Pub Date : 2022-10-06DOI: 10.3389/fsysb.2022.929426
Linda Irons, B. Brook
Healthy lung function depends on a complex system of interactions which regulate the mechanical and biochemical environment of individual cells to the whole organ. Perturbations from these regulated processes give rise to significant lung dysfunction such as chronic inflammation, airway hyperresponsiveness and airway remodelling characteristic of asthma. Importantly, there is ongoing mechanobiological feedback where mechanical factors including airway stiffness and oscillatory loading have considerable influence over cell behavior. The recently proposed area of mechanopharmacology recognises these interactions and aims to highlight the need to consider mechanobiology when identifying and assessing pharmacological targets. However, these multiscale interactions can be difficult to study experimentally due to the need for measurements across a wide range of spatial and temporal scales. On the other hand, integrative multiscale mathematical models have begun to show success in simulating the interactions between different mechanobiological mechanisms or cell/tissue-types across multiple scales. When appropriately informed by experimental data, these models have the potential to serve as extremely useful predictive tools, where physical mechanisms and emergent behaviours can be probed or hypothesised and, more importantly, exploited to propose new mechanopharmacological therapies for asthma and other respiratory diseases. In this review, we first demonstrate via an exemplar, how a multiscale mathematical model of acute bronchoconstriction in an airway could be exploited to propose new mechanopharmacological therapies. We then review current mathematical modelling approaches in respiratory disease and highlight hypotheses generated by such models that could have significant implications for therapies in asthma, but that have not yet been the subject of experimental attention or investigation. Finally we highlight modelling approaches that have shown promise in other biological systems that could be brought to bear in developing mathematical models for optimisation of mechanopharmacological therapies in asthma, with discussion of how they could complement and accelerate current experimental approaches.
{"title":"The role of mathematical models in designing mechanopharmacological therapies for asthma","authors":"Linda Irons, B. Brook","doi":"10.3389/fsysb.2022.929426","DOIUrl":"https://doi.org/10.3389/fsysb.2022.929426","url":null,"abstract":"Healthy lung function depends on a complex system of interactions which regulate the mechanical and biochemical environment of individual cells to the whole organ. Perturbations from these regulated processes give rise to significant lung dysfunction such as chronic inflammation, airway hyperresponsiveness and airway remodelling characteristic of asthma. Importantly, there is ongoing mechanobiological feedback where mechanical factors including airway stiffness and oscillatory loading have considerable influence over cell behavior. The recently proposed area of mechanopharmacology recognises these interactions and aims to highlight the need to consider mechanobiology when identifying and assessing pharmacological targets. However, these multiscale interactions can be difficult to study experimentally due to the need for measurements across a wide range of spatial and temporal scales. On the other hand, integrative multiscale mathematical models have begun to show success in simulating the interactions between different mechanobiological mechanisms or cell/tissue-types across multiple scales. When appropriately informed by experimental data, these models have the potential to serve as extremely useful predictive tools, where physical mechanisms and emergent behaviours can be probed or hypothesised and, more importantly, exploited to propose new mechanopharmacological therapies for asthma and other respiratory diseases. In this review, we first demonstrate via an exemplar, how a multiscale mathematical model of acute bronchoconstriction in an airway could be exploited to propose new mechanopharmacological therapies. We then review current mathematical modelling approaches in respiratory disease and highlight hypotheses generated by such models that could have significant implications for therapies in asthma, but that have not yet been the subject of experimental attention or investigation. Finally we highlight modelling approaches that have shown promise in other biological systems that could be brought to bear in developing mathematical models for optimisation of mechanopharmacological therapies in asthma, with discussion of how they could complement and accelerate current experimental approaches.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44028811","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 : 2022-10-05DOI: 10.3389/fsysb.2022.987135
E. Voit
The past decades have witnessed an astounding rise of the nascent field of systems biology. By and large unknown or ignored for a long time, the field rapidly moved into the limelight and is now in the process of becoming a widely recognized and respected component of mainstream biology. Of course, much remains to be explored and accomplished in systems biology within its parent domain of biology, but the time seems ripe for expansions beyond this domain. The goal of such an expansion should not be the creation of new strongholds or academic silos outside biology, but the true integration of biological systems thinking into educational programs of other disciplines. The expansion should naturally start with closely related fields like biophysics, biochemistry, bioinformatics, and bioengineering, but should continue further into other areas invested in the study of life, such as medicine, epidemiology, and public health, as well as applied mathematics and computer science. This perspective sketches out how systems biological thinking might enrich the training of a new generation of scientists in different fields of scientific endeavor.
