Computational Systems Biology Research Articles

Abstract 3000: A data-driven and AI-empowered systems biology model of MHC class I antigen presentation pathway

Abstract Immunotherapy is a new frontline therapeutic strategy for multiple cancer types. However, non-response mechanisms of immune checkpoint inhibitors generally exist in patients of solid cancer such breast and colorectal cancer. Multiple factors that parallelly regulate T cell functions may cause the non-response mechanism, such as regulators of T-cell’s recognition of cancer cells and cytotoxicity activities. Our recent study revealed that the presentation and salvage of MHC class I antigen presentation on cancer cells is a key deterministic factor of T-cell recognition in multiple cancer types. In this study, we established a systems biology model of the antigen processing and presentation pathways to identify cancer cell-expressing genes that are associated with immune response of breast cancer and effector T cell-mediated cytotoxicity. To the best of our knowledge, there is no established systems biology model of MHC I class antigen presentation pathway. We first manually collected and curated the biological processes that are involved in the MHC class I antigen presentation pathway via an extensive literature review. MHC I class antigen presentation involves a chain of biological processes, namely ubiquitination, proteosome, transporter associated with antigen processing, chaperone-based formation of MHC I class one complex, ER to Golgi, and Golgi to cell membrane exocytosis. We also include branches such as de-ubiquitination, MHC class I complex checking, and endocytosis-based salvage. We further estimated sample-wise activity level of the MHC class I antigen presentation pathway using transcriptomics data. By treating antigen as a “mass carrying” signal, we developed a systems biology model over the established pathway. The whole biological process was treated as a network model, in which the flux represents antigen presentation activity level. Based on this computational method, we further build a machine-learning pipeline that utilizes omics datasets (cancer genomics, scRNA-seq, spatial transcriptomics) collected from tumor tissue samples to correlate the MHC class I antigen presentation activities with immune response in cancer tumor microenvironment. We identified genes expressed by cancer cells that have high impacts on antigen presentation and are associated with T cell recognition and cytotoxicity. The identified genes are involved in (a) the antigen presentation machinery, (b) the immunoproteasome in antigen processing, (c) the dynamics of antigen presentation on the cell surface, and (d) cytokine and chemokine secretion. Citation Format: Jia Wang, Xiao Wang, Pengtao Dang, Haiqi Zhu, Xinyu Zhou, Kaman So, Sha Cao, Chi Zhang. A data-driven and AI-empowered systems biology model of MHC class I antigen presentation pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3000.

Abstract 4275: Averon: informatics platform to discover Actionable Vulnerabilities Enabled by Rewired Oncogenic Networks

Abstract Missense mutations can change protein structure, cellular localization, and functions. Such changes lead to the rewiring of protein-protein interaction (PPI) networks, activation of oncogenic pathways, and acquisition of cancer hallmarks. However, the translation of the landscape of oncogenic mutations into clinically actionable biological models for cancer target discovery remains a major challenge. We address this challenge by leveraging the power of computational systems biology supported by high-throughput screening technologies. While some mutations can disrupt the PPIs, others may induce new PPIs that are not natural for the wild-type counterparts. Recently, we have established a quantitative High Throughput differential Screening (qHT-dS) platform [1] to discover such mutant-enabled or neomorph PPIs (neoPPIs). The screening of more than 13,000 mutant interactions revealed a landscape of gain-of-interactions encompassing both oncogenic and tumor suppressor mutations. This emerging neoPPI landscape may offer new mutant-directed therapeutic approaches for precision medicine. However, to infer clinically actionable mechanistic insights into how neoPPIs promote tumorigenesis special computational tools are needed. To inform the neoPPI-based target discovery, we develop a set of innovative informatics tools for discovering Actionable Vulnerabilities Enabled by Rewired Oncogenic Networks (Averon). Implemented in a widely-used Jupyter Notebook format, the Averon streamlines the identification of the oncogenic programs and clinically significant genes that are regulated by neoPPIs in cancer patients. The Averon can recapitulate well-established connectivity between known mutant-dependent PPIs and specific oncogenic pathways and reveal new, previously unknown mechanisms of neoPPI-mediated oncogenic signaling. To inform new therapeutic strategies in neoPPI-dependent cancers, Averon connects neoPPI-regulated genes with available approved drugs and clinical compounds. Together, the Averon provides a powerful informatics environment to determine therapeutically actionable vulnerabilities created by mutant-regulated protein-protein interactions to inform new personalized therapeutic strategies in cancer. Acknowledgments: This work was supported in part by NCI’s Informatics Technology for Cancer Research (ITCR) Program (R21CA274620, A.A.I.), Winship Cancer Institute #IRG-17-181-06 from the American Cancer Society (A.A.I.). Cancer Target Discovery and Development (CTD2) Network (U01CA217875, H.F.), NCI Emory Lung Cancer SPORE (P50CA217691, H.F.), Career Enhancement Program (A.A.I., P50CA217691), Winship Cancer Institute (NIH 5P30CA138292).

