CDCPreconferenceWorkshop - Revision history

JamesAnderson at 12:14, 7 January 2010

2010-01-07T12:14:26Z

JamesAnderson at 12:12, 7 January 2010

2010-01-07T12:12:59Z

JamesAnderson at 12:12, 7 January 2010

2010-01-07T12:12:29Z

JamesAnderson at 12:09, 7 January 2010

2010-01-07T12:09:03Z

JamesAnderson at 12:08, 7 January 2010

2010-01-07T12:08:26Z

Antonis at 20:12, 11 December 2009

2009-12-11T20:12:40Z

Antonis at 11:53, 4 December 2009

2009-12-04T11:53:38Z

Antonis at 12:14, 8 October 2009

2009-10-08T12:14:39Z

Antonis at 18:36, 2 September 2009

2009-09-02T18:36:43Z

Antonis at 18:19, 8 August 2009

2009-08-08T18:19:51Z

@@ Line 54: / Line 54: @@
 ! Time !! Presenter !! Topic
 |-
-|   8:30-8:45 ||   Professor Domitilla Del Vecchio      || [[Media:DDV_final.pdf | Overview of workshop aims and objectives ]]
+|   8:30-8:45 ||   Professor Domitilla Del Vecchio      ||  Overview of workshop aims and objectives
 |-
 |   8:45-9:45 ||   Professor Mustafa Khammash     ||  [[Media:MK.pdf | Stochastic Modelling and Analysis of Biochemical Networks]]
@@ Line 72: / Line 72: @@
 |   15:45-16:15    ||   Coffee Break ||
 |-
-|   16:15-17:15    ||   Professor Domitilla Del Vecchio ||   From Retroactivity to Modularity in Bio-molecular Circuit Designs
+|   16:15-17:15    ||   Professor Domitilla Del Vecchio ||  [[Media:DDV_final.pdf | From Retroactivity to Modularity in Bio-molecular Circuit Designs]]
 |-
 |   17:15-17:30    ||   Dr Antonis Papachristodoulou||   Wrap-up

@@ Line 66: / Line 66: @@
 |   12:15-13:45    ||   Lunch Break ||
 |-
-|   13:45-14:45    ||   Professor Murat Arcak ||  [[Media:MA_Final.pdf | Passivity-based analysis of reaction networks]]
+|   13:45-14:45    ||   Professor Murat Arcak ||  [[Media:MA_final.pdf | Passivity-based analysis of reaction networks ]]
 |-
 |   14:45-15:45    ||   Professor Richard Murray ||  [[Media:RM_final.pdf | Feedback and Control in Biological Circuit Design ]]

@@ Line 54: / Line 54: @@
 ! Time !! Presenter !! Topic
 |-
-|   8:30-8:45 ||   Professor Domitilla Del Vecchio      ||   Overview of workshop aims and objectives
+|   8:30-8:45 ||   Professor Domitilla Del Vecchio      || [[Media:DDV_final.pdf | Overview of workshop aims and objectives ]]
 |-
-|   8:45-9:45 ||   Professor Mustafa Khammash     ||   Stochastic Modelling and Analysis of Biochemical Networks
+|   8:45-9:45 ||   Professor Mustafa Khammash     ||  [[Media:MK.pdf | Stochastic Modelling and Analysis of Biochemical Networks]]
 |-
 |   9:45-10:45    ||   Dr Antonis Papachristodoulou     ||   ODE Modelling and Analysis of Biochemical Networks
@@ Line 66: / Line 66: @@
 |   12:15-13:45    ||   Lunch Break ||
 |-
-|   13:45-14:45    ||   Professor Murat Arcak ||  Passivity-based analysis of reaction networks
+|   13:45-14:45    ||   Professor Murat Arcak ||  [[Media:MA_Final.pdf | Passivity-based analysis of reaction networks]]
 |-
-|   14:45-15:45    ||   Professor Richard Murray ||   Feedback and Control in Biological Circuit Design
+|   14:45-15:45    ||   Professor Richard Murray ||  [[Media:RM_final.pdf | Feedback and Control in Biological Circuit Design ]]
 |-
 |   15:45-16:15    ||   Coffee Break ||