{"title":"Perspective: Systems biology beyond biology","authors":"E. Voit","doi":"10.3389/fsysb.2022.987135","DOIUrl":"https://doi.org/10.3389/fsysb.2022.987135","url":null,"abstract":"The past decades have witnessed an astounding rise of the nascent field of systems biology. By and large unknown or ignored for a long time, the field rapidly moved into the limelight and is now in the process of becoming a widely recognized and respected component of mainstream biology. Of course, much remains to be explored and accomplished in systems biology within its parent domain of biology, but the time seems ripe for expansions beyond this domain. The goal of such an expansion should not be the creation of new strongholds or academic silos outside biology, but the true integration of biological systems thinking into educational programs of other disciplines. The expansion should naturally start with closely related fields like biophysics, biochemistry, bioinformatics, and bioengineering, but should continue further into other areas invested in the study of life, such as medicine, epidemiology, and public health, as well as applied mathematics and computer science. This perspective sketches out how systems biological thinking might enrich the training of a new generation of scientists in different fields of scientific endeavor.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42491133","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 : 2022-10-03DOI: 10.3389/fsysb.2022.983372
Linlin Li, Xu Wang, Junyi Chai, Xiaoqian Wang, Adrián Buganza-Tepole, David M. Umulis
Embryonic development is a complex phenomenon that integrates genetic regulation and biomechanical cellular behaviors. However, the relative influence of these factors on spatiotemporal morphogen distributions is not well understood. Bone Morphogenetic Proteins (BMPs) are the primary morphogens guiding the dorsal-ventral (DV) patterning of the early zebrafish embryo, and BMP signaling is regulated by a network of extracellular and intracellular factors that impact the range and signaling of BMP ligands. Recent advances in understanding the mechanism of pattern formation support a source-sink mechanism, however, it is not clear how the source-sink mechanism shapes the morphogen patterns in three-dimensional (3D) space, nor how sensitive the pattern is to biophysical rates and boundary conditions along both the anteroposterior (AP) and DV axes of the embryo, nor how the patterns are controlled over time. Throughout blastulation and gastrulation, major cell movement, known as epiboly, happens along with the BMP-mediated DV patterning. The layer of epithelial cells begins to thin as they spread toward the vegetal pole of the embryo until it has completely engulfed the yolk cell. This dynamic domain may influence the distributions of BMP network members through advection. We developed a Finite Element Model (FEM) that incorporates all stages of zebrafish embryonic development data and solves the advection-diffusion-reaction Partial Differential Equations (PDE) in a growing domain. We use the model to investigate mechanisms in underlying BMP-driven DV patterning during epiboly. Solving the PDE is computationally expensive for parameter exploration. To overcome this obstacle, we developed a Neural Network (NN) metamodel of the 3D embryo that is accurate and fast and provided a nonlinear map between high-dimensional input and output that replaces the direct numerical simulation of the PDEs. From the modeling and acceleration by the NN metamodels, we identified the impact of advection on patterning and the influence of the dynamic expression level of regulators on the BMP signaling network.
{"title":"Determining the role of advection in patterning by bone morphogenetic proteins through neural network model-based acceleration of a 3D finite element model of the zebrafish embryo","authors":"Linlin Li, Xu Wang, Junyi Chai, Xiaoqian Wang, Adrián Buganza-Tepole, David M. Umulis","doi":"10.3389/fsysb.2022.983372","DOIUrl":"https://doi.org/10.3389/fsysb.2022.983372","url":null,"abstract":"Embryonic development is a complex phenomenon that integrates genetic regulation and biomechanical cellular behaviors. However, the relative influence of these factors on spatiotemporal morphogen distributions is not well understood. Bone Morphogenetic Proteins (BMPs) are the primary morphogens guiding the dorsal-ventral (DV) patterning of the early zebrafish embryo, and BMP signaling is regulated by a network of extracellular and intracellular factors that impact the range and signaling of BMP ligands. Recent advances in understanding the mechanism of pattern formation support a source-sink mechanism, however, it is not clear how the source-sink mechanism shapes the morphogen patterns in three-dimensional (3D) space, nor how sensitive the pattern is to biophysical rates and boundary conditions along both the anteroposterior (AP) and DV axes of the embryo, nor how the patterns are controlled over time. Throughout blastulation and gastrulation, major cell movement, known as epiboly, happens along with the BMP-mediated DV patterning. The layer of epithelial cells begins to thin as they spread toward the vegetal pole of the embryo until it has completely engulfed the yolk cell. This dynamic domain may influence the distributions of BMP network members through advection. We developed a Finite Element Model (FEM) that incorporates all stages of zebrafish embryonic development data and solves the advection-diffusion-reaction Partial Differential Equations (PDE) in a growing domain. We use the model to investigate mechanisms in underlying BMP-driven DV patterning during epiboly. Solving the PDE is computationally expensive for parameter exploration. To overcome this obstacle, we developed a Neural Network (NN) metamodel of the 3D embryo that is accurate and fast and provided a nonlinear map between high-dimensional input and output that replaces the direct numerical simulation of the PDEs. From the modeling and acceleration by the NN metamodels, we identified the impact of advection on patterning and the influence of the dynamic expression level of regulators on the BMP signaling network.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47808798","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 : 2022-09-23DOI: 10.3389/fsysb.2022.981800
Robert W. Smith, Luis Garcia-Morales, V. M. D. Martins dos Santos, E. Saccenti
Systems and Synthetic Biology are complementary fields emerging side-by-side into mainstream scientific research. Whilst systems biologists focus on understanding natural systems, synthetic biologists wish to modify, adapt and re-purpose biological systems towards certain desired goals, for example enhancing efficiency and robustness of desired biological traits. In both fields, data analysis, predictive mathematical modelling, experimental design, and controlled experimentation are crucial to obtain reproducible results and understand how applications can be scaled to larger systems and processes. As such, students from Life Sciences, Engineering, and Mathematics backgrounds must be taught fundamentals in biological systems, experimental techniques, mathematics, and data analysis/statistics. In addition, students must be trained for future multidisciplinary careers, where the interaction and communication between experimental and modelling researchers is fundamental. With the acceleration of technological developments (both computational and experimental) continuing unabated, educators need to bridge the increasing gap between fundamentally-required knowledge and skills that students need to pursue future academic or industrial research projects. In this paper, we will discuss how we have re-designed an introductory course in Systems and Synthetic Biology at Wageningen University and Research (Netherlands) that is targeted simultaneously to mathematical/computational students with an interest in biology and experimental methods, and to Life Science students interested in learning how biological systems can be mathematically analysed and modelled. The course highlights the links between fundamental methodologies and recently developed technologies within the Systems and Synthetic Biology fields. The course was re-designed for the 2021/22 academic year, we report that students from biology and biotechnology programmes graded their satisfaction of the course as 4.4 out of 5. We discuss how the course can act as a gateway to advanced courses in Systems Biology-oriented curricula (comprising: data infrastructure, modelling, and experimental synthetic biology), and towards future research projects.