Abstract 3162: Genome-scale metabolic modeling suggests novel molecular targets in ER-positive breast cancer

Abstract High expression of the epithelial splicing regulatory protein 1 (ESRP1) is correlated with poor prognosis in estrogen receptor positive (ER+) breast cancer and acquired resistance to tamoxifen/endocrine therapies. Our previous study revealed a novel functionality of ESRP1 in metabolic dysregulation by affecting lipid metabolism and oxidoreductase processes. To further understand how metabolic programming is affected by ESRP1 expression and identify novel therapeutic targets, we developed systems biology models to explore ER+ breast cancer metabolism. Flux balance analysis (FBA) is a computational approach to predict steady-state fluxes through metabolic reactions by posing cellular “objectives” such as maximization of biomass, ATP, or minimization of reactive oxygen species that reflect selective pressures posed by the tumor microenvironment or therapeutic insults. Our custom FBA pipeline leverages transcriptomic data, mutation information, and thermodynamic constraints to generate personalized genome-scale metabolic models. In this study, 100 ESRP1-high and 100 ESRP1-low tumors from the TCGA Breast Cancer Invasive Carcinoma dataset were simulated and compared; in a secondary analysis, we simulated metabolic rerouting of tamoxifen-resistant LCC2 cells and fulvestrant-resistant LCC9 cells upon ESRP1 knockdown. The intersection of statistically significant (p < 0.05) reactions that were altered in both the TCGA and cell line contexts suggested that changes in eicosanoid metabolism and androgen and estrogen synthesis pathways are highly involved in ESRP1's role in breast cancer prognosis and resistance to endocrine therapies. Eicosanoid-utilizing enzymes are consistently altered between the ESRP1-low and high conditions; Hematopoietic prostaglandin D synthase (HPGDS), prostaglandin I2 synthase (PTDIS), and thromboxane A synthase 1 (TBXAS1) are druggable. Furthermore, reactions associated with cytosolic and lysosomal 4-hydroxy-17beta-estradiol production, catalyzed by the enzymes myeloperoxidase, lactoperoxidase, and peroxiredoxin 6, also emerged as consistently altered across the groups. The findings from this methodology have suggested novel metabolic targets to overcome tamoxifen/endocrine therapy resistance in ER+ breast cancer. Experimental validation of these targets is currently underway. Citation Format: Zachary B. Mudge, Kristof Kovacs, Melissa L. Kemp, Sunil S. Badve, Yesim Gokmen-Polar. Genome-scale metabolic modeling suggests novel molecular targets in ER-positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3162.

Abstract 2047: Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer

Abstract Background: Glucose and glutamine are major carbon and energy sources that promote the fast proliferation of cancer cells. The Warburg effect, characterized by shifted flux ratio between aerobic and anaerobic respirations, is considered as a common metabolic reprogramming mechanism in cancer. The further discovery of glutaminolysis pathway elucidated the role of glutamate in providing energy and carbon source in cancer metabolism. Variations in the branches of the central metabolism of glucose and glutamate have also been observed. However, most of these observations were made on cell line or mouse systems, which cannot mimic the dysbalanced nutrient, redox, pH and oxygen levels in a real tumor microenvironment (TME), which may heavily shift in a TME and determines the metabolic phenotypes of a cancer. Analysis: In this study, we conducted a systematic evaluation of the metabolic reprogramming and characteristics via a computational analysis by using pan-cancer transcriptomics data of 11 cancer subtypes and 9 matched adjacent normal tissue types. We focused on the flux distribution and variations of the central energy metabolism and its key branches. We first reconstructed the central metabolism pathway in a subcellular resolution by including glycolytic pathway, production of lactate, TCA cycle, nucleic acids synthesis, glutaminolysis, glutaminate, glutamine and glutathione metabolism, and other amino acids synthesis. We applied our in-house developed scFEA method on TCGA pan-cancer transcriptomics data. Results: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of TCA cycle, i.e., Warburg effect in almost all the analyzed cancer types. However, increased lactate production and the second half of TCA cycle were only seen in certain cancer types. More interestingly, we did not see cancer tissues have highly shifted glutaminolysis compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes, (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls, and (3) the different shifts happened to tissue specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. This study strongly suggests the possibility to have a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors. Citation Format: Alex Lu, Haiqi Zhu, Kevin Hu, Grace Yang, Shaoyang Huang, Pengtao Dang, Sha Cao, Chi Zhang. Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2047.

