Pub Date : 2022-06-24DOI: 10.3389/fsysb.2022.899980
Marilena D. A. Pantziri, M. Klapa
Genome-scale metabolic network models are of great importance in systems biology research, as they are used in metabolic activity dynamics studies and provide the metabolic level representation in multi-omic investigations. Especially for human, accurate metabolic network reconstruction is important in biomedical research and drug discovery. Today, there exist many instances of the human metabolic network as a whole and in its tissue-specific versions. Some are improved updates of models reconstructed from the same research team, while others are combinations of models from various teams, in an effort to include all available information from genome annotation and omic datasets. A major challenge regarding the human stoichiometric models in particular is the standardization of the reconstruction methods, representation formats and model repositories. Stoichiometric model standardization will enable the educated selection of the model that better fits the goals of a study, the direct comparison of results from various flux analysis studies and the identification of model sections that require reconsideration and updating with respect to the annotation of the human genome and proteome. Standardized human metabolic models aligned to the human genome will be a very useful tool in multi-omic studies, enabling the direct and consistent integration of the metabolic with the gene regulation and protein interaction networks. In this work, we provide a thorough overview of the current collection of human metabolic stoichiometric models, describe the current issues regarding their direct comparison and alignment in the context of the various model repositories, exposing the standardization needs, and propose potential solutions.
{"title":"Standardization of Human Metabolic Stoichiometric Models: Challenges and Directions","authors":"Marilena D. A. Pantziri, M. Klapa","doi":"10.3389/fsysb.2022.899980","DOIUrl":"https://doi.org/10.3389/fsysb.2022.899980","url":null,"abstract":"Genome-scale metabolic network models are of great importance in systems biology research, as they are used in metabolic activity dynamics studies and provide the metabolic level representation in multi-omic investigations. Especially for human, accurate metabolic network reconstruction is important in biomedical research and drug discovery. Today, there exist many instances of the human metabolic network as a whole and in its tissue-specific versions. Some are improved updates of models reconstructed from the same research team, while others are combinations of models from various teams, in an effort to include all available information from genome annotation and omic datasets. A major challenge regarding the human stoichiometric models in particular is the standardization of the reconstruction methods, representation formats and model repositories. Stoichiometric model standardization will enable the educated selection of the model that better fits the goals of a study, the direct comparison of results from various flux analysis studies and the identification of model sections that require reconsideration and updating with respect to the annotation of the human genome and proteome. Standardized human metabolic models aligned to the human genome will be a very useful tool in multi-omic studies, enabling the direct and consistent integration of the metabolic with the gene regulation and protein interaction networks. In this work, we provide a thorough overview of the current collection of human metabolic stoichiometric models, describe the current issues regarding their direct comparison and alignment in the context of the various model repositories, exposing the standardization needs, and propose potential solutions.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48710157","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 : 2022-06-23DOI: 10.3389/fsysb.2022.907957
A. Espina, Eduardo Mendoza, Angelyn R. Lao
Alzheimer’s Disease (AD) is a neurodegenerative disorder that causes drastic structural brain atrophy and affects multiple brain functions. Cerebral glucose hypometabolism, associated with senile plaque density formation, is a pre-symptomatic feature of AD and significantly contributes to AD’s future development and progression. As cerebral glucose metabolism gradually slows down due to advanced aging, a healthy adult brain experiences an 8% decrease in cerebral glucose metabolic rate (CGMR) compared to a decline of 20%–40% CGMR in AD patients. To bridge the increasing brain energy gap caused by glucose hypometabolism, ketone bodies (KBs) are used as a supplementary source of energy as cerebral KB metabolism remains unaffected in AD patients. Ketogenic interventions such as Medium-Chain Triglyceride (MCT)-induced treatment can help augment the brain’s energy source availability and might delay further cognitive decline. With this, we constructed a mathematical model on cerebral glucose and KB metabolism to illustrate the drastic effects of glucose hypometabolism on healthy aging individuals, Mild Cognitive Impairment (MCI) subjects, and AD patients. Through the generated simulations, we have shown that KB concentration levels rise during prolonged starvation, and in consideration of glucose hypometabolism, MCT-induced intervention increases the concentration levels of acetyl-CoA (AC) in MCI/AD patients. Furthermore, MCT-induced supplement helps increase the AC concentration levels in healthy adults under normal conditions.
