Murray Wiki - User contributions [en]

CDS 110/ChE 105, Spring 2024

2024-04-02T22:08:05Z

Mkapasia:

{{Course
|Course number=CDS 110
|Course title=Analysis and Design of Feedback Control Systems
|Year=2024
|Term=Spring
|Lecture schedule=MWF, 2-3 pm, 106 Spalding
|Instructors=Richard Murray (CDS/BE), murray@cds.caltech.edu
|Instructor office hours=Wed, 3-4 pm, Annenberg Lounge
|TAs=Natalie Bernat (CMS), Manisha Kapasiawala (BE)
|TA office hours=   Mon, 3-4 pm, 111 Keck   Tue, 4-5 pm, 110 Steele
}}
This course is co-taught with ChE 105 (Dynamics and Control of Chemical Systems).

=== Course Syllabus ===

An introduction to analysis and design of feedback control systems in the time and frequency domain, with an emphasis on state space methods, robustness, and design tradeoffs. Linear input/output systems, including input/output response via convolution, reachability, and observability. State feedback methods, including eigenvalue placement, linear quadratic regulators, and model predictive control. Output feedback including estimators and two-degree of freedom design. Input/output modeling via transfer functions and frequency domain analysis of performance and robustness, including the use of Bode and Nyquist plots. Robustness, tradeoffs and fundamental limits, including the effects of external disturbances and unmodeled dynamics, sensitivity functions, and the Bode integral formula.

=== Lecture Schedule ===

{| class="mw-collapsible wikitable" width=100% border=1 cellpadding=5
|-
! width=10% | Date
! width=40% | Topic
! width=30% | Reading
! width=20% | Homework
|- valign=top
| '''Week 1''' 
1 Apr 3 Apr 5 Apr
| '''Introduction and review'''
* Course overview and logistics
* Introduction to feedback and control
* Introduction to python-control
|
* [[http:fbswiki.org/wiki/index.php/FBS|FBS2e]] 1.1-1.5 (skim), 2.1-2.4
* Lecture materials: {{cds110 sp2024 pdf|L1-1_intro-01Apr2024.pdf|Mon}}
* [https://simons.berkeley.edu/control-theory Feedback Control Theory video tutorial (Simons Institute)]
| HW #1
|- valign=top
| '''Week 2''' 
8 Apr* 10 Apr 12 Apr
| '''Modeling, Stability'''
* State space models
* Continuous and discrete time systems
* Phase portraits and stability
| FBS2e 3.1-3.2, 4.1, 5.1-5.3
| HW #2
|- valign=top
| '''Week 3''' 
15 Apr 17 Apr 19 Apr
| '''Linear Systems'''
* Input/output response of LTI systems
* Matrix exponential, convolution equation
* Linearization around an equilibrium point
| FBS2e 6.1-6.4
| HW #3
|- valign=top
| '''Week 4''' 
22 Apr 24 Apr 26 Apr*
| '''State Feedback'''
* Reachability
* State feedback and eigenvalue placement
* Linear quadratic regulators (LQR)
| FBS2e 7.1-7.4
| HW #4
|- valign=top
| '''Week 5''' 
29 Apr 1 May* 3 May
| '''State estimation'''
* Observers, observability
* Control using estimated state
* Kalman filtering (intro)
| FBS2e 8.1-8.3
| HW #5
|- valign=top
| '''Week 6''' 
6 May 8 May 10 May
| '''Trajectory generation and tracking'''
* Two degree of freedom design
* Gain scheduling
* Model predictive control
| FBS2e, 8.4-8.5 OBC, Ch 1
| HW #6
|- valign=top
| '''Week 7''' 
13 May 15 May 17 May
| '''Frequency domain analysis'''
* Bode and Nyquist plots
* Stability margins
| FBS2e 9.1-9.4, 10.1-10.3
| HW #7
|- valign=top
| '''Week 8''' 
20 May 22 May 24 May*
| '''Robustness and fundamental tradeoffs'''
* Sensitivity functions
* Bode integral formula
| FBS2e, 12.1-12.2, 14.1-14.2
| HW #8
|- valign=top
| '''Week 9''' 
<s>27 May</s> 29 May* 31 May 3 Jun
| '''PID control'''
* Frequency domain design concepts
* Windup and anti-windup
| FBS2e, 11.1-11.4
| HW #9 (Sophomores, Juniors)
|- valign=top
| '''Week 10''' 
5 Jun 7 Jun
| '''Final review and applications'''
* Final exam review (Wed)
| None
| Final exam (Seniors, Graduate Students): 7 Jun (Fri), 2-3 pm
|- valign=top
| '''Finals week (Sophomores, Juniors)''' 
| None
|
| Final exam (Sophomores, Juniors): 12 Jun (Wed), 2-3 pm
|}

=== Grading ===
The final grade will be based on homework sets, a midterm exam, and a final exam:

*''Homework (60%):'' Homework sets will be handed out weekly and due on Wednesdays by 2 pm via Gradescope. Each student is allowed up to two extensions of no more than 2 days each over the course of the term. Homework turned in after Friday at 2 pm or after the two extensions are exhausted will receive 50% credit. Python (or MATLAB) code is considered part of your solution and should be printed and turned in with the problem set (whether the problem asks for it or not).

:(Sophomores and juniors only) The lowest score on your homework sets will be dropped.

* ''Final exam (40%):'' The final exam will be a 1-2 hour, in-class, closed-book exam.
** Seniors and graduate students: the final exam will be on 7 Jun (Fri), 2-4 pm
** Sophomores and juniors: the final exam will be on 12 Jun (Wed), 2-4 pm

=== Collaboration Policy ===

Collaboration on homework assignments is encouraged. You may consult outside reference materials, other students, the TA, or the instructor, but you cannot consult homework solutions from prior years and you must cite any use of material from outside references. All solutions that are handed in should be written up individually and should reflect your own understanding of the subject matter at the time of writing. Python (or MATLAB) scripts and plots are considered part of your writeup and should be done individually (you can share ideas, but not code).

ChatGPT and other AI tools may be used in the same manner as a fellow student in the class: you are allowed to consult online tools and use them to understand the topics, but all solutions should be written up individually. You cannot use online tools to generate solutions for coding problems (cutting and pasting from templates or materials handed out in class and editing them as appropriate is OK).

No collaboration is allowed on the final exam.

=== Course Text and References ===

The primary course text is

* [FBS2e] K. J. Astrom and Richard M. Murray, [http://fbsbook.org ''Feedback Systems: An Introduction for Scientists and Engineers''], Second Edition. Princeton University Press, 2021.

This book is available via free download.

The following additional references, also available for free, may also be useful:

* [Lew], A. D. Lewis, ''A Mathematical Approach to Classical Control'', 2003. [https://mast.queensu.ca/~andrew/teaching/pdf/332-notes.pdf Online access].
* [OBC] R. M. Murray, "Optimization-Based Control", 2023. [https://fbswiki.org/wiki/index.php/Supplement:_Optimization-Based_Control Online access]
* [LST] Richard M. Murray, [https://fbswiki.org/wiki/index.php/Supplement:_Linear_Systems_Theory Feedback Systems: Notes on Linear Systems Theory], 2020. (Updated 30 Oct 2020)

In addition to the books above, the textbooks below may also be useful. They are available in the library (non-reserve), from other students, or you can order them online.

* B. Friedland, ''Control System Design: An Introduction to State-Space Methods'', McGraw-Hill, 1986.
* G. F. Franklin, J. D. Powell, and A. Emami-Naeni, ''Feedback Control of Dynamic Systems'', Addison-Wesley, 2002.

[[Category: Courses]]

Andras Gyorgy, Aug 2023

2023-08-07T16:45:20Z

Mkapasia: /* Schedule */

Andras Gyorgy from NYU Abu Dhabi will visit on 7 Aug (Mon).

