Lymphocyte Dynamics and Repertoires, Modeling

Thomas‐Vaslin, Véronique; Six, Adrien; Bellier, Bertrand; Klatzmann, David

doi:10.1007/978-1-4419-9863-7_96

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…Another very different example of biological symmetry breaking is the dna recombinations in the maturation of lymphocytes (Thomas-Vaslin et al, 2013). The random process of recombination in a cell can be seen as a symmetry breaking from a situation where all the recombinations to come are equivalently possible to a situation where only one recombination is actually realized in each cell.…”

Section: Constraints and Randomnessmentioning

confidence: 99%

Theoretical principles for biology: Variation

Montévil

Mossio

Pocheville

et al. 2016

Progress in Biophysics and Molecular Biology

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Darwin introduced the concept that random variation generates new living forms. In this paper, we elaborate on Darwin's notion of random variation to propose that biological variation should be given the status of a fundamental theoretical principle in biology. We state that biological objects such as organisms are specific objects. Specific objects are special in that they are qualitatively different from each other. They can undergo unpredictable qualitative changes, some of which are not defined before they happen. We express the principle of variation in terms of symmetry changes, where symmetries underlie the theoretical determination of the object. We contrast the biological situation with the physical situation, where objects are generic (that is, different objects can be assumed to be identical) and evolve in well-defined state spaces. We derive several implications of the principle of variation, in particular, biological objects show randomness, historicity and contextuality. We elaborate on the articulation between this principle and the two other principles proposed in this special issue: the principle of default state and the principle of organization.

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Section: Constraints and Randomnessmentioning

confidence: 99%

Theoretical principles for biology: Variation

Montévil

Mossio

Pocheville

et al. 2016

Progress in Biophysics and Molecular Biology

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“…Despite recent systems biology initiatives to understand and model the immune system (2), we are still far from having the appropriate tools to understand its dynamics and to easily communicate among various researchers who observe this system at different levels of granularity and attempt through software modeling to answer different questions. Several complementary experimental methods and models have been used to explore lymphocyte dynamics and turnover (3, 4) and to model it in health, aging and diseases (5). …”

Section: Complexity Of the Immune Systemmentioning

confidence: 99%

“…Models of lymphocyte population dynamics and turnover (3) have primarily been based on mechanistic reconstruction with continuous time models. The fluxes of cell populations are then described by differential equations.…”

Section: Drawbacks Of Current Dynamics Lymphocyte Modeling and Evolutionmentioning

confidence: 99%

Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers

Thomas‐Vaslin¹,

Six²,

Ganascia³

et al. 2013

Front. Immunol.

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Dynamic modeling of lymphocyte behavior has primarily been based on populations based differential equations or on cellular agents moving in space and interacting each other. The final steps of this modeling effort are expressed in a code written in a programing language. On account of the complete lack of standardization of the different steps to proceed, we have to deplore poor communication and sharing between experimentalists, theoreticians and programmers. The adoption of diagrammatic visual computer language should however greatly help the immunologists to better communicate, to more easily identify the models similarities and facilitate the reuse and extension of existing software models. Since immunologists often conceptualize the dynamical evolution of immune systems in terms of “state-transitions” of biological objects, we promote the use of unified modeling language (UML) state-transition diagram. To demonstrate the feasibility of this approach, we present a UML refactoring of two published models on thymocyte differentiation. Originally built with different modeling strategies, a mathematical ordinary differential equation-based model and a cellular automata model, the two models are now in the same visual formalism and can be compared.

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“…However, there remain open questions concerning the rates of thymocyte production that result from various processes, such as differentiation, proliferation, selection, and death of T cells, and that influence the homeostasis and life-span of naïve and effector/memory T cells in secondary lymphoid organs. Several theoretical approaches and experimental immunological protocols are available to investigate lymphocyte dynamics [3] and to model them [14]. A number of studies have tried [15–17], often with the help of modelling [1, 18–21], to quantify thymocyte production, thymic output, and the processes involved in maintenance of a T lymphocyte dynamic equilibrium in the periphery in humans or mice [1, 22–26].…”

Section: Introductionmentioning

confidence: 99%

Modelling T cell proliferation: Dynamics heterogeneity depending on cell differentiation, age, and genetic background

Vibert

Thomas‐Vaslin

2017

PLoS Comput Biol

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Cell proliferation is the common characteristic of all biological systems. The immune system insures the maintenance of body integrity on the basis of a continuous production of diversified T lymphocytes in the thymus. This involves processes of proliferation, differentiation, selection, death and migration of lymphocytes to peripheral tissues, where proliferation also occurs upon antigen recognition. Quantification of cell proliferation dynamics requires specific experimental methods and mathematical modelling. Here, we assess the impact of genetics and aging on the immune system by investigating the dynamics of proliferation of T lymphocytes across their differentiation through thymus and spleen in mice. Our investigation is based on single-cell multicolour flow cytometry analysis revealing the active incorporation of a thymidine analogue during S phase after pulse-chase-pulse experiments in vivo, versus cell DNA content. A generic mathematical model of state transition simulates through Ordinary Differential Equations (ODEs) the evolution of single cell behaviour during various durations of labelling. It allows us to fit our data, to deduce proliferation rates and estimate cell cycle durations in sub-populations. Our model is simple and flexible and is validated with other durations of pulse/chase experiments. Our results reveal that T cell proliferation is highly heterogeneous but with a specific “signature” that depends upon genetic origins, is specific to cell differentiation stages in thymus and spleen and is altered with age. In conclusion, our model allows us to infer proliferation rates and cell cycle phase durations from complex experimental 5-ethynyl-2'-deoxyuridine (EdU) data, revealing T cell proliferation heterogeneity and specific signatures.

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Lymphocyte Dynamics and Repertoires, Modeling

Cited by 6 publications

References 9 publications

Theoretical principles for biology: Variation

Theoretical principles for biology: Variation

Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers

Modelling T cell proliferation: Dynamics heterogeneity depending on cell differentiation, age, and genetic background

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