{"title":"Modelling the Effects of Medium-Chain Triglycerides on Cerebral Ketone Body Metabolism","authors":"A. Espina, Eduardo Mendoza, Angelyn R. Lao","doi":"10.3389/fsysb.2022.907957","DOIUrl":"https://doi.org/10.3389/fsysb.2022.907957","url":null,"abstract":"Alzheimer’s Disease (AD) is a neurodegenerative disorder that causes drastic structural brain atrophy and affects multiple brain functions. Cerebral glucose hypometabolism, associated with senile plaque density formation, is a pre-symptomatic feature of AD and significantly contributes to AD’s future development and progression. As cerebral glucose metabolism gradually slows down due to advanced aging, a healthy adult brain experiences an 8% decrease in cerebral glucose metabolic rate (CGMR) compared to a decline of 20%–40% CGMR in AD patients. To bridge the increasing brain energy gap caused by glucose hypometabolism, ketone bodies (KBs) are used as a supplementary source of energy as cerebral KB metabolism remains unaffected in AD patients. Ketogenic interventions such as Medium-Chain Triglyceride (MCT)-induced treatment can help augment the brain’s energy source availability and might delay further cognitive decline. With this, we constructed a mathematical model on cerebral glucose and KB metabolism to illustrate the drastic effects of glucose hypometabolism on healthy aging individuals, Mild Cognitive Impairment (MCI) subjects, and AD patients. Through the generated simulations, we have shown that KB concentration levels rise during prolonged starvation, and in consideration of glucose hypometabolism, MCT-induced intervention increases the concentration levels of acetyl-CoA (AC) in MCI/AD patients. Furthermore, MCT-induced supplement helps increase the AC concentration levels in healthy adults under normal conditions.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46613355","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 : 2022-06-17DOI: 10.3389/fsysb.2022.899990
C. Spalding, Sandeep Shirgill, E. Taylor, A. Krachler, S. Jabbari
The bacterium Pseudomonas aeruginosa has been shown to undergo a morphological transition akin to L-forms under exposure to antibiotics, a process which may contribute to persistent infections. With the further consideration of antibiotic-resistance mechanisms, this transition renders the design of effective treatment strategies challenging. Through a mathematical model, we illustrate that additionally incorporating the complexities of the host immune response can render somewhat surprising predictions from the simulations. In particular, scenarios arise whereby the addition of a treatment strategy to directly target the L-forms results in a worsened infection, while in others this treatment could turn an antibiotic-resistant infection from persistent to treatable. The study highlights the importance of understanding the in vivo interplay between immune cells and pathogens for successful treatment design.