== Schedule ==

* 9:30 am: Richard, 109 Steele
* 10:00 am: Manisha, 138 Keck
* 10:30 am: Zach Martinez, 138 Keck
* 11:00 am: Seminar, 111 Keck
* 12:15 pm: Lunch with graduate students (organized by John M)
* 1:30 pm: Matt K
* 2:15 pm: Yan, location TBD
* 3:00 pm: John M, location TBD
* 3:45 pm: Inigo, Annenberg 2nd floor lounge
* 4:30 pm: Richard, 109 Steele

Manos Alexis, Oct 2022

2022-10-24T15:28:38Z

Mkapasia: /* 24 Oct (Mon) */

Manos Alexis, a PhD student at Cambridge interested in dynamical systems, control theory, synthetic biology and systems biology will visit our group the week of 24 Oct. You can sign up for a time to meet with him below:

=== 24 Oct (Mon) ===

* 9:00 am: Monica Nolasco, 107 Steele Lab
* 10:00 am: Richard Murray, 109 Steele Lab
* 10:45 am: Yan Zhang, Red Door Cafe
* 11:30 am: Alex Johnson, Red Door Cafe
* 12:00 pm: Lunch w/ soil syn bio students (meet in Keck Lobby)
* 1:30 pm: John Marken
* 2:15 pm: Zoila Jurado, Red Door Cafe
* 3:00 pm: Abhishek Dey, Red Door Cafe
* 3:45 pm: Manisha Kapasiawala, Broad Cafe/Chen (can meet at Keck east entrance and walk over)
* 4:30 pm: open

=== 25 Oct (Tue) ===

* 9:00 am: open
* 10:00 am: Biocircuits group meeting, 111 Keck
* 12:00 pm: Lunch w/ synthetic cell students (leave from group meeting)
* 1:45 pm: Matt, Red Door Cafe
* 2:30 pm: open
* 3:15 pm: Richard Murray, 109 Steele Lab
* 4:00 pm: open

Manos Alexis, Oct 2022

2022-10-24T15:19:54Z

Mkapasia: /* 24 Oct (Mon) */

Manos Alexis, a PhD student at Cambridge interested in dynamical systems, control theory, synthetic biology and systems biology will visit our group the week of 24 Oct. You can sign up for a time to meet with him below:

=== 24 Oct (Mon) ===

* 9:00 am: Monica Nolasco, 107 Steele Lab
* 10:00 am: Richard Murray, 109 Steele Lab
* 10:45 am: Yan Zhang, Red Door Cafe
* 11:30 am: Alex Johnson, Red Door Cafe
* 12:00 pm: Lunch w/ soil syn bio students (meet in Keck Lobby)
* 1:30 pm: John Marken
* 2:15 pm: Zoila Jurado, Red Door Cafe
* 3:00 pm: Abhishek Dey, Red Door Cafe
* 3:45 pm: open
* 4:30 pm: Manisha Kapasiawala

=== 25 Oct (Tue) ===

* 9:00 am: open
* 10:00 am: Biocircuits group meeting, 111 Keck
* 12:00 pm: Lunch w/ synthetic cell students (leave from group meeting)
* 1:45 pm: Matt, Red Door Cafe
* 2:30 pm: open
* 3:15 pm: Richard Murray, 109 Steele Lab
* 4:00 pm: open

Nacho Gispert, 10 Aug 2022

2022-08-08T20:14:28Z

Mkapasia:

Ignacio (Nacho) Gispert, a postdoc with Yuval Elani at Imperial will visit on 10 Aug 2022 (Wed).

Schedule:
* 9:15 am: Richard Murray, 109 Steele
* 10:00 am: Group meeting (Nacho + summer students + lab updates)
* 12:00 pm: Lunch with Manisha and Zoila
* 1:30 pm: Zoila
* 2:15 pm: Manisha
* 3:00 pm: CDS Tea
* 3:45 pm: open
* 4:30 pm: open
* 5:30 pm: Richard, 109 Steele

Research interests: Nacho's research focuses on artificial/biological hybrid cells, specially on how to replicate cell characteristics and behavior in artificial cell-like entities.

Yan Zhang, 28 Mar 2022

2022-03-24T06:06:05Z

Mkapasia:

Yan Zhang, a PhD student at Georgia Tech will visit on 28 Mar 2022.

Schedule:
* 8:30 am: Richard Murray, 109 Steele
* 9:00 am: Open
* 10:00 am: Group meeting presentation
* 12:00 pm: Lunch with Chelsea, Michaelle
* 1:15 pm: Open
* 2:00 pm: Meet with Ayush at Red Door
* 2:45 pm: Meet with Manisha (at Red Door?)
* 3:30 pm: Open
* 4:15 pm: Open
* 5:00 pm: Richard Murray, 109 Steele

Research interests:
* Developed a new diagnostic platform interfacing cell-free biosensors with biphasic polymer systems for simultaneous detection of diverse classes of analytes that are robust to human biofluids and field-deployable.
* Integrated cell-free biosensors to a personal glucose monitor for rapid and reliable biomarker quantification at the point of need
* Designed, characterized, and optimized cell-free bacterial biosensors to detect micronutrient deficiencies, pathogenic bacterial infections, and heavy metal environment contaminants

SURF discussions, Feb 2021

2021-01-28T18:57:33Z

Mkapasia: /* 3 Feb (Wed) */

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 width=100%
|- valign=top
| width=25% |
==== 1 Feb (Mon) ====
* 5:00 pm PST: open
* 5:30 pm PST: open
| width=25% |

==== 2 Feb (Tue) ====
* 4:00 pm PST: Christian/Josefine
* 4:30 pm PST: open
| width=25% |

==== 3 Feb (Wed) ====
* 9:00 am PST: Manisha/Hannah
* 9:30 am PST: open

|}

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 discussions, Feb 2021

2021-01-28T18:57:09Z

Mkapasia: /* 3 Feb (Wed) */

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 width=100%
|- valign=top
| width=25% |
==== 1 Feb (Mon) ====
* 5:00 pm PST: open
* 5:30 pm PST: open
| width=25% |

==== 2 Feb (Tue) ====
* 4:00 pm PST: Christian/Josefine
* 4:30 pm PST: open
| width=25% |

==== 3 Feb (Wed) ====
* 9:00 am PST: Manisha and Hannah
* 9:30 am PST: open

|}

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 2021: Optimizing cell extract for a proto-flagellar system

2021-01-06T09:13:11Z

Mkapasia:

[[File:ATPase_Protoflagellum.png|thumb|400px|right|Figure 1: Vesicle motility induced by rotation of actin-bound ATP synthase]]
* Mentor: Richard Murray
* Co-mentor: Manisha Kapasiawala
= '''Introduction:''' =

Past efforts in synthetic biology have created significant advancements towards the creation of synthetic cells [1]. In the simplest case, synthetic cells are phospholipid vesicles encapsulating a solution of cell extract and energy buffer, which provide the chemical components (e.g. RNA polymerase, ATP, NTPs, amino acids, etc.) for transcription and translation (TX-TL) and basic metabolism [2,3]. As minimal chassis, however, synthetic cells provide a powerful platform for both understanding natural cell behaviors and for programming synthetic behaviors, as the bottom-up construction of these synthetic cells from fully characterized components enables predictability and functionality through mechanistic insight.
Recent work has shown that synthetic cells can be programmed to perform behaviors that are observed in natural cells, such as to deliver cargo, process signals from environmental inputs, and make complex decisions in response to those signals. In the Murray Lab, we are currently focusing on the development of motility in synthetic cells, by repurposing the F1FO-ATP synthase (ATPase) rotary motor to construct an ATPase-based protoflagellum (see figure). Here, proton pumps in the synthetic cell membrane will create a proton gradient that will drive the rotation of ATPase, which will in turn drive the rotation of an actin filament that will propel the synthetic cell.

Currently, the method of introducing the proton pump and ATPase to the synthetic cell membrane is a laborious and expensive process that involves purifying these proteins from E. coli and reconstituting them in synthetic cells. An easier way to reconstitute these proteins in synthetic cells would be to have them make these proteins themselves, but doing so remains challenging due to experimental limitations.

= '''Research overview:''' =

The SURF student will use computational modeling to help optimize cell extract for a proto-flagellar system, by creating a modeling framework for protein production that captures cell extract metabolism. At the most basic level, protein production in cells is modeled as two reactions: transcription and translation. Each reaction can be described mathematically, and the resulting chemical reaction network (CRN) can be simulated to predict protein production (e.g. how much protein is made, how long it takes to reach that steady state level, etc.). Starting from a basic CRN, one can build increasing complex CRNs by considering that there are finite amounts of ribosomes and RNA polymerases in a cell, considering dilution and degradation of mRNA and proteins, etc.

Protein production in cell extract, however, is very different from protein production in a cell, largely because there is no recycling of metabolic products and of energy molecules such as ATP and NADH. Thus, in cell extract, the metabolic reactions going on “in the background”, which we can typically ignore when modeling protein production in a cell, may have a significant impact on protein production, whether via depletion of ATP and other resources, toxic accumulation of phosphate, pH effects, etc. Creating protein production models that capture the effects of cell extract metabolism may lead to the creation of more accurate protein production models.

The SURF project will focus on the creation of a modeling framework for protein production that will capture the effects of cell extract metabolism. To create this modeling framework, the student will use BioCRNpyler [4], a tool developed by the Murray Lab to quickly and easily build CRNs, to model the core metabolic reactions occurring in cell extract. Incorporating transcription and translation reactions will result in the creation of a model where protein production is coupled to cell extract metabolism.

Using parameters derived from literature and previous experiments done in the lab, the SURF student will create and compare models with metabolism vs. those without to determine the effects of cell metabolism on protein production. As far as a specific focus for this project goes, there are two possible directions that a SURF student can pursue as applications of this modeling framework.

