Pub Date : 2024-04-03DOI: 10.3389/fsysb.2024.1270071
Quim Martí-Baena, Andreu Pascuet-Fontanet, Tomas Berjaga-Buisan, Miriam Caravaca-Rodríguez, Jaume Puig-Costa-Jussà, A. Sanchez-Mejias, Dimitrije Ivančić, Sira Mogas-Díez, Marc Güell, Javier Macia
Although blood sampling and medical imaging are well-established techniques in clinical diagnostics, they often require long post-processing procedures. Fast and simple quantification of signaling molecules can enable efficient health monitoring and improve diagnoses. Thyroid hormones (THs) treatment relies on trial-and-error dose adjustments, and requires constant tracking via blood tests. Thus, a fast and reliable method that can constantly track THs levels could substantially improve patient quality of life by reducing their visits to doctors. Synthetic biosensors have shown to be inexpensive and easy tools for sensing molecules, with their use in healthcare increasing over time. This study describes the construction of an engineered THs bacterial biosensor, consisting of a split-intein-based TH receptor ligand binding domain (LBD) biosensor that reconstitutes green fluorescence protein (GFP) after binding to TH. This biosensor could quantitatively measure THs concentrations by evaluating fluorescence intensity. In vitro sensing using Escherichia coli produced GFP over a wide dynamic range. The biosensor was further optimized by adding a double LBD, which enhanced its dynamic range until it reached healthy physiological conditions. Moreover, a mathematical model was developed to assess the dynamic properties of the biosensor and to provide a basis for the characterization of other intein-mediated biosensors. This type of biosensor can be used as the basis for novel treatments of thyroid diseases and can be adapted to measure the concentrations of other hormones, giving rise to a series of mathematically characterized modular biosensors.
{"title":"Intein-mediated thyroid hormone biosensors: towards controlled delivery of hormone therapy","authors":"Quim Martí-Baena, Andreu Pascuet-Fontanet, Tomas Berjaga-Buisan, Miriam Caravaca-Rodríguez, Jaume Puig-Costa-Jussà, A. Sanchez-Mejias, Dimitrije Ivančić, Sira Mogas-Díez, Marc Güell, Javier Macia","doi":"10.3389/fsysb.2024.1270071","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1270071","url":null,"abstract":"Although blood sampling and medical imaging are well-established techniques in clinical diagnostics, they often require long post-processing procedures. Fast and simple quantification of signaling molecules can enable efficient health monitoring and improve diagnoses. Thyroid hormones (THs) treatment relies on trial-and-error dose adjustments, and requires constant tracking via blood tests. Thus, a fast and reliable method that can constantly track THs levels could substantially improve patient quality of life by reducing their visits to doctors. Synthetic biosensors have shown to be inexpensive and easy tools for sensing molecules, with their use in healthcare increasing over time. This study describes the construction of an engineered THs bacterial biosensor, consisting of a split-intein-based TH receptor ligand binding domain (LBD) biosensor that reconstitutes green fluorescence protein (GFP) after binding to TH. This biosensor could quantitatively measure THs concentrations by evaluating fluorescence intensity. In vitro sensing using Escherichia coli produced GFP over a wide dynamic range. The biosensor was further optimized by adding a double LBD, which enhanced its dynamic range until it reached healthy physiological conditions. Moreover, a mathematical model was developed to assess the dynamic properties of the biosensor and to provide a basis for the characterization of other intein-mediated biosensors. This type of biosensor can be used as the basis for novel treatments of thyroid diseases and can be adapted to measure the concentrations of other hormones, giving rise to a series of mathematically characterized modular biosensors.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"20 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140747759","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}
Pub Date : 2024-03-27DOI: 10.3389/fsysb.2024.1367562
Rayna M Nolen, Lene H. Petersen, Karl Kaiser, Antonietta Quigg, D. Hala
Perfluorooctane sulfonate (PFOS) is a ubiquitous pollutant in global aquatic ecosystems with increasing concern for its toxicity to aquatic wildlife through inadvertent exposures. To assess the likely adverse effects of PFOS exposure on aquatic wildlife inhabiting polluted ecosystems, there is a need to identify biomarkers of its exposure and toxicity. We used an integrated systems toxicological framework to identify physiologically relevant biomarkers of PFOS toxicity in fish. An in silico stoichiometric metabolism model of zebrafish (Danio rerio) was used to integrate available (published by other authors) metabolomics and transcriptomics datasets from in vivo toxicological studies with 5 days post fertilized embryo-larval life stage of zebrafish. The experimentally derived omics datasets were used as constraints to parameterize an in silico mathematical model of zebrafish metabolism. In silico simulations using flux balance analysis (FBA) and its extensions showed prominent effects of PFOS exposure on the carnitine shuttle and fatty acid oxidation. Further analysis of metabolites comprising the impacted metabolic reactions indicated carnitine to be the most highly represented cofactor metabolite. Flux simulations also showed a near dose-responsive increase in the pools for fatty acids and acyl-CoAs under PFOS exposure. Taken together, our integrative in silico results showed dyslipidemia effects under PFOS exposure and uniquely identified carnitine as a candidate metabolite biomarker. The verification of this prediction was sought in a subsequent in vivo environmental monitoring study by the authors which showed carnitine to be a modal biomarker of PFOS exposure in wild-caught fish and marine mammals sampled from the northern Gulf of Mexico. Therefore, we highlight the efficacy of FBA to study the properties of large-scale metabolic networks and to identify biomarkers of pollutant exposure in aquatic wildlife.