{"title":"Mathematical Modelling of Pseudomonas aeruginosa L-forms Reveals Complex Interplay Between Host Defence Mechanisms and Putative Treatments","authors":"C. Spalding, Sandeep Shirgill, E. Taylor, A. Krachler, S. Jabbari","doi":"10.3389/fsysb.2022.899990","DOIUrl":"https://doi.org/10.3389/fsysb.2022.899990","url":null,"abstract":"The bacterium Pseudomonas aeruginosa has been shown to undergo a morphological transition akin to L-forms under exposure to antibiotics, a process which may contribute to persistent infections. With the further consideration of antibiotic-resistance mechanisms, this transition renders the design of effective treatment strategies challenging. Through a mathematical model, we illustrate that additionally incorporating the complexities of the host immune response can render somewhat surprising predictions from the simulations. In particular, scenarios arise whereby the addition of a treatment strategy to directly target the L-forms results in a worsened infection, while in others this treatment could turn an antibiotic-resistant infection from persistent to treatable. The study highlights the importance of understanding the in vivo interplay between immune cells and pathogens for successful treatment design.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41786550","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 : 2022-06-02DOI: 10.3389/fsysb.2022.877601
G. Kang, Sedigheh Mirzaei, Hui Zhang, Liang Zhu, S. Rai, D. Srivastava
In the context of high-throughput data, the differences in continuous markers between two groups are usually assessed by ordering the p-values obtained from the two-sample pooled t-test or Wilcoxon–Mann–Whitney test and choosing a stringent cutoff such as 10–8 to control the family-wise error rate ( F W E R ) or false discovery rate ( F D R ) . All markers with p-values below the cutoff are declared to be significantly associated with the phenotype. This inherently assumes that the test procedure provides valid type I error estimates in extreme tails of the null distribution. The aforementioned tests assume homoscedasticity in the two groups, and the t-test further assumes underlying distributions to be normally distributed. Cao et al. (Biometrika, 2013, 100, 495–502) have shown that in the context of multiple hypotheses testing the approach based on F D R may not be valid under non-normality and/or heteroscedasticity. Therefore, having a test statistic that is robust to these violations is needed. In this study, we propose a robust analog of Behrens–Fisher statistic based on trimmed means, conduct an extensive simulation study to compare its performance with other competing approaches, and demonstrate its usefulness by applying it to DNA methylation data used by Teschendorff et al. (Genome Res., 2010, 20, 440–446). An R program to implement the proposed method is provided in the Supplementary Material.
在高通量数据的背景下，通常通过对从两个样本合并t检验或Wilcoxon–Mann–Whitney检验中获得的p值进行排序，并选择严格的截止值（如10–8）来控制家族错误率（F W E R）或错误发现率（F D R），来评估两组之间连续标记的差异。所有p值低于临界值的标记物都被宣布与表型显著相关。这固有地假设测试程序在零分布的极端尾部中提供有效的I型误差估计。上述检验假设两组中存在同方差，t检验进一步假设潜在分布为正态分布。Cao等人（Biometrika，2013100495-502）已经表明，在多个假设测试的背景下，基于F D R的方法在非正态性和/或异方差下可能无效。因此，需要有一个对这些违规行为具有鲁棒性的测试统计数据。在这项研究中，我们提出了一种基于修剪均值的Behrens–Fisher统计的稳健模拟，进行了广泛的模拟研究，以将其性能与其他竞争方法进行比较，并通过将其应用于Teschendorf等人使用的DNA甲基化数据来证明其有用性。（基因组研究，2010，20440-446）。补充材料中提供了一个实施拟议方法的R程序。
{"title":"Robust Behrens–Fisher Statistic Based on Trimmed Means and Its Usefulness in Analyzing High-Throughput Data","authors":"G. Kang, Sedigheh Mirzaei, Hui Zhang, Liang Zhu, S. Rai, D. Srivastava","doi":"10.3389/fsysb.2022.877601","DOIUrl":"https://doi.org/10.3389/fsysb.2022.877601","url":null,"abstract":"In the context of high-throughput data, the differences in continuous markers between two groups are usually assessed by ordering the p-values obtained from the two-sample pooled t-test or Wilcoxon–Mann–Whitney test and choosing a stringent cutoff such as 10–8 to control the family-wise error rate ( F W E R ) or false discovery rate ( F D R ) . All markers with p-values below the cutoff are declared to be significantly associated with the phenotype. This inherently assumes that the test procedure provides valid type I error estimates in extreme tails of the null distribution. The aforementioned tests assume homoscedasticity in the two groups, and the t-test further assumes underlying distributions to be normally distributed. Cao et al. (Biometrika, 2013, 100, 495–502) have shown that in the context of multiple hypotheses testing the approach based on F D R may not be valid under non-normality and/or heteroscedasticity. Therefore, having a test statistic that is robust to these violations is needed. In this study, we propose a robust analog of Behrens–Fisher statistic based on trimmed means, conduct an extensive simulation study to compare its performance with other competing approaches, and demonstrate its usefulness by applying it to DNA methylation data used by Teschendorff et al. (Genome Res., 2010, 20, 440–446). An R program to implement the proposed method is provided in the Supplementary Material.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46131412","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 : 2022-06-02DOI: 10.3389/fsysb.2022.959665
N. Sivakumar, C. Mura, S. Peirce
