Murray Wiki - User contributions [en]

Paul Van den Hof, Dec 2018

2018-12-07T03:02:52Z

Mmayalu: /* Schedule */

Paul van den Hof is Full Professor and Chair of the Control Systems (CS) Group at the Department of Electrical Engineering. He is interested in data-driven modeling, control and optimization of dynamic systems in several technological fields: industrial process control, oil reservoir engineering, high-tech mechatronic and cyber-physical systems, etc. His focus is the development of fundamental techniques, such as data-driven modeling, closed-loop and control-oriented identification and data analytics, experimental design and performance monitoring, and model-based control, monitoring and optimization.

=== Schedule ===

* 10 am: Richard Murray, 107 Steele Lab
* 10:30 am: Petter Nilsson
* 11:00 am: Yuxiao Chen, Thomas Gurriet
* 11:45 am: Lunch with Aaron Ames, Petter Nilsson, Thomas Gurriet, Yuxiao Chen, meet in 266 Gates-Thomas
* 1:00 pm: Seminar, 106 Annenberg
* 2:00 pm: Soon-Jo Chung, 106 Annenberg
* 2:45 pm: Michaelle Mayalu, 2nd Floor lounge, Annenberg
* 3:30 pm: Richard Murray, 107 Steele Lab
* 4:00 pm: Chelsea and Ayush, 2nd floor lounge, Annenberg
* 4:45 pm: Open (if nothing else available)
* 5:30 pm: Depart

=== Seminar ===

'''Data-driven modeling in linear dynamic networks''' 
Friday, December 7th at 1pm, 106 Annenberg

In many areas of science and technology, the complexity of dynamic systems that are being considered, grows beyond the level of single systems into interconnected networks of dynamic systems. In control and optimization this has led to the development of decentralized and distributed algorithms for control/optimization, as e.g. in multi-agent systems.
From the modelling perspective, data-driven modelling tools are typically developed for relatively simple open-loop and closed-loop structures, while the opportunities for big data handling in the current data science era, are becoming abundant. As a result there is a strong need for the development of data-driven modelling tools for large-scale interconnected dynamic networks.
In this seminar we will highlight the main developments and challenges in this area. Besides setting up a modelling framework, we will address problems of local identification of a particular part of the network, including the selection of the appropriate signals to be measured. The concept of network identifiability is highlighted and the role of structural properties of the network, in terms of its topology/graph, is given strong attention. It is also shown how classical closed-loop identification methods need to be generalized to be able to cope with the new situations.

SURF discussions, Jan 2018

2018-01-23T04:55:19Z

Mmayalu: /* 23 Jan (Tue) */

Slots for talking with applicants and co-mentors about SURF projects. Please sign up for one of the slots below. All times are PST. __NOTOC__

In preparation for our conversation, please do the following:
* SURF students should work with their co-mentors to find a time the meeting/Skype call. (For Skype calls, co-mentors should initiate.)
* Please make sure you have read the material in the description of your project, so that you are prepared to talk about what the project is about and we can narrow in on the key ideas that will be the basis of your proposal
* Please take a look at the [[SURF GOTChA chart]] page, which is the format that we will use for the first iteration of your project proposal.

{| border=1
|- valign=top
| width=30% |
==== 23 Jan (Tue) ====
* 1:00 pm PST: open
<hr>
* 4:00 pm PST: Andrey/ Sanjana
<hr>
* 6:00 pm PST: Andy/Emanuel
* 6:30 pm PST: Michaelle/Harman
| width=30% |

==== 24 Jan (Wed) ====
* 7:30 am PST: William/Sannat
* 8:00 am PST: Rory/Elin
<hr>
*12:15 pm PST: Filip / Jin
*12:45 pm PST: open (if needed)
<hr>
* 4:30 pm PST: Reed / Leah
* 5:00 pm PST: open
* 5:30 pm PST: Steve & Tung
|}

The agenda for the phone call is (roughly):

# Description of the basic idea behind the project (based on applicant's understanding)
# Discussion about approaches, things to read, variations to consider, etc
# Discussion of the format of the proposal
# Questions and discussion about the process

SURF 2018: Modeling the Effect of Intracellular Signaling Mechanisms on Population Dynamic Behaviors in the Context of Paradoxical Signaling

2017-12-16T22:50:16Z

Mmayalu: /* Description */

'''[[SURF 2018|2018 SURF]]: project description'''
*'''Mentor:''' Richard M. Murray
*'''Co-mentor:''' Michaëlle N. Mayalu

==Description==
'''Background'''

