Cellular decision-making help cells to infer functionally different phenotypes in response to microenvironmental cues and noise present in the system and the environment, with or without genetic change.
In Cellular Biology, there exists a list of open questions such as, how individual cell decisions influence the dynamics at the population level (an organization of indistinguishable cells) and at the tissue level (a group of nearly identical cells and their corresponding extracellular matrix which simultaneously accomplish a set of biological operations)? As collective cell migration originates from local cellular orientation decisions, can one generate a mathematical model for collective cell migration phenomena without elusive undiscovered biophysical/biochemical mechanisms and further predict the pattern formations which originates inside the collective cell migration? how optimal microenvironmental sensing is related to differentiated tissue at the spatial scale ? How cell sensing radius and total entropy production (which precisely helps us to understand the operating regimes where cells can take decisions about their future fate) is correlated, and how can one understand the limits of sensing radius at robust tissue development ? To partially tackle these sets of questions, the LEUP (Least microEnvironmental Uncertainty Principle) hypothesis has been applied to different biological scenaros.
At first, the LEUP has been enforced to understand the spatio-temporal behavior of a tissue exhibiting phenotypic plasticity (it is a prototype of cell decision-making). Here, two cases have been rigorously studied i.e., migration/resting and migration/proliferation plasticity which underlie the epithelial-mesenchymal transition (EMT) and the Go-or-Grow dichotomy. On the one hand, for the Go-or-Rest plasticity, a bistable switching mechanism between a diffusive (fluid) and an epithelial (solid) tissue phase has been observed from an analogous mean-field approximation which further depends on the sensitivity of the phenotypes to the microenvironment. However, on the other hand, for the Go-or-Grow plasticity, the possibility of Turing pattern formation is inspected for the “solid” tissue phase and its relation to the parameters of the LEUP-driven cell decisions.
Later, LEUP hypothesis has been suggested in the area of collective cell migration such that it can provide a tool for a generative mathematical model of collective migration without precise knowledge about the mechanistic details, where the famous Vicsek model is a special case. In this generative model of collective cell migration, the origin of pattern formation inside collective cell migration has been investigated. Moreover, this hypothesis helps to construct a mathematical model for the collective behavior of spherical \textit{Serratia marcescens} bacteria, where the basic understanding of migration mechanisms remain unknown.
Furthermore, LEUP has been applied to understand tissue robustness, which in turn shows the way how progenitor cell fate decisions are associated with environmental sensing. The regulation of environmental sensing drives the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue, which are verified later with the experimental data. LEUP driven stochastic thermodynamic formalism also shows that the thermodynamic robustness of differentiated tissues depends on cell metabolism, cell sensing properties and the limits of the cell sensing radius, which further ensures the robustness of differentiated tissue spatial order.
Finally, all important results of the thesis have been encapsulated and the extension of the LEUP has been discussed.:Contents
Statement of authorship vii
Abstract ix
I. Introduction to cell decision-making 1
1. What is cell decision-making ? 3
1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Examplesofcelldecision-making. . . . . . . . . . . . . . . . . . . . . . 4
1.2.1. PhenotypicPlasticity . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2. Cellularmigration:orientationdecisions . . . . . . . . . . . . . 5
1.2.3. Celldifferentiation . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3. Challengesandopenquestions . . . . . . . . . . . . . . . . . . . . . . 7
1.4. Solutionstrategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5. Structureofthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
II. Least microEnvironmental Uncertainty Principle (LEUP) 11
2. Least microEnvironmental Uncertainty Principle (LEUP) 13
2.1. HypothesisbehindLEUP . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2. Mathematicalformulation . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1. CellasBayesiandecisionmaker . . . . . . . . . . . . . . . . . . 14
2.2.2. VariationalprincipleforLEUP . . . . . . . . . . . . . . . . . . . . 16
III. LEUP in biological problems 17
3. Phenotypic plasticity : dynamics at the level of tissue from individual cell
decisions 19
3.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2. Individualbasedmodel(IBM) . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3. Mean-fieldapproximation . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.3.1. Phenotypicswitchingdynamics . . . . . . . . . . . . . . . . . . 26
3.3.2. Cellmigrationdynamics . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.3. Superpositionofphenotypicswitchingdynamicsandcellmi-
gration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4. Spatio-temporaldynamicsofcellmigration/proliferationplasticity . . 28
3.4.1. CaseI:Largeinteractionradius . . . . . . . . . . . . . . . . . . 29
3.4.2. CaseII:Finiteinteractionradius . . . . . . . . . . . . . . . . . . 30
3.4.3. Phenotypicswitchingdynamicsintheabsenceofmicroenvi-
ronmentalsensing . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4. Cellular orientation decisions: origin of pattern formations in collective
cell migrations 39
4.1. Mathematicalframework . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.1.1. Self-propelledparticlemodelwithleupbaseddecision-making 41
4.1.2. Orderparametersandobservables . . . . . . . . . . . . . . . . 42
4.1.3. Statisticaltest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2. ComparisonwithVicsekmodel . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.1. Patternsindifferentparameterregimes . . . . . . . . . . . . . 45
4.3. Application:thesphericalbacteriacase. . . . . . . . . . . . . . . . . . 47
4.4. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5. Cell differentiation and sensing: tissue robustness from optimal environ-
mental sensing 53
5.1. LEUPbasedmathematicalmodelforcelldifferentiation . . . . . . . . 56
5.1.1. StatisticalresultsfromLEUP . . . . . . . . . . . . . . . . . . . . 59
5.2. RelationbetweenLEUPandcellsensing . . . . . . . . . . . . . . . . . 60
5.3. LEUPdrivenfluctuationtheorem: confirmsthethermodynamicro-
bustnessofdifferentiatedtissues . . . . . . . . . . . . . . . . . . . . . 61
5.3.1. Application: differentiated photoreceptor mosaics are ther-
modynamicallyrobust . . . . . . . . . . . . . . . . . . . . . . . . 65
5.4. Thelimitforcellsensingradius . . . . . . . . . . . . . . . . . . . . . . . 67
5.4.1. Application:Theaveragesensingradiusoftheavianconecell 69
5.5. Summaryandoutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6. Discussions 75
7. Supplementary Material 91
8. Erklärung 115
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:77565 |
| Date | 26 January 2022 |
| Creators | Barua, Arnab |
| Contributors | Voigt, Axel, Meyer-Hermann, Michael, Hatzikirou, Haralampos, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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