{"title":"Research-driven education: An introductory course to systems and synthetic biology","authors":"Robert W. Smith, Luis Garcia-Morales, V. M. D. Martins dos Santos, E. Saccenti","doi":"10.3389/fsysb.2022.981800","DOIUrl":"https://doi.org/10.3389/fsysb.2022.981800","url":null,"abstract":"Systems and Synthetic Biology are complementary fields emerging side-by-side into mainstream scientific research. Whilst systems biologists focus on understanding natural systems, synthetic biologists wish to modify, adapt and re-purpose biological systems towards certain desired goals, for example enhancing efficiency and robustness of desired biological traits. In both fields, data analysis, predictive mathematical modelling, experimental design, and controlled experimentation are crucial to obtain reproducible results and understand how applications can be scaled to larger systems and processes. As such, students from Life Sciences, Engineering, and Mathematics backgrounds must be taught fundamentals in biological systems, experimental techniques, mathematics, and data analysis/statistics. In addition, students must be trained for future multidisciplinary careers, where the interaction and communication between experimental and modelling researchers is fundamental. With the acceleration of technological developments (both computational and experimental) continuing unabated, educators need to bridge the increasing gap between fundamentally-required knowledge and skills that students need to pursue future academic or industrial research projects. In this paper, we will discuss how we have re-designed an introductory course in Systems and Synthetic Biology at Wageningen University and Research (Netherlands) that is targeted simultaneously to mathematical/computational students with an interest in biology and experimental methods, and to Life Science students interested in learning how biological systems can be mathematically analysed and modelled. The course highlights the links between fundamental methodologies and recently developed technologies within the Systems and Synthetic Biology fields. The course was re-designed for the 2021/22 academic year, we report that students from biology and biotechnology programmes graded their satisfaction of the course as 4.4 out of 5. We discuss how the course can act as a gateway to advanced courses in Systems Biology-oriented curricula (comprising: data infrastructure, modelling, and experimental synthetic biology), and towards future research projects.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41854382","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 : 2022-09-01Epub Date: 2022-09-09DOI: 10.3389/fsysb.2022.898858
Eric Mjolsness
Many emergent, non-fundamental models of complex systems can be described naturally by the temporal evolution of spatial structures with some nontrivial discretized topology, such as a graph with suitable parameter vectors labeling its vertices. For example, the cytoskeleton of a single cell, such as the cortical microtubule network in a plant cell or the actin filaments in a synapse, comprises many interconnected polymers whose topology is naturally graph-like and dynamic. The same can be said for cells connected dynamically in a developing tissue. There is a mathematical framework suitable for expressing such emergent dynamics, "stochastic parameterized graph grammars," composed of a collection of the graph- and parameter-altering rules, each of which has a time-evolution operator that suitably moves probability. These rule-level operators form an operator algebra, much like particle creation/annihilation operators or Lie group generators. Here, we present an explicit and constructive calculation, in terms of elementary basis operators and standard component notation, of what turns out to be a general combinatorial expression for the operator algebra that reduces products and, therefore, commutators of graph grammar rule operators to equivalent integer-weighted sums of such operators. We show how these results extend to "dynamical graph grammars," which include rules that bear local differential equation dynamics for some continuous-valued parameters. Commutators of such time-evolution operators have analytic uses, including deriving efficient simulation algorithms and approximations and estimating their errors. The resulting formalism is complementary to spatial models in the form of partial differential equations or stochastic reaction-diffusion processes. We discuss the potential application of this framework to the remodeling dynamics of the microtubule cytoskeleton in cortical microtubule networks relevant to plant development and of the actin cytoskeleton in, for example, a growing or shrinking synaptic spine head. Both cytoskeletal systems underlie biological morphodynamics.
