# Difference between revisions of "Klinke:Talks"

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Rosa and Co Webinar Series:<br> | Rosa and Co Webinar Series:<br> | ||

'''In silico model-based inference: a contemporary approach for hypothesis testing in systems pharmacology'''<br> | '''In silico model-based inference: a contemporary approach for hypothesis testing in systems pharmacology'''<br> | ||

− | Wednesday, March 13, 2013.([http://rosaandco.com/webinarPresentations/webinarKlinke2013.wmv Webinar broadcast]) <br></div> | + | Wednesday, March 13, 2013. ([http://rosaandco.com/webinarPresentations/webinarKlinke2013.wmv Webinar broadcast]) <br></div> |

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, which is 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]. | 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, which is 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]. | ||

## Revision as of 14:13, 14 March 2013

**The Klinke Lab @ West Virginia University**

**Home**
**Research**
**Lab Members**
**Publications**
**Talks**
**Positions**
**Contact**

Penn State Department of Chemical Engineering Seminar Series:

**A Bayesian Perspective on Understanding How Cells Make Decisions**

In this seminar, Dr. Klinke discusses some of the recent work from the lab where experimental and computational methods are used to help understand how cells make decisions.

This Week in WV - Mountain State Science - WV Public Broadcasting:

**WVU Nanotechnology**

Reporter Ben Adducchio talks with Dr. Klinke about nanoscience and his work in cancer immunology. In this brief segment, Dr. Klinke refers to nanoscale structures, called exosomes, that are thought to play key roles in intercellular communication - such as between cancer and immune cells. Exosomes are 50 - 100 nm in diameter membrane vesicles that contain transmembrane proteins embedded within a lipid bilayer, cytosolic proteins and, potentially, microRNAs derived from donor cells. Understanding the role of these naturally designed nanoscale materials in facilitating cell-to-cell communication will ultimately aid in engineering nanoscale structures as immunotherapies.

Rosa and Co Webinar Series:

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

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, which is 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].

[1] Klinke DJ, An empirical Bayesian approach for model-based inference of cellular signaling networks. BMC Bioinformatics. (2009) 10:371.

[2] Klinke DJ, Cheng N, Chambers E, Quantifying crosstalk among Interferon-gamma, Interleukin-12, and Tumor Necrosis Factor signaling pathways within a TH1 cell model. Sci Signal. (2012) 5(220):ra32.

[3] Kulkarni YM, Chambers E, McGray AJ, Ware JS, Bramson JL, Klinke DJ, A quantitative systems approach to identify paracrine mechanisms that locally suppress immune response to Interleukin-12 in the B16 melanoma model. Integr Biol. (2012) 4(8):925-36.