Yaron Ben-Ami, Joe M. Pitt-Francis, Philip K. Maini, Helen M. Byrne
Interstitial fluid flow is a feature of many solid tumours. In vitro experiments have shown that such fluid flow can direct tumour cell movement upstream or downstream depending on the balance between the competing mechanisms of tensotaxis and autologous chemotaxis. In this work we develop a probabilistic-continuum, two-phase model for cell migration in response to interstitial flow. We use a Fokker-Planck type equation for the cell-velocity probability density function, and model the flow-dependent mechanochemical stimulus as a forcing term which biases cell migration upstream and downstream. Using velocity-space averaging, we reformulate the model as a system of continuum equations for the spatio-temporal evolution of the cell volume fraction and flux, in response to forcing terms which depend on the local direction and magnitude of the mechanochemical cues. We specialise our model to describe a one-dimensional cell layer subject to fluid flow. Using a combination of numerical simulations and asymptotic analysis, we delineate the parameter regime where transitions from downstream to upstream cell migration occur. As has been observed experimentally, the model predicts downstream-oriented, chemotactic migration at low cell volume fractions, and upstream-oriented, tensotactic migration at larger volume fractions. We show that the locus of the critical volume fraction, at which the system transitions from downstream to upstream migration, is dominated by the ratio of the rate of chemokine secretion and advection. Our model predicts that, because the tensotactic stimulus depends strongly on the cell volume fraction, upstream migration occurs only transiently when the cells are initially seeded, and transitions to downstream migration occur at later times due to the dispersive effect of cell diffusion.
{"title":"Using a probabilistic approach to derive a two-phase model of flow-induced cell migration","authors":"Yaron Ben-Ami, Joe M. Pitt-Francis, Philip K. Maini, Helen M. Byrne","doi":"arxiv-2309.13982","DOIUrl":"https://doi.org/arxiv-2309.13982","url":null,"abstract":"Interstitial fluid flow is a feature of many solid tumours. In vitro\u0000experiments have shown that such fluid flow can direct tumour cell movement\u0000upstream or downstream depending on the balance between the competing\u0000mechanisms of tensotaxis and autologous chemotaxis. In this work we develop a\u0000probabilistic-continuum, two-phase model for cell migration in response to\u0000interstitial flow. We use a Fokker-Planck type equation for the cell-velocity\u0000probability density function, and model the flow-dependent mechanochemical\u0000stimulus as a forcing term which biases cell migration upstream and downstream.\u0000Using velocity-space averaging, we reformulate the model as a system of\u0000continuum equations for the spatio-temporal evolution of the cell volume\u0000fraction and flux, in response to forcing terms which depend on the local\u0000direction and magnitude of the mechanochemical cues. We specialise our model to\u0000describe a one-dimensional cell layer subject to fluid flow. Using a\u0000combination of numerical simulations and asymptotic analysis, we delineate the\u0000parameter regime where transitions from downstream to upstream cell migration\u0000occur. As has been observed experimentally, the model predicts\u0000downstream-oriented, chemotactic migration at low cell volume fractions, and\u0000upstream-oriented, tensotactic migration at larger volume fractions. We show\u0000that the locus of the critical volume fraction, at which the system transitions\u0000from downstream to upstream migration, is dominated by the ratio of the rate of\u0000chemokine secretion and advection. Our model predicts that, because the\u0000tensotactic stimulus depends strongly on the cell volume fraction, upstream\u0000migration occurs only transiently when the cells are initially seeded, and\u0000transitions to downstream migration occur at later times due to the dispersive\u0000effect of cell diffusion.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"23 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523140","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}
Robert Chojowski, Ulrich S. Schwarz, Falko Ziebert
The nucleus of eukaryotic cells typically makes up around 30 % of the cell volume and tends to be up to ten times stiffer than the surrounding cytoplasm. Therefore it is an important element for cell mechanics, but a quantitative understanding of its mechanical role is largely missing. Here we demonstrate that elastic phase fields can be used to describe dynamical cell processes in adhesive or confining environments in which the nucleus plays an important role. We first introduce and verify our computational method and then study several applications of large relevance. For cells on adhesive patterns, we find that nuclear stress is shielded by the adhesive pattern. For cell compression between two parallel plates, we obtain force-compression curves that allow us to extract an effective modulus for the cell-nucleus composite. For micropipette aspiration, the effect of the nucleus on the effective modulus is found to be much weaker, highlighting the complicated interplay between extracellular geometry and cell mechanics that is captured by our approach.