{"title":"Explicit Calculation of Structural Commutation Relations for Stochastic and Dynamical Graph Grammar Rule Operators in Biological Morphodynamics.","authors":"Eric Mjolsness","doi":"10.3389/fsysb.2022.898858","DOIUrl":"10.3389/fsysb.2022.898858","url":null,"abstract":"<p><p>Many emergent, non-fundamental models of complex systems can be described naturally by the temporal evolution of spatial structures with some nontrivial discretized topology, such as a graph with suitable parameter vectors labeling its vertices. For example, the cytoskeleton of a single cell, such as the cortical microtubule network in a plant cell or the actin filaments in a synapse, comprises many interconnected polymers whose topology is naturally graph-like and dynamic. The same can be said for cells connected dynamically in a developing tissue. There is a mathematical framework suitable for expressing such emergent dynamics, \"stochastic parameterized graph grammars,\" composed of a collection of the graph- and parameter-altering rules, each of which has a time-evolution operator that suitably moves probability. These rule-level operators form an operator algebra, much like particle creation/annihilation operators or Lie group generators. Here, we present an explicit and constructive calculation, in terms of elementary basis operators and standard component notation, of what turns out to be a general combinatorial expression for the operator algebra that reduces products and, therefore, commutators of graph grammar rule operators to equivalent integer-weighted sums of such operators. We show how these results extend to \"dynamical graph grammars,\" which include rules that bear local differential equation dynamics for some continuous-valued parameters. Commutators of such time-evolution operators have analytic uses, including deriving efficient simulation algorithms and approximations and estimating their errors. The resulting formalism is complementary to spatial models in the form of partial differential equations or stochastic reaction-diffusion processes. We discuss the potential application of this framework to the remodeling dynamics of the microtubule cytoskeleton in cortical microtubule networks relevant to plant development and of the actin cytoskeleton in, for example, a growing or shrinking synaptic spine head. Both cytoskeletal systems underlie biological morphodynamics.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"2 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10592048","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 : 2022-08-12DOI: 10.3389/fsysb.2022.955611
Alexandre J. Kennang Ouamba, Mérilie Gagnon, Thibault V. Varin, P. Chouinard, G. LaPointe, Denis Roy
The microbiota of silage is a key determinant of its quality. Although commercial inoculants are often used to improve silage quality, studies to analyze their impact on the microbiota of preserved forage at farm-scale facilities are scarce. We assessed the diversity of viable bacterial communities of hay (unfermented dry forage) and grass or legume (GL) and corn (C) silage to deepen our knowledge of how inoculant addition drives microbial occurrence patterns on dairy farms. Forage samples were collected from 24 dairy farms over two sampling periods. Samples were analyzed by high-throughput sequencing and quantitative PCR after being treated with propidium monoazide to account for viable cells. We found consistent significant differences between hay and silage community structures across sampling periods. Silage was generally dominated by lactic acid bacteria (LAB), while Pantoea and Sphingomonas were the main co-dominant genera in hay. The GL silage dominated by Pediococcus, Weissella, and Bacillus was phylogenetically different from C silage enriched in Acetobacter. The use of inoculants including Lentilactobacillus buchneri either alone or in combination with Lactiplantibacillus plantarum, Lacticaseibacillus casei, Pediococcus pentosaceus, or Enterococcus faecium did not systematically prevent the occurrence of undesirable bacteria, especially when corn-based, probably because of factors that can mitigate the effect of inoculation on the microbiota. The core Lactobacillales constituted the dominant LAB in silage with up to 96% relative abundance, indicating either the ubiquity of inoculants or the high competitiveness of epiphytes. Silage chemical profiles varied inconsistently with sampling periods and the use of inoculants. Multivariate multi-table analyses allowed the identification of bacterial clusters mainly driven by moisture and magnesium content in hay, while pH, lactic, and fatty acids were the main drivers for silage. Bacterial network analyses showed considerable variations in the topological roles with the use of inoculants. These results may help evaluate the effectiveness of forage management practices implemented on dairy farms and, therefore, are useful for fine-tuning the search for new additives. Such knowledge can be used by forage makers to adjust processing routines to improve the hygienic quality, nutritional potential, and aerobic stability of preserved forage.
