Cytoplasm Subsystem - Revision history

Murray: /* Implementation in BioCRNpyler */

2025-07-08T16:29:48Z

Implementation in BioCRNpyler

← Older revision		Revision as of 09:29, 8 July 2025
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	=== Protein-only systems ===		=== Protein-only systems ===

	== Implementation in BioCRNpyler ==		== Modeling and Specification ==
			=== Implementation in BioCRNpyler ===
	[[BioCRNpyler]] contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.		[[BioCRNpyler]] contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.

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	[[Image:gfp_sim-plot.png]]		[[Image:gfp_sim-plot.png]]

			=== Subsystem specifications ===

			* Primary inputs
			* Primary outputs
			* Other interfaces
			* Safety specifications
			* Performance specifications

Murray: /* Non-Transcription/Translation-Based Systems */

2025-07-07T16:37:56Z

Non-Transcription/Translation-Based Systems

← Older revision		Revision as of 09:37, 7 July 2025
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	=== Genelets ===		=== Genelets ===

			=== Protein-only systems ===

	== Implementation in BioCRNpyler ==		== Implementation in BioCRNpyler ==

Murray: /* Implementation in BioCRNpyler */

2025-07-07T15:33:43Z

Implementation in BioCRNpyler

← Older revision		Revision as of 08:33, 7 July 2025
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	[[BioCRNpyler]] contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.		[[BioCRNpyler]] contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.

	The simplest implementation of transcription and translation makes use of the pre-defined <~~pre~~>TxTlExtract</~~pre~~> mixture:		The simplest implementation of transcription and translation makes use of the pre-defined <code>TxTlExtract</code> mixture: <pre>
	<pre>
	import numpy as np		import numpy as np
	import matplotlib.pyplot as plt		import matplotlib.pyplot as plt

Murray: /* Implementation in BioCRNpyler */

2025-07-07T15:32:51Z

Implementation in BioCRNpyler

← Older revision		Revision as of 08:32, 7 July 2025
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	[[BioCRNpyler]] contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.		[[BioCRNpyler]] contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.

	The simplest implementation of transcription and translation makes use of the pre-defined `TxTlExtract~~` mixt~~		The simplest implementation of transcription and translation makes use of the pre-defined <pre>TxTlExtract</pre> mixture:
	<pre>		<pre>
	import numpy as np		import numpy as np

Murray: /* Implementation in BioCRNpyler */

2025-07-07T15:31:55Z

Implementation in BioCRNpyler

← Older revision		Revision as of 08:31, 7 July 2025
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	The simplest implementation of transcription and translation makes use of the pre-defined `TxTlExtract` mixt		The simplest implementation of transcription and translation makes use of the pre-defined `TxTlExtract` mixt
			<pre>
			import numpy as np
			import matplotlib.pyplot as plt
			import biocrnpyler as bcp

			# Create a simple GFP DNA construct
			gfp_DNA = bcp.DNAassembly('GFP', promoter='pconst', rbs='rbs')

	~~import numpy as np~~		# Create a TXTL mixture containing the GFP
	~~import matplotlib.pyplot as plt~~		mixture = bcp.TxTlExtract(
	~~import biocrnpyler as bcp~~		"TX-TL of GFP", components=[gfp_DNA],
			parameters={'ktx': 0.5, 'ktl': 2, 'kb': 100, 'ku':10, 'kdeg': 100})
	~~# Create a simple GFP DNA construct~~
	~~gfp_DNA = bcp.DNAassembly('GFP', promoter='pconst', rbs='rbs')~~		# Compile the CRN and generate a simulation
			gfp_CRN = mixture.compile_crn()
	# Create a TXTL mixture containing the GFP
	mixture = bcp.TxTlExtract(		timepts = np.linspace(0, 100)
	"TX-TL of GFP", components=[gfp_DNA],		gfp_sim = gfp_CRN.simulate_with_bioscrape_via_sbml(
	parameters={'ktx': 0.5, 'ktl': 2, 'kb': 100, 'ku':10, 'kdeg': 100})		timepts, initial_condition_dict={
			'dna_GFP': 1, 'protein_RNAP': 10, 'protein_Ribo': 50})
	# Compile the CRN and generate a simulation
	gfp_CRN = mixture.compile_crn()		plt.plot(timepts, gfp_sim['protein_GFP'])
			plt.xlabel("Time, min") # default units = minutes
	timepts = np.linspace(0, 100)		plt.ylabel("[GFP], nM") # default units = nM
	gfp_sim = gfp_CRN.simulate_with_bioscrape_via_sbml(		</pre>
	timepts, initial_condition_dict={
	'dna_GFP': 1, 'protein_RNAP': 10, 'protein_Ribo': 50})

	plt.plot(timepts, gfp_sim['protein_GFP'])
	plt.xlabel("Time, min") # default units = minutes
	plt.ylabel("[GFP], nM") # default units = nM

	yielding the plot		yielding the plot

	[[Image:gfp_sim-plot.png]]		[[Image:gfp_sim-plot.png]]

Murray: /* Implementation in BioCRNpyler */

2025-07-07T15:30:36Z

Implementation in BioCRNpyler

Murray at 15:29, 7 July 2025

2025-07-07T15:29:56Z

← Older revision		Revision as of 08:29, 7 July 2025
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	# Compile the CRN and generate a simulation		# Compile the CRN and generate a simulation
	gfp_CRN = mixture.compile_crn()		gfp_CRN = mixture.compile_crn()

	timepts = np.linspace(0, 100)		timepts = np.linspace(0, 100)
	gfp_sim = gfp_CRN.simulate_with_bioscrape_via_sbml(		gfp_sim = gfp_CRN.simulate_with_bioscrape_via_sbml(
	timepts, initial_condition_dict={		timepts, initial_condition_dict={
	'dna_GFP': 1, 'protein_RNAP': 10, 'protein_Ribo': 50})		'dna_GFP': 1, 'protein_RNAP': 10, 'protein_Ribo': 50})

	plt.plot(timepts, gfp_sim['protein_GFP'])		plt.plot(timepts, gfp_sim['protein_GFP'])
	plt.xlabel("Time, min") # default units = minutes		plt.xlabel("Time, min") # default units = minutes

Murray at 15:29, 7 July 2025

2025-07-07T15:29:02Z

← Older revision		Revision as of 08:29, 7 July 2025
Line 27:		Line 27:
	[[BioCRNpyler]] contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.		[[BioCRNpyler]] contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.