{"title":"The role of the nucleus for cell mechanics: an elastic phase field approach","authors":"Robert Chojowski, Ulrich S. Schwarz, Falko Ziebert","doi":"arxiv-2309.12777","DOIUrl":"https://doi.org/arxiv-2309.12777","url":null,"abstract":"The nucleus of eukaryotic cells typically makes up around 30 % of the cell\u0000volume and tends to be up to ten times stiffer than the surrounding cytoplasm.\u0000Therefore it is an important element for cell mechanics, but a quantitative\u0000understanding of its mechanical role is largely missing. Here we demonstrate\u0000that elastic phase fields can be used to describe dynamical cell processes in\u0000adhesive or confining environments in which the nucleus plays an important\u0000role. We first introduce and verify our computational method and then study\u0000several applications of large relevance. For cells on adhesive patterns, we\u0000find that nuclear stress is shielded by the adhesive pattern. For cell\u0000compression between two parallel plates, we obtain force-compression curves\u0000that allow us to extract an effective modulus for the cell-nucleus composite.\u0000For micropipette aspiration, the effect of the nucleus on the effective modulus\u0000is found to be much weaker, highlighting the complicated interplay between\u0000extracellular geometry and cell mechanics that is captured by our approach.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523139","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}
Fahad Ahmed, Reem Abdel-Salam, Leon Hamnett, Mary Adewunmi, Temitope Ayano
Breast cancer is one of the leading causes of death for women worldwide. Early screening is essential for early identification, but the chance of survival declines as the cancer progresses into advanced stages. For this study, the most recent BRACS dataset of histological (H&E) stained images was used to classify breast cancer tumours, which contains both the whole-slide images (WSI) and region-of-interest (ROI) images, however, for our study we have considered ROI images. We have experimented using different pre-trained deep learning models, such as Xception, EfficientNet, ResNet50, and InceptionResNet, pre-trained on the ImageNet weights. We pre-processed the BRACS ROI along with image augmentation, upsampling, and dataset split strategies. For the default dataset split, the best results were obtained by ResNet50 achieving 66% f1-score. For the custom dataset split, the best results were obtained by performing upsampling and image augmentation which results in 96.2% f1-score. Our second approach also reduced the number of false positive and false negative classifications to less than 3% for each class. We believe that our study significantly impacts the early diagnosis and identification of breast cancer tumors and their subtypes, especially atypical and malignant tumors, thus improving patient outcomes and reducing patient mortality rates. Overall, this study has primarily focused on identifying seven (7) breast cancer tumor subtypes, and we believe that the experimental models can be fine-tuned further to generalize over previous breast cancer histology datasets as well.
{"title":"Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images","authors":"Fahad Ahmed, Reem Abdel-Salam, Leon Hamnett, Mary Adewunmi, Temitope Ayano","doi":"arxiv-2309.08745","DOIUrl":"https://doi.org/arxiv-2309.08745","url":null,"abstract":"Breast cancer is one of the leading causes of death for women worldwide.\u0000Early screening is essential for early identification, but the chance of\u0000survival declines as the cancer progresses into advanced stages. For this\u0000study, the most recent BRACS dataset of histological (H&E) stained images was\u0000used to classify breast cancer tumours, which contains both the whole-slide\u0000images (WSI) and region-of-interest (ROI) images, however, for our study we\u0000have considered ROI images. We have experimented using different pre-trained\u0000deep learning models, such as Xception, EfficientNet, ResNet50, and\u0000InceptionResNet, pre-trained on the ImageNet weights. We pre-processed the\u0000BRACS ROI along with image augmentation, upsampling, and dataset split\u0000strategies. For the default dataset split, the best results were obtained by\u0000ResNet50 achieving 66% f1-score. For the custom dataset split, the best\u0000results were obtained by performing upsampling and image augmentation which\u0000results in 96.2% f1-score. Our second approach also reduced the number of\u0000false positive and false negative classifications to less than 3% for each\u0000class. We believe that our study significantly impacts the early diagnosis and\u0000identification of breast cancer tumors and their subtypes, especially atypical\u0000and malignant tumors, thus improving patient outcomes and reducing patient\u0000mortality rates. Overall, this study has primarily focused on identifying seven\u0000(7) breast cancer tumor subtypes, and we believe that the experimental models\u0000can be fine-tuned further to generalize over previous breast cancer histology\u0000datasets as well.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522603","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}
Aarthi Venkat, Joyce Chew, Ferran Cardoso Rodriguez, Christopher J. Tape, Michael Perlmutter, Smita Krishnaswamy
Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.