{"title":"Metataxonomic insights into the microbial ecology of farm-scale hay, grass or legume, and corn silage produced with and without inoculants","authors":"Alexandre J. Kennang Ouamba, Mérilie Gagnon, Thibault V. Varin, P. Chouinard, G. LaPointe, Denis Roy","doi":"10.3389/fsysb.2022.955611","DOIUrl":"https://doi.org/10.3389/fsysb.2022.955611","url":null,"abstract":"The microbiota of silage is a key determinant of its quality. Although commercial inoculants are often used to improve silage quality, studies to analyze their impact on the microbiota of preserved forage at farm-scale facilities are scarce. We assessed the diversity of viable bacterial communities of hay (unfermented dry forage) and grass or legume (GL) and corn (C) silage to deepen our knowledge of how inoculant addition drives microbial occurrence patterns on dairy farms. Forage samples were collected from 24 dairy farms over two sampling periods. Samples were analyzed by high-throughput sequencing and quantitative PCR after being treated with propidium monoazide to account for viable cells. We found consistent significant differences between hay and silage community structures across sampling periods. Silage was generally dominated by lactic acid bacteria (LAB), while Pantoea and Sphingomonas were the main co-dominant genera in hay. The GL silage dominated by Pediococcus, Weissella, and Bacillus was phylogenetically different from C silage enriched in Acetobacter. The use of inoculants including Lentilactobacillus buchneri either alone or in combination with Lactiplantibacillus plantarum, Lacticaseibacillus casei, Pediococcus pentosaceus, or Enterococcus faecium did not systematically prevent the occurrence of undesirable bacteria, especially when corn-based, probably because of factors that can mitigate the effect of inoculation on the microbiota. The core Lactobacillales constituted the dominant LAB in silage with up to 96% relative abundance, indicating either the ubiquity of inoculants or the high competitiveness of epiphytes. Silage chemical profiles varied inconsistently with sampling periods and the use of inoculants. Multivariate multi-table analyses allowed the identification of bacterial clusters mainly driven by moisture and magnesium content in hay, while pH, lactic, and fatty acids were the main drivers for silage. Bacterial network analyses showed considerable variations in the topological roles with the use of inoculants. These results may help evaluate the effectiveness of forage management practices implemented on dairy farms and, therefore, are useful for fine-tuning the search for new additives. Such knowledge can be used by forage makers to adjust processing routines to improve the hygienic quality, nutritional potential, and aerobic stability of preserved forage.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44933043","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 : 2022-08-08DOI: 10.3389/fsysb.2022.912974
Eliezer Flores-Garza, Mario A. Zetter, R. Hernández-Pando, Elisa Domínguez-Hüttinger
Tuberculosis is a worldwide persistent infectious disease. It is caused by bacteria from the Mycobacterium tuberculosis complex that mainly affects the lungs and can be fatal. Using an integrative systems biology approach, we study the immunopathological progression of this disease, analyzing the key interactions between the cells involved in the different phases of the infectious process. We integrated multiple in vivo and in vitro data from immunohistochemical, serological, molecular biology, and cell count assays into a mechanistic mathematical model. The ordinary differential equation (ODE) model captures the regulatory interplay between the phenotypic variation of the main cells involved in the disease progression and the inflammatory microenvironment. The model reproduces in vivo time course data of an experimental model of progressive pulmonary TB in mouse, accurately reflecting the functional adaptations of the host–pathogen interactions as the disease progresses through three phenotypically different phases. We used the model to assess the effect of genotypic variations (encoded as changes in parameters) on disease outcomes. For all genotypes, we found an all-or-nothing response, where the virtual mouse either completely clears the infection or suffers uncontrolled Tb growth. Results show that it is 84% probable that a mouse submitted to a progressive pulmonary TB assay will end up with an uncontrolled infection. The simulations also showed how the genotypic variations shape the transitions across phases, showing that 100% of the genotypes evaluated eventually progress to phase two of the disease, suggesting that adaptive immune response activation was unavoidable. All the genotypes of the network that avoided progressing to phase 3 cleared the infection. Later, by analyzing the three different phases separately, we saw that the anti-inflammatory genotype of phase 3 was the one with the highest probability of leading to uncontrolled bacterial growth, and the proinflammatory genotype associated with phase 2 had the highest probability of bacterial clearance. Forty-two percent of the genotypes evaluated showed a bistable response, with one stable steady state corresponding to infection clearance and the other one to bacteria reaching its carrying capacity. Our mechanistic model can be used to predict the outcomes of different experimental conditions through in silico assays.