ИЗУЧЕНИЕ ХИМИЧЕСКОГО СОСТАВА И БИОЛОГИЧЕСКОГО ДЕЙСТВИЯ ГИДРОЛАТА HYSSOPUS OFFICINALIS L.

Products of plant raw materials processing, in particular hyssop herb are becoming more and more popular nowadays. This plant raw material is used in traditional medicine in many countries all over of the world and is included as an officinal raw material in pharmacopoeias of several European countries: France, Portugal, Romania, Sweden and Germany. Hyssop hydrolat is used in perfumery, cosmetics and food industries, but it is not used in medicine yet, due to the lack of a complex research. This article presents the results of a research of the chemical composition, antibacterial and antioxidant activity of Hyssopus officinalis L. hydrolate. The main aim of the article is to study the chemical composition, antibacterial and antioxidant activity of hydrolate of Hyssopus officinalis L. Materials and methods. The object of the research is a hydrolate of hyssop obtained by the method of steam distillation from freshly harvested raw materials of hyssop (Hyssopus officinalis L.). The study of the chemical composition of the hydrolate was carried out by gas-liquid chromatography. The antibacterial activity of the hydrolate was assessed by the growth and luminescence characteristics of the marine luminescent bacteria Aliivibrio fischeri F1 and recombinant bioluminescent test-objects based on E. coli MG1655. Antioxidant activity was determined by the method of non-enzymatic initiation of lipid peroxidation in a model biological system of yolk lipoproteins. Results. The concentration of essential oil in the hyssop’s hydrolate was defined as 0.032%. It was found that the hydrolate contains isopinocamphone, pinocamphone; β-cariophillen; β-pinen; sabinen and 1.8-cineol. The dynamics of the accumulation of lipid peroxide products in the presence of hyssop’s hydrolate was significantly lower in comparison with the control by 39.47%, which indicates the ability of this hydrolate to suppress free radical oxidation processes, and thus exhibit antioxidant activity. Conclusion. As a result of the research, hydrolate of Hyssopus officinalis L. was found to exhibit antibacterial and antioxidant properties. Prospects for further research are the development of medicines and cosmetics based on hydrolate of Hyssopus officinalis L. with antibacterial and antioxidant properties.

Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients

Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients’ health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system’s coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79–0.95 (sensitivity 80–92%, specificity 62–94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice.

Deciphering the complete human-monkeypox virus interactome: Identifying immune responses and potential drug targets.

Monkeypox virus (MPXV) is a dsDNA virus, belonging to Poxviridae family. The outbreak of monkeypox disease in humans is critical in European and Western countries, owing to its origin in African regions. The highest number of cases of the disease were found in the United States, followed by Spain and Brazil. Understanding the complete infection mechanism of diverse MPXV strains and their interaction with humans is important for therapeutic drug development, and to avoid any future epidemics. Using computational systems biology, we deciphered the genome-wide protein-protein interactions (PPIs) between 22 MPXV strains and human proteome. Based on phylogenomics and disease severity, 3 different strains of MPXV: Zaire-96-I-16, MPXV-UK_P2, and MPXV_USA_2022_MA001 were selected for comparative functional analysis of the proteins involved in the interactions. On an average, we predicted around 92,880 non-redundant PPIs between human and MPXV proteomes, involving 8014 host and 116 pathogen proteins from the 3 strains. The gene ontology (GO) enrichment analysis revealed 10,624 common GO terms in which the host proteins of 3 strains were highly enriched. These include significant GO terms such as platelet activation (GO:0030168), GABA-A receptor complex (GO:1902711), and metalloendopeptidase activity (GO:0004222). The host proteins were also significantly enriched in calcium signaling pathway (hsa04020), MAPK signaling pathway (hsa04010), and inflammatory mediator regulation of TRP channels (hsa04750). These significantly enriched GO terms and KEGG pathways are known to be implicated in immunomodulatory and therapeutic role in humans during viral infection. The protein hubs analysis revealed that most of the MPXV proteins form hubs with the protein kinases and AGC kinase C-terminal domains. Furthermore, subcellular localization revealed that most of the human proteins were localized in cytoplasm (29.22%) and nucleus (26.79%). A few drugs including Fostamatinib, Tamoxifen and others were identified as potential drug candidates against the monkeypox virus disease. This study reports the genome-scale PPIs elucidation in human-monkeypox virus pathosystem, thus facilitating the research community with functional insights into the monkeypox disease infection mechanism and augment the drug development.

