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Cellular level models as tools for cytokine design.

ABSTRACT: Cytokines and growth factors are critical regulators that connect intracellular and extracellular environments through binding to specific cell-surface receptors. They regulate a wide variety of immunological, growth, and inflammatory response processes. The overall signal initiated by a population of cytokine molecules over long time periods is controlled by the subtle interplay of binding, signaling, and trafficking kinetics. Building on the work of others, we abstract a simple kinetic model that captures relevant features from cytokine systems as well as related growth factor systems. We explore a large range of potential biochemical behaviors, through systematic examination of the model's parameter space. Different rates for the same reaction topology lead to a dramatic range of biochemical network properties and outcomes. Evolution might productively explore varied and different portions of parameter space to create beneficial behaviors, and effective human therapeutic intervention might be achieved through altering network kinetic properties. Quantitative analysis of the results reveals the basis for tensions among a number of different network characteristics. For example, strong binding of cytokine to receptor can increase short-term receptor activation and signal initiation but decrease long-term signaling due to internalization and degradation. Further analysis reveals the role of specific biochemical processes in modulating such tensions. For instance, the kinetics of cytokine binding and receptor activation modulate whether ligand-receptor dissociation can generally occur before signal initiation or receptor internalization. Beyond analysis, the same models and model behaviors provide an important basis for the design of more potent cytokine therapeutics by providing insight into how binding kinetics affect ligand potency.

SUBMITTER: Radhakrishnan ML

PROVIDER: S-EPMC3496272 | biostudies-literature | 2010 Jul-Aug

REPOSITORIES: biostudies-literature

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Cellular level models as tools for cytokine design.

Radhakrishnan Mala L ML Tidor Bruce B

Biotechnology progress 20100701 4

Cytokines and growth factors are critical regulators that connect intracellular and extracellular environments through binding to specific cell-surface receptors. They regulate a wide variety of immunological, growth, and inflammatory response processes. The overall signal initiated by a population of cytokine molecules over long time periods is controlled by the subtle interplay of binding, signaling, and trafficking kinetics. Building on the work of others, we abstract a simple kinetic model t ...[more]

PMID: 20568274

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Project description:OBJECTIVE:Immune-mediated inflammatory arthritis (IMIA) is a heterogeneous group of diseases including rheumatoid arthritis (RA), psoriatic arthritis (PsA), and spondyloarthritis (SpA). Disease-modifying antirheumatic drugs (DMARDs) target very different cellular components of the disease processes. Characterization of the pathobiological subtypes of IMIA could provide more specific treatment approaches for each disease. For example, RA has been proposed to consist of at least three synovial pathotypes (lymphoid, myeloid, and fibroid), and only a subgroup of RA patients have erosive disease. The objective of this study was to evaluate the effects of various DMARDs on different synovial cell subsets using human ex vivo models of IMIA. METHODS:Synovial fluid and blood samples were obtained from a study population consisting of patients with RA, PsA, or peripheral SpA with at least one swollen joint (n = 18). The DMARDs used in this study were methotrexate, adalimumab, etanercept, tocilizumab, anakinra, ustekinumab, secukinumab, tofacitinib, and baricitinib. Paired synovial fluid mononuclear cells (SFMCs), peripheral blood mononuclear cells (PBMCs), and fibroblast-like synovial cells (FLSs) were used in three different previously optimized ex vivo models. RESULTS:In SFMCs cultured for 48 hours, all DMARDs except anakinra decreased the production of monocyte chemoattractant protein (MCP)-1. In SFMCs cultured for 21 days, only the two tumor necrosis factor alpha (TNFα) inhibitors adalimumab and etanercept decreased the secretion of tartrate-resistant acid phosphatase (P < 0.01, P < 0.001). In the FLS and PBMC 48-hour co-cultures, only tocilizumab (P < 0.001) and the two Janus kinase inhibitors tofacitinib and baricitinib (both P < 0.05) decreased the production of MCP-1 by around 50%. CONCLUSION:TNFα inhibition was effective in preventing inflammatory osteoclastogenesis, whereas tocilizumab, tofacitinib, and baricitinib had superior efficacy in cultures dominated by FLSs. Taken together, this study reveals that responses to cytokine inhibitors associate with cellular composition in models of IMIA. In particular, this study provides new evidence on the differential effect of DMARDs on leukocytes compared with stromal cells.

Project description:Computational modeling and simulation have become essential tools in the quest to better understand the brain's makeup and to decipher the causal interrelations of its components. The breadth of biochemical and biophysical processes and structures in the brain has led to the development of a large variety of model abstractions and specialized tools, often times requiring high performance computing resources for their timely execution. What has been missing so far was an in-depth analysis of the complexity of the computational kernels, hindering a systematic approach to identifying bottlenecks of algorithms and hardware. If whole brain models are to be achieved on emerging computer generations, models and simulation engines will have to be carefully co-designed for the intrinsic hardware tradeoffs. For the first time, we present a systematic exploration based on analytic performance modeling. We base our analysis on three in silico models, chosen as representative examples of the most widely employed modeling abstractions: current-based point neurons, conductance-based point neurons and conductance-based detailed neurons. We identify that the synaptic modeling formalism, i.e. current or conductance-based representation, and not the level of morphological detail, is the most significant factor in determining the properties of memory bandwidth saturation and shared-memory scaling of in silico models. Even though general purpose computing has, until now, largely been able to deliver high performance, we find that for all types of abstractions, network latency and memory bandwidth will become severe bottlenecks as the number of neurons to be simulated grows. By adapting and extending a performance modeling approach, we deliver a first characterization of the performance landscape of brain tissue simulations, allowing us to pinpoint current bottlenecks for state-of-the-art in silico models, and make projections for future hardware and software requirements.

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Cellular level models as tools for cytokine design.

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Cellular level models as tools for cytokine design.

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