[[File:PARADOX.jpg|thumb|Schematic diagram of Paradoxical Signaling. Image From:Hart, Y. et al. Paradoxical signaling by a secreted molecule leads to homeostasis of cell levels. Cell 158, 1022–1032 (2014). |right]]

Cells can communicate with each other by secreting signaling molecules that diffuse between them. Cell detection of an extracellular signaling molecule triggers cascades of intracellular signaling events that regulate diverse cell behaviors such as the proliferation, apoptosis, differentiation, and gene expression. In some cases, the exact same signaling molecule can affect opposing or antagonistic cell behaviors. This scenario is referred to as paradoxical signaling. Two important examples of paradoxical signaling are: 

1. Beta cells secrete insulin, which lowers blood glucose levels. Glucose has both mitogenic and toxic effects on the cell1.
2. T cells secret a cytokine IL-2 which promotes T cell proliferation2 and also affects cell death3.

Cells can use paradoxical signaling to balance between proliferation and death rates and it is therefore an effective method to regulate homeostasis of cell population. By modeling these paradoxical signaling systems we can gain insight into how defects in population control affect diseases such as cancer, type II diabetes and autoimmunity4. In addition, synthetic biology has begun to exploit paradoxical signaling mechanisms to engineer new cell population control circuits.

Although cellular behavior and cell-cell communication are regulated by the internal biochemical signaling mechanisms within individual cells, connecting these mechanisms to the overall population dynamics poses a considerable challenge for multiple reasons including:

1. Complexity and/or limited knowledge of intracellular mechanisms
2. Differences in length and time scales between molecular-level reactions and cell-level dynamics

'''Goal'''
The goal of this project is to develop and explore different modeling techniques that can describe how intracellular mechanisms lead to overall cell population behavior in the context of paradoxical signaling. Possible tasks include (but are not limited to):

1. Extending, modifying and/or combining modeling frameworks found in literature
2. Developing reasonable justifications for modeling simplifications and assumptions
3. Conducting model parameter studies and drawing insights into system behavior

'''Recommended Skills:'''

1. Systems Biology
2. Strong familiarity in Linear and Nonlinear Differential Equations
3. Basic Linear Algebra
4. Statistics
5. Proficiency in Matlab and or Python

==References:==
1. [https://www.ncbi.nlm.nih.gov/m/pubmed/22928570/ Dadon, D. et al. Glucose metabolism: key endogenous regulator of β-cell replication and survival. Diabetes Obes. Metab. 14 Suppl 3, 101–108 (2012).]
2. [https://www.ncbi.nlm.nih.gov/pubmed/18725574 Zhu, J. & Paul, W. E. CD4 T cells: fates, functions, and faults. Blood 112, 1557–1569 (2008).]
3. [https://www.ncbi.nlm.nih.gov/pubmed/12734339 Deenick, E. K., Gett, A. V. & Hodgkin, P. D. Stochastic model of T cell proliferation: a calculus revealing IL-2 regulation of precursor frequencies, cell cycle time, and survival. J. Immunol. Baltim. Md 1950 170, 4963–4972 (2003).]
4. [https://www.ncbi.nlm.nih.gov/pubmed/25171404 Hart, Y. et al. Paradoxical signaling by a secreted molecule leads to homeostasis of cell levels. Cell 158, 1022–1032 (2014).]
5. [https://www.hindawi.com/journals/bmri/2010/541609/ Zheng, Y. & Sriram, G. Mathematical Modeling: Bridging the Gap between Concept and Realization in Synthetic Biology. J. Biomed. Biotechnol. 2010, (2010).]
6. [http://www.cell.com/cell/fulltext/S0092-8674(14)01044-7 Youk, H. & Lim, W. A. Sending Mixed Messages for Cell Population Control. Cell 158, 973–975 (2014).]
7. [http://www.cell.com/molecular-cell/abstract/S1097-2765(13)00006-3 Hart, Y. & Alon, U. The Utility of Paradoxical Components in Biological Circuits. Mol. Cell 49, 213–221 (2013).]
8. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4332153/ Sgro, A. E. et al. From intracellular signaling to population oscillations: bridging size- and time-scales in collective behavior. Mol. Syst. Biol. 11, (2015).]

