Dataset Information

A simple work flow for biologically inspired model reduction--application to early JAK-STAT signaling.

ABSTRACT:

Background

Modeling of biological pathways is a key issue in systems biology. When constructing a model, it is tempting to incorporate all known interactions of pathway species, which results in models with a large number of unknown parameters. Fortunately, unknown parameters need not necessarily be measured directly, but some parameter values can be estimated indirectly by fitting the model to experimental data. However, parameter fitting, or, more precisely, maximum likelihood parameter estimation, only provides valid results, if the complexity of the model is in balance with the amount and quality of the experimental data. If this is the case the model is said to be identifiable for the given data. If a model turns out to be unidentifiable, two steps can be taken. Either additional experiments need to be conducted, or the model has to be simplified.

Results

We propose a systematic procedure for model simplification, which consists of the following steps: estimate the parameters of the model, create an identifiability ranking for the estimated parameters, and simplify the model based on the identifiability analysis results. These steps need to be applied iteratively until the resulting model is identifiable, or equivalently, until parameter variances are small. We choose parameter variances as stopping criterion, since they are concise and easy to interpret. For both, the parameter estimation and the calculation of parameter variances, multi-start parameter estimations are run on a parallel cluster. In contrast to related work in systems biology, we do not suggest simplifying a model by fixing some of its parameters, but change the structure of the model.

Conclusions

We apply the proposed approach to a model of early signaling events in the JAK-STAT pathway. The resulting model is not only identifiable with small parameter variances, but also shows the best trade-off between goodness of fit and model complexity.

SUBMITTER: Quaiser T

PROVIDER: S-EPMC3050741 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Publications

A simple work flow for biologically inspired model reduction--application to early JAK-STAT signaling.

Quaiser Tom T Dittrich Anna A Schaper Fred F Mönnigmann Martin M

BMC systems biology 20110221

<h4>Background</h4>Modeling of biological pathways is a key issue in systems biology. When constructing a model, it is tempting to incorporate all known interactions of pathway species, which results in models with a large number of unknown parameters. Fortunately, unknown parameters need not necessarily be measured directly, but some parameter values can be estimated indirectly by fitting the model to experimental data. However, parameter fitting, or, more precisely, maximum likelihood paramete ...[more]

PMID: 21338487

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Project description:BackgroundThe JAK/STAT pathway transduces signals from multiple cytokines and controls haematopoiesis, immunity and inflammation. In addition, pathological activation is seen in multiple malignancies including the myeloproliferative neoplasms (MPNs). Given this, drug development efforts have targeted the pathway with JAK inhibitors such as ruxolitinib. Although effective, high costs and side effects have limited its adoption. Thus, a need for effective low cost treatments remains.Methods & findingsWe used the low-complexity Drosophila melanogaster pathway to screen for small molecules that modulate JAK/STAT signalling. This screen identified methotrexate and the closely related aminopterin as potent suppressors of STAT activation. We show that methotrexate suppresses human JAK/STAT signalling without affecting other phosphorylation-dependent pathways. Furthermore, methotrexate significantly reduces STAT5 phosphorylation in cells expressing JAK2 V617F, a mutation associated with most human MPNs. Methotrexate acts independently of dihydrofolate reductase (DHFR) and is comparable to the JAK1/2 inhibitor ruxolitinib. However, cells treated with methotrexate still retain their ability to respond to physiological levels of the ligand erythropoietin.ConclusionsAminopterin and methotrexate represent the first chemotherapy agents developed and act as competitive inhibitors of DHFR. Methotrexate is also widely used at low doses to treat inflammatory and immune-mediated conditions including rheumatoid arthritis. In this low-dose regime, folate supplements are given to mitigate side effects by bypassing the biochemical requirement for DHFR. Although independent of DHFR, the mechanism-of-action underlying the low-dose effects of methotrexate is unknown. Given that multiple pro-inflammatory cytokines signal through the pathway, we suggest that suppression of the JAK/STAT pathway is likely to be the principal anti-inflammatory and immunosuppressive mechanism-of-action of low-dose methotrexate. In addition, we suggest that patients with JAK/STAT-associated haematological malignancies may benefit from low-dose methotrexate treatments. While the JAK1/2 inhibitor ruxolitinib is effective, a £43,200 annual cost precludes widespread adoption. With an annual methotrexate cost of around £32, our findings represent an important development with significant future potential.

Dataset Information

A simple work flow for biologically inspired model reduction--application to early JAK-STAT signaling.

Background

Results

Conclusions

Publications

A simple work flow for biologically inspired model reduction--application to early JAK-STAT signaling.

Similar Datasets

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