=== Creating design specifications for ''in vesicle'' production of the protoflagellar system ===

As a proof-of-concept for the modeling framework described above, the student will first work on a protein production model for bacteriorhodopsin (proton pump) and ATP synthase, two components that are necessary for the proto-flagellar system. If sufficient progress is made, using insights from this model, the student can also work experimentally towards the goal of achieving and improving protein production of bacteriorhodopsin and ATP synthase, both in bulk extract and in encapsulated vesicles. Questions that the student may pursue include the following: how much ATP is needed for the production of sufficient bacteriorhodopsins and ATP synthases? does changing the starting pH of the cell extract improve protein production? how does the starting concentration of NADH/NADPH/other molecules impact protein production in this system?

=== Creating guidelines for metabolic engineering of cell-free protein synthesis ===

Cell extract contains a lot of enzymes that are useful for E. coli, but not necessarily useful for cell-free protein production. These enzymes participate in reactions that divert resources like ATP and amino acids away from protein production and towards pathways that are unproductive (one study found that only 12% of energy resources in cell extract went towards protein production!). An interesting long-term goal would be to create a cell line where the enzymes that participate in these unproductive pathways have been experimentally knocked out, so that when we make cell extract, we will have fewer resources diverted away from protein production. However, knocking out important pathways may result in E. coli that are not viable. This project will aim to computationally "knock out" enzymes in their cell-free protein production model, while modeling the effects of these knockouts in living cells, to determine the optimal metabolic engineering strategy for improving protein production. While similar work has been explored in other labs, the work has either been experimental work unguided by mathematical intuition, or theoretical work that fails to consider possible experimental limitations [5, 6, 7]. If successful, this project will help pave the way for future experimental work in creating this optimized cell extract.

'''SURF student qualifications:'''
* Prerequisite coursework: Bi1x (or similar introductory biology course that provides a basic foundation in molecular biology)
* Preferred coursework: BE 150, BE/APh 161
* Other: basic familiarity with coding preferred (Python, Java, etc.)

'''References:'''
# P. Stano, “Is Research on ‘Synthetic Cells’ Moving to the Next Level?,” Life, vol. 9, no. 1, 2019, doi: 10.3390/life9010003.
# N. E. Gregorio, M. Z. Levine, and J. P. Oza, “A User’s Guide to Cell-Free Protein Synthesis,” Methods and Protocols, vol. 2, no. 1, 2019, doi: 10.3390/mps2010024.
# H. Jia, M. Heymann, F. Bernhard, P. Schwille, and L. Kai, “Cell-free protein synthesis in micro compartments: building a minimal cell from biobricks,” New Biotechnology, vol. 39, pp. 199–205, Oct. 2017, doi: 10.1016/j.nbt.2017.06.014.
# W. Poole, A. Pandey, A. Shur, Z. A. Tuza, and R. M. Murray, “BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts,” bioRxiv, p. 2020.08.02.233478, Jan. 2020, doi: 10.1101/2020.08.02.233478.
# N. Horvath, M. Vilkhovoy, J. A. Wayman, K. Calhoun, J. Swartz, and J. D. Varner, “Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli,” Metabolic Engineering Communications, vol. 10, p. e00113, Jun. 2020, doi: 10.1016/j.mec.2019.e00113.
# R. W. Martin et al., “Cell-free protein synthesis from genomically recoded bacteria enables multisite incorporation of noncanonical amino acids,” Nature Communications, vol. 9, no. 1, p. 1203, Mar. 2018, doi: 10.1038/s41467-018-03469-5.
# H. J. Lim and D.-M. Kim, “Cell-Free Metabolic Engineering: Recent Developments and Future Prospects,” Methods and Protocols, vol. 2, no. 2, 2019, doi: 10.3390/mps2020033.

SURF 2021: Optimizing cell extract for a proto-flagellar system

2021-01-06T08:46:46Z

Mkapasia:

[[File:ATPase_Protoflagellum.png|thumb|400px|right|Figure 1: Vesicle motility induced by rotation of actin-bound ATP synthase]]
* Mentor: Richard Murray
* Co-mentor: Manisha Kapasiawala
== '''Introduction:''' ==

Past efforts in synthetic biology have created significant advancements towards the creation of synthetic cells [1]. In the simplest case, synthetic cells are phospholipid vesicles encapsulating a solution of cell extract and energy buffer, which provide the chemical components (e.g. RNA polymerase, ATP, NTPs, amino acids, etc.) for transcription and translation (TX-TL) and basic metabolism [2,3]. As minimal chassis, however, synthetic cells provide a powerful platform for both understanding natural cell behaviors and for programming synthetic behaviors, as the bottom-up construction of these synthetic cells from fully characterized components enables predictability and functionality through mechanistic insight.
Recent work has shown that synthetic cells can be programmed to perform behaviors that are observed in natural cells, such as to deliver cargo, process signals from environmental inputs, and make complex decisions in response to those signals. In the Murray Lab, we are currently focusing on the development of motility in synthetic cells, by repurposing the F1FO-ATP synthase (ATPase) rotary motor to construct an ATPase-based protoflagellum (see figure). Here, proton pumps in the synthetic cell membrane will create a proton gradient that will drive the rotation of ATPase, which will in turn drive the rotation of an actin filament that will propel the synthetic cell.

Currently, the method of introducing the proton pump and ATPase to the synthetic cell membrane is a laborious and expensive process that involves purifying these proteins from E. coli and reconstituting them in synthetic cells. An easier way to reconstitute these proteins in synthetic cells would be to have them make these proteins themselves, but doing so remains challenging due to experimental limitations.

== '''Research overview:''' ==

The SURF student will use computational modeling to help optimize cell extract for a proto-flagellar system, by creating a modeling framework for protein production that captures cell extract metabolism. At the most basic level, protein production in cells is modeled as two reactions: transcription and translation. Each reaction can be described mathematically, and the resulting chemical reaction network (CRN) can be simulated to predict protein production (e.g. how much protein is made, how long it takes to reach that steady state level, etc.). Starting from a basic CRN, one can build increasing complex CRNs by considering that there are finite amounts of ribosomes and RNA polymerases in a cell, considering dilution and degradation of mRNA and proteins, etc.

Protein production in cell extract, however, is very different from protein production in a cell, largely because there is no recycling of metabolic products and of energy molecules such as ATP and NADH. Thus, in cell extract, the metabolic reactions going on “in the background”, which we can typically ignore when modeling protein production in a cell, may have a significant impact on protein production, whether via depletion of ATP and other resources, toxic accumulation of phosphate, pH effects, etc. Creating protein production models that capture the effects of cell extract metabolism may lead to the creation of more accurate protein production models.

The SURF project will focus on the creation of a modeling framework for protein production that will capture the effects of cell extract metabolism. To create this modeling framework, the student will use BioCRNpyler [4], a tool developed by the Murray Lab to quickly and easily build CRNs, to model the core metabolic reactions occurring in cell extract. Incorporating transcription and translation reactions will result in the creation of a model where protein production is coupled to cell extract metabolism.

Using parameters derived from literature and previous experiments done in the lab, the SURF student will create and compare models with metabolism vs. those without to determine the effects of cell metabolism on protein production. As far as a specific focus for this project goes, there are two possible directions that a SURF student can pursue as applications of this modeling framework.

'''Creating design specifications for "in vesicle" production of the protoflagellar system'''

As a proof-of-concept for the modeling framework described above, the student will first work on a protein production model for bacteriorhodopsin (proton pump) and ATP synthase, two components that are necessary for the proto-flagellar system. If sufficient progress is made, using insights from this model, the student can also work experimentally towards the goal of achieving and improving protein production of bacteriorhodopsin and ATP synthase, both in bulk extract and in encapsulated vesicles. Questions that the student may pursue include the following: how much ATP is needed for the production of sufficient bacteriorhodopsins and ATP synthases? does changing the starting pH of the cell extract improve protein production? how does the starting concentration of NADH/NADPH/other molecules impact protein production in this system?

'''Creating guidelines for metabolic engineering of cell-free protein synthesis'''

Cell extract contains a lot of enzymes that are useful for E. coli, but not necessarily useful for cell-free protein production. These enzymes participate in reactions that divert resources like ATP and amino acids away from protein production and towards pathways that are unproductive (one study found that only 12% of energy resources in cell extract went towards protein production!). An interesting long-term goal would be to create a cell line where the enzymes that participate in these unproductive pathways have been experimentally knocked out, so that when we make cell extract, we will have fewer resources diverted away from protein production. However, knocking out important pathways may result in E. coli that are not viable. This project will aim to computationally "knock out" enzymes in their cell-free protein production model, while modeling the effects of these knockouts in living cells, to determine the optimal metabolic engineering strategy for improving protein production. While similar work has been explored in other labs, the work has either been experimental work unguided by mathematical intuition, or theoretical work that fails to consider possible experimental limitations [5, 6, 7]. If successful, this project will help pave the way for future experimental work in creating this optimized cell extract.