SURF 2018: Modeling the Effect of Intracellular Signaling Mechanisms on Population Dynamic Behaviors in the Context of Paradoxical Signaling

2017-12-16T22:49:19Z

Mmayalu: /* Description */

'''[[SURF 2018|2018 SURF]]: project description'''
*'''Mentor:''' Richard M. Murray
*'''Co-mentor:''' Michaëlle N. Mayalu

==Description==
'''Background'''

Cells can communicate with each other by secreting signaling molecules that diffuse between them. Cell detection of an extracellular signaling molecule triggers cascades of intracellular signaling events that regulate diverse cell behaviors such as the proliferation, apoptosis, differentiation, and gene expression. In some cases, the exact same signaling molecule can affect opposing or antagonistic cell behaviors. This scenario is referred to as paradoxical signaling. Two important examples of paradoxical signaling are: 

1. Beta cells secrete insulin, which lowers blood glucose levels. Glucose has both mitogenic and toxic effects on the cell1.
2. T cells secret a cytokine IL-2 which promotes T cell proliferation2 and also affects cell death3.

Cells can use paradoxical signaling to balance between proliferation and death rates and it is therefore an effective method to regulate homeostasis of cell population. By modeling these paradoxical signaling systems we can gain insight into how defects in population control affect diseases such as cancer, type II diabetes and autoimmunity4. In addition, synthetic biology has begun to exploit paradoxical signaling mechanisms to engineer new cell population control circuits.

[[File:PARADOX.jpg|thumb|Schematic diagram of Paradoxical Signaling. Image From:Hart, Y. et al. Paradoxical signaling by a secreted molecule leads to homeostasis of cell levels. Cell 158, 1022–1032 (2014). |center]]

Although cellular behavior and cell-cell communication are regulated by the internal biochemical signaling mechanisms within individual cells, connecting these mechanisms to the overall population dynamics poses a considerable challenge for multiple reasons including:

1. Complexity and/or limited knowledge of intracellular mechanisms
2. Differences in length and time scales between molecular-level reactions and cell-level dynamics

'''Goal'''
The goal of this project is to develop and explore different modeling techniques that can describe how intracellular mechanisms lead to overall cell population behavior in the context of paradoxical signaling. Possible tasks include (but are not limited to):

1. Extending, modifying and/or combining modeling frameworks found in literature
2. Developing reasonable justifications for modeling simplifications and assumptions
3. Conducting model parameter studies and drawing insights into system behavior

'''Recommended Skills:'''

1. Systems Biology
2. Strong familiarity in Linear and Nonlinear Differential Equations
3. Basic Linear Algebra
4. Statistics
5. Proficiency in Matlab and or Python

==References:==
1. [https://www.ncbi.nlm.nih.gov/m/pubmed/22928570/ Dadon, D. et al. Glucose metabolism: key endogenous regulator of β-cell replication and survival. Diabetes Obes. Metab. 14 Suppl 3, 101–108 (2012).]
2. [https://www.ncbi.nlm.nih.gov/pubmed/18725574 Zhu, J. & Paul, W. E. CD4 T cells: fates, functions, and faults. Blood 112, 1557–1569 (2008).]
3. [https://www.ncbi.nlm.nih.gov/pubmed/12734339 Deenick, E. K., Gett, A. V. & Hodgkin, P. D. Stochastic model of T cell proliferation: a calculus revealing IL-2 regulation of precursor frequencies, cell cycle time, and survival. J. Immunol. Baltim. Md 1950 170, 4963–4972 (2003).]
4. [https://www.ncbi.nlm.nih.gov/pubmed/25171404 Hart, Y. et al. Paradoxical signaling by a secreted molecule leads to homeostasis of cell levels. Cell 158, 1022–1032 (2014).]
5. [https://www.hindawi.com/journals/bmri/2010/541609/ Zheng, Y. & Sriram, G. Mathematical Modeling: Bridging the Gap between Concept and Realization in Synthetic Biology. J. Biomed. Biotechnol. 2010, (2010).]
6. [http://www.cell.com/cell/fulltext/S0092-8674(14)01044-7 Youk, H. & Lim, W. A. Sending Mixed Messages for Cell Population Control. Cell 158, 973–975 (2014).]
7. [http://www.cell.com/molecular-cell/abstract/S1097-2765(13)00006-3 Hart, Y. & Alon, U. The Utility of Paradoxical Components in Biological Circuits. Mol. Cell 49, 213–221 (2013).]
8. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4332153/ Sgro, A. E. et al. From intracellular signaling to population oscillations: bridging size- and time-scales in collective behavior. Mol. Syst. Biol. 11, (2015).]