@@ Line 62: / Line 62: @@
 |   10:45-11:15    ||   Coffee Break  ||
 |-
-|   11:15-12:15    ||   Professor Frank Allgöwer ||   [[FA.pdf‎ | Robustness and Bifurcation Analysis of biochemical reaction networks ]]
+|   11:15-12:15    ||   Professor Frank Allgöwer ||   [[Media:FA.pdf‎ | Robustness and Bifurcation Analysis of biochemical reaction networks ]]
 |-
 |   12:15-13:45    ||   Lunch Break ||

@@ Line 62: / Line 62: @@
 |   10:45-11:15    ||   Coffee Break  ||
 |-
-|   11:15-12:15    ||   Professor Frank Allgöwer ||   Robustness and Bifurcation Analysis of biochemical reaction networks
+|   11:15-12:15    ||   Professor Frank Allgöwer ||   [[FA.pdf‎ | Robustness and Bifurcation Analysis of biochemical reaction networks ]]
 |-
 |   12:15-13:45    ||   Lunch Break ||

← Older revision		Revision as of 20:12, 11 December 2009
Line 83:		Line 83:

	Modeling biochemical reaction networks is key to understanding life at the most basic level. One of the challenges to the analysis and synthesis of genetic networks is that the cellular environment in which these circuits function is abuzz with noise. Cellular noise results in random fluctuations (over time) within individual cells and is a potential source of phenotypic variability among clonal cellular populations. The richness of stochastic phenomena in biology underscores the importance of proper modeling, and analysis of cellular noise. In this talk, we motivate the need for stochastic models and outline the key tools for the modeling and analysis of stochasticity inside living cells. We also show that noise induced ﬂuctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a biochemical network, carries its distinctive ﬁngerprint that encodes a wealth of information about that network. This establishes a potentially powerful approach for the identiﬁcation of genetic networks and offers a new window into their dynamic character.		Modeling biochemical reaction networks is key to understanding life at the most basic level. One of the challenges to the analysis and synthesis of genetic networks is that the cellular environment in which these circuits function is abuzz with noise. Cellular noise results in random fluctuations (over time) within individual cells and is a potential source of phenotypic variability among clonal cellular populations. The richness of stochastic phenomena in biology underscores the importance of proper modeling, and analysis of cellular noise. In this talk, we motivate the need for stochastic models and outline the key tools for the modeling and analysis of stochasticity inside living cells. We also show that noise induced ﬂuctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a biochemical network, carries its distinctive ﬁngerprint that encodes a wealth of information about that network. This establishes a potentially powerful approach for the identiﬁcation of genetic networks and offers a new window into their dynamic character.
		+
		+	* Dr Antonis Papachristodoulou, "Modelling and Analysis of Biochemical Reaction Networks"
		+
		+	Mathematical modeling is a key tool in Systems Biology. Modeling and analyzing biological networks presents a number of mathematical and computational challenges, as the models that are considered are usually complicated nonlinear differential equations with several time-scales and unknown parameters. In this part of the workshop, we review various modeling techniques for biological systems at different time and space scales and then present mathematical and algorithmic tools to quantify their robustness - both the stability and performance in the presence of parametric uncertainties. These tools are rooted in robust control and dynamical systems theory, but use recent developments in the theory of positive polynomials. At the same time, they have great practical promise and relevance, which we explore through a series of biologically meaningful examples. We discuss how these models can be model-reduced or decomposed into meaningful modules ready for more detailed analysis and design.

	* Professor Frank Allgöwer, "Robustness and Bifurcation Analysis of biochemical reaction networks"		* Professor Frank Allgöwer, "Robustness and Bifurcation Analysis of biochemical reaction networks"

