Pub Date : 2016-09-01DOI: 10.1109/LLS.2017.2652448
John H. Abel;Brian Drawert;Andreas Hellander;Linda R. Petzold
GillesPy is an open-source Python package for model construction and simulation of stochastic biochemical systems. GillesPy consists of a Python framework for model building and an interface to the StochKit2 suite of efficient simulation algorithms based on the Gillespie stochastic simulation algorithms. To enable intuitive model construction and seamless integration into the scientific Python stack, we present an easy-to-understand action-oriented programming interface. Here, we describe the components of this package and provide a detailed example relevant to the computational biology community.
{"title":"GillesPy: A Python Package for Stochastic Model Building and Simulation","authors":"John H. Abel;Brian Drawert;Andreas Hellander;Linda R. Petzold","doi":"10.1109/LLS.2017.2652448","DOIUrl":"10.1109/LLS.2017.2652448","url":null,"abstract":"GillesPy is an open-source Python package for model construction and simulation of stochastic biochemical systems. GillesPy consists of a Python framework for model building and an interface to the StochKit2 suite of efficient simulation algorithms based on the Gillespie stochastic simulation algorithms. To enable intuitive model construction and seamless integration into the scientific Python stack, we present an easy-to-understand action-oriented programming interface. Here, we describe the components of this package and provide a detailed example relevant to the computational biology community.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 3","pages":"35-38"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2017.2652448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35103720","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.2016.2646560
Robert S. Parker
The complete sequencing of the human genome has undoubtedly advanced the study of biology and the practice of medicine, including some dramatic and rapid advances in human health. This progress has been slowed, however, by the challenge of understanding how the genetic players, and their regulation, interact to yield systemic responses to disease and treatment. Taking a puzzle as an analogy for life, the landmark achievement of identifying the human genome provided a list of the possible puzzle pieces, but it did not provide the completed picture on the cover. The search since has focused on the relationships between this genomic information and the (individual or patient) systemic response or function—the “omics” efforts in mapping proteins (proteomics) and metabolites (metabolomics). The primary avenues in this search are: 1) defining the causal connections between the plethora of transcriptional, protein, and metabolite players; 2) linking these microscale networks to system-level response; and 3) capturing the dynamics of the system in response to changes at lower scales. The fields of systems biology, and its translational science counterpart systems medicine, have emerged as the bridge between reductionist molecular and cellular biology approaches and the systems-level understanding required to use this knowledge to advance the human condition.
{"title":"Guest Editorial: Special Issue on the Foundations of Systems Biology in Engineering (FOSBE)","authors":"Robert S. Parker","doi":"10.1109/LLS.2016.2646560","DOIUrl":"https://doi.org/10.1109/LLS.2016.2646560","url":null,"abstract":"The complete sequencing of the human genome has undoubtedly advanced the study of biology and the practice of medicine, including some dramatic and rapid advances in human health. This progress has been slowed, however, by the challenge of understanding how the genetic players, and their regulation, interact to yield systemic responses to disease and treatment. Taking a puzzle as an analogy for life, the landmark achievement of identifying the human genome provided a list of the possible puzzle pieces, but it did not provide the completed picture on the cover. The search since has focused on the relationships between this genomic information and the (individual or patient) systemic response or function—the “omics” efforts in mapping proteins (proteomics) and metabolites (metabolomics). The primary avenues in this search are: 1) defining the causal connections between the plethora of transcriptional, protein, and metabolite players; 2) linking these microscale networks to system-level response; and 3) capturing the dynamics of the system in response to changes at lower scales. The fields of systems biology, and its translational science counterpart systems medicine, have emerged as the bridge between reductionist molecular and cellular biology approaches and the systems-level understanding required to use this knowledge to advance the human condition.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 3","pages":"17-18"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2646560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49909175","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;Marc Birtwistle;Calin Belta;Agung 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;Marc Birtwistle;Calin Belta;Agung 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 3","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":"49909176","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;Thomas K. Wood;Zuyi 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;Thomas K. Wood;Zuyi 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 3","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":"49909174","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 : 2016-06-02DOI: 10.1109/LLS.2016.2646383