File:PARADOX.jpg

2017-12-16T22:35:40Z

Mmayalu:

SURF 2018: Modeling the Effect of Intracellular Signaling Mechanisms on Population Dynamic Behaviors in the Context of Paradoxical Signaling

2017-12-16T22:34:43Z

Mmayalu: /* Description */

'''[[SURF 2018|2018 SURF]]: project description'''
*'''Mentor:''' Richard M. Murray
*'''Co-mentor:''' Michaëlle N. Mayalu

==Description==
'''Background'''

Cells can communicate with each other by secreting signaling molecules that diffuse between them. Cell detection of an extracellular signaling molecule triggers cascades of intracellular signaling events that regulate diverse cell behaviors such as the proliferation, apoptosis, differentiation, and gene expression. In some cases, the exact same signaling molecule can affect opposing or antagonistic cell behaviors. This scenario is referred to as paradoxical signaling. Two important examples of paradoxical signaling are: 

1. Beta cells secrete insulin, which lowers blood glucose levels. Glucose has both mitogenic and toxic effects on the cell1.
2. T cells secret a cytokine IL-2 which promotes T cell proliferation2 and also affects cell death3.

Cells can use paradoxical signaling to balance between proliferation and death rates and it is therefore an effective method to regulate homeostasis of cell population. By modeling these paradoxical signaling systems we can gain insight into how defects in population control affect diseases such as cancer, type II diabetes and autoimmunity4. In addition, synthetic biology has begun to exploit paradoxical signaling mechanisms to engineer new cell population control circuits.

[[File:ASS_DNAPEN.jpg|thumb|Schematic diagram of integrase-based continuous event logger circuit. Incoming stimuli direct the recording circuit to insert an ink plasmid corresponding to the stimulus being recorded, at the end of a "DNA tape" where previously detected stimuli have been recorded.]]

Although cellular behavior and cell-cell communication are regulated by the internal biochemical signaling mechanisms within individual cells, connecting these mechanisms to the overall population dynamics poses a considerable challenge for multiple reasons including:

1. Complexity and/or limited knowledge of intracellular mechanisms
2. Differences in length and time scales between molecular-level reactions and cell-level dynamics

'''Goal'''
The goal of this project is to develop and explore different modeling techniques that can describe how intracellular mechanisms lead to overall cell population behavior in the context of paradoxical signaling. Possible tasks include (but are not limited to):

1. Extending, modifying and/or combining modeling frameworks found in literature
2. Developing reasonable justifications for modeling simplifications and assumptions
3. Conducting model parameter studies and drawing insights into system behavior

'''Recommended Skills:'''

1. Systems Biology
2. Strong familiarity in Linear and Nonlinear Differential Equations
3. Basic Linear Algebra
4. Statistics
5. Proficiency in Matlab and or Python

==References:==
1. [https://www.ncbi.nlm.nih.gov/m/pubmed/22928570/ Dadon, D. et al. Glucose metabolism: key endogenous regulator of β-cell replication and survival. Diabetes Obes. Metab. 14 Suppl 3, 101–108 (2012).]
2. [https://www.ncbi.nlm.nih.gov/pubmed/18725574 Zhu, J. & Paul, W. E. CD4 T cells: fates, functions, and faults. Blood 112, 1557–1569 (2008).]
3. [https://www.ncbi.nlm.nih.gov/pubmed/12734339 Deenick, E. K., Gett, A. V. & Hodgkin, P. D. Stochastic model of T cell proliferation: a calculus revealing IL-2 regulation of precursor frequencies, cell cycle time, and survival. J. Immunol. Baltim. Md 1950 170, 4963–4972 (2003).]
4. [https://www.ncbi.nlm.nih.gov/pubmed/25171404 Hart, Y. et al. Paradoxical signaling by a secreted molecule leads to homeostasis of cell levels. Cell 158, 1022–1032 (2014).]
5. [https://www.hindawi.com/journals/bmri/2010/541609/ Zheng, Y. & Sriram, G. Mathematical Modeling: Bridging the Gap between Concept and Realization in Synthetic Biology. J. Biomed. Biotechnol. 2010, (2010).]
6. [http://www.cell.com/cell/fulltext/S0092-8674(14)01044-7 Youk, H. & Lim, W. A. Sending Mixed Messages for Cell Population Control. Cell 158, 973–975 (2014).]
7. [http://www.cell.com/molecular-cell/abstract/S1097-2765(13)00006-3 Hart, Y. & Alon, U. The Utility of Paradoxical Components in Biological Circuits. Mol. Cell 49, 213–221 (2013).]
8. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4332153/ Sgro, A. E. et al. From intracellular signaling to population oscillations: bridging size- and time-scales in collective behavior. Mol. Syst. Biol. 11, (2015).]