@@ Line 47: / Line 47: @@
 * Professor Richard Murray, Control and Dynamical Systems, California Institute of Technology.
 * Professor Frank Allgöwer, Institute for Systems Theory and Automatic Control, University of Stuttgart.
 * Professor Murat Arcak, Electrical Engineering and Computer Science, University of California at Berkeley.
@@ Line 69: / Line 68: @@
 |   12:15-13:45    ||   Lunch Break ||
 |-
-|   13:45-14:45    ||   Professor Pablo Iglesias ||   Control motifs regulating sterol homeostasis
+|   13:45-14:45    ||   ** ||   **
 |-
 |   14:45-15:30    ||   Professor Richard Murray ||   Feedback and Control in Biological Circuit Design
@@ Line 102: / Line 101: @@
 * Professor Domitilla Del Vecchio, "From Retroactivity to Modularity in Bio-molecular Circuit Designs"
-Modularity plays a fundamental role in the prediction of the
+Modularity plays a fundamental role in the prediction of the behavior of a system from the behavior of its components, guaranteeing that the properties of individual components do not change upon interconnection. Just as electrical, hydraulic, and other physical systems often do not display modularity, nor do many bio-molecular systems, and specifically, genetic and signaling networks. Here, we study the effect of interconnections on the input/output dynamic characteristics of transcriptional components, focusing on a property, which we call "retroactivity," that plays a role similar to impedance in electrical circuits. In order to attenuate the effect of retroactivity, we propose to
-behavior of a system from the behavior of its components, guaranteeing that
+design insulation devices based on a feedback mechanism inspired by the design of amplifiers in electronics and provide a bio-molecular realization. We show that natural bio-molecular systems already employ recurrent motifs, that is, phosphorylation/dephosphorylation cycles, which can work as remarkable insulation devices.

@@ Line 59: / Line 59: @@
 |   8:45-9:30 ||   Professor Mustafa Khammash     ||   Stochastic Modelling and Analysis of Biochemical Networks
 |-
-|   9:30-10:15    ||   Dr Antonis Papachristodoulou     ||   ODE Modelling and analysis of biological networks
+|   9:30-10:15    ||   Dr Antonis Papachristodoulou     ||   ODE Modelling and Analysis of Biochemical Networks
 |-
 |   10:15-10:45    ||   Coffee Break  ||

← Older revision		Revision as of 18:19, 8 August 2009
Line 86:		Line 86:

	Modeling biochemical reaction networks is key to understanding life at the most basic level. One of the challenges to the analysis and synthesis of genetic networks is that the cellular environment in which these circuits function is abuzz with noise. Cellular noise results in random fluctuations (over time) within individual cells and is a potential source of phenotypic variability among clonal cellular populations. The richness of stochastic phenomena in biology underscores the importance of proper modeling, and analysis of cellular noise. In this talk, we motivate the need for stochastic models and outline the key tools for the modeling and analysis of stochasticity inside living cells. We also show that noise induced ﬂuctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a biochemical network, carries its distinctive ﬁngerprint that encodes a wealth of information about that network. This establishes a potentially powerful approach for the identiﬁcation of genetic networks and offers a new window into their dynamic character.		Modeling biochemical reaction networks is key to understanding life at the most basic level. One of the challenges to the analysis and synthesis of genetic networks is that the cellular environment in which these circuits function is abuzz with noise. Cellular noise results in random fluctuations (over time) within individual cells and is a potential source of phenotypic variability among clonal cellular populations. The richness of stochastic phenomena in biology underscores the importance of proper modeling, and analysis of cellular noise. In this talk, we motivate the need for stochastic models and outline the key tools for the modeling and analysis of stochasticity inside living cells. We also show that noise induced ﬂuctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a biochemical network, carries its distinctive ﬁngerprint that encodes a wealth of information about that network. This establishes a potentially powerful approach for the identiﬁcation of genetic networks and offers a new window into their dynamic character.
		+
		+	* Professor Frank Allgöwer, "Robustness and Bifurcation Analysis of biochemical reaction networks"
		+
		+	Unlike in the engineering disciplines or for example in physics, measured data in biology typically have rather large uncertainties associated with them. Many important quantities cannot be measured at all or can only be measured by destroying the biological entity to be investigated. Together with the fact that often the fundamental knowledge about the biological systems under investigation is also still in its early stages, this leads to significant uncertainties that have to be considered when investigating biological systems. In this part of the workshop we give an overview over some new methods to analyze both the steady state and the dynamical behavior of biochemical reaction networks under uncertainty. With a number of examples we will show that suitably developed systems theoretical methods are well suited to make an important contribution to
		+	biological research.

	* Professor Murat Arcak, "Passivity-based analysis of reaction networks"		* Professor Murat Arcak, "Passivity-based analysis of reaction networks"