C. A. Vargas-Garcia, Mohammad Soltani, Abhyudai Singh
How isogenic cell populations maintain size homeostasis, i.e., a narrow distribution of cell size, is an intriguing fundamental problem. We model cell size using a stochastic hybrid system, where a cell grows exponentially in size (volume) over time and probabilistic division events are triggered at discrete-time intervals. Moreover, whenever division occurs, size is randomly partitioned among daughter cells. We first consider a scenario where a timer (cell-cycle clock) that measures the time elapsed since the last division event regulates both the cellular growth and division rates. The analysis reveals that such a timer-controlled system cannot achieve size homeostasis, in the sense that the cell-to-cell size variation grows unboundedly with time. To explore biologically meaningful mechanisms for controlling size, we consider two classes of regulation: a size-dependent growth rate and a size-dependent division rate. Our results show that these strategies can provide bounded intercellular variation in cell size and exact mathematical conditions on the form of regulation needed for size homeostasis are derived. Different known forms of size control strategies, such as the adder and the sizer, are shown to be consistent with these results. Finally, we discuss how organisms ranging from bacteria to mammalian cells have adopted different control approaches for maintaining size homeostasis.
{"title":"Conditions for Cell Size Homeostasis: A Stochastic Hybrid System Approach","authors":"C. A. Vargas-Garcia, Mohammad Soltani, Abhyudai Singh","doi":"10.1109/LLS.2016.2646383","DOIUrl":"https://doi.org/10.1109/LLS.2016.2646383","url":null,"abstract":"How isogenic cell populations maintain size homeostasis, i.e., a narrow distribution of cell size, is an intriguing fundamental problem. We model cell size using a stochastic hybrid system, where a cell grows exponentially in size (volume) over time and probabilistic division events are triggered at discrete-time intervals. Moreover, whenever division occurs, size is randomly partitioned among daughter cells. We first consider a scenario where a timer (cell-cycle clock) that measures the time elapsed since the last division event regulates both the cellular growth and division rates. The analysis reveals that such a timer-controlled system cannot achieve size homeostasis, in the sense that the cell-to-cell size variation grows unboundedly with time. To explore biologically meaningful mechanisms for controlling size, we consider two classes of regulation: a size-dependent growth rate and a size-dependent division rate. Our results show that these strategies can provide bounded intercellular variation in cell size and exact mathematical conditions on the form of regulation needed for size homeostasis are derived. Different known forms of size control strategies, such as the adder and the sizer, are shown to be consistent with these results. Finally, we discuss how organisms ranging from bacteria to mammalian cells have adopted different control approaches for maintaining size homeostasis.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"47-50"},"PeriodicalIF":0.0,"publicationDate":"2016-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2646383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509678","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 : 2016-06-01DOI: 10.1109/LLS.2016.2615091
Mehmet Eren Ahsen;Hitay Özbay;Silviu-Iulian 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":"Mehmet Eren Ahsen;Hitay Özbay;Silviu-Iulian 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 2","pages":"5-8"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","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":"49947146","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 : 2016-06-01DOI: 10.1109/LLS.2016.2615081
Daniel Kaschek;Frauke Henjes;Max Hasmann;Ulrike Korf;Jens Timmer
Dynamic modeling has become one of the pillars of understanding complex biological systems from a mechanistic point of view. In particular, ordinary differential equations are frequently used to model the dynamics of the interacting states, e.g., molecular species in cell signaling pathways. The equations typically contain many unknown parameters, such as reaction rates and initial conditions, but also time-dependent parameters, i.e., input functions driving the system. Both are a priori unknown and need to be estimated from experimental, time-resolved data. Here, we discuss an application of indirect optimal control methods for input estimation and parameter estimation in the mammalian target of rapamycin (mTOR) signaling. Whereas the direct identification and quantification of different active mTOR complexes, e.g., mTOR complex $textit {2}$ (mTORC2), is only possible by highly challenging experiments, the mathematical framework allows to reconstruct its dynamics by solving an appropriate Euler–Lagrange equation based on Pontryagin’s maximum principle. The inherently large search space underlying this approach allows to test specific biological hypotheses about the activation of protein kinase B (AKT) by mTORC2 and to reject an alternative model with high statistical power. Hereby, we identify a minimal model that has AKT threonine phosphorylation as a prerequisite for serine phosphorylation by mTORC2. Based on this model, the activation of mTORC2 is predicted to be inhibited by drugs, targeting the receptors of the ERBB receptor family.