{"title":"Mathematical Model of the Immunopathological Progression of Tuberculosis","authors":"Eliezer Flores-Garza, Mario A. Zetter, R. Hernández-Pando, Elisa Domínguez-Hüttinger","doi":"10.3389/fsysb.2022.912974","DOIUrl":"https://doi.org/10.3389/fsysb.2022.912974","url":null,"abstract":"Tuberculosis is a worldwide persistent infectious disease. It is caused by bacteria from the Mycobacterium tuberculosis complex that mainly affects the lungs and can be fatal. Using an integrative systems biology approach, we study the immunopathological progression of this disease, analyzing the key interactions between the cells involved in the different phases of the infectious process. We integrated multiple in vivo and in vitro data from immunohistochemical, serological, molecular biology, and cell count assays into a mechanistic mathematical model. The ordinary differential equation (ODE) model captures the regulatory interplay between the phenotypic variation of the main cells involved in the disease progression and the inflammatory microenvironment. The model reproduces in vivo time course data of an experimental model of progressive pulmonary TB in mouse, accurately reflecting the functional adaptations of the host–pathogen interactions as the disease progresses through three phenotypically different phases. We used the model to assess the effect of genotypic variations (encoded as changes in parameters) on disease outcomes. For all genotypes, we found an all-or-nothing response, where the virtual mouse either completely clears the infection or suffers uncontrolled Tb growth. Results show that it is 84% probable that a mouse submitted to a progressive pulmonary TB assay will end up with an uncontrolled infection. The simulations also showed how the genotypic variations shape the transitions across phases, showing that 100% of the genotypes evaluated eventually progress to phase two of the disease, suggesting that adaptive immune response activation was unavoidable. All the genotypes of the network that avoided progressing to phase 3 cleared the infection. Later, by analyzing the three different phases separately, we saw that the anti-inflammatory genotype of phase 3 was the one with the highest probability of leading to uncontrolled bacterial growth, and the proinflammatory genotype associated with phase 2 had the highest probability of bacterial clearance. Forty-two percent of the genotypes evaluated showed a bistable response, with one stable steady state corresponding to infection clearance and the other one to bacteria reaching its carrying capacity. Our mechanistic model can be used to predict the outcomes of different experimental conditions through in silico assays.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49096585","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 : 2022-07-14DOI: 10.3389/fsysb.2022.923085
Daniela S. Santurio, L. Barros
CAR-T cell immunotherapy involves genetically reprogrammed T-lymphocytes that interact with cancer cells and activate an anti-tumor immune response. This therapy has been approved for clinical use for hematological cancers, but new challenges have emerged in the treatment of solid tumors. Some of the challenges include the heterogeneity of antigen expression found in solid tumors, including antigen-positive non-tumoral cells, the immune inhibitory molecule expression, and CAR-T cell trafficking difficulty within the tumor microenvironment. We proposed a mathematical model to describe the “on-target” and “off-tumor” effects of CAR-T cell therapy on gliomas, and we investigated which parameters influenced the final outcome using a global sensitivity analysis. Our model highlights the dynamics of CAR-T cell therapy, tumor, and healthy populations (antigen-positive glia, antigen-negative glia, and neurons), and it provides novel insight into the consequences of “on-target” “off-tumor” effects, particularly in the neuronal loss.
{"title":"A Mathematical Model for On-Target Off-Tumor Effect of CAR-T Cells on Gliomas","authors":"Daniela S. Santurio, L. Barros","doi":"10.3389/fsysb.2022.923085","DOIUrl":"https://doi.org/10.3389/fsysb.2022.923085","url":null,"abstract":"CAR-T cell immunotherapy involves genetically reprogrammed T-lymphocytes that interact with cancer cells and activate an anti-tumor immune response. This therapy has been approved for clinical use for hematological cancers, but new challenges have emerged in the treatment of solid tumors. Some of the challenges include the heterogeneity of antigen expression found in solid tumors, including antigen-positive non-tumoral cells, the immune inhibitory molecule expression, and CAR-T cell trafficking difficulty within the tumor microenvironment. We proposed a mathematical model to describe the “on-target” and “off-tumor” effects of CAR-T cell therapy on gliomas, and we investigated which parameters influenced the final outcome using a global sensitivity analysis. Our model highlights the dynamics of CAR-T cell therapy, tumor, and healthy populations (antigen-positive glia, antigen-negative glia, and neurons), and it provides novel insight into the consequences of “on-target” “off-tumor” effects, particularly in the neuronal loss.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46204299","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 : 2022-07-11DOI: 10.3389/fsysb.2022.925791
Naouel Zerrouk, Sahar Aghakhani, Vidisha Singh, F. Augé, A. Niarakis
Rheumatoid Arthritis (RA) is an autoimmune disease of unknown aetiology involving complex interactions between environmental and genetic factors. Its pathogenesis is suspected to arise from intricate interplays between signalling, gene regulation and metabolism, leading to synovial inflammation, bone erosion and cartilage destruction in the patients’ joints. In addition, the resident synoviocytes of macrophage and fibroblast types can interact with innate and adaptive immune cells and contribute to the disease’s debilitating symptoms. Therefore, a detailed, mechanistic mapping of the molecular pathways and cellular crosstalks is essential to understand the complex biological processes and different disease manifestations. In this regard, we present the RA-Atlas, an SBGN-standardized, interactive, manually curated representation of existing knowledge related to the onset and progression of RA. This state-of-the-art RA-Atlas includes an updated version of the global RA-map covering relevant metabolic pathways and cell-specific molecular interaction maps for CD4+ Th1 cells, fibroblasts, and M1 and M2 macrophages. The molecular interaction maps were built using information extracted from published literature and pathway databases and enriched using omic data. The RA-Atlas is freely accessible on the webserver MINERVA (https://ramap.uni.lu/minerva/), allowing easy navigation using semantic zoom, cell-specific or experimental data overlay, gene set enrichment analysis, pathway export or drug query.