{"title":"Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis","authors":"Aarthi Venkat, Joyce Chew, Ferran Cardoso Rodriguez, Christopher J. Tape, Michael Perlmutter, Smita Krishnaswamy","doi":"arxiv-2309.07813","DOIUrl":"https://doi.org/arxiv-2309.07813","url":null,"abstract":"Directed graphs are a natural model for many phenomena, in particular\u0000scientific knowledge graphs such as molecular interaction or chemical reaction\u0000networks that define cellular signaling relationships. In these situations,\u0000source nodes typically have distinct biophysical properties from sinks. Due to\u0000their ordered and unidirectional relationships, many such networks also have\u0000hierarchical and multiscale structure. However, the majority of methods\u0000performing node- and edge-level tasks in machine learning do not take these\u0000properties into account, and thus have not been leveraged effectively for\u0000scientific tasks such as cellular signaling network inference. We propose a new\u0000framework called Directed Scattering Autoencoder (DSAE) which uses a directed\u0000version of a geometric scattering transform, combined with the non-linear\u0000dimensionality reduction properties of an autoencoder and the geometric\u0000properties of the hyperbolic space to learn latent hierarchies. We show this\u0000method outperforms numerous others on tasks such as embedding directed graphs\u0000and learning cellular signaling networks.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523142","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}
Goutham Kodakandla, Askar Akimzhanov, Darren Boehning
Plasma membrane calcium influx through ion channels is crucial for many events in cellular physiology. Cell surface stimuli lead to the production of inositol 1,4,5-trisphosphate (IP3), which binds to IP3 receptors in the endoplasmic reticulum (ER) to release calcium pools from the ER lumen. This leads to depletion of ER calcium pools which has been termed store-depletion. Store-depletion leads the dissociation of calcium ions from the EF-hand motif of the ER calcium sensor Stromal Interaction Molecule 1 (STIM1). This leads to a conformational change in STIM1 which helps it to interact with a plasma membrane (PM) at ER:PM junctions. At these ER:PM junctions, STIM1 binds to and activates a calcium channel known as Orai1 to form calcium-release activated calcium (CRAC) channels. Activation of Orai1 leads to calcium influx, known as store-operated calcium entry (SOCE). In addition to Orai1 and STIM1, the homologs of Orai1 and STIM1, such as Orai2/3 and STIM2 also play a crucial role in calcium homeostasis. The influx of calcium through the Orai channel activates a calcium current that has been termed CRAC currents. CRAC channels form multimers and cluster together in large macromolecular assemblies termed puncta. How these CRAC channels form puncta has been contentious since their discovery. In this review, we will outline the history of SOCE, the molecular players involved in this process (Orai and STIM proteins, TRP channels, SOCE-associated regulatory factor etc.), as well as the models that have been proposed to explain this important mechanism in cellular physiology.
{"title":"Regulation of store-operated calcium entry","authors":"Goutham Kodakandla, Askar Akimzhanov, Darren Boehning","doi":"arxiv-2309.06907","DOIUrl":"https://doi.org/arxiv-2309.06907","url":null,"abstract":"Plasma membrane calcium influx through ion channels is crucial for many\u0000events in cellular physiology. Cell surface stimuli lead to the production of\u0000inositol 1,4,5-trisphosphate (IP3), which binds to IP3 receptors in the\u0000endoplasmic reticulum (ER) to release calcium pools from the ER lumen. This\u0000leads to depletion of ER calcium pools which has been termed store-depletion.\u0000Store-depletion leads the dissociation of calcium ions from the EF-hand motif\u0000of the ER calcium sensor Stromal Interaction Molecule 1 (STIM1). This leads to\u0000a conformational change in STIM1 which helps it to interact with a plasma\u0000membrane (PM) at ER:PM junctions. At these ER:PM junctions, STIM1 binds to and\u0000activates a calcium channel known as Orai1 to form calcium-release activated\u0000calcium (CRAC) channels. Activation of Orai1 leads to calcium influx, known as\u0000store-operated calcium entry (SOCE). In addition to Orai1 and STIM1, the\u0000homologs of Orai1 and STIM1, such as Orai2/3 and STIM2 also play a crucial role\u0000in calcium homeostasis. The influx of calcium through the Orai channel\u0000activates a calcium current that has been termed CRAC currents. CRAC channels\u0000form multimers and cluster together in large macromolecular assemblies termed\u0000puncta. How these CRAC channels form puncta has been contentious since their\u0000discovery. In this review, we will outline the history of SOCE, the molecular\u0000players involved in this process (Orai and STIM proteins, TRP channels,\u0000SOCE-associated regulatory factor etc.), as well as the models that have been\u0000proposed to explain this important mechanism in cellular physiology.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522604","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}
