Pub Date : 2016-12-01DOI: 10.1109/LLS.2016.2644647
M. K. Toroghi, W. R. Cluett, R. Mahadevan
Alcohol is one of the most widely consumed and abused substances, and is a major factor in many alcohol-related diseases, incidents of impaired driving, and crimes. In this letter, we develop a mechanistic model for alcohol metabolism in the human body based on the dynamic parsimonious flux balance analysis technique. The developed whole body alcohol metabolic model contains two main mechanisms for ethanol metabolism in the body, namely, oxidative and non-oxidative mechanisms. The model is able to demonstrate the effect of variations in biochemical kinetics associated with the alcohol dehydrogenase enzyme, gender differences, physiological properties of the human body such as age, weight, and height, and the meal effect on the alcohol clearance from the body. Simulation results show that the model predictions are consistent with in vivo studies. The results from this letter indicate that the proposed metabolic modeling approach may open the door to new opportunities in the area of metabolic nutrition research and personalized medicine since it accounts for physiological properties and biochemical information related to the human body.
{"title":"Multiscale Metabolic Modeling Approach for Predicting Blood Alcohol Concentration","authors":"M. K. Toroghi, W. R. Cluett, R. Mahadevan","doi":"10.1109/LLS.2016.2644647","DOIUrl":"https://doi.org/10.1109/LLS.2016.2644647","url":null,"abstract":"Alcohol is one of the most widely consumed and abused substances, and is a major factor in many alcohol-related diseases, incidents of impaired driving, and crimes. In this letter, we develop a mechanistic model for alcohol metabolism in the human body based on the dynamic parsimonious flux balance analysis technique. The developed whole body alcohol metabolic model contains two main mechanisms for ethanol metabolism in the body, namely, oxidative and non-oxidative mechanisms. The model is able to demonstrate the effect of variations in biochemical kinetics associated with the alcohol dehydrogenase enzyme, gender differences, physiological properties of the human body such as age, weight, and height, and the meal effect on the alcohol clearance from the body. Simulation results show that the model predictions are consistent with in vivo studies. The results from this letter indicate that the proposed metabolic modeling approach may open the door to new opportunities in the area of metabolic nutrition research and personalized medicine since it accounts for physiological properties and biochemical information related to the human body.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"59-62"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2644647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509620","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 : 2016-12-01DOI: 10.1109/LLS.2016.2644647
Masood Khaksar Toroghi;William R. Cluett;Radhakrishnan Mahadevan
Alcohol is one of the most widely consumed and abused substances, and is a major factor in many alcohol-related diseases, incidents of impaired driving, and crimes. In this letter, we develop a mechanistic model for alcohol metabolism in the human body based on the dynamic parsimonious flux balance analysis technique. The developed whole body alcohol metabolic model contains two main mechanisms for ethanol metabolism in the body, namely, oxidative and non-oxidative mechanisms. The model is able to demonstrate the effect of variations in biochemical kinetics associated with the alcohol dehydrogenase enzyme, gender differences, physiological properties of the human body such as age, weight, and height, and the meal effect on the alcohol clearance from the body. Simulation results show that the model predictions are consistent with in vivo studies. The results from this letter indicate that the proposed metabolic modeling approach may open the door to new opportunities in the area of metabolic nutrition research and personalized medicine since it accounts for physiological properties and biochemical information related to the human body.