'''Qualifications'''
* Prerequisite coursework: Bi1x (or similar intro. biology course)
* Preferred coursework: BE 150, BE/APh 161
* Other: basic familiarity with coding preferred (Python, Java, etc.)

'''References:'''
# P. Stano, “Is Research on ‘Synthetic Cells’ Moving to the Next Level?,” Life, vol. 9, no. 1, 2019, doi: 10.3390/life9010003.
# N. E. Gregorio, M. Z. Levine, and J. P. Oza, “A User’s Guide to Cell-Free Protein Synthesis,” Methods and Protocols, vol. 2, no. 1, 2019, doi: 10.3390/mps2010024.
# H. Jia, M. Heymann, F. Bernhard, P. Schwille, and L. Kai, “Cell-free protein synthesis in micro compartments: building a minimal cell from biobricks,” New Biotechnology, vol. 39, pp. 199–205, Oct. 2017, doi: 10.1016/j.nbt.2017.06.014.
# W. Poole, A. Pandey, A. Shur, Z. A. Tuza, and R. M. Murray, “BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts,” bioRxiv, p. 2020.08.02.233478, Jan. 2020, doi: 10.1101/2020.08.02.233478.
# N. Horvath, M. Vilkhovoy, J. A. Wayman, K. Calhoun, J. Swartz, and J. D. Varner, “Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli,” Metabolic Engineering Communications, vol. 10, p. e00113, Jun. 2020, doi: 10.1016/j.mec.2019.e00113.
# R. W. Martin et al., “Cell-free protein synthesis from genomically recoded bacteria enables multisite incorporation of noncanonical amino acids,” Nature Communications, vol. 9, no. 1, p. 1203, Mar. 2018, doi: 10.1038/s41467-018-03469-5.
# H. J. Lim and D.-M. Kim, “Cell-Free Metabolic Engineering: Recent Developments and Future Prospects,” Methods and Protocols, vol. 2, no. 2, 2019, doi: 10.3390/mps2020033.

SURF 2021: Optimizing cell extract for a proto-flagellar system

2021-01-06T08:43:20Z

Mkapasia:

[[File:ATPase_Protoflagellum.png|thumb|400px|right|Figure 1: Vesicle motility induced by rotation of actin-bound ATP synthase]]
* Mentor: Richard Murray
* Co-mentor: Manisha Kapasiawala
== '''Introduction:''' ==

Past efforts in synthetic biology have created significant advancements towards the creation of synthetic cells [1]. In the simplest case, synthetic cells are phospholipid vesicles encapsulating a solution of cell extract and energy buffer, which provide the chemical components (e.g. RNA polymerase, ATP, NTPs, amino acids, etc.) for transcription and translation (TX-TL) and basic metabolism [2,3]. As minimal chassis, however, synthetic cells provide a powerful platform for both understanding natural cell behaviors and for programming synthetic behaviors, as the bottom-up construction of these synthetic cells from fully characterized components enables predictability and functionality through mechanistic insight.
Recent work has shown that synthetic cells can be programmed to perform behaviors that are observed in natural cells, such as to deliver cargo, process signals from environmental inputs, and make complex decisions in response to those signals. In the Murray Lab, we are currently focusing on the development of motility in synthetic cells, by repurposing the F1FO-ATP synthase (ATPase) rotary motor to construct an ATPase-based protoflagellum (see figure). Here, proton pumps in the synthetic cell membrane will create a proton gradient that will drive the rotation of ATPase, which will in turn drive the rotation of an actin filament that will propel the synthetic cell.

Currently, the method of introducing the proton pump and ATPase to the synthetic cell membrane is a laborious and expensive process that involves purifying these proteins from E. coli and reconstituting them in synthetic cells. An easier way to reconstitute these proteins in synthetic cells would be to have them make these proteins themselves, but doing so remains challenging due to experimental limitations.

== '''Research overview:''' ==

The SURF student will use computational modeling to help optimize cell extract for a proto-flagellar system, by creating a modeling framework for protein production that captures cell extract metabolism. At the most basic level, protein production in cells is modeled as two reactions: transcription and translation. Each reaction can be described mathematically, and the resulting chemical reaction network (CRN) can be simulated to predict protein production (e.g. how much protein is made, how long it takes to reach that steady state level, etc.). Starting from a basic CRN, one can build increasing complex CRNs by considering that there are finite amounts of ribosomes and RNA polymerases in a cell, considering dilution and degradation of mRNA and proteins, etc.

Protein production in cell extract, however, is very different from protein production in a cell, largely because there is no recycling of metabolic products and of energy molecules such as ATP and NADH. Thus, in cell extract, the metabolic reactions going on “in the background”, which we can typically ignore when modeling protein production in a cell, may have a significant impact on protein production, whether via depletion of ATP and other resources, toxic accumulation of phosphate, pH effects, etc. Creating protein production models that capture the effects of cell extract metabolism may lead to the creation of more accurate protein production models.

The SURF project will focus on the creation of a modeling framework for protein production that will capture the effects of cell extract metabolism. To create this modeling framework, the student will use BioCRNpyler [4], a tool developed by the Murray Lab to quickly and easily build CRNs, to model the core metabolic reactions occurring in cell extract. Incorporating transcription and translation reactions will result in the creation of a model where protein production is coupled to cell extract metabolism.

Using parameters derived from literature and previous experiments done in the lab, the SURF student will create and compare models with metabolism vs. those without to determine the effects of cell metabolism on protein production. As far as a specific focus for this project goes, there are two possible directions that a SURF student can pursue as applications of this modeling framework.

'''Creating design specifications for "in vesicle" production of the protoflagellar system'''

As a proof-of-concept for the modeling framework described above, the student will first work on a protein production model for bacteriorhodopsin (proton pump) and ATP synthase, two components that are necessary for the proto-flagellar system. If sufficient progress is made, using insights from this model, the student can also work experimentally towards the goal of achieving and improving protein production of bacteriorhodopsin and ATP synthase, both in bulk extract and in encapsulated vesicles. Questions that the student may pursue include the following: how much ATP is needed for the production of sufficient bacteriorhodopsins and ATP synthases? does changing the starting pH of the cell extract improve protein production? how does the starting concentration of NADH/NADPH/other molecules impact protein production in this system?

'''Creating guidelines for metabolic engineering of cell-free protein synthesis'''

Cell extract contains a lot of enzymes that are useful for E. coli, but not necessarily useful for cell-free protein production. These enzymes participate in reactions that divert resources like ATP and amino acids away from protein production and towards pathways that are unproductive (one study found that only 12% of energy resources in cell extract went towards protein production!). An interesting long-term goal would be to create a cell line where the enzymes that participate in these unproductive pathways have been experimentally knocked out, so that when we make cell extract, we will have fewer resources diverted away from protein production. However, knocking out important pathways may result in E. coli that are not viable. This project will aim to computationally "knock out" enzymes in their cell-free protein production model, while modeling the effects of these knockouts in living cells, to determine the optimal metabolic engineering strategy for improving protein production.

'''Qualifications'''
* Prerequisite coursework: Bi1x (or similar intro. biology course)
* Preferred coursework: BE 150, BE/APh 161
* Other: basic familiarity with coding preferred (Python, Java, etc.)

'''References:'''
# P. Stano, “Is Research on ‘Synthetic Cells’ Moving to the Next Level?,” Life, vol. 9, no. 1, 2019, doi: 10.3390/life9010003.
# N. E. Gregorio, M. Z. Levine, and J. P. Oza, “A User’s Guide to Cell-Free Protein Synthesis,” Methods and Protocols, vol. 2, no. 1, 2019, doi: 10.3390/mps2010024.
# H. Jia, M. Heymann, F. Bernhard, P. Schwille, and L. Kai, “Cell-free protein synthesis in micro compartments: building a minimal cell from biobricks,” New Biotechnology, vol. 39, pp. 199–205, Oct. 2017, doi: 10.1016/j.nbt.2017.06.014.
# W. Poole, A. Pandey, A. Shur, Z. A. Tuza, and R. M. Murray, “BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts,” bioRxiv, p. 2020.08.02.233478, Jan. 2020, doi: 10.1101/2020.08.02.233478.
# N. Horvath, M. Vilkhovoy, J. A. Wayman, K. Calhoun, J. Swartz, and J. D. Varner, “Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli,” Metabolic Engineering Communications, vol. 10, p. e00113, Jun. 2020, doi: 10.1016/j.mec.2019.e00113.
# R. W. Martin et al., “Cell-free protein synthesis from genomically recoded bacteria enables multisite incorporation of noncanonical amino acids,” Nature Communications, vol. 9, no. 1, p. 1203, Mar. 2018, doi: 10.1038/s41467-018-03469-5.
# H. J. Lim and D.-M. Kim, “Cell-Free Metabolic Engineering: Recent Developments and Future Prospects,” Methods and Protocols, vol. 2, no. 2, 2019, doi: 10.3390/mps2020033.