Agent-based modeling (ABM) is a well-established computational paradigm for simulating complex systems in terms of the interactions between individual entities that comprise the system’s population. Machine learning (ML) refers to computational approaches whereby algorithms use statistical methods to “learn” from data on their own, i.e., without imposing any a priori model/theory onto a system or its behavior. Biological systems—ranging from molecules, to cells, to entire organisms, to whole populations and even ecosystems—consist of vast numbers of discrete entities, governed by complex webs of interactions that span various spatiotemporal scales and exhibit nonlinearity, stochasticity, and variable degrees of coupling between entities. For these reasons, the macroscopic properties and collective dynamics of biological systems are generally difficult to accurately model or predict via continuum modeling techniques and mean-field formalisms. ABM takes a “bottom-up” approach that obviates common difficulties of other modeling approaches by enabling one to relatively easily create (or at least propose, for testing) a set of well-defined “rules” to be applied to the individual entities (agents) in a system. Quantitatively evaluating a system and propagating its state over a series of discrete time-steps effectively simulates the system, allowing various observables to be computed and the system’s properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, at least in an unbiased way, there is a uniquely synergistic opportunity to employ ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, running ABM calculations can generate a wealth of data, and ML can be applied in that context too—for example, to generate statistical measures that accurately and meaningfully describe the stochastic outputs of a system and its properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate plausible (realistic) datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision a variety of synergistic ABM⇄ML loops. After introducing some basic ideas about ABMs and ML, and their limitations, this Review describes examples of how ABM and ML have been integrated in diverse contexts, spanning spatial scales that include multicellular and tissue-scale biology to human population-level epidemiology. In so doing, we have used published studies as a guide to identify ML approaches that are well-suited to particular types of ABM applications, based on the scale of the biological system and the properties of the available data.
{"title":"Innovations in integrating machine learning and agent-based modeling of biomedical systems","authors":"N. Sivakumar, C. Mura, S. Peirce","doi":"10.3389/fsysb.2022.959665","DOIUrl":"https://doi.org/10.3389/fsysb.2022.959665","url":null,"abstract":"Agent-based modeling (ABM) is a well-established computational paradigm for simulating complex systems in terms of the interactions between individual entities that comprise the system’s population. Machine learning (ML) refers to computational approaches whereby algorithms use statistical methods to “learn” from data on their own, i.e., without imposing any a priori model/theory onto a system or its behavior. Biological systems—ranging from molecules, to cells, to entire organisms, to whole populations and even ecosystems—consist of vast numbers of discrete entities, governed by complex webs of interactions that span various spatiotemporal scales and exhibit nonlinearity, stochasticity, and variable degrees of coupling between entities. For these reasons, the macroscopic properties and collective dynamics of biological systems are generally difficult to accurately model or predict via continuum modeling techniques and mean-field formalisms. ABM takes a “bottom-up” approach that obviates common difficulties of other modeling approaches by enabling one to relatively easily create (or at least propose, for testing) a set of well-defined “rules” to be applied to the individual entities (agents) in a system. Quantitatively evaluating a system and propagating its state over a series of discrete time-steps effectively simulates the system, allowing various observables to be computed and the system’s properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, at least in an unbiased way, there is a uniquely synergistic opportunity to employ ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, running ABM calculations can generate a wealth of data, and ML can be applied in that context too—for example, to generate statistical measures that accurately and meaningfully describe the stochastic outputs of a system and its properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate plausible (realistic) datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision a variety of synergistic ABM⇄ML loops. After introducing some basic ideas about ABMs and ML, and their limitations, this Review describes examples of how ABM and ML have been integrated in diverse contexts, spanning spatial scales that include multicellular and tissue-scale biology to human population-level epidemiology. In so doing, we have used published studies as a guide to identify ML approaches that are well-suited to particular types of ABM applications, based on the scale of the biological system and the properties of the available data.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48806822","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 : 2022-05-30DOI: 10.3389/fsysb.2022.896265
Andrea Angarita-Rodríguez, Nicolás Mendoza-Mejía, Janneth González, A. Aristizabal, Oscar Hidalgo-lanussa, Juan J. Rubio-Mesa, G. Barreto, A. Pinzón
Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatory pathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies, with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β-oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA-mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation in metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases.