Maintaining tissue homeostasis requires appropriate regulation of stem cell differentiation. The Waddington landscape posits that gene circuits in a cell form a potential landscape of different cell types, wherein cells follow attractors of the probability landscape to develop into distinct cell types. However, how adult stem cells achieve a delicate balance between self-renewal and differentiation remains unclear. We propose that random inheritance of epigenetic states plays a pivotal role in stem cell differentiation and present a hybrid model of stem cell differentiation induced by epigenetic modifications. Our comprehensive model integrates gene regulation networks, epigenetic state inheritance, and cell regeneration, encompassing multi-scale dynamics ranging from transcription regulation to cell population. Through model simulations, we demonstrate that random inheritance of epigenetic states during cell divisions can spontaneously induce cell differentiation, dedifferentiation, and transdifferentiation. Furthermore, we investigate the influences of interfering with epigenetic modifications and introducing additional transcription factors on the probabilities of dedifferentiation and transdifferentiation, revealing the underlying mechanism of cell reprogramming. This textit{in silico} model provides valuable insights into the intricate mechanism governing stem cell differentiation and cell reprogramming and offers a promising path to enhance the field of regenerative medicine.
{"title":"Dynamics of cell-type transition mediated by epigenetic modifications","authors":"Rongsheng Huang, Qiaojun Situ, Jinzhi Lei","doi":"arxiv-2309.07356","DOIUrl":"https://doi.org/arxiv-2309.07356","url":null,"abstract":"Maintaining tissue homeostasis requires appropriate regulation of stem cell\u0000differentiation. The Waddington landscape posits that gene circuits in a cell\u0000form a potential landscape of different cell types, wherein cells follow\u0000attractors of the probability landscape to develop into distinct cell types.\u0000However, how adult stem cells achieve a delicate balance between self-renewal\u0000and differentiation remains unclear. We propose that random inheritance of\u0000epigenetic states plays a pivotal role in stem cell differentiation and present\u0000a hybrid model of stem cell differentiation induced by epigenetic\u0000modifications. Our comprehensive model integrates gene regulation networks,\u0000epigenetic state inheritance, and cell regeneration, encompassing multi-scale\u0000dynamics ranging from transcription regulation to cell population. Through\u0000model simulations, we demonstrate that random inheritance of epigenetic states\u0000during cell divisions can spontaneously induce cell differentiation,\u0000dedifferentiation, and transdifferentiation. Furthermore, we investigate the\u0000influences of interfering with epigenetic modifications and introducing\u0000additional transcription factors on the probabilities of dedifferentiation and\u0000transdifferentiation, revealing the underlying mechanism of cell reprogramming.\u0000This textit{in silico} model provides valuable insights into the intricate\u0000mechanism governing stem cell differentiation and cell reprogramming and offers\u0000a promising path to enhance the field of regenerative medicine.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"213 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522679","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}
Divakar Badal, Aloke Kumar, Varsha Singh, Danny Raj M
Microbial interactions regulate their spread and survival in competitive environments. It is not clear if the physical parameters of the environment regulate the outcome of these interactions. In this work, we show that the opportunistic pathogen Pseudomonas aeruginosa occupies a larger area on the substratum in the presence of yeast such as Cryptococcus neoformans , than without it. At the microscopic level, bacterial cells show an enhanced activity in the vicinity of yeast cells. We observe this behaviour even when the live yeast cells are replaced with heat-killed cells or with spherical glass beads of similar morphology, which suggests that the observed behaviour is not specific to the biology of microbes. Upon careful investigation, we find that a fluid pool is formed around yeast cells which facilitates the swimming of the flagellated P. aeruginosa , causing their enhanced motility. Using mathematical modeling we demonstrate how this local enhancement of bacterial motility leads to the enhanced spread observed at the level of the plate. We find that the dynamics of the fluid landscape around the bacteria, mediated by the growing yeast lawn, affects the spreading. For instance, when the yeast lawn grows faster, a bacterial colony prefers a lower initial loading of yeast cells for optimum enhancement in the spread. We confirm our predictions using Candida albicans and C. neoformans, at different initial compositions. In summary, our work shows the importance of considering the dynamically changing physical environment while studying bacterial motility in complex environments.
