulated 25 proteins via utilizing the "Monte Carlo iterated search technique" and the "Emodel and Glide score" function. Three thousand five hundred eighty-three genes were identified for Parkinson's disease and 31 genes for IMPHY000797 compound, among which 25 common genes were identified. Further, the "FOXO-signaling pathway" was identified to be a modulated pathway. Among the 25 proteins, the highest modulated genes and highest binding affinity were exhibited by SIRT3, FOXO1, and PPARGC1A with the compound IMPHY000797. Further, rat toxicity analysis provided the efficacy and safety of the compound. The study was required to identify the probable molecular mechanism, which needs more confirmation from other studies, which is still a significant hit-back.

Systems immunology approaches to study T cells in health and disease.

T cells are dynamically regulated immune cells that are implicated in a variety of diseases ranging from infection, cancer and autoimmunity. Recent advancements in sequencing methods have provided valuable insights in the transcriptional and epigenetic regulation of T cells in various disease settings. In this review, we identify the key sequencing-based methods that have been applied to understand the transcriptomic and epigenomic regulation of T cells in diseases.

Systems biology approaches identify metabolic signatures of dietary lifespan and healthspan across species.

Dietary restriction (DR) is a potent method to enhance lifespan and healthspan, but individual responses are influenced by genetic variations. Understanding how metabolism-related genetic differences impact longevity and healthspan are unclear. To investigate this, we used metabolites as markers to reveal how different genotypes respond to diet to influence longevity and healthspan traits. We analyzed data from Drosophila Genetic Reference Panel (DGRP) strains raised under AL and DR conditions, combining metabolomic, phenotypic, and genome-wide information. We employed two computational and complementary methods across species-random forest modeling within the DGRP as our primary analysis and Mendelian randomization in human cohorts as a secondary analysis. We pinpointed key traits with cross-species relevance as well as underlying heterogeneity and pleiotropy that influence lifespan and healthspan. Notably, orotate was linked to parental age at death in humans and blocked the DR lifespan extension in flies, while threonine supplementation extended lifespan, in a strain- and sex-specific manner. Thus, utilizing natural genetic variation data from flies and humans, we employed a systems biology approach to elucidate potential therapeutic pathways and metabolomic targets for diet-dependent changes in lifespan and healthspan.

An automated network-based tool to search for metabolic vulnerabilities in cancer.

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gmctool, a freely accessible online tool that allows us to accomplish this task in a simple, efficient and intuitive environment. gmctool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. gmctool introduces qualitative and quantitative improvements over our previously developed algorithms to predict, visualize and analyze metabolic vulnerabilities in cancer, demonstrating a superior performance than competing algorithms. A detailed illustration of gmctool is presented for multiple myeloma (MM), an incurable hematological malignancy. We provide in vitro experimental evidence for the essentiality of CTPS1 (CTPS synthase) and UAP1 (UDP-N-Acetylglucosamine Pyrophosphorylase 1) in specific MM patient subgroups.

Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli.

Engineered microorganisms have emerged as viable alternatives for limonene production. However, issues such as low enzyme abundance or activities, and regulatory feedback/forward inhibition may reduce yields. To understand the underlying metabolism, we adopted a systems biology approach for an engineered limonene-producing Escherichia coli strain K-12 MG1655. Firstly, we generated time-series metabolomics data and, secondly, developed a dynamic model based on enzyme dynamics to track the native metabolic networks and the engineered mevalonate pathway. After several iterations of model fitting with experimental profiles, which also included 13C-tracer studies, we performed in silico knockouts (KOs) of all enzymes to identify bottleneck(s) for optimal limonene yields. The simulations indicated that ALDH/ADH (aldehyde dehydrogenase/alcohol dehydrogenase) and LDH (lactate dehydrogenase) suppression, and HK (hexokinase) enhancement would increase limonene yields. Experimental confirmation was achieved, where ALDH-ADH and LDH KOs, and HK overexpression improved limonene yield by 8- to 11-fold. Our systems biology approach can guide microbial strain re-engineering for optimal target production.

Optimal performance objectives in the highly conserved bone morphogenetic protein signaling pathway.

Throughout development, complex networks of cell signaling pathways drive cellular decision-making across different tissues and contexts. The transforming growth factor ß (TGF-ß) pathways, including the BMP/Smad pathway, play crucial roles in determining cellular responses. However, as the Smad pathway is used reiteratively throughout the life cycle of all animals, its systems-level behavior varies from one context to another, despite the pathway connectivity remaining nearly constant. For instance, some cellular systems require a rapid response, while others require high noise filtering. In this paper, we examine how the BMP-Smad pathway balances trade-offs among three such systems-level behaviors, or "Performance Objectives (POs)": response speed, noise amplification, and the sensitivity of pathway output to receptor input. Using a Smad pathway model fit to human cell data, we show that varying non-conserved parameters (NCPs) such as protein concentrations, the Smad pathway can be tuned to emphasize any of the three POs and that the concentration of nuclear phosphatase has the greatest effect on tuning the POs. However, due to competition among the POs, the pathway cannot simultaneously optimize all three, but at best must balance trade-offs among the POs. We applied the multi-objective optimization concept of the Pareto Front, a widely used concept in economics to identify optimal trade-offs among various requirements. We show that the BMP pathway efficiently balances competing POs across species and is largely Pareto optimal. Our findings reveal that varying the concentration of NCPs allows the Smad signaling pathway to generate a diverse range of POs. This insight identifies how signaling pathways can be optimally tuned for each context.

Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response.

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body's response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome's role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome's function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis.

BACKGROUND: Cystic Fibrosis (CF) is a monogenic disease caused by mutations in the gene coding the Cystic Fibrosis Transmembrane Regulator (CFTR) protein, but its overall physio-pathology cannot be solely explained by the loss of the CFTR chloride channel function. Indeed, CFTR belongs to a yet not fully deciphered network of proteins participating in various signalling pathways. METHODS: We propose a systems biology approach to study how the absence of the CFTR protein at the membrane leads to perturbation of these pathways, resulting in a panel of deleterious CF cellular phenotypes. RESULTS: Based on publicly available transcriptomic datasets, we built and analyzed a CF network that recapitulates signalling dysregulations. The CF network topology and its resulting phenotypes were found to be consistent with CF pathology. CONCLUSION: Analysis of the network topology highlighted a few proteins that may initiate the propagation of dysregulations, those that trigger CF cellular phenotypes, and suggested several candidate therapeutic targets. Although our research is focused on CF, the global approach proposed in the present paper could also be followed to study other rare monogenic diseases.

Higher-order correction of persistent batch effects in correlation networks.

MOTIVATION: Systems biology analyses often use correlations in gene expression profiles to infer co-expression networks that are then used as input for gene regulatory network inference or to identify functional modules of co-expressed or putatively co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene, resulting in improved fidelity of DE analysis. However, such adjustments do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading to the identification of false, non-biological associations, even when the input data are corrected using standard batch correction. RESULTS: In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. We then introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data. AVAILABILITY AND IMPLEMENTATION: COBRA is available under the GLP3 open source license in R and Python in netZoo (https://netzoo.github.io).

Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease.

One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and planetary ecosystem health. For example, not only lung cancer (LC) and gastroesophageal reflux disease (GERD) pose a significant burden on planetary health, but also the coexistence of GERD in patients with LC is often associated with a poor prognosis. This study reports on the genetic overlaps between these two conditions using systems biology-driven bioinformatics and machine learning-based algorithms. A total of nine hub genes including IGHV1-3, COL3A1, ITGA11, COL1A1, MS4A1, SPP1, MMP9, MMP7, and LOC102723407 were found to be significantly altered in both LC and GERD as compared with controls and with pathway analyses suggesting a significant association with the matrix remodeling pathway. The expression of these genes was validated in two additional datasets. Random forest and K-nearest neighbor, two machine learning-based algorithms, achieved accuracies of 89% and 85% for distinguishing LC and GERD, respectively, from controls using these hub genes. Additionally, potential drug targets were identified, with molecular docking confirming the binding affinity of doxycycline to matrix metalloproteinase 7 (binding affinity: -6.8 kcal/mol). The present study is the first of its kind that combines in silico and machine learning algorithms to identify the gene signatures that relate to both LC and GERD and promising drug candidates that warrant further research in relation to therapeutic innovation in LC and GERD. Finally, this study also suggests upstream regulators, including microRNAs and transcription factors, that can inform future mechanistic research on LC and GERD.

How Do You Start a Revolution for Systems Medicine in a Health Innovation Ecosystem? Think Orthogonally and Change Assumptions.

This paper defines a revolution as an orthogonal change in direction, a 90-degree perpendicular turn from the status quo ways of thinking, being and doing, so as to create a complete break, an abolitionist rupture with current and past ways of producing knowledge. David Bowie was a relatable example of a revolutionary and orthogonal innovator who completely and courageously broke with the past and the present and opened up new vistas in music and performing arts. The late anthropologist and public intellectual David Graeber also argued that a revolution fundamentally changes the assumptions in a given field of inquiry. Changing the entrenched assumptions that are long ossified, outdated or uncritically internalized by a knowledge community and profession can have multiplying revolutionary effects on downstream knowledge production. Thinking orthogonally to change the prevailing assumptions is indeed a revolutionary act. Orthogonal innovation as described in this paper is not a repackaging of an innovation in a different field. An orthogonal innovation is proposed as coalescence of ideas drawn from orthogonal domains, e.g., epistemologically speaking as in medicine and political theory, with an eye to pave the way for unprecedented social change and innovation. Grounding systems medicine in political determinants of planetary health, to link two fields of inquiry that have remained isolated and orthogonal since the 17th century, is nothing short of a revolution and orthogonal innovation in the making. For systems medicine to be a truly revolutionary field, it ought to acknowledge that there is no single-issue health nor single-issue politics.