SURF 2021: Optimizing cell extract for a proto-flagellar system

2021-01-06T08:41:19Z

Mkapasia:

[[File:ATPase_Protoflagellum.png|thumb|400px|right|Figure 1: Vesicle motility induced by rotation of actin-bound ATP synthase]]
* Mentor: Richard Murray
* Co-mentor: Manisha Kapasiawala
== '''Introduction:''' ==

Past efforts in synthetic biology have created significant advancements towards the creation of synthetic cells [1]. In the simplest case, synthetic cells are phospholipid vesicles encapsulating a solution of cell extract and energy buffer, which provide the chemical components (e.g. RNA polymerase, ATP, NTPs, amino acids, etc.) for transcription and translation (TX-TL) and basic metabolism [2,3]. As minimal chassis, however, synthetic cells provide a powerful platform for both understanding natural cell behaviors and for programming synthetic behaviors, as the bottom-up construction of these synthetic cells from fully characterized components enables predictability and functionality through mechanistic insight.
Recent work has shown that synthetic cells can be programmed to perform behaviors that are observed in natural cells, such as to deliver cargo, process signals from environmental inputs, and make complex decisions in response to those signals. In the Murray Lab, we are currently focusing on the development of motility in synthetic cells, by repurposing the F1FO-ATP synthase (ATPase) rotary motor to construct an ATPase-based protoflagellum (see figure). Here, proton pumps in the synthetic cell membrane will create a proton gradient that will drive the rotation of ATPase, which will in turn drive the rotation of an actin filament that will propel the synthetic cell.

Currently, the method of introducing the proton pump and ATPase to the synthetic cell membrane is a laborious and expensive process that involves purifying these proteins from E. coli and reconstituting them in synthetic cells. An easier way to reconstitute these proteins in synthetic cells would be to have them make these proteins themselves, but doing so remains challenging due to experimental limitations.

== '''Research overview:''' ==

The SURF student will use computational modeling to help optimize cell extract for a proto-flagellar system, by creating a modeling framework for protein production that captures cell extract metabolism. At the most basic level, protein production in cells is modeled as two reactions: transcription and translation. Each reaction can be described mathematically, and the resulting chemical reaction network (CRN) can be simulated to predict protein production (e.g. how much protein is made, how long it takes to reach that steady state level, etc.). Starting from a basic CRN, one can build increasing complex CRNs by considering that there are finite amounts of ribosomes and RNA polymerases in a cell, considering dilution and degradation of mRNA and proteins, etc.

Protein production in cell extract, however, is very different from protein production in a cell, largely because there is no recycling of metabolic products and of energy molecules such as ATP and NADH. Thus, in cell extract, the metabolic reactions going on “in the background”, which we can typically ignore when modeling protein production in a cell, may have a significant impact on protein production, whether via depletion of ATP and other resources, toxic accumulation of phosphate, pH effects, etc. Creating protein production models that capture the effects of cell extract metabolism may lead to the creation of more accurate protein production models.

The SURF project will focus on the creation of a modeling framework for protein production that will capture the effects of cell extract metabolism. To create this modeling framework, the student will use BioCRNpyler [4], a tool developed by the Murray Lab to quickly and easily build CRNs, to model the core metabolic reactions occurring in cell extract. Incorporating transcription and translation reactions will result in the creation of a model where protein production is coupled to cell extract metabolism.

Using parameters derived from literature and previous experiments done in the lab, the SURF student will create and compare models with metabolism vs. those without to determine the effects of cell metabolism on protein production. As far as a specific focus for this project goes, there are two possible directions that a SURF student can pursue as applications of this modeling framework.

'''Creating design specifications for "in vesicle" production of the protoflagellar system'''

As a proof-of-concept for the modeling framework described above, the student will first work on a protein production model for bacteriorhodopsin (proton pump) and ATP synthase, two components that are necessary for the proto-flagellar system. If sufficient progress is made, using insights from this model, the student can also work experimentally towards the goal of achieving and improving protein production of bacteriorhodopsin and ATP synthase, both in bulk extract and in encapsulated vesicles. Questions that the student may pursue include the following: (1) how much ATP is needed for the production of sufficient bacteriorhodopsins and ATP synthases? (2) does changing the starting pH of the cell extract improve protein production? (3) how does the starting concentration of NADH/NADPH/other molecules impact protein production in this system?

'''Creating guidelines for metabolic engineering of cell-free protein synthesis'''

Cell extract contains a lot of enzymes that are useful for E. coli, but not necessarily useful for cell-free protein production. These enzymes participate in reactions that divert resources like ATP and amino acids away from protein production and towards pathways that are unproductive (one study found that only 12% of energy resources in cell extract went towards protein production!). An interesting long-term goal would be to create a cell line where the enzymes that participate in these unproductive pathways have been experimentally knocked out, so that when we make cell extract, we will have fewer resources diverted away from protein production. However, knocking out important pathways may result in E. coli that are not viable. This project will aim to computationally "knock out" enzymes in their cell-free protein production model, while modeling the effects of these knockouts in living cells, to determine the optimal metabolic engineering strategy for improving protein production.

'''Qualifications'''
* Prerequisite coursework: Bi1x (or similar intro. biology course)
* Preferred coursework: BE150, BE/APh 161
* Other: basic familiarity with coding preferred (Python, Java, etc.)

'''References:'''
# P. Stano, “Is Research on ‘Synthetic Cells’ Moving to the Next Level?,” Life, vol. 9, no. 1, 2019, doi: 10.3390/life9010003.
# N. E. Gregorio, M. Z. Levine, and J. P. Oza, “A User’s Guide to Cell-Free Protein Synthesis,” Methods and Protocols, vol. 2, no. 1, 2019, doi: 10.3390/mps2010024.
# H. Jia, M. Heymann, F. Bernhard, P. Schwille, and L. Kai, “Cell-free protein synthesis in micro compartments: building a minimal cell from biobricks,” New Biotechnology, vol. 39, pp. 199–205, Oct. 2017, doi: 10.1016/j.nbt.2017.06.014.
# W. Poole, A. Pandey, A. Shur, Z. A. Tuza, and R. M. Murray, “BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts,” bioRxiv, p. 2020.08.02.233478, Jan. 2020, doi: 10.1101/2020.08.02.233478.
# N. Horvath, M. Vilkhovoy, J. A. Wayman, K. Calhoun, J. Swartz, and J. D. Varner, “Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli,” Metabolic Engineering Communications, vol. 10, p. e00113, Jun. 2020, doi: 10.1016/j.mec.2019.e00113.
# [1]R. W. Martin et al., “Cell-free protein synthesis from genomically recoded bacteria enables multisite incorporation of noncanonical amino acids,” Nature Communications, vol. 9, no. 1, p. 1203, Mar. 2018, doi: 10.1038/s41467-018-03469-5.
# H. J. Lim and D.-M. Kim, “Cell-Free Metabolic Engineering: Recent Developments and Future Prospects,” Methods and Protocols, vol. 2, no. 2, 2019, doi: 10.3390/mps2020033.

SURF 2021: Optimizing cell extract for a proto-flagellar system

2021-01-06T08:35:55Z

Mkapasia:

[[File:ATPase_Protoflagellum.png|thumb|400px|right|Figure 1: Vesicle motility induced by rotation of actin-bound ATP synthase]]
* Mentor: Richard Murray
* Co-mentor: Manisha Kapasiawala
== '''Introduction:''' ==

Past efforts in synthetic biology have created significant advancements towards the creation of synthetic cells. In the simplest case, synthetic cells are phospholipid vesicles encapsulating a solution of cell extract and energy buffer, which provide the chemical components (e.g. RNA polymerase, ATP, NTPs, amino acids, etc.) for transcription and translation (TX-TL) and basic metabolism. As minimal chassis, however, synthetic cells provide a powerful platform for both understanding natural cell behaviors and for programming synthetic behaviors, as the bottom-up construction of these synthetic cells from fully characterized components enables predictability and functionality through mechanistic insight.
Recent work has shown that synthetic cells can be programmed to perform behaviors that are observed in natural cells, such as to deliver cargo, process signals from environmental inputs, and make complex decisions in response to those signals. In the Murray Lab, we are currently focusing on the development of motility in synthetic cells, by repurposing the F1FO-ATP synthase (ATPase) rotary motor to construct an ATPase-based protoflagellum (see figure). Here, proton pumps in the synthetic cell membrane will create a proton gradient that will drive the rotation of ATPase, which will in turn drive the rotation of an actin filament that will propel the synthetic cell.