{"title":"Multi-Omics Integrative Analysis Coupled to Control Theory and Computational Simulation of a Genome-Scale metabolic Model Reveal Controlling Biological Switches in Human Astrocytes Under Palmitic Acid-Induced Lipotoxicity","authors":"Andrea Angarita-Rodríguez, Nicolás Mendoza-Mejía, Janneth González, A. Aristizabal, Oscar Hidalgo-lanussa, Juan J. Rubio-Mesa, G. Barreto, A. Pinzón","doi":"10.3389/fsysb.2022.896265","DOIUrl":"https://doi.org/10.3389/fsysb.2022.896265","url":null,"abstract":"Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatory pathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies, with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β-oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA-mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation in metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44273875","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 : 2022-05-25DOI: 10.3389/fsysb.2022.877717
Xiaoxi Shen, Chang Jiang, Yalu Wen, Chenxi Li, Qing Lu
Deep learning is a powerful tool for capturing complex structures within the data. It holds great promise for genomic research due to its capacity of learning complex features in genomic data. In this paper, we provide a brief review on deep learning techniques and various applications of deep learning to genomic studies. We also briefly mention current challenges and future perspectives on using emerging deep learning techniques for ongoing and future genomic research.
{"title":"A Brief Review on Deep Learning Applications in Genomic Studies","authors":"Xiaoxi Shen, Chang Jiang, Yalu Wen, Chenxi Li, Qing Lu","doi":"10.3389/fsysb.2022.877717","DOIUrl":"https://doi.org/10.3389/fsysb.2022.877717","url":null,"abstract":"Deep learning is a powerful tool for capturing complex structures within the data. It holds great promise for genomic research due to its capacity of learning complex features in genomic data. In this paper, we provide a brief review on deep learning techniques and various applications of deep learning to genomic studies. We also briefly mention current challenges and future perspectives on using emerging deep learning techniques for ongoing and future genomic research.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41654756","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 : 2022-04-29DOI: 10.3389/fsysb.2022.893007
G. N’guyen, C. Roblet, L. Lagacé, M. Filteau
Maple syrup, an emblematic food product of Canada is produced from the concentration of sap collected from maple trees during spring. During this season, the trees come out of dormancy, which modifies sap composition. Meanwhile, microorganisms that contaminate sap as it is collected can also modify its composition. As these two factors can impact the quality of maple syrup, we aimed to better understand how microbial communities vary along dormancy release. We estimated the absolute abundance of bacteria and fungi in maple sap along a dormancy release index using high-throughput amplicon sequencing and digital droplet PCR (ddPCR). Several members were identified as indicators of maple sap composition, syrup organoleptic conformity and color, some of which are also hubs in the microbial association networks. We further explored bacterial communities by performing a predictive functional analysis, revealing various metabolic pathways correlated to dormancy release. Finally, we performed an experimental investigation of maple sap carrying capacity and limiting nutrients along dormancy release and found that maple sap composition variation influences its carrying capacity. Taken together, our results indicate that an increase in nitrogen supply in the form of allantoate combined with possible metabolite excretion could lead microbial communities towards different paths. Indeed, we observed a greater heterogeneity during late dormancy release which in turn could explain the variation in maple syrup quality. Further experimental investigation into the contribution of microbial, vegetal, environmental, technological, and processing factors to the final composition of maple syrup will be required to improve our understanding of this complex and flavorful food matrix and to develop quality control strategies.