Systems biology of electrogenic Pseudomonas putida - multi-omics insights and metabolic engineering for enhanced 2-ketogluconate production.

BACKGROUND: Pseudomonas putida KT2440 has emerged as a promising host for industrial bioproduction. However, its strictly aerobic nature limits the scope of applications. Remarkably, this microbe exhibits high bioconversion efficiency when cultured in an anoxic bio-electrochemical system (BES), where the anode serves as the terminal electron acceptor instead of oxygen. This environment facilitates the synthesis of commercially attractive chemicals, including 2-ketogluconate (2KG). To better understand this interesting electrogenic phenotype, we studied the BES-cultured strain on a systems level through multi-omics analysis. Inspired by our findings, we constructed novel mutants aimed at improving 2KG production. RESULTS: When incubated on glucose, P. putida KT2440 did not grow but produced significant amounts of 2KG, along with minor amounts of gluconate, acetate, pyruvate, succinate, and lactate. 13C tracer studies demonstrated that these products are partially derived from biomass carbon, involving proteins and lipids. Over time, the cells exhibited global changes on both the transcriptomic and proteomic levels, including the shutdown of translation and cell motility, likely to conserve energy. These adaptations enabled the cells to maintain significant metabolic activity for several weeks. Acetate formation was shown to contribute to energy supply. Mutants deficient in acetate production demonstrated superior 2KG production in terms of titer, yield, and productivity. The ∆aldBI ∆aldBII double deletion mutant performed best, accumulating 2KG at twice the rate of the wild type and with an increased yield (0.96 mol/mol). CONCLUSIONS: By integrating transcriptomic, proteomic, and metabolomic analyses, this work provides the first systems biology insight into the electrogenic phenotype of P. putida KT2440. Adaptation to anoxic-electrogenic conditions involved coordinated changes in energy metabolism, enabling cells to sustain metabolic activity for extended periods. The metabolically engineered mutants are promising for enhanced 2KG production under these conditions. The attenuation of acetate synthesis represents the first systems biology-informed metabolic engineering strategy for enhanced 2KG production in P. putida. This non-growth anoxic-electrogenic mode expands our understanding of the interplay between growth, glucose phosphorylation, and glucose oxidation into gluconate and 2KG in P. putida.

A systematic investigation of clear cell renal cell carcinoma using meta-analysis and systems biology approaches.

Renal cell carcinoma with clear cells (ccRCC) is the most frequent kind; it accounts for almost 70% of all kidney cancers. A primary objective of current research was to find genes that may be used in ccRCC gene therapy to understand better the molecular pathways underlying the disease. Based on PubMed microarray searches and meta-analyses, we compared overall survival and recurrence-free survival rates in ccRCC patients with those in healthy samples. The technique was followed by a KEGG pathway and Gene Ontology (GO) function analyses, both performed in conjunction with the approach. Tumor immune estimate and multi-gene biomarkers validation for clinical outcomes were performed at the molecular and clinical cohort levels. Our analysis included fourteen GEO datasets based on inclusion and exclusion criteria. A meta-analysis procedure, network construction using PPIs, and four significant gene identification standard algorithms indicated that 11 genes had the most important differences. Ten genes were upregulated, and one was downregulated in the study. In order to analyze RFS and OS survival rates, 11 genes expressed in the GEPIA2 database were examined. Nearly nine of eleven significant genes have been found to beinvolved in tumor immunity. Furthermore, it was found that mRNA expression levels of these genes were significantly correlated with experimental literature studies on ccRCCs, which explained these findings. This study identified eleven gene panels associated with ccRCC growth and metastasis, as well as their immune system infiltration.

Energy Landscape Reveals the Underlying Mechanism of Cancer-Adipose Conversion in Gene Network Models.

Cancer is a systemic heterogeneous disease involving complex molecular networks. Tumor formation involves an epithelial-mesenchymal transition (EMT), which promotes both metastasis and plasticity of cancer cells. Recent experiments have proposed that cancer cells can be transformed into adipocytes via a combination of drugs. However, the underlying mechanisms for how these drugs work, from a molecular network perspective, remain elusive. To reveal the mechanism of cancer-adipose conversion (CAC), this study adopts a systems biology approach by combing mathematical modeling and molecular experiments, based on underlying molecular regulatory networks. Four types of attractors are identified, corresponding to epithelial (E), mesenchymal (M), adipose (A) and partial/intermediate EMT (P) cell states on the CAC landscape. Landscape and transition path results illustrate that intermediate states play critical roles in the cancer to adipose transition. Through a landscape control approach, two new therapeutic strategies for drug combinations are identified, that promote CAC. These predictions are verified by molecular experiments in different cell lines. The combined computational and experimental approach provides a powerful tool to explore molecular mechanisms for cell fate transitions in cancer networks. The results reveal underlying mechanisms of intermediate cell states that govern the CAC, and identified new potential drug combinations to induce cancer adipogenesis.