Currently, the method of introducing the proton pump and ATPase to the synthetic cell membrane is a laborious and expensive process that involves purifying these proteins from E. coli and reconstituting them in synthetic cells. An easier way to reconstitute these proteins in synthetic cells would be to have them make these proteins themselves, but doing so remains challenging due to experimental limitations.

== '''Research overview:''' ==

The SURF student will use computational modeling to help optimize cell extract for a proto-flagellar system, by creating a modeling framework for protein production that captures cell extract metabolism. At the most basic level, protein production in cells is modeled as two reactions: transcription and translation. Each reaction can be described mathematically, and the resulting chemical reaction network (CRN) can be simulated to predict protein production (e.g. how much protein is made, how long it takes to reach that steady state level, etc.). Starting from a basic CRN, one can build increasing complex CRNs by considering that there are finite amounts of ribosomes and RNA polymerases in a cell, considering dilution and degradation of mRNA and proteins, etc.

Protein production in cell extract, however, is very different from protein production in a cell, largely because there is no recycling of metabolic products and of energy molecules such as ATP and NADH. Thus, in cell extract, the metabolic reactions going on “in the background”, which we can typically ignore when modeling protein production in a cell, may have a significant impact on protein production, whether via depletion of ATP and other resources, toxic accumulation of phosphate, pH effects, etc. Creating protein production models that capture the effects of cell extract metabolism may lead to the creation of more accurate protein production models.

The SURF project will focus on the creation of a modeling framework for protein production that will capture the effects of cell extract metabolism. To create this modeling framework, the student will use BioCRNpyler, a tool developed by the Murray Lab to quickly and easily build CRNs, to model the core metabolic reactions occurring in cell extract. Incorporating transcription and translation reactions will result in the creation of a model where protein production is coupled to cell extract metabolism.

Using parameters derived from literature and previous experiments done in the lab, the SURF student will create and compare models with metabolism vs. those without to determine the effects of cell metabolism on protein production. As far as a specific focus for this project goes, there are two possible directions that a SURF student can pursue as applications of this modeling framework.

'''Creating design specifications for "in vesicle" production of the protoflagellar system'''

As a proof-of-concept for the modeling framework described above, the student will first work on a protein production model for bacteriorhodopsin (proton pump) and ATP synthase, two components that are necessary for the proto-flagellar system. If sufficient progress is made, using insights from this model, the student can also work experimentally towards the goal of achieving and improving protein production of bacteriorhodopsin and ATP synthase, both in bulk extract and in encapsulated vesicles. Questions that the student may pursue include the following: (1) how much ATP is needed for the production of sufficient bacteriorhodopsins and ATP synthases? (2) does changing the starting pH of the cell extract improve protein production? (3) how does the starting concentration of NADH/NADPH/other molecules impact protein production in this system?

'''Creating guidelines for metabolic engineering of cell-free protein synthesis'''

Cell extract contains a lot of enzymes that are useful for E. coli, but not necessarily useful for cell-free protein production. These enzymes participate in reactions that divert resources like ATP and amino acids away from protein production and towards pathways that are unproductive (one study found that only 12% of energy resources in cell extract went towards protein production!). An interesting long-term goal would be to create a cell line where the enzymes that participate in these unproductive pathways have been experimentally knocked out, so that when we make cell extract, we will have fewer resources diverted away from protein production. However, knocking out important pathways may result in E. coli that are not viable. This project will aim to computationally "knock out" enzymes in their cell-free protein production model, while modeling the effects of these knockouts in living cells, to determine the optimal metabolic engineering strategy for improving protein production.

'''Qualifications'''
* Prerequisite coursework: Bi1x (or similar intro. biology course)
* Preferred coursework: BE150, BE/APh 161
* Other: basic familiarity with coding preferred (Python, Java, etc.)

'''References:'''
# P. Stano, “Is Research on ‘Synthetic Cells’ Moving to the Next Level?,” Life, vol. 9, no. 1, 2019, doi: 10.3390/life9010003.
# N. E. Gregorio, M. Z. Levine, and J. P. Oza, “A User’s Guide to Cell-Free Protein Synthesis,” Methods and Protocols, vol. 2, no. 1, 2019, doi: 10.3390/mps2010024.
# H. Jia, M. Heymann, F. Bernhard, P. Schwille, and L. Kai, “Cell-free protein synthesis in micro compartments: building a minimal cell from biobricks,” New Biotechnology, vol. 39, pp. 199–205, Oct. 2017, doi: 10.1016/j.nbt.2017.06.014.
# W. Poole, A. Pandey, A. Shur, Z. A. Tuza, and R. M. Murray, “BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts,” bioRxiv, p. 2020.08.02.233478, Jan. 2020, doi: 10.1101/2020.08.02.233478.
# N. Horvath, M. Vilkhovoy, J. A. Wayman, K. Calhoun, J. Swartz, and J. D. Varner, “Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli,” Metabolic Engineering Communications, vol. 10, p. e00113, Jun. 2020, doi: 10.1016/j.mec.2019.e00113.
# H. J. Lim and D.-M. Kim, “Cell-Free Metabolic Engineering: Recent Developments and Future Prospects,” Methods and Protocols, vol. 2, no. 2, 2019, doi: 10.3390/mps2020033.

SURF 2021: Optimizing cell extract for a proto-flagellar system

2021-01-06T08:35:23Z

Mkapasia:

[[File:ATPase_Protoflagellum.png|thumb|500px|right|Figure 1: Vesicle motility induced by rotation of actin-bound ATP synthase]]
* Mentor: Richard Murray
* Co-mentor: Manisha Kapasiawala
== '''Introduction:''' ==

Past efforts in synthetic biology have created significant advancements towards the creation of synthetic cells. In the simplest case, synthetic cells are phospholipid vesicles encapsulating a solution of cell extract and energy buffer, which provide the chemical components (e.g. RNA polymerase, ATP, NTPs, amino acids, etc.) for transcription and translation (TX-TL) and basic metabolism. As minimal chassis, however, synthetic cells provide a powerful platform for both understanding natural cell behaviors and for programming synthetic behaviors, as the bottom-up construction of these synthetic cells from fully characterized components enables predictability and functionality through mechanistic insight.
Recent work has shown that synthetic cells can be programmed to perform behaviors that are observed in natural cells, such as to deliver cargo, process signals from environmental inputs, and make complex decisions in response to those signals. In the Murray Lab, we are currently focusing on the development of motility in synthetic cells, by repurposing the F1FO-ATP synthase (ATPase) rotary motor to construct an ATPase-based protoflagellum (see figure). Here, proton pumps in the synthetic cell membrane will create a proton gradient that will drive the rotation of ATPase, which will in turn drive the rotation of an actin filament that will propel the synthetic cell.

Currently, the method of introducing the proton pump and ATPase to the synthetic cell membrane is a laborious and expensive process that involves purifying these proteins from E. coli and reconstituting them in synthetic cells. An easier way to reconstitute these proteins in synthetic cells would be to have them make these proteins themselves, but doing so remains challenging due to experimental limitations.

== '''Research overview:''' ==

The SURF student will use computational modeling to help optimize cell extract for a proto-flagellar system, by creating a modeling framework for protein production that captures cell extract metabolism. At the most basic level, protein production in cells is modeled as two reactions: transcription and translation. Each reaction can be described mathematically, and the resulting chemical reaction network (CRN) can be simulated to predict protein production (e.g. how much protein is made, how long it takes to reach that steady state level, etc.). Starting from a basic CRN, one can build increasing complex CRNs by considering that there are finite amounts of ribosomes and RNA polymerases in a cell, considering dilution and degradation of mRNA and proteins, etc.

Protein production in cell extract, however, is very different from protein production in a cell, largely because there is no recycling of metabolic products and of energy molecules such as ATP and NADH. Thus, in cell extract, the metabolic reactions going on “in the background”, which we can typically ignore when modeling protein production in a cell, may have a significant impact on protein production, whether via depletion of ATP and other resources, toxic accumulation of phosphate, pH effects, etc. Creating protein production models that capture the effects of cell extract metabolism may lead to the creation of more accurate protein production models.

The SURF project will focus on the creation of a modeling framework for protein production that will capture the effects of cell extract metabolism. To create this modeling framework, the student will use BioCRNpyler, a tool developed by the Murray Lab to quickly and easily build CRNs, to model the core metabolic reactions occurring in cell extract. Incorporating transcription and translation reactions will result in the creation of a model where protein production is coupled to cell extract metabolism.

Using parameters derived from literature and previous experiments done in the lab, the SURF student will create and compare models with metabolism vs. those without to determine the effects of cell metabolism on protein production. As far as a specific focus for this project goes, there are two possible directions that a SURF student can pursue as applications of this modeling framework.