SURF 2018: Modeling the Effect of Intracellular Signaling Mechanisms on Population Dynamic Behaviors in the Context of Paradoxical Signaling

2017-12-16T22:22:30Z

Mmayalu: Created page with "'''2018 SURF: project description''' *'''Mentor:''' Richard M. Murray *'''Co-mentor:''' Michaëlle N. Mayalu ==Description== '''Background''' Cells..."

'''[[SURF 2018|2018 SURF]]: project description'''
*'''Mentor:''' Richard M. Murray
*'''Co-mentor:''' Michaëlle N. Mayalu

==Description==
'''Background'''

Cells can communicate with each other by secreting signaling molecules that diffuse between them. Cell detection of an extracellular signaling molecule triggers cascades of intracellular signaling events that regulate diverse cell behaviors such as the proliferation, apoptosis, differentiation, and gene expression. In some cases, the exact same signaling molecule can affect opposing or antagonistic cell behaviors. This scenario is referred to as paradoxical signaling. Two important examples of paradoxical signaling are: 

1. Beta cells secrete insulin, which lowers blood glucose levels. Glucose has both mitogenic and toxic effects on the cell1.
2. T cells secret a cytokine IL-2 which promotes T cell proliferation2 and also affects cell death3.

Cells can use paradoxical signaling to balance between proliferation and death rates and it is therefore an effective method to regulate homeostasis of cell population. By modeling these paradoxical signaling systems we can gain insight into how defects in population control affect diseases such as cancer, type II diabetes and autoimmunity4. In addition, synthetic biology has begun to exploit paradoxical signaling mechanisms to engineer new cell population control circuits.

Although cellular behavior and cell-cell communication are regulated by the internal biochemical signaling mechanisms within individual cells, connecting these mechanisms to the overall population dynamics poses a considerable challenge for multiple reasons including:

1. Complexity and/or limited knowledge of intracellular mechanisms
2. Differences in length and time scales between molecular-level reactions and cell-level dynamics

'''Goal'''
The goal of this project is to develop and explore different modeling techniques that can describe how intracellular mechanisms lead to overall cell population behavior in the context of paradoxical signaling. Possible tasks include (but are not limited to):

1. Extending, modifying and/or combining modeling frameworks found in literature
2. Developing reasonable justifications for modeling simplifications and assumptions
3. Conducting model parameter studies and drawing insights into system behavior

'''Recommended Skills:'''

1. Systems Biology
2. Strong familiarity in Linear and Nonlinear Differential Equations
3. Basic Linear Algebra
4. Statistics
5. Proficiency in Matlab and or Python

==References:==
1. [https://www.ncbi.nlm.nih.gov/m/pubmed/22928570/ Dadon, D. et al. Glucose metabolism: key endogenous regulator of β-cell replication and survival. Diabetes Obes. Metab. 14 Suppl 3, 101–108 (2012).]
2. [https://www.ncbi.nlm.nih.gov/pubmed/18725574 Zhu, J. & Paul, W. E. CD4 T cells: fates, functions, and faults. Blood 112, 1557–1569 (2008).]
3. [https://www.ncbi.nlm.nih.gov/pubmed/12734339 Deenick, E. K., Gett, A. V. & Hodgkin, P. D. Stochastic model of T cell proliferation: a calculus revealing IL-2 regulation of precursor frequencies, cell cycle time, and survival. J. Immunol. Baltim. Md 1950 170, 4963–4972 (2003).]
4. [https://www.ncbi.nlm.nih.gov/pubmed/25171404 Hart, Y. et al. Paradoxical signaling by a secreted molecule leads to homeostasis of cell levels. Cell 158, 1022–1032 (2014).]
5. [https://www.hindawi.com/journals/bmri/2010/541609/ Zheng, Y. & Sriram, G. Mathematical Modeling: Bridging the Gap between Concept and Realization in Synthetic Biology. J. Biomed. Biotechnol. 2010, (2010).]
6. [http://www.cell.com/cell/fulltext/S0092-8674(14)01044-7 Youk, H. & Lim, W. A. Sending Mixed Messages for Cell Population Control. Cell 158, 973–975 (2014).]
7. [http://www.cell.com/molecular-cell/abstract/S1097-2765(13)00006-3 Hart, Y. & Alon, U. The Utility of Paradoxical Components in Biological Circuits. Mol. Cell 49, 213–221 (2013).]
8. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4332153/ Sgro, A. E. et al. From intracellular signaling to population oscillations: bridging size- and time-scales in collective behavior. Mol. Syst. Biol. 11, (2015).]