{"title":"A Metataxonomic Analysis of Maple Sap Microbial Communities Reveals New Insights Into Maple Syrup Complexity","authors":"G. N’guyen, C. Roblet, L. Lagacé, M. Filteau","doi":"10.3389/fsysb.2022.893007","DOIUrl":"https://doi.org/10.3389/fsysb.2022.893007","url":null,"abstract":"Maple syrup, an emblematic food product of Canada is produced from the concentration of sap collected from maple trees during spring. During this season, the trees come out of dormancy, which modifies sap composition. Meanwhile, microorganisms that contaminate sap as it is collected can also modify its composition. As these two factors can impact the quality of maple syrup, we aimed to better understand how microbial communities vary along dormancy release. We estimated the absolute abundance of bacteria and fungi in maple sap along a dormancy release index using high-throughput amplicon sequencing and digital droplet PCR (ddPCR). Several members were identified as indicators of maple sap composition, syrup organoleptic conformity and color, some of which are also hubs in the microbial association networks. We further explored bacterial communities by performing a predictive functional analysis, revealing various metabolic pathways correlated to dormancy release. Finally, we performed an experimental investigation of maple sap carrying capacity and limiting nutrients along dormancy release and found that maple sap composition variation influences its carrying capacity. Taken together, our results indicate that an increase in nitrogen supply in the form of allantoate combined with possible metabolite excretion could lead microbial communities towards different paths. Indeed, we observed a greater heterogeneity during late dormancy release which in turn could explain the variation in maple syrup quality. Further experimental investigation into the contribution of microbial, vegetal, environmental, technological, and processing factors to the final composition of maple syrup will be required to improve our understanding of this complex and flavorful food matrix and to develop quality control strategies.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46191643","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 : 2022-04-29DOI: 10.3389/fsysb.2022.876075
Adriana Zanca, J. Flegg, J. Osborne
Wound healing of the skin is a complex process that is still not well-understood. Wound management is expensive for both individuals and the health system overall, and can reduce quality of life for patients. Given these significant socio-economic impacts, wound healing has long been a focus of scientific research. Recent in vivo mouse studies have identified two key regions in wounded skin tissue: A non-proliferative leading edge that actively migrates into wounded space, and a proliferative hub in which cells have enhanced mitotic properties. This work uses mathematical and computational modelling to investigate the effect of changing the mechanical characteristics of cells in these two key regions. In this paper we explore what characteristics are sufficient for wound healing, particularly focusing on cell proliferation, since wounds are not able to repair successfully without sufficient levels of cell division. By considering contact inhibited proliferation, where small cells are unable to divide, we find that a quiescent region develops if the proliferative hub is able to grow over time, essentially limiting the number of cells that are able to divide. In contrast, if the size of the proliferative hub is kept below some threshold, then contact inhibition has a less significant role in wound repair. This work builds upon existing cell-based computational studies of wound healing and could be modified to investigate different stages of wound healing, impaired healing and wound treatments.
{"title":"Push or Pull? Cell Proliferation and Migration During Wound Healing","authors":"Adriana Zanca, J. Flegg, J. Osborne","doi":"10.3389/fsysb.2022.876075","DOIUrl":"https://doi.org/10.3389/fsysb.2022.876075","url":null,"abstract":"Wound healing of the skin is a complex process that is still not well-understood. Wound management is expensive for both individuals and the health system overall, and can reduce quality of life for patients. Given these significant socio-economic impacts, wound healing has long been a focus of scientific research. Recent in vivo mouse studies have identified two key regions in wounded skin tissue: A non-proliferative leading edge that actively migrates into wounded space, and a proliferative hub in which cells have enhanced mitotic properties. This work uses mathematical and computational modelling to investigate the effect of changing the mechanical characteristics of cells in these two key regions. In this paper we explore what characteristics are sufficient for wound healing, particularly focusing on cell proliferation, since wounds are not able to repair successfully without sufficient levels of cell division. By considering contact inhibited proliferation, where small cells are unable to divide, we find that a quiescent region develops if the proliferative hub is able to grow over time, essentially limiting the number of cells that are able to divide. In contrast, if the size of the proliferative hub is kept below some threshold, then contact inhibition has a less significant role in wound repair. This work builds upon existing cell-based computational studies of wound healing and could be modified to investigate different stages of wound healing, impaired healing and wound treatments.