{"title":"A dynamic fluid landscape mediates the spread of bacteria","authors":"Divakar Badal, Aloke Kumar, Varsha Singh, Danny Raj M","doi":"arxiv-2309.05351","DOIUrl":"https://doi.org/arxiv-2309.05351","url":null,"abstract":"Microbial interactions regulate their spread and survival in competitive\u0000environments. It is not clear if the physical parameters of the environment\u0000regulate the outcome of these interactions. In this work, we show that the\u0000opportunistic pathogen Pseudomonas aeruginosa occupies a larger area on the\u0000substratum in the presence of yeast such as Cryptococcus neoformans , than\u0000without it. At the microscopic level, bacterial cells show an enhanced activity\u0000in the vicinity of yeast cells. We observe this behaviour even when the live\u0000yeast cells are replaced with heat-killed cells or with spherical glass beads\u0000of similar morphology, which suggests that the observed behaviour is not\u0000specific to the biology of microbes. Upon careful investigation, we find that a\u0000fluid pool is formed around yeast cells which facilitates the swimming of the\u0000flagellated P. aeruginosa , causing their enhanced motility. Using mathematical\u0000modeling we demonstrate how this local enhancement of bacterial motility leads\u0000to the enhanced spread observed at the level of the plate. We find that the\u0000dynamics of the fluid landscape around the bacteria, mediated by the growing\u0000yeast lawn, affects the spreading. For instance, when the yeast lawn grows\u0000faster, a bacterial colony prefers a lower initial loading of yeast cells for\u0000optimum enhancement in the spread. We confirm our predictions using Candida\u0000albicans and C. neoformans, at different initial compositions. In summary, our\u0000work shows the importance of considering the dynamically changing physical\u0000environment while studying bacterial motility in complex environments.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522608","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}
Kathrin Hellmuth, Christian Klingenberg, Qin Li, Min Tang
Directed bacterial motion due to external stimuli (chemotaxis) can, on the mesoscopic phase space, be described by a velocity change parameter $K$. The numerical reconstruction for $K$ from experimental data provides useful insights and plays a crucial role in model fitting, verification and prediction. In this article, the PDE-constrained optimization framework is deployed to perform the reconstruction of $K$ from velocity-averaged, localized data taken in the interior of a 1D domain. Depending on the data preparation and experimental setup, this problem can either be well- or ill-posed. We analyze these situations, and propose a very specific design that guarantees local convergence. The design is adapted to the discretization of $K$ and decouples the reconstruction of local values into smaller cell problem, opening up opportunities for parallelization. We further provide numerical evidence as a showcase for the theoretical results.
{"title":"Numerical reconstruction of the kinetic chemotaxis kernel from macroscopic measurement, wellposedness and illposedness","authors":"Kathrin Hellmuth, Christian Klingenberg, Qin Li, Min Tang","doi":"arxiv-2309.05004","DOIUrl":"https://doi.org/arxiv-2309.05004","url":null,"abstract":"Directed bacterial motion due to external stimuli (chemotaxis) can, on the\u0000mesoscopic phase space, be described by a velocity change parameter $K$. The\u0000numerical reconstruction for $K$ from experimental data provides useful\u0000insights and plays a crucial role in model fitting, verification and\u0000prediction. In this article, the PDE-constrained optimization framework is\u0000deployed to perform the reconstruction of $K$ from velocity-averaged, localized\u0000data taken in the interior of a 1D domain. Depending on the data preparation\u0000and experimental setup, this problem can either be well- or ill-posed. We\u0000analyze these situations, and propose a very specific design that guarantees\u0000local convergence. The design is adapted to the discretization of $K$ and\u0000decouples the reconstruction of local values into smaller cell problem, opening\u0000up opportunities for parallelization. We further provide numerical evidence as\u0000a showcase for the theoretical results.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"44 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522611","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}
Yue LiuMathematical Institute, University of Oxford, Kevin SuhDepartment of Chemical and Biological Engineering, Princeton University, Philip K. MainiMathematical Institute, University of Oxford, Daniel J. CohenDepartment of Chemical and Biological Engineering, Princeton UniversityDepartment of Mechanical and Aerospace Engineering, Princeton University, Ruth E. BakerMathematical Institute, University of Oxford
When employing a mechanistic model to study biological systems, practical parameter identifiability is important for making predictions in a wide range of scenarios, as well as for understanding the mechanisms driving the system behaviour. We argue that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. To demonstrate, we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and require more data to be practically identifiable. The results from identifiability analysis can inform model selection, as well as data collection and experimental design.
