Multiscale Modeling of the Early CD8 T-Cell Immune Response in Lymph Nodes: An Integrative Study

Prokopiou, Sotiris; Barbarroux, Loic; Bernard, Samuel; Mafille, Julien; Leverrier, Yann; Arpin, Christophe; Marvel, Jacqueline; Gandrillon, Olivier; Crauste, Fabien

doi:10.3390/computation2040159

Cited by 29 publications

(71 citation statements)

References 53 publications

(89 reference statements)

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“…However, only more recently have truly multiscale models emerged, where both the intracellular biochemistry and cell–cell interactions can be analyzed simultaneously (42). This is due, in part, to advances in computer hardware.…”

Section: Agent-based Models Can Integrate Intracellular and Intercellmentioning

confidence: 99%

The Role of Multiscale Protein Dynamics in Antigen Presentation and T Lymphocyte Recognition

et al. 2017

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T lymphocytes are stimulated when they recognize short peptides bound to class I proteins of the major histocompatibility complex (MHC) protein, as peptide–MHC complexes. Due to the diversity in T-cell receptor (TCR) molecules together with both the peptides and MHC proteins they bind to, it has been difficult to design vaccines and treatments based on these interactions. Machine learning has made some progress in trying to predict the immunogenicity of peptide sequences in the context of specific MHC class I alleles but, as such approaches cannot integrate temporal information and lack explanatory power, their scope will always be limited. Here, we advocate a mechanistic description of antigen presentation and TCR activation which is explanatory, predictive, and quantitative, drawing on modeling approaches that collectively span several length and time scales, being capable of furnishing reliable biological descriptions that are difficult for experimentalists to provide. It is a form of multiscale systems biology. We propose the use of chemical rate equations to describe the time evolution of the foreign and host proteins to explain how the original proteins end up being presented on the cell surface as peptide fragments, while we invoke molecular dynamics to describe the key binding processes on the molecular level, including those of peptide–MHC complexes with TCRs which lie at the heart of the immune response. On each level, complementary methods based on machine learning are available, and we discuss the relationship between these divergent approaches. The pursuit of predictive mechanistic modeling approaches requires experimentalists to adapt their work so as to acquire, store, and expose data that can be used to verify and validate such models.

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Section: Agent-based Models Can Integrate Intracellular and Intercellmentioning

confidence: 99%

The Role of Multiscale Protein Dynamics in Antigen Presentation and T Lymphocyte Recognition

et al. 2017

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“…Multi-scale models are usually based on the continuum modeling and/or MAS approaches which can be decomposed into N single-scale mathematical models and several physical processes. Various works have already been done which apply many multi-scale methods in several biological systems [14,[17][18][19][20][21][22][36][37][38][39][40][41][42][43][44][45][46][47][48][49]: a good review is proposed in Table 1.…”

Section: Strategy Referencesmentioning

confidence: 99%

“…These diverse levels coupling intra and interscale interactions make a biological system extremely complex, requiring advanced mathematical and computational models for the integration of the different scales [15,16]. There is a range of multi-scale modelling methods that could potentially be employed in systems biology [17][18][19][20][21][22] and consequently in the biofabrication of tissues and organs. Then, in the next section, we will describe some multi-scale approaches and/or software which may be part of the BioCAE and that are playing or will play important roles in multi-scale problems in systems biology and tissues biofabricated, thereby preventing a large amount of trial and error experiments in laboratories.…”