{"title":"Testing the Pattern of AKT Activation by Variational Parameter Estimation","authors":"Daniel Kaschek;Frauke Henjes;Max Hasmann;Ulrike Korf;Jens Timmer","doi":"10.1109/LLS.2016.2615081","DOIUrl":"https://doi.org/10.1109/LLS.2016.2615081","url":null,"abstract":"Dynamic modeling has become one of the pillars of understanding complex biological systems from a mechanistic point of view. In particular, ordinary differential equations are frequently used to model the dynamics of the interacting states, e.g., molecular species in cell signaling pathways. The equations typically contain many unknown parameters, such as reaction rates and initial conditions, but also time-dependent parameters, i.e., input functions driving the system. Both are \u0000<italic>a priori</i>\u0000 unknown and need to be estimated from experimental, time-resolved data. Here, we discuss an application of indirect optimal control methods for input estimation and parameter estimation in the \u0000<italic>mammalian target of rapamycin</i>\u0000 (mTOR) signaling. Whereas the direct identification and quantification of different active mTOR complexes, e.g., \u0000<italic>mTOR complex</i>\u0000 \u0000<inline-formula> <tex-math>$textit {2}$ </tex-math></inline-formula>\u0000 (mTORC2), is only possible by highly challenging experiments, the mathematical framework allows to reconstruct its dynamics by solving an appropriate Euler–Lagrange equation based on Pontryagin’s maximum principle. The inherently large search space underlying this approach allows to test specific biological hypotheses about the activation of \u0000<italic>protein kinase B</i>\u0000 (AKT) by mTORC2 and to reject an alternative model with high statistical power. Hereby, we identify a minimal model that has AKT threonine phosphorylation as a prerequisite for serine phosphorylation by mTORC2. Based on this model, the activation of mTORC2 is predicted to be inhibited by drugs, targeting the receptors of the ERBB receptor family.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 2","pages":"13-16"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2615081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49947148","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 : 2016-06-01DOI: 10.1109/LLS.2016.2615089
Steffen Waldherr;Philip Trennt;Mubashir 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":"Steffen Waldherr;Philip Trennt;Mubashir 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 2","pages":"9-12"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","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":"49947147","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 : 2016-06-01DOI: 10.1109/LLS.2016.2615081
Daniel Kaschek, F. Henjes, M. Hasmann, U. Korf, J. Timmer
Dynamic modeling has become one of the pillars of understanding complex biological systems from a mechanistic point of view. In particular, ordinary differential equations are frequently used to model the dynamics of the interacting states, e.g., molecular species in cell signaling pathways. The equations typically contain many unknown parameters, such as reaction rates and initial conditions, but also time-dependent parameters, i.e., input functions driving the system. Both are a priori unknown and need to be estimated from experimental, time-resolved data. Here, we discuss an application of indirect optimal control methods for input estimation and parameter estimation in the mammalian target of rapamycin (mTOR) signaling. Whereas the direct identification and quantification of different active mTOR complexes, e.g., mTOR complex $textit {2}$ (mTORC2), is only possible by highly challenging experiments, the mathematical framework allows to reconstruct its dynamics by solving an appropriate Euler–Lagrange equation based on Pontryagin’s maximum principle. The inherently large search space underlying this approach allows to test specific biological hypotheses about the activation of protein kinase B (AKT) by mTORC2 and to reject an alternative model with high statistical power. Hereby, we identify a minimal model that has AKT threonine phosphorylation as a prerequisite for serine phosphorylation by mTORC2. Based on this model, the activation of mTORC2 is predicted to be inhibited by drugs, targeting the receptors of the ERBB receptor family.