{"title":"In silico biomarker analysis of the adverse effects of perfluorooctane sulfonate (PFOS) exposure on the metabolic physiology of embryo-larval zebrafish","authors":"Rayna M Nolen, Lene H. Petersen, Karl Kaiser, Antonietta Quigg, D. Hala","doi":"10.3389/fsysb.2024.1367562","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1367562","url":null,"abstract":"Perfluorooctane sulfonate (PFOS) is a ubiquitous pollutant in global aquatic ecosystems with increasing concern for its toxicity to aquatic wildlife through inadvertent exposures. To assess the likely adverse effects of PFOS exposure on aquatic wildlife inhabiting polluted ecosystems, there is a need to identify biomarkers of its exposure and toxicity. We used an integrated systems toxicological framework to identify physiologically relevant biomarkers of PFOS toxicity in fish. An in silico stoichiometric metabolism model of zebrafish (Danio rerio) was used to integrate available (published by other authors) metabolomics and transcriptomics datasets from in vivo toxicological studies with 5 days post fertilized embryo-larval life stage of zebrafish. The experimentally derived omics datasets were used as constraints to parameterize an in silico mathematical model of zebrafish metabolism. In silico simulations using flux balance analysis (FBA) and its extensions showed prominent effects of PFOS exposure on the carnitine shuttle and fatty acid oxidation. Further analysis of metabolites comprising the impacted metabolic reactions indicated carnitine to be the most highly represented cofactor metabolite. Flux simulations also showed a near dose-responsive increase in the pools for fatty acids and acyl-CoAs under PFOS exposure. Taken together, our integrative in silico results showed dyslipidemia effects under PFOS exposure and uniquely identified carnitine as a candidate metabolite biomarker. The verification of this prediction was sought in a subsequent in vivo environmental monitoring study by the authors which showed carnitine to be a modal biomarker of PFOS exposure in wild-caught fish and marine mammals sampled from the northern Gulf of Mexico. Therefore, we highlight the efficacy of FBA to study the properties of large-scale metabolic networks and to identify biomarkers of pollutant exposure in aquatic wildlife.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"71 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140376211","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}
Pub Date : 2024-03-25DOI: 10.3389/fsysb.2024.1256398
Lingjun Lu, Yannuo Li, Rene Schloss, Ioannis P. Androulakis
The central circadian pacemaker in the suprachiasmatic nuclei (SCN) aligns the phase and period of autonomous molecular oscillators in peripheral cells to daily light/dark cycles via physiological, neuronal, hormonal, and metabolic signals. Among different entrainment factors, temperature entrainment has been proposed as an essential alternative for inducing and sustaining circadian rhythms in vitro. While the synchronization mechanisms for hormones such as glucocorticoids have been widely studied, little is known about the crucial role of body temperature as a systemic cue. In this work, we develop a semi-mechanistic mathematical model describing the entrainment of peripheral clocks to temperature rhythms. The model incorporates a temperature sensing-transduction cascade involving a heat shock transcription factor-1 (HSF1) and heat shock response (HSR) pathway to simulate the entrainment of clock genes. The model is used to investigate the mammalian temperature entrainment and synchronization of cells subject to temperature oscillations of different amplitudes and magnitudes and examine the effects of transitioning between temperature schedules. Our computational analyses of the system’s dynamic responses reveal that 1) individual cells gradually synchronize to the rhythmic temperature signal by resetting their intrinsic phases to achieve coherent dynamics while oscillations are abolished in the absence of temperature rhythmicity; 2) alterations in the amplitude and period of temperature rhythms impact the peripheral synchronization behavior; 3) personalized synchronization strategies allow for differential, adaptive responses to temperature rhythms. Our results demonstrate that temperature can be a potent entrainer of circadian rhythms. Therefore, in vitro systems subjected to temperature modulation can serve as a potential tool for studying the adjustment or disruption of circadian rhythms.