Section: Biological Computational Aided Engineering For Biofabricatiomentioning

confidence: 99%

“…Modelling and simulation of diffusion process in tissue spheroids CFD [14] Agent-based virtual tissue simulations CC3D [17] Cell compressibility, motility and contact inhibition on the growth of tumor cell clusters CC3D [18] Multiscale modeling of the early CD8 T cell immune response in lymph nodes CC3D [19] Multi-scale knowledge on cardiac development CC3D [20] The cell behavior ontology CC3D [21] Cell differentiation in the transition to multicellularity CC3D [22] Dynamics of cell aggregates fusion CC3D [36] A multi-cell model of tumor evolution CC3D [37] Virtual tissues MAS [38] Disruption of blood vessel development CC3D [39] Tumor growth and angiogenesis CC3D [40] Agent-oriented in silico liver (ILS) MAS [41] Model of thrombus development MAS (TS) [42] Three-dimensional multi-scale tumor model MAS [43] A multi-scale model of dendritic cell education and trafficking in the lung CM [44] Multi-scale model of follicular development CM [45] Multi-scale in silico leukocytes model MAS [46] Multi-scale model of organogenesis CM [47] Limitations of spheroids under inappropriate conditions FE [48] Finite lower levels occur at multiple time scales influencing the behavior of the organ. Multi-scale models are usually based on the continuum modeling and/or MAS approaches which can be decomposed into N single-scale mathematical models and several physical processes.…”

Section: Strategy Referencesmentioning

confidence: 99%

See 1 more Smart Citation

BioCAE: A New Strategy of Complex Biological Systems for Biofabrication of Tissues and Organs

Dernowsek¹,

Ra²,

Silva³

2017

J Tissue Sci Eng

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Next sessions we expose a new approach, based on Information *Corresponding author: Janaina de Andrea Dernowsek, Center for information technology Renato Archer (CTI), 3D Technologies Research Group (NT3D), Campinas, Brazil, AbstractBiofabrication as an interdisciplinary area is fostering new knowledge and integration of areas like nanotechnology, chemistry, biology, physics, materials science, control systems, among many others, necessary to accomplish the challenge of bioengineering functional complex tissues. The emergence of integrated platforms and systems biology to understand complex biological systems in multiscale levels will enable the prediction and creation of biofabricated biological structures. This systematic analysis (meta-analysis) or integrated platforms for estimating biological process have been named as BioCAE, which will become the key for important steps of the biofabrication processes. Biological Computational Aided Engineering (BioCAE) is a new computational approach to understanding and bioengineer complex tissues (biofabrication) using a combination of different methods as multiscale modelling, computer simulations, data mining and systems biology. In addition, multi-agent systems (MAS), which are composed of different interacting computing entities called agents, also provide an interesting way to design and implement simulations of biological systems, integrating them with all steps of the BioCAE. MAS as a part of computational science have become a growing area to manipulate and solve complex problems. This paper presents an approach that will allow predicting the development and behavior of different biological processes such as molecular networks, gene interactions, cells, tissues and organs due to its flexibility, beyond to provide a new outlook in the biofabrication of tissues and organs.

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“…Some studies have been performed in which the architecture of the LN is modeled as either 2D-or 3D regular orthogonal lattice [9][10][11][12][13][14] or a paradigmatic 3D view composed of a simplified set of basic structures [15]. The broad availability of various imaging techniques has enabled the characterization of the LN structures with a high degree of spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Computational Approach to 3D Modeling of the Lymph Node Geometry

et al. 2015

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Abstract:In this study we present a computational approach to the generation of the major geometric structures of an idealized murine lymph node (LN). In this generation, we consider the major compartments such as the subcapsular sinus, B cell follicles, trabecular and medullar sinuses, blood vessels and the T cell zone with a primary focus on the fibroblastic reticular cell (FRC) network. Confocal microscopy data of LN macroscopic structures and structural properties of the FRC network have been generated and utilized in the present model. The methodology sets a library of modules that can be used to assemble a solid geometric LN model and subsequently generate an adaptive mesh model capable of implementing transport phenomena. Overall, based on the use of high-resolution confocal microscopy and morphological analysis of cell 3D reconstructions, we have developed a computational model of the LN geometry, suitable for further investigation in studies of fluid transport and cell migration in this immunologically essential organ.

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Multiscale Modeling of the Early CD8 T-Cell Immune Response in Lymph Nodes: An Integrative Study

Cited by 29 publications

References 53 publications

The Role of Multiscale Protein Dynamics in Antigen Presentation and T Lymphocyte Recognition

The Role of Multiscale Protein Dynamics in Antigen Presentation and T Lymphocyte Recognition

BioCAE: A New Strategy of Complex Biological Systems for Biofabrication of Tissues and Organs

Computational Approach to 3D Modeling of the Lymph Node Geometry

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