In silico model-based inference: a contemporary approach for hypothesis testing in systems pharmacology

9 November 2012

David J Klinke II
Department of Chemical Engineering
West Virginia University

Abstract

Quantitative and systems pharmacology is an emerging field that integrates a systems perspective with advances in quantitative biology, mathematical modeling, and simulation to improve human health using therapies. Conventionally, mathematical modeling provides a quantitative framework for integrating fragmented knowledge of a system and for simulating the dynamic response of the system to environmental changes. However, one of the major challenges in drug discovery and development is determining whether our understanding of how a therapeutic acts across multiple time and length scales is consistent with observed biological response — an inductive inference problem. Despite advances in computational power, conventional methods for inference remain largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early-1900's. In this talk, I will summarize conventional methods for model-based inference and suggest a simulation-based alternative [1]. This simulation-based alternative accounts for the specific data at hand and the uncertainty associated with the model parameters. I will illustrate the approach using aspects from two recent publications [2, 3].

References

1. D J Klinke, "An empirical Bayesian approach for model-based inference of cellular signaling networks", BMC Bioinformatics 10:371 2009. PubMed
2. D J Klinke, N Cheng, E Chambers, "Quantifying crosstalk among interferon-γ, interleukin-12, and tumor necrosis factor signaling pathways within a TH1 cell model", Sci Signal 5:ra2 2012. PubMed
3. Y M Kulkarni, E Chambers, A J McGray, J S Ware, J L Bramson, D J Klinke, "A quantitative systems approach to identify paracrine mechanisms that locally suppress immune response to Interleukin-12 in the B16 melanoma model", Integr Biol 4:925-36 2012.