{"title":"Parameter identifiability and model selection for partial differential equation models of cell invasion","authors":"Yue LiuMathematical Institute, University of Oxford, Kevin SuhDepartment of Chemical and Biological Engineering, Princeton University, Philip K. MainiMathematical Institute, University of Oxford, Daniel J. CohenDepartment of Chemical and Biological Engineering, Princeton UniversityDepartment of Mechanical and Aerospace Engineering, Princeton University, Ruth E. BakerMathematical Institute, University of Oxford","doi":"arxiv-2309.01476","DOIUrl":"https://doi.org/arxiv-2309.01476","url":null,"abstract":"When employing a mechanistic model to study biological systems, practical\u0000parameter identifiability is important for making predictions in a wide range\u0000of scenarios, as well as for understanding the mechanisms driving the system\u0000behaviour. We argue that parameter identifiability should be considered\u0000alongside goodness-of-fit and model complexity as criteria for model selection.\u0000To demonstrate, we use a profile likelihood approach to investigate parameter\u0000identifiability for four extensions of the Fisher--KPP model, given\u0000experimental data from a cell invasion assay. We show that more complicated\u0000models tend to be less identifiable, with parameter estimates being more\u0000sensitive to subtle differences in experimental procedures, and require more\u0000data to be practically identifiable. The results from identifiability analysis\u0000can inform model selection, as well as data collection and experimental design.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"35 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522605","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}
Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualizing the emergent behavior of cells. We first review how this inference problem has been addressed in freely migrating cells on two-dimensional substrates and in structured, confining systems. Moreover, we discuss how data-driven methods can be connected with molecular mechanisms, either by integrating mechanistic bottom-up biophysical models, or by performing inference on subcellular degrees of freedom. Finally, we provide an overview of applications of data-driven modelling in developing frameworks for cell-to-cell variability in behaviours, and for learning the collective dynamics of multicellular systems. Specifically, we review inference and machine learning approaches to recover cell-cell interactions and collective dynamical modes, and how these can be integrated into physical active matter models of collective migration.
{"title":"Learning dynamical models of single and collective cell migration: a review","authors":"David B. Brückner, Chase P. Broedersz","doi":"arxiv-2309.00545","DOIUrl":"https://doi.org/arxiv-2309.00545","url":null,"abstract":"Single and collective cell migration are fundamental processes critical for\u0000physiological phenomena ranging from embryonic development and immune response\u0000to wound healing and cancer metastasis. To understand cell migration from a\u0000physical perspective, a broad variety of models for the underlying physical\u0000mechanisms that govern cell motility have been developed. A key challenge in\u0000the development of such models is how to connect them to experimental\u0000observations, which often exhibit complex stochastic behaviours. In this\u0000review, we discuss recent advances in data-driven theoretical approaches that\u0000directly connect with experimental data to infer dynamical models of stochastic\u0000cell migration. Leveraging advances in nanofabrication, image analysis, and\u0000tracking technology, experimental studies now provide unprecedented large\u0000datasets on cellular dynamics. In parallel, theoretical efforts have been\u0000directed towards integrating such datasets into physical models from the single\u0000cell to the tissue scale with the aim of conceptualizing the emergent behavior\u0000of cells. We first review how this inference problem has been addressed in\u0000freely migrating cells on two-dimensional substrates and in structured,\u0000confining systems. Moreover, we discuss how data-driven methods can be\u0000connected with molecular mechanisms, either by integrating mechanistic\u0000bottom-up biophysical models, or by performing inference on subcellular degrees\u0000of freedom. Finally, we provide an overview of applications of data-driven\u0000modelling in developing frameworks for cell-to-cell variability in behaviours,\u0000and for learning the collective dynamics of multicellular systems.\u0000Specifically, we review inference and machine learning approaches to recover\u0000cell-cell interactions and collective dynamical modes, and how these can be\u0000integrated into physical active matter models of collective migration.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"26 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522607","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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