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; PHEIDI > 0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR < 0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including “cell aging”, “metal ion binding” and “oxidative damage”. Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

In Silico Identification of Peptides with PPARγ Antagonism in Protein Hydrolysate from Rice (Oryza sativa).

At least half the population in industrialized countries suffers from obesity due to excessive accumulation of adipose tissue. Recently, rice (Oryza sativa) proteins have been considered valuable sources of bioactive peptides with antiadipogenic potential. In this study, the digestibility and bioaccessibility in vitro of a novel protein concentrate (NPC) from rice were determined through INFOGEST protocols. Furthermore, the presence of prolamin and glutelin was evaluated via SDS-PAGE, and their potential digestibility and the bioactivity of ligands against peroxisome proliferator-activated receptor gamma (PPARγ) were explored by BIOPEP UWM and HPEPDOCK. For the top candidates, molecular simulations were conducted using Autodock Vina to evaluate their binding affinity against the antiadipogenic region of PPARγ and their pharmacokinetics and drug-likeness using SwissADME. Simulating gastrointestinal digestion showed a recovery of 43.07% and 35.92% bioaccessibility. The protein banding patterns showed the presence of prolamin (57 kDa) and glutelin (12 kDa) as the predominant proteins in the NPC. The in silico hydrolysis predicts the presence of three and two peptide ligands in glutelin and prolamin fraction, respectively, with high affinity for PPARγ (≤160). Finally, the docking studies suggest that the prolamin-derived peptides QSPVF and QPY (-6.38 & -5.61 kcal/mol, respectively) have expected affinity and pharmacokinetic properties to act as potential PPARγ antagonists. Hence, according to our results, bioactive peptides resulting from NPC rice consumption might have an antiadipogenic effect via PPARγ interactions, but further experimentation and validation in suitable biological model systems are necessary to gain more insight and to provide evidence to support our in silico findings.

Dynamical Analysis of Generalized Tumor Model with Caputo Fractional-Order Derivative

In this study, we perform a dynamical analysis of a generalized tumor model using the Caputo fractional-order derivative. Tumor growth models are widely used in biomedical research to understand the dynamics of tumor development and to evaluate potential treatments. The Caputo fractional-order derivative is a mathematical tool that is recently being applied to model biological systems, including tumor growth. We present a detailed mathematical analysis of the generalized tumor model with the Caputo fractional-order derivative and examine its dynamical behavior. Our results show that the Caputo fractional-order derivative provides a more accurate description of the tumor growth dynamics compared to classical integer-order derivatives. We also provide a comprehensive stability analysis of the tumor model and show that the fractional-order derivative allows for a more nuanced understanding of the stability of the system. The least-square curve fitting method fits several biological parameters, including the fractional-order parameter α. In conclusion, our study provides new insights into the dynamics of tumor growth and highlights the potential of the Caputo fractional-order derivative as a valuable tool in biomedical research. The results of this study shell have significant implications for the development of more effective treatments for tumor growth and the design of more accurate mathematical models of tumor development.

A Review of Quantitative Systems Pharmacology Models of the Coagulation Cascade: Opportunities for Improved Usability.