{"title":"Mathematical modeling of temperature-induced circadian rhythms","authors":"Lingjun Lu, Yannuo Li, Rene Schloss, Ioannis P. Androulakis","doi":"10.3389/fsysb.2024.1256398","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1256398","url":null,"abstract":"The central circadian pacemaker in the suprachiasmatic nuclei (SCN) aligns the phase and period of autonomous molecular oscillators in peripheral cells to daily light/dark cycles via physiological, neuronal, hormonal, and metabolic signals. Among different entrainment factors, temperature entrainment has been proposed as an essential alternative for inducing and sustaining circadian rhythms in vitro. While the synchronization mechanisms for hormones such as glucocorticoids have been widely studied, little is known about the crucial role of body temperature as a systemic cue. In this work, we develop a semi-mechanistic mathematical model describing the entrainment of peripheral clocks to temperature rhythms. The model incorporates a temperature sensing-transduction cascade involving a heat shock transcription factor-1 (HSF1) and heat shock response (HSR) pathway to simulate the entrainment of clock genes. The model is used to investigate the mammalian temperature entrainment and synchronization of cells subject to temperature oscillations of different amplitudes and magnitudes and examine the effects of transitioning between temperature schedules. Our computational analyses of the system’s dynamic responses reveal that 1) individual cells gradually synchronize to the rhythmic temperature signal by resetting their intrinsic phases to achieve coherent dynamics while oscillations are abolished in the absence of temperature rhythmicity; 2) alterations in the amplitude and period of temperature rhythms impact the peripheral synchronization behavior; 3) personalized synchronization strategies allow for differential, adaptive responses to temperature rhythms. Our results demonstrate that temperature can be a potent entrainer of circadian rhythms. Therefore, in vitro systems subjected to temperature modulation can serve as a potential tool for studying the adjustment or disruption of circadian rhythms.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" 571","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140382927","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}
Pub Date : 2024-03-21DOI: 10.3389/fsysb.2024.1355595
Phi Le, Xingyue Gong, Leah Ung, Hai Yang, Bridget P Keenan, Li Zhang, Tao He
Exploring features associated with the clinical outcome of interest is a rapidly advancing area of research. However, with contemporary sequencing technologies capable of identifying over thousands of genes per sample, there is a challenge in constructing efficient prediction models that balance accuracy and resource utilization. To address this challenge, researchers have developed feature selection methods to enhance performance, reduce overfitting, and ensure resource efficiency. However, applying feature selection models to survival analysis, particularly in clinical datasets characterized by substantial censoring and limited sample sizes, introduces unique challenges. We propose a robust ensemble feature selection approach integrated with group Lasso to identify compelling features and evaluate its performance in predicting survival outcomes. Our approach consistently outperforms established models across various criteria through extensive simulations, demonstrating low false discovery rates, high sensitivity, and high stability. Furthermore, we applied the approach to a colorectal cancer dataset from The Cancer Genome Atlas, showcasing its effectiveness by generating a composite score based on the selected genes to correctly distinguish different subtypes of the patients. In summary, our proposed approach excels in selecting impactful features from high-dimensional data, yielding better outcomes compared to contemporary state-of-the-art models.