{"title":"Multiscale Metabolic Modeling Approach for Predicting Blood Alcohol Concentration","authors":"Masood Khaksar Toroghi;William R. Cluett;Radhakrishnan Mahadevan","doi":"10.1109/LLS.2016.2644647","DOIUrl":"https://doi.org/10.1109/LLS.2016.2644647","url":null,"abstract":"Alcohol is one of the most widely consumed and abused substances, and is a major factor in many alcohol-related diseases, incidents of impaired driving, and crimes. In this letter, we develop a mechanistic model for alcohol metabolism in the human body based on the dynamic parsimonious flux balance analysis technique. The developed whole body alcohol metabolic model contains two main mechanisms for ethanol metabolism in the body, namely, oxidative and non-oxidative mechanisms. The model is able to demonstrate the effect of variations in biochemical kinetics associated with the alcohol dehydrogenase enzyme, gender differences, physiological properties of the human body such as age, weight, and height, and the meal effect on the alcohol clearance from the body. Simulation results show that the model predictions are consistent with in vivo studies. The results from this letter indicate that the proposed metabolic modeling approach may open the door to new opportunities in the area of metabolic nutrition research and personalized medicine since it accounts for physiological properties and biochemical information related to the human body.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 4","pages":"59-62"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2644647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49986488","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 : 2016-10-05DOI: 10.1109/LLS.2016.2615086
Gunjan Thakur, Bernie J. Daigle, Meng Qian, Kelsey R. Dean, Yuanyang Zhang, Ruoting Yang, Taek‐Kyun Kim, Xiaogang Wu, Meng Li, Inyoul Y. Lee, L. Petzold, Francis J. Doyle
Accurate classification of biological phenotypes is an essential task for medical decision making. The selection of subjects for classifier training and validation sets is a crucial step within this task. To evaluate the impact of two approaches for subject selection—randomization and clinical balancing, we applied six classification algorithms to a highly replicated publicly available breast cancer data set. Using six performance metrics, we demonstrate that clinical balancing improves both training and validation performance for all methods on average. We also observed a smaller discrepancy between training and validation performance. Furthermore, a simple analytical argument is presented which suggests that we need only two metrics from the class of metrics based on the entries of the confusion matrix. In light of our results, we recommend: 1) clinical balancing of training and validation data to improve signal-to-noise ratio and 2) the use of multiple classification algorithms and evaluation metrics for a comprehensive evaluation of the decision making process.
{"title":"A Multimetric Evaluation of Stratified Random Sampling for Classification: A Case Study","authors":"Gunjan Thakur, Bernie J. Daigle, Meng Qian, Kelsey R. Dean, Yuanyang Zhang, Ruoting Yang, Taek‐Kyun Kim, Xiaogang Wu, Meng Li, Inyoul Y. Lee, L. Petzold, Francis J. Doyle","doi":"10.1109/LLS.2016.2615086","DOIUrl":"https://doi.org/10.1109/LLS.2016.2615086","url":null,"abstract":"Accurate classification of biological phenotypes is an essential task for medical decision making. The selection of subjects for classifier training and validation sets is a crucial step within this task. To evaluate the impact of two approaches for subject selection—randomization and clinical balancing, we applied six classification algorithms to a highly replicated publicly available breast cancer data set. Using six performance metrics, we demonstrate that clinical balancing improves both training and validation performance for all methods on average. We also observed a smaller discrepancy between training and validation performance. Furthermore, a simple analytical argument is presented which suggests that we need only two metrics from the class of metrics based on the entries of the confusion matrix. In light of our results, we recommend: 1) clinical balancing of training and validation data to improve signal-to-noise ratio and 2) the use of multiple classification algorithms and evaluation metrics for a comprehensive evaluation of the decision making process.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"43-46"},"PeriodicalIF":0.0,"publicationDate":"2016-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2615086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509475","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 : 2016-10-05DOI: 10.1109/LLS.2016.2615086
Gunjan S. Thakur;Bernie J. Daigle;Meng Qian;Kelsey R. Dean;Yuanyang Zhang;Ruoting Yang;Taek-Kyun Kim;Xiaogang Wu;Meng Li;Inyoul Lee;Linda R. Petzold;Francis J. Doyle
Accurate classification of biological phenotypes is an essential task for medical decision making. The selection of subjects for classifier training and validation sets is a crucial step within this task. To evaluate the impact of two approaches for subject selection-randomization and clinical balancing, we applied six classification algorithms to a highly replicated publicly available breast cancer data set. Using six performance metrics, we demonstrate that clinical balancing improves both training and validation performance for all methods on average. We also observed a smaller discrepancy between training and validation performance. Furthermore, a simple analytical argument is presented which suggests that we need only two metrics from the class of metrics based on the entries of the confusion matrix. In light of our results, we recommend: 1) clinical balancing of training and validation data to improve signal-to-noise ratio and 2) the use of multiple classification algorithms and evaluation metrics for a comprehensive evaluation of the decision making process.