'''Creating design specifications for "in vesicle" production of the protoflagellar system'''

As a proof-of-concept for the modeling framework described above, the student will first work on a protein production model for bacteriorhodopsin (proton pump) and ATP synthase, two components that are necessary for the proto-flagellar system. If sufficient progress is made, using insights from this model, the student can also work experimentally towards the goal of achieving and improving protein production of bacteriorhodopsin and ATP synthase, both in bulk extract and in encapsulated vesicles. Questions that the student may pursue include the following: (1) how much ATP is needed for the production of sufficient bacteriorhodopsins and ATP synthases? (2) does changing the starting pH of the cell extract improve protein production? (3) how does the starting concentration of NADH/NADPH/other molecules impact protein production in this system?

'''Creating guidelines for metabolic engineering of cell-free protein synthesis'''

Cell extract contains a lot of enzymes that are useful for E. coli, but not necessarily useful for cell-free protein production. These enzymes participate in reactions that divert resources like ATP and amino acids away from protein production and towards pathways that are unproductive (one study found that only 12% of energy resources in cell extract went towards protein production!). An interesting long-term goal would be to create a cell line where the enzymes that participate in these unproductive pathways have been experimentally knocked out, so that when we make cell extract, we will have fewer resources diverted away from protein production. However, knocking out important pathways may result in E. coli that are not viable. This project will aim to computationally "knock out" enzymes in their cell-free protein production model, while modeling the effects of these knockouts in living cells, to determine the optimal metabolic engineering strategy for improving protein production.

'''Qualifications'''
* Prerequisite coursework: Bi1x (or similar intro. biology course)
* Preferred coursework: BE150, BE/APh 161
* Other: basic familiarity with coding preferred (Python, Java, etc.)

'''References:'''
# P. Stano, “Is Research on ‘Synthetic Cells’ Moving to the Next Level?,” Life, vol. 9, no. 1, 2019, doi: 10.3390/life9010003.
# N. E. Gregorio, M. Z. Levine, and J. P. Oza, “A User’s Guide to Cell-Free Protein Synthesis,” Methods and Protocols, vol. 2, no. 1, 2019, doi: 10.3390/mps2010024.
# H. Jia, M. Heymann, F. Bernhard, P. Schwille, and L. Kai, “Cell-free protein synthesis in micro compartments: building a minimal cell from biobricks,” New Biotechnology, vol. 39, pp. 199–205, Oct. 2017, doi: 10.1016/j.nbt.2017.06.014.
# W. Poole, A. Pandey, A. Shur, Z. A. Tuza, and R. M. Murray, “BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts,” bioRxiv, p. 2020.08.02.233478, Jan. 2020, doi: 10.1101/2020.08.02.233478.
# N. Horvath, M. Vilkhovoy, J. A. Wayman, K. Calhoun, J. Swartz, and J. D. Varner, “Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli,” Metabolic Engineering Communications, vol. 10, p. e00113, Jun. 2020, doi: 10.1016/j.mec.2019.e00113.
# H. J. Lim and D.-M. Kim, “Cell-Free Metabolic Engineering: Recent Developments and Future Prospects,” Methods and Protocols, vol. 2, no. 2, 2019, doi: 10.3390/mps2020033.

File:ATPase Protoflagellum.png

2021-01-06T08:32:54Z

Mkapasia: Concept for vesicle motility. Proton pump creates proton gradient that drives rotation of ATP synthase, and subsequently drives rotation of a bound actin filament that drives vesicle motility.

Concept for vesicle motility. Proton pump creates proton gradient that drives rotation of ATP synthase, and subsequently drives rotation of a bound actin filament that drives vesicle motility.

SURF 2021: Optimizing cell extract for a proto-flagellar system

2021-01-06T08:27:44Z

Mkapasia:

[[File:Example.jpg]]
* Mentor: Richard Murray
* Co-mentor: Manisha Kapasiawala
== '''Introduction:''' ==

Past efforts in synthetic biology have created significant advancements towards the creation of synthetic cells. In the simplest case, synthetic cells are phospholipid vesicles encapsulating a solution of cell extract and energy buffer, which provide the chemical components (e.g. RNA polymerase, ATP, NTPs, amino acids, etc.) for transcription and translation (TX-TL) and basic metabolism. As minimal chassis, however, synthetic cells provide a powerful platform for both understanding natural cell behaviors and for programming synthetic behaviors, as the bottom-up construction of these synthetic cells from fully characterized components enables predictability and functionality through mechanistic insight.
Recent work has shown that synthetic cells can be programmed to perform behaviors that are observed in natural cells, such as to deliver cargo, process signals from environmental inputs, and make complex decisions in response to those signals. In the Murray Lab, we are currently focusing on the development of motility in synthetic cells, by repurposing the F1FO-ATP synthase (ATPase) rotary motor to construct an ATPase-based protoflagellum (see figure). Here, proton pumps in the synthetic cell membrane will create a proton gradient that will drive the rotation of ATPase, which will in turn drive the rotation of an actin filament that will propel the synthetic cell.

Currently, the method of introducing the proton pump and ATPase to the synthetic cell membrane is a laborious and expensive process that involves purifying these proteins from E. coli and reconstituting them in synthetic cells. An easier way to reconstitute these proteins in synthetic cells would be to have them make these proteins themselves, but doing so remains challenging due to experimental limitations.

== '''Research overview:''' ==

The SURF student will use computational modeling to help optimize cell extract for a proto-flagellar system, by creating a modeling framework for protein production that captures cell extract metabolism. At the most basic level, protein production in cells is modeled as two reactions: transcription and translation. Each reaction can be described mathematically, and the resulting chemical reaction network (CRN) can be simulated to predict protein production (e.g. how much protein is made, how long it takes to reach that steady state level, etc.). Starting from a basic CRN, one can build increasing complex CRNs by considering that there are finite amounts of ribosomes and RNA polymerases in a cell, considering dilution and degradation of mRNA and proteins, etc.

Protein production in cell extract, however, is very different from protein production in a cell, largely because there is no recycling of metabolic products and of energy molecules such as ATP and NADH. Thus, in cell extract, the metabolic reactions going on “in the background”, which we can typically ignore when modeling protein production in a cell, may have a significant impact on protein production, whether via depletion of ATP and other resources, toxic accumulation of phosphate, pH effects, etc. Creating protein production models that capture the effects of cell extract metabolism may lead to the creation of more accurate protein production models.

The SURF project will focus on the creation of a modeling framework for protein production that will capture the effects of cell extract metabolism. To create this modeling framework, the student will use BioCRNpyler, a tool developed by the Murray Lab to quickly and easily build CRNs, to model the core metabolic reactions occurring in cell extract. Incorporating transcription and translation reactions will result in the creation of a model where protein production is coupled to cell extract metabolism.

Using parameters derived from literature and previous experiments done in the lab, the SURF student will create and compare models with metabolism vs. those without to determine the effects of cell metabolism on protein production. As far as a specific focus for this project goes, there are two possible directions that a SURF student can pursue as applications of this modeling framework.

'''Creating design specifications for "in vesicle" production of the protoflagellar system'''

As a proof-of-concept for the modeling framework described above, the student will first work on a protein production model for bacteriorhodopsin (proton pump) and ATP synthase, two components that are necessary for the proto-flagellar system. If sufficient progress is made, using insights from this model, the student can also work experimentally towards the goal of achieving and improving protein production of bacteriorhodopsin and ATP synthase, both in bulk extract and in encapsulated vesicles. Questions that the student may pursue include the following: (1) how much ATP is needed for the production of sufficient bacteriorhodopsins and ATP synthases? (2) does changing the starting pH of the cell extract improve protein production? (3) how does the starting concentration of NADH/NADPH/other molecules impact protein production in this system?

'''Creating guidelines for metabolic engineering of cell-free protein synthesis'''

Cell extract contains a lot of enzymes that are useful for E. coli, but not necessarily useful for cell-free protein production. These enzymes participate in reactions that divert resources like ATP and amino acids away from protein production and towards pathways that are unproductive (one study found that only 12% of energy resources in cell extract went towards protein production!). An interesting long-term goal would be to create a cell line where the enzymes that participate in these unproductive pathways have been experimentally knocked out, so that when we make cell extract, we will have fewer resources diverted away from protein production. However, knocking out important pathways may result in E. coli that are not viable. This project will aim to computationally "knock out" enzymes in their cell-free protein production model, while modeling the effects of these knockouts in living cells, to determine the optimal metabolic engineering strategy for improving protein production.

'''Qualifications'''
* Prerequisite coursework: Bi1x (or similar intro. biology course)
* Preferred coursework: BE150, BE/APh 161
* Other: basic familiarity with coding preferred (Python, Java, etc.)