{"title":"A robust ensemble feature selection approach to prioritize genes associated with survival outcome in high-dimensional gene expression data","authors":"Phi Le, Xingyue Gong, Leah Ung, Hai Yang, Bridget P Keenan, Li Zhang, Tao He","doi":"10.3389/fsysb.2024.1355595","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1355595","url":null,"abstract":"Exploring features associated with the clinical outcome of interest is a rapidly advancing area of research. However, with contemporary sequencing technologies capable of identifying over thousands of genes per sample, there is a challenge in constructing efficient prediction models that balance accuracy and resource utilization. To address this challenge, researchers have developed feature selection methods to enhance performance, reduce overfitting, and ensure resource efficiency. However, applying feature selection models to survival analysis, particularly in clinical datasets characterized by substantial censoring and limited sample sizes, introduces unique challenges. We propose a robust ensemble feature selection approach integrated with group Lasso to identify compelling features and evaluate its performance in predicting survival outcomes. Our approach consistently outperforms established models across various criteria through extensive simulations, demonstrating low false discovery rates, high sensitivity, and high stability. Furthermore, we applied the approach to a colorectal cancer dataset from The Cancer Genome Atlas, showcasing its effectiveness by generating a composite score based on the selected genes to correctly distinguish different subtypes of the patients. In summary, our proposed approach excels in selecting impactful features from high-dimensional data, yielding better outcomes compared to contemporary state-of-the-art models.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" 88","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221450","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}
Pub Date : 2024-03-15DOI: 10.3389/fsysb.2024.1343006
Clara Octors, Ryan E. Yoast, Scott M. Emrich, Mohamed Trebak, James Sneyd
The concentration of free cytosolic Ca2+ is a critical second messenger in almost every cell type, with the signal often being carried by the period of oscillations, or spikes, in the cytosolic Ca2+ concentration. We have previously studied how Ca2+ influx across the plasma membrane affects the period and shape of Ca2+ oscillations in HEK293 cells. However, our theoretical work was unable to explain how the shape of Ca2+ oscillations could change qualitatively, from thin spikes to broad oscillations, during the course of a single time series. Such qualitative changes in oscillation shape are a common feature of HEK293 cells in which STIM1 and 2 have been knocked out. Here, we present an extended version of our earlier model that suggests that such time-dependent qualitative changes in oscillation shape might be the result of balanced positive and negative feedback from Ca2+ to the production and degradation of inositol trisphosphate.
{"title":"Calcium oscillations in HEK293 cells lacking SOCE suggest the existence of a balanced regulation of IP3 production and degradation","authors":"Clara Octors, Ryan E. Yoast, Scott M. Emrich, Mohamed Trebak, James Sneyd","doi":"10.3389/fsysb.2024.1343006","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1343006","url":null,"abstract":"The concentration of free cytosolic Ca2+ is a critical second messenger in almost every cell type, with the signal often being carried by the period of oscillations, or spikes, in the cytosolic Ca2+ concentration. We have previously studied how Ca2+ influx across the plasma membrane affects the period and shape of Ca2+ oscillations in HEK293 cells. However, our theoretical work was unable to explain how the shape of Ca2+ oscillations could change qualitatively, from thin spikes to broad oscillations, during the course of a single time series. Such qualitative changes in oscillation shape are a common feature of HEK293 cells in which STIM1 and 2 have been knocked out. Here, we present an extended version of our earlier model that suggests that such time-dependent qualitative changes in oscillation shape might be the result of balanced positive and negative feedback from Ca2+ to the production and degradation of inositol trisphosphate.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"8 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140240991","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}
Pub Date : 2024-03-13DOI: 10.3389/fsysb.2024.1335885
Geneviève Dupont, Didier Gonze
Physiological processes are governed by intricate networks of transcriptional and post-translational regulations. Inter-cellular interactions and signaling pathways further modulate the response of the cells to environmental conditions. Understanding the dynamics of these systems in healthy conditions and their alterations in pathologic situations requires a “systems” approach. Computational models allow to formalize and to simulate the dynamics of complex networks. Here, we briefly illustrate, through a few selected examples, how modeling helps to answer non-trivial questions regarding rhythmic phenomena, signaling and decision-making in cellular systems. These examples relate to cell differentiation, metabolic regulation, chronopharmacology and calcium dynamics.
