Frontiers in systems biology最新文献

{"title":"Editorial: Combining mechanistic modeling with machine learning to study multiscale biological processes","authors":"S. Peirce-Cottler, Y. Vodovotz","doi":"10.3389/fsysb.2024.1367549","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1367549","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"15 2-4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139870267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-scale flux balance analysis reveals redox trade-offs in the metabolism of the thermoacidophile Methylacidiphilum fumariolicum under auto-, hetero-and methanotrophic conditions","authors":"Alexis Saldivar, Patricia Ruiz-Ruiz, Sergio Revah, C. Zuñiga","doi":"10.3389/fsysb.2024.1291612","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1291612","url":null,"abstract":"Members of the genus Methylacidiphilum are thermoacidophile methanotrophs with optimal growth temperatures between 50°C and 60°C, and pH between 1.0 and 3.0. These microorganisms, as well as other extremophile bacteria, offer an attractive platform for environmental and industrial biotechnology because of their robust operating conditions and capacity to grow using low-cost substrates. In this study, we isolated Methylacidiphilum fumariolicum str. Pic from a crater lake located in the state of Chiapas, Mexico. We sequenced the genome and built a genome-scale metabolic model. The manually curated model contains 667 metabolites, 729 reactions, and 473 genes. Predicted flux distributions using flux balance analysis identified changes in redox trade-offs under methanotrophic and autotrophic conditions (H2+CO2). This was also predicted under heterotrophic conditions (acetone, isopropanol, and propane). Model validation was performed by testing the capacity of the strains to grow using four substrates: CH4, acetone, isopropanol, and LP-Gas. The results suggest that the metabolism of M. fumariolicum str. Pic is limited by the regeneration of redox equivalents such as NAD(P)H and reduced cytochromes.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140489025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons","authors":"Christian T. Michael, Sayed A. Almohri, J. Linderman, Denise E. Kirschner","doi":"10.3389/fsysb.2023.1283341","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1283341","url":null,"abstract":"Computational models of disease progression have been constructed for a myriad of pathologies. Typically, the conceptual implementation for pathology-related in silico intervention studies has been ad hoc and similar in design to experimental studies. We introduce a multi-scale interventional design (MID) framework toward two key goals: tracking of disease dynamics from within-body to patient to population scale; and tracking impact(s) of interventions across these same spatial scales. Our MID framework prioritizes investigation of impact on individual patients within virtual pre-clinical trials, instead of replicating the design of experimental studies. We apply a MID framework to develop, organize, and analyze a cohort of virtual patients for the study of tuberculosis (TB) as an example disease. For this study, we use HostSim: our next-generation whole patient-scale computational model of individuals infected with Mycobacterium tuberculosis. HostSim captures infection within lungs by tracking multiple granulomas, together with dynamics occurring with blood and lymph node compartments, the compartments involved during pulmonary TB. We extend HostSim to include a simple drug intervention as an example of our approach and use our MID framework to quantify the impact of treatment at cellular and tissue (granuloma), patient (lungs, lymph nodes and blood), and population scales. Sensitivity analyses allow us to determine which features of virtual patients are the strongest predictors of intervention efficacy across scales. These insights allow us to identify patient-heterogeneous mechanisms that drive outcomes across scales.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"17 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139609220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolic perturbation studies using a Nash Equilibrium model of liver machine perfusion: modeling oxidative stress and effect of glutathione supplementation","authors":"Angelo Lucia, Korkut Uygun","doi":"10.3389/fsysb.2023.1260315","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1260315","url":null,"abstract":"The current clinical standard of Static Cold Storage (SCS) which involves preservation on ice (about +4°C) in a hypoxic state limits storage to a few hours for metabolically active tissues such as the liver and the heart. This period of hypoxia during can generate superoxide and other free radicals from purine metabolism, a well-established component of ischemia/reperfusion injury (IRI). Machine perfusion is at the cutting edge of organ preservation, which provides a functional, oxygenated preservation modality that can avoid/attenuate IRI. In clinical application, perfusion usually follows a period of SCS. This presentation of oxygen following hypoxia can lead to superoxide and hydrogen peroxide generation, but machine perfusion also allows manipulation of the temperature profiles and supply of antioxidant treatments, which could be used to minimize