Despite the numerous therapeutic options to treat bleeding or thrombosis, a comprehensive quantitative mechanistic understanding of the effects of these and potential novel therapies is lacking. Recently, the quality of quantitative systems pharmacology (QSP) models of the coagulation cascade has improved, simulating the interactions between proteases, cofactors, regulators, fibrin, and therapeutic responses under different clinical scenarios. We aim to review the literature on QSP models to assess the unique capabilities and reusability of these models. We systematically searched the literature and BioModels database reviewing systems biology (SB) and QSP models. The purpose and scope of most of these models are redundant with only two SB models serving as the basis for QSP models. Primarily three QSP models have a comprehensive scope and are systematically linked between SB and more recent QSP models. The biological scope of recent QSP models has expanded to enable simulations of previously unexplainable clotting events and the drug effects for treating bleeding or thrombosis. Overall, the field of coagulation appears to suffer from unclear connections between models and irreproducible code as previously reported. The reusability of future QSP models can improve by adopting model equations from validated QSP models, clearly documenting the purpose and modifications, and sharing reproducible code. The capabilities of future QSP models can improve from more rigorous validation by capturing a broader range of responses to therapies from individual patient measurements and integrating blood flow and platelet dynamics to closely represent in vivo bleeding or thrombosis risk.

Combinatorial Network of Transcriptional and miRNA Regulation in Colorectal Cancer

Colorectal cancer is one of the leading causes of cancer-associated mortality across the worldwide. One of the major challenges in colorectal cancer is the understanding of the regulatory mechanisms of biological molecules. In this study, we aimed to identify novel key molecules in colorectal cancer by using a computational systems biology approach. We constructed the colorectal protein-protein interaction network which followed hierarchical scale-free nature. We identified TP53, CTNBB1, AKT1, EGFR, HRAS, JUN, RHOA, and EGF as bottleneck-hubs. The HRAS showed the largest interacting strength with functional subnetworks, having strong correlation with protein phosphorylation, kinase activity, signal transduction, and apoptotic processes. Furthermore, we constructed the bottleneck-hubs' regulatory networks with their transcriptional (transcription factor) and post-transcriptional (miRNAs) regulators, which exhibited the important key regulators. We observed miR-429, miR-622, and miR-133b and transcription factors (EZH2, HDAC1, HDAC4, AR, NFKB1, and KLF4) regulates four bottleneck-hubs (TP53, JUN, AKT1 and EGFR) at the motif level. In future, biochemical investigation of the observed key regulators could provide further understanding about their role in the pathophysiology of colorectal cancer.

Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves

BackgroundHigh-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar (Populus sp.). Here, the authors report the relative abundance of extractable aromatic metabolites in Populus trichocarpa leaves rapidly estimated using pyrolysis-molecular beam mass spectrometry (py-MBMS). Poplar leaves were analyzed in conjunction with and validated by GC/MS analysis of extracts to determine key spectral features used to build PLS models to predict the relative composition of extractable aromatic metabolites in whole poplar leaves.ResultsThe Pearson correlation coefficient for the relative abundance of extractable aromatic metabolites based on ranking between GC/MS analysis and py-MBMS analysis of the Boardman leaf set was 0.86 with R2 = 0.76 using a simplified prediction approach from select ions in MBMS spectra. Metabolites most influential to py-MBMS spectral features in the Clatskanie set included the following compounds: catechol, salicortin, salicyloyl-coumaroyl-glucoside conjugates, α-salicyloylsalicin, tremulacin, as well as other salicylates, trichocarpin, salicylic acid, and various tremuloidin conjugates. Ions in py-MBMS spectra with the highest correlation to the abundance of extractable aromatic metabolites as determined by GC/MS analysis of extracts, included m/z 68, 71, 77, 91, 94, 105, 107, 108, and 122, and were used to develop the simplified prediction approach without PLS models or a priori measurements.ConclusionsThe simplified py-MBMS method is capable of rapidly screening leaf tissue for relative abundance of extractable aromatic secondary metabolites to enable prioritization of samples in large populations requiring comprehensive metabolomics that will ultimately inform plant systems biology models and advance the development of optimized biomass feedstocks for renewable fuels and chemicals.

Gene network reveals LASP1, TUBA1C, and S100A6 are likely playing regulatory roles in multiple sclerosis.