'''References:'''
# P. Stano, “Is Research on ‘Synthetic Cells’ Moving to the Next Level?,” Life, vol. 9, no. 1, 2019, doi: 10.3390/life9010003.
# N. E. Gregorio, M. Z. Levine, and J. P. Oza, “A User’s Guide to Cell-Free Protein Synthesis,” Methods and Protocols, vol. 2, no. 1, 2019, doi: 10.3390/mps2010024.
# H. Jia, M. Heymann, F. Bernhard, P. Schwille, and L. Kai, “Cell-free protein synthesis in micro compartments: building a minimal cell from biobricks,” New Biotechnology, vol. 39, pp. 199–205, Oct. 2017, doi: 10.1016/j.nbt.2017.06.014.
# W. Poole, A. Pandey, A. Shur, Z. A. Tuza, and R. M. Murray, “BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts,” bioRxiv, p. 2020.08.02.233478, Jan. 2020, doi: 10.1101/2020.08.02.233478.
# N. Horvath, M. Vilkhovoy, J. A. Wayman, K. Calhoun, J. Swartz, and J. D. Varner, “Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli,” Metabolic Engineering Communications, vol. 10, p. e00113, Jun. 2020, doi: 10.1016/j.mec.2019.e00113.
# H. J. Lim and D.-M. Kim, “Cell-Free Metabolic Engineering: Recent Developments and Future Prospects,” Methods and Protocols, vol. 2, no. 2, 2019, doi: 10.3390/mps2020033.

SURF 2021: Optimizing cell extract for a proto-flagellar system

2021-01-06T07:47:59Z

Mkapasia: Created page with "== '''THIS PAGE IS INCOMPLETE. I need to add references and figures (will be up by 9am on 2021/01/06)'''== * Mentor: Richard Murray * Co-mentor: Manisha Kapasiawala == '''Int..."

== '''THIS PAGE IS INCOMPLETE. I need to add references and figures (will be up by 9am on 2021/01/06)'''==

* Mentor: Richard Murray
* Co-mentor: Manisha Kapasiawala
== '''Introduction:''' ==

Past efforts in synthetic biology have created significant advancements towards the creation of synthetic cells. In the simplest case, synthetic cells are phospholipid vesicles encapsulating a solution of cell extract and energy buffer, which provide the chemical components (e.g. RNA polymerase, ATP, NTPs, amino acids, etc.) for transcription and translation (TX-TL) and basic metabolism. As minimal chassis, however, synthetic cells provide a powerful platform for both understanding natural cell behaviors and for programming synthetic behaviors, as the bottom-up construction of these synthetic cells from fully characterized components enables predictability and functionality through mechanistic insight.
Recent work has shown that synthetic cells can be programmed to perform behaviors that are observed in natural cells, such as to deliver cargo, process signals from environmental inputs, and make complex decisions in response to those signals. In the Murray Lab, we are currently focusing on the development of motility in synthetic cells, by repurposing the F1FO-ATP synthase (ATPase) rotary motor to construct an ATPase-based protoflagellum (see figure). Here, proton pumps in the synthetic cell membrane will create a proton gradient that will drive the rotation of ATPase, which will in turn drive the rotation of an actin filament that will propel the synthetic cell.

Currently, the method of introducing the proton pump and ATPase to the synthetic cell membrane is a laborious and expensive process that involves purifying these proteins from E. coli and reconstituting them in synthetic cells. An easier way to reconstitute these proteins in synthetic cells would be to have them make these proteins themselves, but doing so remains challenging due to experimental limitations.

== '''Research overview:''' ==

The SURF student will use computational modeling to help optimize cell extract for a proto-flagellar system, by creating a modeling framework for protein production that captures cell extract metabolism. At the most basic level, protein production in cells is modeled as two reactions: transcription and translation. Each reaction can be described mathematically, and the resulting chemical reaction network (CRN) can be simulated to predict protein production (e.g. how much protein is made, how long it takes to reach that steady state level, etc.). Starting from a basic CRN, one can build increasing complex CRNs by considering that there are finite amounts of ribosomes and RNA polymerases in a cell, considering dilution and degradation of mRNA and proteins, etc.

Protein production in cell extract, however, is very different from protein production in a cell, largely because there is no recycling of metabolic products and of energy molecules such as ATP and NADH. Thus, in cell extract, the metabolic reactions going on “in the background”, which we can typically ignore when modeling protein production in a cell, may have a significant impact on protein production, whether via depletion of ATP and other resources, toxic accumulation of phosphate, pH effects, etc. Creating protein production models that capture the effects of cell extract metabolism may lead to the creation of more accurate protein production models.

The SURF project will focus on the creation of a modeling framework for protein production that will capture the effects of cell extract metabolism. To create this modeling framework, the student will use BioCRNpyler, a tool developed by the Murray Lab to quickly and easily build CRNs, to model the core metabolic reactions occurring in cell extract. Incorporating transcription and translation reactions will result in the creation of a model where protein production is coupled to cell extract metabolism.

Using parameters derived from literature and previous experiments done in the lab, the SURF student will create and compare models with metabolism vs. those without to determine the effects of cell metabolism on protein production. As far as a specific focus for this project goes, there are two possible directions that a SURF student can pursue as applications of this modeling framework.

'''Creating design specifications for "in vesicle" production of the protoflagellar system'''

As a proof-of-concept for the modeling framework described above, the student will first work on a protein production model for bacteriorhodopsin (proton pump) and ATP synthase, two components that are necessary for the proto-flagellar system. If sufficient progress is made, using insights from this model, the student can also work experimentally towards the goal of achieving and improving protein production of bacteriorhodopsin and ATP synthase, both in bulk extract and in encapsulated vesicles. Questions that the student may pursue include the following: (1) how much ATP is needed for the production of sufficient bacteriorhodopsins and ATP synthases? (2) does changing the starting pH of the cell extract improve protein production? (3) how does the starting concentration of NADH/NADPH/other molecules impact protein production in this system?

'''Creating guidelines for metabolic engineering of cell-free protein synthesis'''

Cell extract contains a lot of enzymes that are useful for E. coli, but not necessarily useful for cell-free protein production. These enzymes participate in reactions that divert resources like ATP and amino acids away from protein production and towards pathways that are unproductive (one study found that only 12% of energy resources in cell extract went towards protein production!). An interesting long-term goal would be to create a cell line where the enzymes that participate in these unproductive pathways have been experimentally knocked out, so that when we make cell extract, we will have fewer resources diverted away from protein production. However, knocking out important pathways may result in E. coli that are not viable. This project will aim to computationally "knock out" enzymes in their cell-free protein production model, while modeling the effects of these knockouts in living cells, to determine the optimal metabolic engineering strategy for improving protein production.

'''Qualifications'''
* Prerequisite coursework: Bi1x (or similar intro. biology course)
* Preferred coursework: BE150, BE/APh 161
* Other: basic familiarity with coding preferred (Python, Java, etc.)

'''References:'''
# some review about txtl
# some review about artificial cells
# github and/or biocrnpyler paper
# txtl metabolism paper
#

UG meetings, Dec 2019

2019-11-25T18:26:35Z

Mkapasia:

Please put your initials by a time when you can meet. You can edit this page by logging in to the wiki using your IMSS credentials (same as REGIS).

If none of the open times work, send e-mail to Richard.

* 2 Dec (Mon), 12:15 pm: Open
* 2 Dec (Mon), 12:30 pm: Open
* 2 Dec (Mon), 12:45 pm: Open
<hr>
* 3 Dec (Tue): 4:45 pm: M.K.
* 3 Dec (Tue): 5:15 pm: Open
* 3 Dec (Tue): 5:30 pm: Open
<hr>
* 4 Dec (Wed), 10:00 am: Open
* 4 Dec (Wed), 10:15 am: Open
* 4 Dec (Wed), 10:30 am: Open
* 4 Dec (Wed), 10:45 am: Open
<hr>
* 6 Dec (Wed), 10:00 am: Open
* 6 Dec (Wed), 10:15 am: Open
* 6 Dec (Wed), 10:30 am: Open
* 6 Dec (Wed), 10:45 am: Open

UG meetings, Dec 2019

2019-11-25T18:26:09Z

Mkapasia:

Please put your initials by a time when you can meet. You can edit this page by logging in to the wiki using your IMSS credentials (same as REGIS).

If none of the open times work, send e-mail to Richard.

* 2 Dec (Mon), 12:15 pm: Open
* 2 Dec (Mon), 12:30 pm: Open
* 2 Dec (Mon), 12:45 pm: Open
<hr>
* 3 Dec (Tue): 4:45 pm: Manisha
* 3 Dec (Tue): 5:15 pm: Open
* 3 Dec (Tue): 5:30 pm: Open
<hr>
* 4 Dec (Wed), 10:00 am: Open
* 4 Dec (Wed), 10:15 am: Open
* 4 Dec (Wed), 10:30 am: Open
* 4 Dec (Wed), 10:45 am: Open
<hr>
* 6 Dec (Wed), 10:00 am: Open
* 6 Dec (Wed), 10:15 am: Open
* 6 Dec (Wed), 10:30 am: Open
* 6 Dec (Wed), 10:45 am: Open