{"title":"A Mechanistic Cellular Atlas of the Rheumatic Joint","authors":"Naouel Zerrouk, Sahar Aghakhani, Vidisha Singh, F. Augé, A. Niarakis","doi":"10.3389/fsysb.2022.925791","DOIUrl":"https://doi.org/10.3389/fsysb.2022.925791","url":null,"abstract":"Rheumatoid Arthritis (RA) is an autoimmune disease of unknown aetiology involving complex interactions between environmental and genetic factors. Its pathogenesis is suspected to arise from intricate interplays between signalling, gene regulation and metabolism, leading to synovial inflammation, bone erosion and cartilage destruction in the patients’ joints. In addition, the resident synoviocytes of macrophage and fibroblast types can interact with innate and adaptive immune cells and contribute to the disease’s debilitating symptoms. Therefore, a detailed, mechanistic mapping of the molecular pathways and cellular crosstalks is essential to understand the complex biological processes and different disease manifestations. In this regard, we present the RA-Atlas, an SBGN-standardized, interactive, manually curated representation of existing knowledge related to the onset and progression of RA. This state-of-the-art RA-Atlas includes an updated version of the global RA-map covering relevant metabolic pathways and cell-specific molecular interaction maps for CD4+ Th1 cells, fibroblasts, and M1 and M2 macrophages. The molecular interaction maps were built using information extracted from published literature and pathway databases and enriched using omic data. The RA-Atlas is freely accessible on the webserver MINERVA (https://ramap.uni.lu/minerva/), allowing easy navigation using semantic zoom, cell-specific or experimental data overlay, gene set enrichment analysis, pathway export or drug query.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46432364","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 : 2022-07-04DOI: 10.3389/fsysb.2022.951295
Gretter González-Blanco, José Manuel Jáuregui-Wade, Tea Anastasia Ruiz-Luis, Y. Saito-Nakano, J. Valdés
Eukaryotic circular RNAs (circRNAs) emerged in a common ancestor of the land-plant Arabidopsis thaliana, the fungi Saccharomyces cerevisiae and Schizosaccharomyces pombe, and the protists Plasmodium falciparum and Dictyostelium discoideum, more than a billion years ago (Wang et al., 2014). Due to their resistance to exonucleases, these molecules are very stable, and modern-day circRNAs are capable of interacting with proteins and other RNAs (Lasda and Parker, 2014), thus regulating multiple cellular mechanisms (Qu et al., 2015) ranging from cell-cell communication (Yu and Kuo, 2019) to gene expression regulation (Garcia-Lerena et al., 2022) and, together with miRNAs and mRNAs, participating in complex regulatory networks (Cao et al., 2020). Molecular dating and species number analyses suggest that after their marine origin, some Amoebozoans colonized the land ecosystems, and others diversified with land plant radiation (FizPalacios et al., 2013; Fiz-Palacios et al., 2014). Plants and amoebozoans co-evolved and interacted within these new ecosystems generating modern-day enteric Entamoeba species such as Entamoeba histolytica, which causes dysentery in humans, and E. invadens, which invades multiple tissues of reptiles (Loftus et al., 2005; Lorenzi et al., 2010; Ehrenkaufer et al., 2013; Tanaka et al., 2019). Furthermore, the parasitic E. histolytica speciation processes culminated in a very characteristic Sulfur metabolism (Jeelani and Nozaki, 2014; Mi-Ichi and Yoshida, 2019) including sulfate activation localized in mitochondria-related organelles (mitosomes), and sulfolipid metabolism pathways. The latter is crucial for the encystation of the reptilian parasite E. invadens (Jauregui-Wade et al., 2019; Jauregui-Wade et al., 2020), which is the model of choice to study amoebic differentiation so far. Recently, 12 intronic (flicRNAs), and 748 exonic and exonic-intronic (circRNAs) circular RNAs have been identified in E. histolytica and E. invadens. In the human parasite, flicRNAs and circRNAs are differentially expressed between virulent (HM1-IMSS) and avirulent (Rahman) amoebic strains (Mendoza-Figueroa et al., 2018; López-Luis, 2022). In contrast, the reported E. invadens circRNAs correspond to 20 h encysting-induced cultures (López-Luis, 2022). As expected, in addition to strainand encystment-specific circular RNAs, numerous circRNAs derived from genes of multiple functions were reported. We reasoned that the comparison of circular RNAs indicative of species-specific Sulfur metabolism with those indicative of previously acquired differentiation mechanisms, and with those indicative of more recently acquired parasitic behavior (virulence) could suggest their episodic origin (or repurposing) and their functional relationships. Edited by: Juan David Ospina-Villa, Colombian Institute of Tropical Medicine (ICMT), Colombia