such issues. However, metabolomic data is difficult to gather, and there are currently no mathematical models present to allow rational design of experiments or guide clinical practice. In this article, the effects of a gradual warming temperature policy and glutathione supplementation to minimize oxidative stress are studied. An optimal gradual warming temperature policy for mid-thermic machine perfusion of a liver metabolic model is determined using a combination of Nash Equilibrium and Monte Carlo optimization. Using this optimal gradual warming temperature policy, minimum GSH requirements to maintain hydrogen peroxide concentrations in the normal region are calculated using a different Monte Carlo optimization methodology. In addition, the dynamic behavior of key metabolites and cofactors are determined. Results show that the minimum GSH requirement increases and that the ratio of GSH/GSSG decreases with increasing hydrogen peroxide concentration. In addition, at high concentrations of hydrogen peroxide it is shown that cytochrome C undergoes dysfunction leading to a decrease in useful oxygen consumption and ATP synthesis from the electron transport chain and an overall reduction in the energy charge for the liver cells.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"31 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crosstalking with Dendritic Cells: A Path to Engineer Advanced T Cell Immunotherapy.","authors":"Sogand Schafer, Kaige Chen, Leyuan Ma","doi":"10.3389/fsysb.2024.1372995","DOIUrl":"10.3389/fsysb.2024.1372995","url":null,"abstract":"<p><p>Crosstalk between dendritic cells (DCs) and T cells plays a crucial role in modulating immune responses in natural and pathological conditions. DC-T cell crosstalk is achieved through contact-dependent (i.e., immunological synapse) and contact-independent mechanisms (i.e., cytokines). Activated DCs upregulate co-stimulatory signals and secrete proinflammatory cytokines to orchestrate T cell activation and differentiation. Conversely, activated T helper cells \"license\" DCs towards maturation, while regulatory T cells (Tregs) silence DCs to elicit tolerogenic immunity. Strategies to efficiently modulate the DC-T cell crosstalk can be harnessed to promote immune activation for cancer immunotherapy or immune tolerance for the treatment of autoimmune diseases. Here, we review the natural crosstalk mechanisms between DC and T cells. We highlight bioengineering approaches to modulate DC-T cell crosstalk, including conventional vaccines, synthetic vaccines, and DC-mimics, and key seminal studies leveraging these approaches to steer immune response for the treatment of cancer and autoimmune diseases.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11192543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141443864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Integrative systems biology and big data for agricultural improvement and understanding","authors":"Liliana Fadul-Pacheco, Antony T. Vincent, Eric R. Paquet","doi":"10.3389/fsysb.2023.1347323","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1347323","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"25 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139168561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An enhanced transcription factor repressilator that buffers stochasticity and entrains to an erratic external circadian signal.","authors":"Steven A Frank","doi":"10.3389/fsysb.2023.1276734","DOIUrl":"10.3389/fsysb.2023.1276734","url":null,"abstract":"<p><p>How do cellular regulatory networks solve the challenges of life? This article presents computer software to study that question, focusing on how transcription factor networks transform internal and external inputs into cellular response outputs. The example challenge concerns maintaining a circadian rhythm of molecular concentrations. The system must buffer intrinsic stochastic fluctuations in molecular concentrations and entrain to an external circadian signal that appears and disappears randomly. The software optimizes a stochastic differential equation of transcription factor protein dynamics and the associated mRNAs that produce those transcription factors. The cellular network takes as inputs the concentrations of the transcription factors and produces as outputs the transcription rates of the mRNAs that make the transcription factors. An artificial neural network encodes the cellular input-output function, allowing efficient search for solutions to the complex stochastic challenge. Several good solutions are discovered, measured by the probability distribution for the tracking deviation between the stochastic cellular circadian trajectory and the deterministic external circadian pattern. The solutions differ significantly from each other, showing that overparameterized cellular networks may solve a given challenge in a variety of ways. The computation method provides a major advance in its ability to find transcription factor network dynamics that can solve environmental challenges. The article concludes by drawing an analogy between overparameterized cellular networks and the dense and deeply connected overparameterized artificial neural networks that have succeeded so well in deep learning. Understanding how overparameterized networks solve challenges may provide insight into the evolutionary design of cellular regulation.