{"title":"A Multimetric Evaluation of Stratified Random Sampling for Classification: A Case Study","authors":"Gunjan S. Thakur;Bernie J. Daigle;Meng Qian;Kelsey R. Dean;Yuanyang Zhang;Ruoting Yang;Taek-Kyun Kim;Xiaogang Wu;Meng Li;Inyoul Lee;Linda R. Petzold;Francis J. Doyle","doi":"10.1109/LLS.2016.2615086","DOIUrl":"https://doi.org/10.1109/LLS.2016.2615086","url":null,"abstract":"Accurate classification of biological phenotypes is an essential task for medical decision making. The selection of subjects for classifier training and validation sets is a crucial step within this task. To evaluate the impact of two approaches for subject selection-randomization and clinical balancing, we applied six classification algorithms to a highly replicated publicly available breast cancer data set. Using six performance metrics, we demonstrate that clinical balancing improves both training and validation performance for all methods on average. We also observed a smaller discrepancy between training and validation performance. Furthermore, a simple analytical argument is presented which suggests that we need only two metrics from the class of metrics based on the entries of the confusion matrix. In light of our results, we recommend: 1) clinical balancing of training and validation data to improve signal-to-noise ratio and 2) the use of multiple classification algorithms and evaluation metrics for a comprehensive evaluation of the decision making process.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 4","pages":"43-46"},"PeriodicalIF":0.0,"publicationDate":"2016-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2615086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49986484","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 : 2016-10-04DOI: 10.1109/LLS.2016.2615091
M. Ahsen, H. Ozbay, S. Niculescu
A cyclic model for gene regulatory networks with time delayed negative feedback is analyzed using an extension of the so-called secant condition, which is originally developed for systems without time delays. It is shown that sufficient conditions obtained earlier for delay-independent local stability can be further improved for homogenous networks to obtain delay-dependent necessary and sufficient conditions, which are expressed in terms of the parameters of the Hill-type nonlinearity.
{"title":"Analysis of a Gene Regulatory Network Model With Time Delay Using the Secant Condition","authors":"M. Ahsen, H. Ozbay, S. Niculescu","doi":"10.1109/LLS.2016.2615091","DOIUrl":"https://doi.org/10.1109/LLS.2016.2615091","url":null,"abstract":"A cyclic model for gene regulatory networks with time delayed negative feedback is analyzed using an extension of the so-called secant condition, which is originally developed for systems without time delays. It is shown that sufficient conditions obtained earlier for delay-independent local stability can be further improved for homogenous networks to obtain delay-dependent necessary and sufficient conditions, which are expressed in terms of the parameters of the Hill-type nonlinearity.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"5-8"},"PeriodicalIF":0.0,"publicationDate":"2016-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2615091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509540","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 : 2016-10-04DOI: 10.1109/LLS.2016.2615089
S. Waldherr, Philip Trennt, M. Hussain
The modeling of heterogeneous dynamic cell populations based on population balance equations is an important tool to describe the interaction between intracellular dynamics and population dynamics. However, the numerical simulation of such models remains challenging for models with high-dimensional intracellular dynamics, when these dynamics influence the growth rate of the cells. To cope with this challenge, we propose a hybrid simulation scheme based on the method of partial characteristics. We show that important features of the population density function, such as its moments or marginals, can be approximated by this scheme in a statistically converging way. In a case study with a population of differentiating cells, we illustrate how to obtain the growth dynamics of the individual subpopulations and deduce the extent of cell differentiation under a time-varying stimulus.
{"title":"Hybrid Simulation of Heterogeneous Cell Populations","authors":"S. Waldherr, Philip Trennt, M. Hussain","doi":"10.1109/LLS.2016.2615089","DOIUrl":"https://doi.org/10.1109/LLS.2016.2615089","url":null,"abstract":"The modeling of heterogeneous dynamic cell populations based on population balance equations is an important tool to describe the interaction between intracellular dynamics and population dynamics. However, the numerical simulation of such models remains challenging for models with high-dimensional intracellular dynamics, when these dynamics influence the growth rate of the cells. To cope with this challenge, we propose a hybrid simulation scheme based on the method of partial characteristics. We show that important features of the population density function, such as its moments or marginals, can be approximated by this scheme in a statistically converging way. In a case study with a population of differentiating cells, we illustrate how to obtain the growth dynamics of the individual subpopulations and deduce the extent of cell differentiation under a time-varying stimulus.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"9-12"},"PeriodicalIF":0.0,"publicationDate":"2016-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2615089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509533","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 : 2016-09-01DOI: 10.1109/LLS.2016.2646498
Daniel P. Howsmon;Juergen Hahn
Models of biochemical reaction networks commonly contain a large number of parameters, while at the same time, there is only a limited amount of (noisy) data available for their estimation. As such, the values of many parameters are not well known as nominal parameter values have to be determined from the open scientific literature and a significant number of the values may have been derived in different cell types or organisms than that which is modeled. There clearly is a need to estimate at least some of the parameter values from experimental data; however, the small amount of available data and the large number of parameters commonly found in these types of models require the use of regularization techniques to avoid overfitting. A tutorial of regularization techniques, including parameter set selection, precedes a case study of estimating parameters in a signal transduction network. Cross-validation results rather than fitting results are presented to further emphasize the need for models that generalize well to new data instead of simply fitting the current data.