{"title":"Computational insights in cell physiology","authors":"Geneviève Dupont, Didier Gonze","doi":"10.3389/fsysb.2024.1335885","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1335885","url":null,"abstract":"Physiological processes are governed by intricate networks of transcriptional and post-translational regulations. Inter-cellular interactions and signaling pathways further modulate the response of the cells to environmental conditions. Understanding the dynamics of these systems in healthy conditions and their alterations in pathologic situations requires a “systems” approach. Computational models allow to formalize and to simulate the dynamics of complex networks. Here, we briefly illustrate, through a few selected examples, how modeling helps to answer non-trivial questions regarding rhythmic phenomena, signaling and decision-making in cellular systems. These examples relate to cell differentiation, metabolic regulation, chronopharmacology and calcium dynamics.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"35 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247824","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}
Pub Date : 2024-03-06DOI: 10.3389/fsysb.2024.1385458
Jennifer C. Lovejoy
{"title":"Expanding our thought horizons in systems biology and medicine","authors":"Jennifer C. Lovejoy","doi":"10.3389/fsysb.2024.1385458","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1385458","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261892","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}
Pub Date : 2024-03-01DOI: 10.3389/fsysb.2024.1346076
Jonatan Blais, Julie Jeukens
One of the main challenges in cancer treatment is the selection of treatment resistant clones which leads to the emergence of resistance to previously efficacious therapies. Identifying vulnerabilities in the form of cellular trade-offs constraining the phenotypic possibility space could allow to avoid the emergence of resistance by simultaneously targeting cellular processes that are involved in different alternative phenotypic strategies linked by trade-offs. The Pareto optimality theory has been proposed as a framework allowing to identify such trade-offs in biological data from its prediction that it would lead to the presence of specific geometrical patterns (polytopes) in, e.g., gene expression space, with vertices representing specialized phenotypes. We tested this approach in diffuse large B-cell lymphoma (DLCBL) transcriptomic data. As predicted, there was highly statistically significant evidence for the data forming a tetrahedron in gene expression space, defining four specialized phenotypes (archetypes). These archetypes were significantly enriched in certain biological functions, and contained genes that formed a pattern of shared and unique elements among archetypes, as expected if trade-offs between essential functions underlie the observed structure. The results can be interpreted as reflecting trade-offs between aerobic energy production and protein synthesis, and between immunotolerant and immune escape strategies. Targeting genes on both sides of these trade-offs simultaneously represent potential promising avenues for therapeutic applications.
癌症治疗面临的主要挑战之一是耐药克隆的选择，这导致对以前有效的疗法产生抗药性。以细胞权衡的形式识别限制表型可能性空间的弱点，可以通过同时针对参与由权衡联系在一起的不同替代表型策略的细胞过程来避免抗药性的出现。帕累托最优化理论被认为是一种框架，它可以识别生物数据中的这种权衡，因为它预测在基因表达空间等方面会出现特定的几何模式（多面体），其顶点代表专门的表型。我们在弥漫大 B 细胞淋巴瘤（DLCBL）转录组数据中测试了这种方法。正如预测的那样，数据在基因表达空间中形成了一个四面体，定义了四种特化表型（原型），这在统计学上具有非常显著的证据。这些原型明显富集了某些生物功能，并包含了在原型之间形成共享和独特元素模式的基因，如果基本功能之间的权衡是所观察到的结构的基础，那么就会出现这种情况。这些结果可以解释为反映了有氧能量生产和蛋白质合成之间的权衡，以及免疫耐受和免疫逃逸策略之间的权衡。同时以这些权衡两边的基因为靶标，是治疗应用的潜在可行途径。
{"title":"Pareto task inference analysis reveals cellular trade-offs in diffuse large B-Cell lymphoma transcriptomic data","authors":"Jonatan Blais, Julie Jeukens","doi":"10.3389/fsysb.2024.1346076","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1346076","url":null,"abstract":"One of the main challenges in cancer treatment is the selection of treatment resistant clones which leads to the emergence of resistance to previously efficacious therapies. Identifying vulnerabilities in the form of cellular trade-offs constraining the phenotypic possibility space could allow to avoid the emergence of resistance by simultaneously targeting cellular processes that are involved in different alternative phenotypic strategies linked by trade-offs. The Pareto optimality theory has been proposed as a framework allowing to identify such trade-offs in biological data from its prediction that it would lead to the presence of specific geometrical patterns (polytopes) in, e.g., gene expression space, with vertices representing specialized phenotypes. We tested this approach in diffuse large B-cell lymphoma (DLCBL) transcriptomic data. As predicted, there was highly statistically significant evidence for the data forming a tetrahedron in gene expression space, defining four specialized phenotypes (archetypes). These archetypes were significantly enriched in certain biological functions, and contained genes that formed a pattern of shared and unique elements among archetypes, as expected if trade-offs between essential functions underlie the observed structure. The results can be interpreted as reflecting trade-offs between aerobic energy production and protein synthesis, and between immunotolerant and immune escape strategies. Targeting genes on both sides of these trade-offs simultaneously represent potential promising avenues for therapeutic applications.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"25 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086210","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}