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48559521","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 : 2022-04-29DOI: 10.3389/fsysb.2022.815237
Kalyani B. Karunakaran, N. Balakrishnan, M. Ganapathiraju
Accelerated efforts to identify intervention strategies for the COVID-19 pandemic caused by SARS-CoV-2 need to be supported by deeper investigations into host invasion and response mechanisms. We constructed the neighborhood interactome network of the 332 human proteins targeted by SARS-CoV-2 proteins, augmenting it with 1,941 novel human protein-protein interactions predicted using our High-precision Protein-Protein Interaction Prediction (HiPPIP) model. Novel interactors, and the interactome as a whole, showed significant enrichment for genes differentially expressed in SARS-CoV-2-infected A549 and Calu-3 cells, postmortem lung samples of COVID-19 patients and blood samples of COVID-19 patients with severe clinical outcomes. The PPIs connected host proteins to COVID-19 blood biomarkers, ACE2 (SARS-CoV-2 entry receptor), genes differentiating SARS-CoV-2 infection from other respiratory virus infections, and SARS-CoV-targeted host proteins. Novel PPIs facilitated identification of the cilium organization functional module; we deduced the potential antiviral role of an interaction between the virus-targeted NUP98 and the cilia-associated CHMP5. Functional enrichment analyses revealed promyelocytic leukaemia bodies, midbody, cell cycle checkpoints and tristetraprolin pathway as potential viral targets. Network proximity of diabetes and hypertension associated genes to host proteins indicated a mechanistic basis for these co-morbidities in critically ill/non-surviving patients. Twenty-four drugs were identified using comparative transcriptome analysis, which include those undergoing COVID-19 clinical trials, showing broad-spectrum antiviral properties or proven activity against SARS-CoV-2 or SARS-CoV/MERS-CoV in cell-based assays. The interactome is available on a webserver at http://severus.dbmi.pitt.edu/corona/.
{"title":"Interactome of SARS-CoV-2 Modulated Host Proteins With Computationally Predicted PPIs: Insights From Translational Systems Biology Studies","authors":"Kalyani B. Karunakaran, N. Balakrishnan, M. Ganapathiraju","doi":"10.3389/fsysb.2022.815237","DOIUrl":"https://doi.org/10.3389/fsysb.2022.815237","url":null,"abstract":"Accelerated efforts to identify intervention strategies for the COVID-19 pandemic caused by SARS-CoV-2 need to be supported by deeper investigations into host invasion and response mechanisms. We constructed the neighborhood interactome network of the 332 human proteins targeted by SARS-CoV-2 proteins, augmenting it with 1,941 novel human protein-protein interactions predicted using our High-precision Protein-Protein Interaction Prediction (HiPPIP) model. Novel interactors, and the interactome as a whole, showed significant enrichment for genes differentially expressed in SARS-CoV-2-infected A549 and Calu-3 cells, postmortem lung samples of COVID-19 patients and blood samples of COVID-19 patients with severe clinical outcomes. The PPIs connected host proteins to COVID-19 blood biomarkers, ACE2 (SARS-CoV-2 entry receptor), genes differentiating SARS-CoV-2 infection from other respiratory virus infections, and SARS-CoV-targeted host proteins. Novel PPIs facilitated identification of the cilium organization functional module; we deduced the potential antiviral role of an interaction between the virus-targeted NUP98 and the cilia-associated CHMP5. Functional enrichment analyses revealed promyelocytic leukaemia bodies, midbody, cell cycle checkpoints and tristetraprolin pathway as potential viral targets. Network proximity of diabetes and hypertension associated genes to host proteins indicated a mechanistic basis for these co-morbidities in critically ill/non-surviving patients. Twenty-four drugs were identified using comparative transcriptome analysis, which include those undergoing COVID-19 clinical trials, showing broad-spectrum antiviral properties or proven activity against SARS-CoV-2 or SARS-CoV/MERS-CoV in cell-based assays. The interactome is available on a webserver at http://severus.dbmi.pitt.edu/corona/.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48608230","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}
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