{"title":"Regularization Techniques to Overcome Overparameterization of Complex Biochemical Reaction Networks","authors":"Daniel P. Howsmon;Juergen Hahn","doi":"10.1109/LLS.2016.2646498","DOIUrl":"10.1109/LLS.2016.2646498","url":null,"abstract":"Models of biochemical reaction networks commonly contain a large number of parameters, while at the same time, there is only a limited amount of (noisy) data available for their estimation. As such, the values of many parameters are not well known as nominal parameter values have to be determined from the open scientific literature and a significant number of the values may have been derived in different cell types or organisms than that which is modeled. There clearly is a need to estimate at least some of the parameter values from experimental data; however, the small amount of available data and the large number of parameters commonly found in these types of models require the use of regularization techniques to avoid overfitting. A tutorial of regularization techniques, including parameter set selection, precedes a case study of estimating parameters in a signal transduction network. Cross-validation results rather than fitting results are presented to further emphasize the need for models that generalize well to new data instead of simply fitting the current data.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 3","pages":"31-34"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2646498","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35523890","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}
Pub Date : 2016-09-01DOI: 10.1109/LLS.2016.2644648
Lorenzo Castelli;Raffaele Pesenti;Daniel Segrè
Each living cell needs to solve a resource allocation problem, in which multiple inputs (uptake fluxes) and outputs (secretion fluxes) are the outcome of the stoichiometry of biochemical pathways and the regulation of metabolic enzymes. Quantifying the efficiency with which a cell solves this resource allocation problem constitutes a basic question in “cellular economics.” In this letter, we propose the use of data envelopment analysis (DEA) to define multidimensional yields that can capture the multidimensional nature of cell input–output processes. The DEA, by treating cells as decision-making units, enables one to introduce the concept of efficiency frontier that is both intimately connected to the shadow prices of flux balance analysis and useful to estimate the phenotypic phase space from experimental measurements of fluxes.
{"title":"The Cell as a Decision-Making Unit","authors":"Lorenzo Castelli;Raffaele Pesenti;Daniel Segrè","doi":"10.1109/LLS.2016.2644648","DOIUrl":"https://doi.org/10.1109/LLS.2016.2644648","url":null,"abstract":"Each living cell needs to solve a resource allocation problem, in which multiple inputs (uptake fluxes) and outputs (secretion fluxes) are the outcome of the stoichiometry of biochemical pathways and the regulation of metabolic enzymes. Quantifying the efficiency with which a cell solves this resource allocation problem constitutes a basic question in “cellular economics.” In this letter, we propose the use of data envelopment analysis (DEA) to define multidimensional yields that can capture the multidimensional nature of cell input–output processes. The DEA, by treating cells as decision-making units, enables one to introduce the concept of efficiency frontier that is both intimately connected to the shadow prices of flux balance analysis and useful to estimate the phenotypic phase space from experimental measurements of fluxes.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 3","pages":"27-30"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2644648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49909173","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 : 2016-09-01DOI: 10.1109/LLS.2016.2644646
Zhe Xu, M. Birtwistle, C. Belta, A. Julius
We propose a method for discriminating among competing models for biological systems. Our approach is based on learning temporal logic formulas from data obtained by simulating the models. We apply this method to find dynamic features of epidermal growth factor induced extracellular signal-regulated kinase (ERK) activation that are strictly unique to positive versus negative feedback models. We first search for a temporal logic formula from a training set that can eliminate ERK dynamics observed with both models and then identify the ERK dynamics that are unique to each model. The obtained formulas are tested with a validation sample set and the decision rates and classification rates are estimated using the Chernoff bound. The results can be used in guiding and optimizing the design of experiments for model discrimination.
