Systems Biology in Immunology: A Computational Modeling Perspective

Germain, Ronald N.; Meier‐Schellersheim, Martin; Nita‐Lazar, Aleksandra; Fraser, Iain D. C.

doi:10.1146/annurev-immunol-030409-101317

Cited by 168 publications

(137 citation statements)

References 326 publications

(311 reference statements)

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“…While such rule-based simulation tools have overcome many of the technical challenges involved in computational analysis of complex biochemical pathways, the lack of accurate measurements of important model parameters, such as intracellular protein concentrations, still represents a major bottleneck in using such methods to better understand biological processes (22). Among the experimental approaches that allow measuring protein abundances, mass spectrometry is establishing itself as the tool of choice in cases where more than just a handful of signaling components need to be quantified.…”

mentioning

confidence: 99%

Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing

Manes

Angermann

Koppenol-Raab

et al. 2015

Molecular & Cellular Proteomics

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Osteoclasts are monocyte-derived multinuclear cells that directly attach to and resorb bone. Sphingosine-1-phosphate (S1P) 1 regulates bone resorption by functioning as both a chemoattractant and chemorepellent of osteoclast precursors through two G-protein coupled receptors that antagonize each other in an S1P-concentration-dependent manner. To quantitatively explore the behavior of this chemosensing pathway, we applied targeted proteomics, transcriptomics, and rule-based pathway modeling using the Simmune toolset. RAW264.7 cells (a mouse monocyte/macrophage cell line) were used as model osteoclast precursors, RNA-seq was used to identify expressed target proteins, and selected reaction monitoring (SRM) mass spectrometry using internal peptide standards was used to perform absolute abundance measurements of pathway proteins. The resulting transcript and protein abundance values were strongly correlated. Measured protein abundance values, used as simulation input parameters, led to in silico pathway behavior matching in vitro measurements. Moreover, once model parameters were established, even simulated responses toward stimuli that were not used for parameterization were consistent with experimental findings. These findings demonstrate the feasibility and value of combining targeted mass spectrometry with pathway modeling for advancing biological insight. Molecular & Cellular Proteomics 14: 10.1074/mcp.M115.048918, 2661-2681, 2015.Chemotaxis is defined as directed movement of a cell (or of an organism) resulting from stimulation by a chemokine or other chemotactic chemical. Eukaryotic cells employ intricate intracellular pathways to sense concentration differences of chemoattractants at their surface and move along such gradients using multiple synchronized cellular processes, including cell protrusion and adhesion at the leading end, de-adhesion at the trailing end, and mechanical force generation at both the leading and trailing ends (1-4).Activation of chemotactic receptors through chemoattractant concentration gradients results in nonuniform intracellular signaling responses that depend on numerous feedback mechanisms (5-7). Downstream, F-actin synthesis at the leading end causes the formation of cell protrusions (for example, filopodia, lamellipodia, and lamellae) that, in concert with actomyosin contraction, generates force at both the leading and trailing ends (8 -10). Chemotactic traction is produced by cell protrusions interacting with a confined environment and/or by adhesions that bind to the extracellular matrix and/or to cell adhesion molecules (11).Chemotaxis plays a major role in a wide range of physiological and pathophysiological processes. Sphingosine-1-phosphate (S1P), a phosphosphingolipid, mediates chemotaxis of many circulating cell types, including osteoclast precursors (OPs) (12)(13)(14). Osteoclasts are monocyte-derived multinuclear cells that directly attach to the bone matrix and resorb bone. They are solely responsible for bone resorption, and their misregulated activity has been imp...

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mentioning

confidence: 99%

Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing

Manes

Angermann

Koppenol-Raab

et al. 2015

Molecular & Cellular Proteomics

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“…Current knowledge of the roles of individual IFN subtypes in health and disease has not been forthcoming from clinical investigations. Both viral and host genome studies and transcriptome analyses have the potential to drive identification of clinically relevant IFN biomarkers [146]. Too often, clinical trials have not measured IFN subtypes and/or explored only limited subtypes for disease, some with poor outcomes.…”

Section: Ifns In Human Therapymentioning

confidence: 99%

Interferons as Biomarkers and Effectors: Lessons Learned from Animal Models

2012

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Interferons (IFNs) comprise type I, II and III families with multiple subtypes. Via transcription of IFNstimulated genes (ISGs), IFNs can exert multiple biological effects on the cell. In infectious and chronic inflammatory diseases, the IFNs and their ISG sets can be potentially utilized as biomarkers of disease outcome. Animal models allow investigations into disease pathogenesis and gene knockout models have proved cause and effect relationships of molecules related to the IFN response. Sets of IFN subtypes and their ISG products provide immunological signature patterns for different viral and other diseases. In this article, we give an overview of IFNs in several virus infection models and autoimmune diseases of medical relevance. Lessons learned from animal models inform us of IFN system parameters as indicators of disease outcome and whether clinical research is warranted. Moreover, validated IFN biomarkers for prognosis enhance our understanding of therapeutic and vaccine development.

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“…Systems immunology represents a fundamental paradigm underlying the research in modern immunology which puts strong emphasis on a comprehensive measurements of immunological parameters [10]. The organization and behavior of the immune system is highly complex and cannot be understood without mathematical modeling.…”

Section: Introductionmentioning

confidence: 99%

Tensor based approach to the numerical treatment of the parameter estimation problems in mathematical immunology

Zheltkova

Zheltkov

Grossman

et al. 2017

Journal of Inverse and Ill-Posed Problems

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Abstract:The development of efficient computational tools for data assimilation and analysis using multiparameter models is one of the major issues in systems immunology. The mathematical description of the immune processes across different scales calls for the development of multiscale models characterized by a high dimensionality of the state space and a large number of parameters. In this study we consider a standard parameter estimation problem for two models, formulated as ODEs systems: the model of HIV infection and BrdU-labeled cell division model. The data fitting is formulated as global optimization of variants of least squares objective function. A new computational method based on Tensor Train (TT) decomposition is applied to solve the formulated problem. The idea of proposed method is to extract the tensor structure of the optimized functional and use it for optimization. The method demonstrated a better performance in comparison with some other broadly used global optimization techniques.

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Systems Biology in Immunology: A Computational Modeling Perspective

Cited by 168 publications

References 326 publications

Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing

Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing

Interferons as Biomarkers and Effectors: Lessons Learned from Animal Models

Tensor based approach to the numerical treatment of the parameter estimation problems in mathematical immunology

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