Multiple sclerosis (MS), a non-contagious and chronic disease of the central nervous system, is an unpredictable and indirectly inherited disease affecting different people in different ways. Using Omics platforms genomics, transcriptomics, proteomics, epigenomics, interactomics, and metabolomics database, it is now possible to construct sound systems biology models to extract full knowledge of the MS and recognize the pathway to uncover the personalized therapeutic tools. In this study, we used several Bayesian Networks in order to find the transcriptional gene regulation networks that drive MS disease. We used a set of BN algorithms using the R add-on package bnlearn. The BN results underwent further downstream analysis and were validated using a wide range of Cytoscape algorithms, web based computational tools and qPCR amplification of blood samples from 56 MS patients and 44 healthy controls. The results were semantically integrated to improve understanding of the complex molecular architecture underlying MS, distinguishing distinct metabolic pathways and providing a valuable foundation for the discovery of involved genes and possibly new treatments. Results show that the LASP1, TUBA1C, and S100A6 genes were most likely playing a biological role in MS development. Results from qPCR showed a significant increase (P < 0.05) in LASP1 and S100A6 gene expression levels in MS patients compared to that in controls. However, a significant down regulation of TUBA1C gene was observed in the same comparison. This study provides potential diagnostic and therapeutic biomarkers for enhanced understanding of gene regulation underlying MS.

Sloppiness: Fundamental study, new formalism and its application in model assessment.

Computational modelling of biological processes poses multiple challenges in each stage of the modelling exercise. Some significant challenges include identifiability, precisely estimating parameters from limited data, informative experiments and anisotropic sensitivity in the parameter space. One of these challenges' crucial but inconspicuous sources is the possible presence of large regions in the parameter space over which model predictions are nearly identical. This property, known as sloppiness, has been reasonably well-addressed in the past decade, studying its possible impacts and remedies. However, certain critical unanswered questions concerning sloppiness, particularly related to its quantification and practical implications in various stages of system identification, still prevail. In this work, we systematically examine sloppiness at a fundamental level and formalise two new theoretical definitions of sloppiness. Using the proposed definitions, we establish a mathematical relationship between the parameter estimates' precision and sloppiness in linear predictors. Further, we develop a novel computational method and a visual tool to assess the goodness of a model around a point in parameter space by identifying local structural identifiability and sloppiness and finding the most sensitive and least sensitive parameters for non-infinitesimal perturbations. We demonstrate the working of our method in benchmark systems biology models of various complexities. The pharmacokinetic HIV infection model analysis identified a new set of biologically relevant parameters that can be used to control the free virus in an active HIV infection.

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.

EPISOL: A software package with expanded functions to perform 3D-RISM calculations for the solvation of chemical and biological molecules.

Integral equation theory (IET) provides an effective solvation model for chemical and biological systems that balances computational efficiency and accuracy. We present a new software package, the expanded package for IET-based solvation (EPISOL), that performs 3D-reference interaction site model (3D-RISM) calculations to obtain the solvation structure and free energies of solute molecules in different solvents. In EPISOL, we have implemented 22 different closures, multiple free energy functionals, and new variations of 3D-RISM theory, including the recent hydrophobicity-induced density inhomogeneity (HI) theory for hydrophobic solutes and ion-dipole correction (IDC) theory for negatively charged solutes. To speed up the convergence and enhance the stability of the self-consistent iterations, we have introduced several numerical schemes in EPISOL, including a newly developed dynamic mixing approach. We show that these schemes have significantly reduced the failure rate of 3D-RISM calculations compared to AMBER-RISM software. EPISOL consists of both a user-friendly graphic interface and a kernel library that allows users to call its routines and adapt them to other programs. EPISOL is compatible with the force-field and coordinate files from both AMBER and GROMACS simulation packages. Moreover, EPISOL is equipped with an internal memory control to efficiently manage the use of physical memory, making it suitable for performing calculations on large biomolecules. We demonstrate that EPISOL can efficiently and accurately calculate solvation density distributions around various solute molecules (including a protein chaperone consisting of 120,715 atoms) and obtain solvent free energy for a wide range of organic compounds. We expect that EPISOL can be widely applied as a solvation model for chemical and biological systems. EPISOL is available at https://github.com/EPISOLrelease/EPISOL.

Towards AI-driven longevity research: An overview.

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.