{"title":"Testing the Pattern of AKT Activation by Variational Parameter Estimation","authors":"Daniel Kaschek, F. Henjes, M. Hasmann, U. Korf, J. Timmer","doi":"10.1109/LLS.2016.2615081","DOIUrl":"https://doi.org/10.1109/LLS.2016.2615081","url":null,"abstract":"Dynamic modeling has become one of the pillars of understanding complex biological systems from a mechanistic point of view. In particular, ordinary differential equations are frequently used to model the dynamics of the interacting states, e.g., molecular species in cell signaling pathways. The equations typically contain many unknown parameters, such as reaction rates and initial conditions, but also time-dependent parameters, i.e., input functions driving the system. Both are a priori unknown and need to be estimated from experimental, time-resolved data. Here, we discuss an application of indirect optimal control methods for input estimation and parameter estimation in the mammalian target of rapamycin (mTOR) signaling. Whereas the direct identification and quantification of different active mTOR complexes, e.g., mTOR complex $textit {2}$ (mTORC2), is only possible by highly challenging experiments, the mathematical framework allows to reconstruct its dynamics by solving an appropriate Euler–Lagrange equation based on Pontryagin’s maximum principle. The inherently large search space underlying this approach allows to test specific biological hypotheses about the activation of protein kinase B (AKT) by mTORC2 and to reject an alternative model with high statistical power. Hereby, we identify a minimal model that has AKT threonine phosphorylation as a prerequisite for serine phosphorylation by mTORC2. Based on this model, the activation of mTORC2 is predicted to be inhibited by drugs, targeting the receptors of the ERBB receptor family.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"13-16"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2615081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509380","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 : 2016-03-01DOI: 10.1109/LLS.2016.2568259
Xiaodong Yang, A. Ren, Tianqiao Zhu, Fangming Hu
Digital phantoms are vital for various biomedical researches. Traditional phantoms include theoretical models and voxel models reconstructed from medical images. It has been demonstrated that the homogeneous phantom filled with uniform tissue is accurate enough for wearable antenna design, body-centric channel modeling, etc. Therefore, it is interesting and necessary to investigate the novel approach of generating digital phantoms using an optical noncontact measurement system. In this letter, the point cloud data are first obtained; then, they are simplified via principal component analysis; finally, by applying surface reconstruction and mesh simplification techniques, a digital Chinese phantom is established. To verify the usability of the phantom, numerical calculation is performed to check E-fields at different positions on the body. Results sufficiently prove the feasibility of the train of thought presented in this letter.
{"title":"A Novel Digital Phantom Using an Optical Noncontact Measurement System","authors":"Xiaodong Yang, A. Ren, Tianqiao Zhu, Fangming Hu","doi":"10.1109/LLS.2016.2568259","DOIUrl":"https://doi.org/10.1109/LLS.2016.2568259","url":null,"abstract":"Digital phantoms are vital for various biomedical researches. Traditional phantoms include theoretical models and voxel models reconstructed from medical images. It has been demonstrated that the homogeneous phantom filled with uniform tissue is accurate enough for wearable antenna design, body-centric channel modeling, etc. Therefore, it is interesting and necessary to investigate the novel approach of generating digital phantoms using an optical noncontact measurement system. In this letter, the point cloud data are first obtained; then, they are simplified via principal component analysis; finally, by applying surface reconstruction and mesh simplification techniques, a digital Chinese phantom is established. To verify the usability of the phantom, numerical calculation is performed to check E-fields at different positions on the body. Results sufficiently prove the feasibility of the train of thought presented in this letter.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2568259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509293","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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