Pub Date : 2024-03-01DOI: 10.3389/fsysb.2024.1291293
Kayla Chun, Eric VanArsdale, Elebeoba May, Gregory F. Payne, William E. Bentley
Introduction: Molecular communication is the transfer of information encoded by molecular structure and activity. We examine molecular communication within bacterial consortia as cells with diverse biosynthetic capabilities can be assembled for enhanced function. Their coordination, both in terms of engineered genetic circuits within individual cells as well as their population-scale functions, is needed to ensure robust performance. We have suggested that “electrogenetics,” the use of electronics to activate specific genetic circuits, is a means by which electronic devices can mediate molecular communication, ultimately enabling programmable control.Methods: Here, we have developed a graphical network model for dynamically assessing electronic and molecular signal propagation schemes wherein nodes represent individual cells, and their edges represent communication channels by which signaling molecules are transferred. We utilize graph properties such as edge dynamics and graph topology to interrogate the signaling dynamics of specific engineered bacterial consortia.Results: We were able to recapitulate previous experimental systems with our model. In addition, we found that networks with more distinct subpopulations (high network modularity) propagated signals more slowly than randomized networks, while strategic arrangement of subpopulations with respect to the inducer source (an electrode) can increase signal output and outperform otherwise homogeneous networks.Discussion: We developed this model to better understand our previous experimental results, but also to enable future designs wherein subpopulation composition, genetic circuits, and spatial configurations can be varied to tune performance. We suggest that this work may provide insight into the signaling which occurs in synthetically assembled systems as well as native microbial communities.
{"title":"Assessing electrogenetic activation via a network model of biological signal propagation","authors":"Kayla Chun, Eric VanArsdale, Elebeoba May, Gregory F. Payne, William E. Bentley","doi":"10.3389/fsysb.2024.1291293","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1291293","url":null,"abstract":"Introduction: Molecular communication is the transfer of information encoded by molecular structure and activity. We examine molecular communication within bacterial consortia as cells with diverse biosynthetic capabilities can be assembled for enhanced function. Their coordination, both in terms of engineered genetic circuits within individual cells as well as their population-scale functions, is needed to ensure robust performance. We have suggested that “electrogenetics,” the use of electronics to activate specific genetic circuits, is a means by which electronic devices can mediate molecular communication, ultimately enabling programmable control.Methods: Here, we have developed a graphical network model for dynamically assessing electronic and molecular signal propagation schemes wherein nodes represent individual cells, and their edges represent communication channels by which signaling molecules are transferred. We utilize graph properties such as edge dynamics and graph topology to interrogate the signaling dynamics of specific engineered bacterial consortia.Results: We were able to recapitulate previous experimental systems with our model. In addition, we found that networks with more distinct subpopulations (high network modularity) propagated signals more slowly than randomized networks, while strategic arrangement of subpopulations with respect to the inducer source (an electrode) can increase signal output and outperform otherwise homogeneous networks.Discussion: We developed this model to better understand our previous experimental results, but also to enable future designs wherein subpopulation composition, genetic circuits, and spatial configurations can be varied to tune performance. We suggest that this work may provide insight into the signaling which occurs in synthetically assembled systems as well as native microbial communities.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"120 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089081","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}
Pub Date : 2024-02-27DOI: 10.3389/fsysb.2024.1363884
Rahuman S. Malik Sheriff, Hiroki Asari, Henning Hermjakob, Wolfgang Huber, Thomas Quail, Silvia D. M. Santos, Amber M. Smith, Virginie Uhlmann
Mathematical modeling is a pivotal tool for deciphering the complexities of biological systems and their control mechanisms, providing substantial benefits for industrial applications and answering relevant biological questions. BioModels’ Model of the Year 2023 competition was established to recognize and highlight exciting modeling-based research in the life sciences, particularly by non-independent early-career researchers. It further aims to endorse reproducibility and FAIR principles of model sharing among these researchers. We here delineate the competition’s criteria for participation and selection, introduce the award recipients, and provide an overview of their contributions. Their models provide crucial insights into cell division regulation, protein stability, and cell fate determination, illustrating the role of mathematical modeling in advancing biological research.
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