10多亿年前，真核环状RNA（circRNA）出现在陆地植物拟南芥、真菌酿酒酵母（Saccharomyces cerevisiae）和球裂殖酵母（Schizosaccharomyces pombe）以及原生生物恶性疟原虫（Plasmodium falciparum）和盘基网柄菌（Dictyosterium discoideum）的共同祖先中（Wang et al.，2014）。由于它们对核酸外切酶的抗性，这些分子非常稳定，现代circRNA能够与蛋白质和其他RNA相互作用（Lasda和Parker，2014），从而调节多种细胞机制（Qu et al.，2015），从细胞间通讯（Yu和Kuo，2019）到基因表达调控（Garcia Lerena et al.，2022），参与复杂的调控网络（Cao et al.，2020）。分子年代测定和物种数量分析表明，一些变形虫在海洋起源后定居在陆地生态系统中，而另一些则随着陆地植物辐射而多样化（FizPalacios等人，2013；Fiz-Palacios et al.，2014）。植物和变形虫在这些新的生态系统中共同进化和相互作用，产生了现代肠道内阿米巴物种，如引起人类痢疾的溶组织内阿米巴和入侵爬行动物多种组织的E.入侵者（Loftus等人，2005；Lorenzi等人，2010；Ehrenkaufer等人，2013；Tanaka等人，2019）。此外，寄生溶组织E.histolytica的物种形成过程最终导致了非常具有特征性的硫代谢（Jeelani和Nozaki，2014；Mi-Ichi和Yoshida，2019），包括线粒体相关细胞器（有丝分裂体）中的硫酸盐激活和硫脂代谢途径。后者对入侵爬行动物寄生虫E.的包壳至关重要（Jauregui Wade等人，2019；Jauregui Wade等人，2020），这是迄今为止研究阿米巴分化的首选模型。最近，在溶组织大肠杆菌和入侵大肠杆菌中鉴定出12个内含子（flicRNA）和748个外显子和外显子内含子（circRNA）环状RNA。在人类寄生虫中，flicRNA和circRNA在强毒株（HM1-IMSS）和弱毒株（Rahman）阿米巴菌株之间差异表达（Mendoza Figueroa等人，2018；洛佩斯·路易斯，2022）。相反，报道的E.入侵者circRNA对应于20小时的包壳诱导培养物（López-Luis，2022）。正如预期的那样，除了菌株和外壳特异性环状RNA外，还报道了许多来源于多种功能基因的环状RNA。我们推断，指示物种特异性硫代谢的环状RNA与指示先前获得的分化机制的环状RNA以及指示最近获得的寄生行为（毒力）的环状RNA的比较可能表明它们的偶发起源（或重新利用）及其功能关系。编辑：Juan David Ospina Villa，哥伦比亚热带医学研究所，哥伦比亚
{"title":"Old Circular RNAs, New Habits: Repurposing Noncoding RNAs in Parasitic Amebozoa","authors":"Gretter González-Blanco, José Manuel Jáuregui-Wade, Tea Anastasia Ruiz-Luis, Y. Saito-Nakano, J. Valdés","doi":"10.3389/fsysb.2022.951295","DOIUrl":"https://doi.org/10.3389/fsysb.2022.951295","url":null,"abstract":"Eukaryotic circular RNAs (circRNAs) emerged in a common ancestor of the land-plant Arabidopsis thaliana, the fungi Saccharomyces cerevisiae and Schizosaccharomyces pombe, and the protists Plasmodium falciparum and Dictyostelium discoideum, more than a billion years ago (Wang et al., 2014). Due to their resistance to exonucleases, these molecules are very stable, and modern-day circRNAs are capable of interacting with proteins and other RNAs (Lasda and Parker, 2014), thus regulating multiple cellular mechanisms (Qu et al., 2015) ranging from cell-cell communication (Yu and Kuo, 2019) to gene expression regulation (Garcia-Lerena et al., 2022) and, together with miRNAs and mRNAs, participating in complex regulatory networks (Cao et al., 2020). Molecular dating and species number analyses suggest that after their marine origin, some Amoebozoans colonized the land ecosystems, and others diversified with land plant radiation (FizPalacios et al., 2013; Fiz-Palacios et al., 2014). Plants and amoebozoans co-evolved and interacted within these new ecosystems generating modern-day enteric Entamoeba species such as Entamoeba histolytica, which causes dysentery in humans, and E. invadens, which invades multiple tissues of reptiles (Loftus et al., 2005; Lorenzi et al., 2010; Ehrenkaufer et al., 2013; Tanaka et al., 2019). Furthermore, the parasitic E. histolytica speciation processes culminated in a very characteristic Sulfur metabolism (Jeelani and Nozaki, 2014; Mi-Ichi and Yoshida, 2019) including sulfate activation localized in mitochondria-related organelles (mitosomes), and sulfolipid metabolism pathways. The latter is crucial for the encystation of the reptilian parasite E. invadens (Jauregui-Wade et al., 2019; Jauregui-Wade et al., 2020), which is the model of choice to study amoebic differentiation so far. Recently, 12 intronic (flicRNAs), and 748 exonic and exonic-intronic (circRNAs) circular RNAs have been identified in E. histolytica and E. invadens. In the human parasite, flicRNAs and circRNAs are differentially expressed between virulent (HM1-IMSS) and avirulent (Rahman) amoebic strains (Mendoza-Figueroa et al., 2018; López-Luis, 2022). In contrast, the reported E. invadens circRNAs correspond to 20 h encysting-induced cultures (López-Luis, 2022). As expected, in addition to strainand encystment-specific circular RNAs, numerous circRNAs derived from genes of multiple functions were reported. We reasoned that the comparison of circular RNAs indicative of species-specific Sulfur metabolism with those indicative of previously acquired differentiation mechanisms, and with those indicative of more recently acquired parasitic behavior (virulence) could suggest their episodic origin (or repurposing) and their functional relationships. Edited by: Juan David Ospina-Villa, Colombian Institute of Tropical Medicine (ICMT), Colombia","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44473806","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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