Abstract P5-11-01: Epithelial-Mesenchymal Plasticity is Regulated by Inflammatory Signaling Networks Coupled to Cell Morphology

Abstract Purpose: During development and homeostatic processes such as wound repair, certain cells undergo a remarkable process where they radically transform in cell shape and state, from epithelial to mesenchymal cells. This ability is referred to as ‘Epithelial-Mesenchymal Plasticity’ (EMP), triggered by both mechanical (i.e. loss of cell-cell contact) and soluble cues (i.e. TGFβ), and is absolutely essential in both embryonic and adult organisms. Dysregulation of EMP also occurs in cancer; where tumor cells undergo EMP to become metastatic, stem-like, and drug resistant. Critically, increased EMP correlates with increased cancer severity. However, it is largely a black box as to how EMP is regulated and how epithelial cells sense physical, geometrical, and soluble cues in their environment to assume a mesenchymal fate. This work inferred that cell shape is a determinant of not only fates – but of long-term outcomes. That is, we provide mechanistic explanations between cell context, environment, and cancer severity. In this work, we investigated how mechanical and soluble cues are coupled to the dynamics of signaling pathways that regulate transcriptional and post-transcriptional events that underpin EMP. Especially with regards to mechanical cues, we attempt to unlock the ‘black box’ as to how changes in adhesion, ECM (Extracellular Matrix) stiffness, and environment geometry are coupled to the transcriptional events that drive EMP. Results: We show that changes in cell and nuclear shape result from the actions of the cytoskeleton and important drivers of EMP in upregulating ‘interlocking’ networks that promote EMP-driving inflammation and suppressing insulin signaling. Using a combination of cell biology, proteomics, and new statistical methods, we provide a systems biology model demonstrating: Cell shape → MT bound Kinesin-1 activity and nuclear shape → inflammation (IKK, JNK), insulin signaling (IRS), and YAP/TAZ → EMP Our work connects observable changes in phenotype to causal changes in signaling network architecture and cell fates. We used an integrative -omic approach to analyze tumors from breast cancer patients. We identified a novel tumor suppressor – JAM3 – whose loss is associated with altered nuclear shape in vivo, inhibiting JAM3 in cells, or stimulation with canonical EMP inducer TGFβ to promote EMP, and thus changes the organization of microtubules and alters nuclear shape. During EMP we observe there is upregulation of a pro-inflammatory, insulin resistant, signaling network that is predictive of mesenchymal states across cancer. EMP following JAM3 depletion and/or TGFβ stimulation is rescued by inhibition of Kinesin-1 motors. This rescue is explained by changes in inflammatory and insulin signaling. We show that while Kinesin-1 activity is responsible for upregulation in canonical signalling and network ‘hubs’, changes in nuclear shape upregulate ‘effectors’ of these hubs. Thus, microtubules and nuclei differentially regulate different parts of ‘interlocking’ networks. Conclusions: This work has integrated image-omics, comprehensive global proteomics, and quantitative cell biology to provide a mechanistic ¬and systems-level understanding of how epithelial cells differentiate into mesenchymal forms during disease development and progression. This work is of major significance for three reasons. First, it shows how cell shape can mechanistically regulate cell fates on an unprecedented systems-level. Second, we identify an EMP network that is conserved across cancers and may indeed be conserved across both normal and diseased mesenchymal cells. Indeed, we speculate that different types of diseased cells may all share the same network. Finally, we introduce the concept of interlocking networks – where hubs and effectors are regulated by different cellular components. Our work has been extensively validated, using chemical and genetic approaches and in vivo model of in human breast cancer. Citation Format: Zheng Yin. Epithelial-Mesenchymal Plasticity is Regulated by Inflammatory Signaling Networks Coupled to Cell Morphology [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-11-01.

Efficient Emulation of Computer Models Utilising Multiple Known Boundaries of Differing Dimension

Emulation has been successfully applied across a wide variety of scientific disciplines for efficiently analysing computationally intensive models. We develop known boundary emulation strategies which utilise the fact that, for many computer models, there exist hyperplanes in the input parameter space for which the model output can be evaluated far more efficiently, whether this be analytically or just significantly faster using a more efficient and simpler numerical solver. The information contained on these known hyperplanes, or boundaries, can be incorporated into the emulation process via analytical update, thus involving no additional computational cost. In this article, we show that such analytical updates are available for multiple boundaries of various dimensions. We subsequently demonstrate which configurations of boundaries such analytical updates are available for, in particular by presenting a set of conditions that such a set of boundaries must satisfy. We demonstrate the powerful computational advantages of the known boundary emulation techniques developed on both an illustrative low-dimensional simulated example and a scientifically relevant and high-dimensional systems biology model of hormonal crosstalk in the roots of an Arabidopsis plant.