Klinke:Talks

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Penn State Department of Chemical Engineering Seminar Series:<br>
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Biotechnology and Bioengineering Journal Video Highlights:<br>
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'''A Bayesian Perspective on Understanding How Cells Make Decisions'''<br>
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'''Inferring alterations in cell-to-cell communication in HER2+ breast cancer using secretome profiling of three cell models'''<br>
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Thursday, January 13, 2011. ([http://mediasite.lib.wvu.edu/Mediasite1/Viewer/?peid=0e6d756fa1814541a7da6a99c4efcff31d MediaSite Video ])<br></div>
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October 30, 2014. ([http://youtu.be/_Ifs5b8ksvw YouTube Video ])<br></div>
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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.<br>
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This video highlight of a recent publication in Biotechnology and Bioengineering provides background information related to somatic evolution in cancer and summarizes our research findings where we used proteomics to test a hypothesis related to how cell-to-cell communication becomes altered in cancer.<br>
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Rosa and Co World-Wide Webinar Series: Impact of Modeling & Simulation in Drug Development:<br>
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'''In silico model-based inference: a contemporary approach for hypothesis testing in systems pharmacology'''<br>
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Wednesday, March 13, 2013. ([http://rosaandco.com/webinarPresentations/webinarKlinke2013.wmv Webinar broadcast]) <br></div>
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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].
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[1] Klinke DJ, An empirical Bayesian approach for model-based inference of cellular signaling networks. BMC Bioinformatics. (2009) 10:371.
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[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.
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[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.<br>
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<div style="text-align: center;">
<div style="text-align: center;">
-
Rosa and Co Webinar Series:<br>
+
Penn State Department of Chemical Engineering Seminar Series:<br>
-
'''In silico model-based inference: a contemporary approach for hypothesis testing in systems pharmacology'''<br>
+
'''A Bayesian Perspective on Understanding How Cells Make Decisions'''<br>
-
Wednesday, March 13, 2013.([http://rosaandco.com/webinarPresentations/webinarKlinke2013.wmv Webinar broadcast]) <br></div>
+
Thursday, January 13, 2011. ([http://mediasite.lib.wvu.edu/Mediasite1/Viewer/?peid=0e6d756fa1814541a7da6a99c4efcff31d MediaSite Video ])<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].
+
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.<br>
-
[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.
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[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.<br>
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The Klinke Lab @ West Virginia University

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Biotechnology and Bioengineering Journal Video Highlights:
Inferring alterations in cell-to-cell communication in HER2+ breast cancer using secretome profiling of three cell models

October 30, 2014. (YouTube Video )

This video highlight of a recent publication in Biotechnology and Bioengineering provides background information related to somatic evolution in cancer and summarizes our research findings where we used proteomics to test a hypothesis related to how cell-to-cell communication becomes altered in cancer.


Rosa and Co World-Wide Webinar Series: Impact of Modeling & Simulation in Drug Development:
In silico model-based inference: a contemporary approach for hypothesis testing in systems pharmacology

Wednesday, March 13, 2013. (Webinar broadcast)

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.


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

Friday, May 27, 2011. (YouTube Video )

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.


Penn State Department of Chemical Engineering Seminar Series:
A Bayesian Perspective on Understanding How Cells Make Decisions

Thursday, January 13, 2011. (MediaSite Video )

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.


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