{"title":"A Temporal Logic Inference Approach for Model Discrimination","authors":"Zhe Xu, M. Birtwistle, C. Belta, A. Julius","doi":"10.1109/LLS.2016.2644646","DOIUrl":"https://doi.org/10.1109/LLS.2016.2644646","url":null,"abstract":"We propose a method for discriminating among competing models for biological systems. Our approach is based on learning temporal logic formulas from data obtained by simulating the models. We apply this method to find dynamic features of epidermal growth factor induced extracellular signal-regulated kinase (ERK) activation that are strictly unique to positive versus negative feedback models. We first search for a temporal logic formula from a training set that can eliminate ERK dynamics observed with both models and then identify the ERK dynamics that are unique to each model. The obtained formulas are tested with a validation sample set and the decision rates and classification rates are estimated using the Chernoff bound. The results can be used in guiding and optimizing the design of experiments for model discrimination.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"19-22"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2644646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509584","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 : 2016-09-01DOI: 10.1109/LLS.2017.2652473
Zhaobin Xu, Nicholas Ribaudo, Xianhua Li, T. Wood, Z. Huang
Recent studies indicate that pretreating microorganisms with ribosome-targeting antibiotics may promote a transition in the microbial phenotype, such as the formation of persister cells; i.e., those cells that survive antibiotic treatment by becoming metabolically dormant. In this letter, we developed the first genome-scale modeling approach to systematically investigate the influence of ribosome-targeting antibiotics on the metabolism of Pseudomonas aeruginosa. An approach for integrating gene expression data with metabolic networks was first developed to identify the metabolic reactions whose fluxes were positively correlated with gene activation levels. The fluxes of these reactions were further constrained via a flux balance analysis to mimic the inhibition of antibiotics on microbial metabolism. It was found that some of metabolic reactions with large flux change, including metabolic reactions for homoserine metabolism, the production of 2-heptyl-4-quinolone, and isocitrate lyase, were confirmed by existing experimental data for their important role in promoting persister cell formation. Metabolites with large exchange-rate change, such as acetate, agmatine, and oxoglutarate, were found important for persister cell formation in previous experiments. The predicted results on the flux change triggered by ribosome-targeting antibiotics can be used to generate hypotheses for future experimental design to combat antibiotic-resistant pathogens.
{"title":"A Genome-Scale Modeling Approach to Investigate the Antibiotics-Triggered Perturbation in the Metabolism of Pseudomonas aeruginosa","authors":"Zhaobin Xu, Nicholas Ribaudo, Xianhua Li, T. Wood, Z. Huang","doi":"10.1109/LLS.2017.2652473","DOIUrl":"https://doi.org/10.1109/LLS.2017.2652473","url":null,"abstract":"Recent studies indicate that pretreating microorganisms with ribosome-targeting antibiotics may promote a transition in the microbial phenotype, such as the formation of persister cells; i.e., those cells that survive antibiotic treatment by becoming metabolically dormant. In this letter, we developed the first genome-scale modeling approach to systematically investigate the influence of ribosome-targeting antibiotics on the metabolism of Pseudomonas aeruginosa. An approach for integrating gene expression data with metabolic networks was first developed to identify the metabolic reactions whose fluxes were positively correlated with gene activation levels. The fluxes of these reactions were further constrained via a flux balance analysis to mimic the inhibition of antibiotics on microbial metabolism. It was found that some of metabolic reactions with large flux change, including metabolic reactions for homoserine metabolism, the production of 2-heptyl-4-quinolone, and isocitrate lyase, were confirmed by existing experimental data for their important role in promoting persister cell formation. Metabolites with large exchange-rate change, such as acetate, agmatine, and oxoglutarate, were found important for persister cell formation in previous experiments. The predicted results on the flux change triggered by ribosome-targeting antibiotics can be used to generate hypotheses for future experimental design to combat antibiotic-resistant pathogens.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"39-42"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2017.2652473","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509739","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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