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"3 ","pages":"1276734"},"PeriodicalIF":2.3,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The development of an ingestible biosensor for the characterization of gut metabolites related to major depressive disorder: hypothesis and theory","authors":"Amanda Densil, Mya Elisabeth George, Hala Mahdi, Andrew Chami, Alyssa Mark, Chantal Luo, Yifan Wang, Aribah Ali, Pengpeng Tang, Audrey Yihui Dong, Sin Yu Pao, Rubani Singh Suri, Isabella Valentini, Lila Al-Arabi, Fanxiao Liu, Alesha Singh, Linda Wu, Helen Peng, Anjana Sudharshan, Zoha Naqvi, Jayda Hewitt, Catherine Andary, Vincent Leung, Paul Forsythe, Jianping Xu","doi":"10.3389/fsysb.2023.1274184","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1274184","url":null,"abstract":"The diagnostic process for psychiatric conditions is guided by the Diagnostic and Statistical Manual of Mental Disorders (DSM) in North America. Revisions of the DSM over the years have led to lowered diagnostic thresholds across the board, incurring increased rates of both misdiagnosis and over-diagnosis. Coupled with stigma, this ambiguity and lack of consistency exacerbates the challenges that clinicians and scientists face in the clinical assessment and research of mood disorders such as Major Depressive Disorder (MDD). While current efforts to characterize MDD have largely focused on qualitative approaches, the broad variations in physiological traits, such as those found in the gut, suggest the immense potential of using biomarkers to provide a quantitative and objective assessment. Here, we propose the development of a probiotic Escherichia coli (E. coli) multi-input ingestible biosensor for the characterization of key gut metabolites implicated in MDD. DNA writing with CRISPR based editors allows for the molecular recording of signals while riboflavin detection acts as a means to establish temporal and spatial specificity for the large intestine. We test the feasibility of this approach through kinetic modeling of the system which demonstrates targeted sensing and robust recording of metabolites within the large intestine in a time- and dose- dependent manner. Additionally, a post-hoc normalization model successfully controlled for confounding factors such as individual variation in riboflavin concentrations, producing a linear relationship between actual and predicted metabolite concentrations. We also highlight indole, butyrate, tetrahydrofolate, hydrogen peroxide, and tetrathionate as key gut metabolites that have the potential to direct our proposed biosensor specifically for MDD. Ultimately, our proposed biosensor has the potential to allow for a greater understanding of disease pathophysiology, assessment, and treatment response for many mood disorders.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"96 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization","authors":"Å. Flobak, John Zobolas, Miguel Vazquez, T. S. Steigedal, L. Thommesen, Asle Grislingås, B. Niederdorfer, Evelina Folkesson, Martin Kuiper","doi":"10.3389/fsysb.2023.1252961","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1252961","url":null,"abstract":"Treatment with combinations of drugs carries great promise for personalized therapy for a variety of diseases. We have previously shown that synergistic combinations of cancer signaling inhibitors can be identified based on a logical framework, by manual model definition. We now demonstrate how automated adjustments of model topology and logic equations both can greatly reduce the workload traditionally associated with logical model optimization. Our methodology allows the exploration of larger model ensembles that all obey a set of observations, while being less restrained for parts of the model where parameterization is not guided by biological data. We benchmark the synergy prediction performance of our logical models in a dataset of 153 targeted drug combinations. We show that well-performing manual models faithfully represent measured biomarker data and that their performance can be outmatched by automated parameterization using a genetic algorithm. Whereas the predictive performance of a curated model is strongly affected by simulated curation errors, data-guided deletion of a small subset of regulatory model edges can significantly improve prediction quality. With correct topology we find evidence of some tolerance to simulated errors in the biomarker calibration data, yet performance decreases with reduced data quality. Moreover, we show that predictive logical models are valuable for proposing mechanisms underpinning observed synergies. With our framework we predict the synergy of joint inhibition of PI3K and TAK1, and further substantiate this prediction with observations in cancer cell cultures and in xenograft experiments.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"172 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Virtual patients and digital twins in the systems analysis of drug discovery and development","authors":"Chen Zhao, Hua He, Huilin Ma","doi":"10.3389/fsysb.2023.1293076","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1293076","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139334720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}