	* Simple example of TX-TL ~~gene expression~~		The simplest implementation of transcription and translation makes use of the pre-defined `TxTlExtract` mixt

			import numpy as np
			import matplotlib.pyplot as plt
			import biocrnpyler as bcp

			# Create a simple GFP DNA construct
			gfp_DNA = bcp.DNAassembly('GFP', promoter='pconst', rbs='rbs')

			# Create a TXTL mixture containing the GFP
			mixture = bcp.TxTlExtract(
			"TX-TL of GFP", components=[gfp_DNA],
			parameters={'ktx': 0.5, 'ktl': 2, 'kb': 100, 'ku':10, 'kdeg': 100})

			# Compile the CRN and generate a simulation
			gfp_CRN = mixture.compile_crn()

			timepts = np.linspace(0, 100)
			gfp_sim = gfp_CRN.simulate_with_bioscrape_via_sbml(
			timepts, initial_condition_dict={
			'dna_GFP': 1, 'protein_RNAP': 10, 'protein_Ribo': 50})

			plt.plot(timepts, gfp_sim['protein_GFP'])
			plt.xlabel("Time, min") # default units = minutes
			plt.ylabel("[GFP], nM") # default units = nM

			yielding the plot

			[[Image:gfp_sim-plot.png]]

Murray: Initial outline

2025-07-07T13:25:44Z

Initial outline

New page

The "cytoplasm" subsystem is responsible for implementing and maintaining the internal environment of the synthetic cell, including key mechanisms such as transcription, translation, and degradation. There are many different approaches to implementing the cytoplasm subsystem, depending on the desired functionality of the synthetic cell. On this page we describe some of the key functions and approaches to implementing those functions.

== Transcription and Translation ==
The functions of transcription and translation enable to expression of RNA and proteins from DNA. They are often key elements in synthetic cells since they allow genetic programs that can conditionally express molecules with prescribed functionality.

=== Transcription only systems ===
Systems that implement just transcription, such as genelets.

=== Purified transcription and translation systems (PURE) ===
PURExpress, PUREFrex, OnePot pure

=== Extract-based transcription and translation systems (TX-TL) ===

== Degradation and Waste Management ==
Another function of the cytoplasm subsystem is to help degrade products of transcription and translation when they are no longer needed and to manage any waste products that are produced by the cytoplasm. These functions often make use of other subsystems, such as the [[Transport Subsystem]].

=== Degradation tags ===

=== External buffers ===

== Non-Transcription/Translation-Based Systems ==
In some cases, a synthetic cell may not make use of transcription nor translation as its mechanism for implementing the functionality of synthetic cells.

=== Genelets ===

== Implementation in BioCRNpyler ==
[[BioCRNpyler]] contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.

* Simple example of TX-TL gene expression

← Older revision		Revision as of 08:30, 7 July 2025
Line 29:		Line 29:
	The simplest implementation of transcription and translation makes use of the pre-defined `TxTlExtract` mixt		The simplest implementation of transcription and translation makes use of the pre-defined `TxTlExtract` mixt

	import numpy as np		import numpy as np
	import matplotlib.pyplot as plt		import matplotlib.pyplot as plt
	import biocrnpyler as bcp		import biocrnpyler as bcp

	# Create a simple GFP DNA construct		# Create a simple GFP DNA construct
	gfp_DNA = bcp.DNAassembly('GFP', promoter='pconst', rbs='rbs')		gfp_DNA = bcp.DNAassembly('GFP', promoter='pconst', rbs='rbs')

	# Create a TXTL mixture containing the GFP		# Create a TXTL mixture containing the GFP
	mixture = bcp.TxTlExtract(		mixture = bcp.TxTlExtract(
	"TX-TL of GFP", components=[gfp_DNA],		"TX-TL of GFP", components=[gfp_DNA],
	parameters={'ktx': 0.5, 'ktl': 2, 'kb': 100, 'ku':10, 'kdeg': 100})		parameters={'ktx': 0.5, 'ktl': 2, 'kb': 100, 'ku':10, 'kdeg': 100})

	# Compile the CRN and generate a simulation		# Compile the CRN and generate a simulation
	gfp_CRN = mixture.compile_crn()		gfp_CRN = mixture.compile_crn()

	timepts = np.linspace(0, 100)		timepts = np.linspace(0, 100)
	gfp_sim = gfp_CRN.simulate_with_bioscrape_via_sbml(		gfp_sim = gfp_CRN.simulate_with_bioscrape_via_sbml(
	timepts, initial_condition_dict={		timepts, initial_condition_dict={
	'dna_GFP': 1, 'protein_RNAP': 10, 'protein_Ribo': 50})		'dna_GFP': 1, 'protein_RNAP': 10, 'protein_Ribo': 50})

	plt.plot(timepts, gfp_sim['protein_GFP'])		plt.plot(timepts, gfp_sim['protein_GFP'])
	plt.xlabel("Time, min") # default units = minutes		plt.xlabel("Time, min") # default units = minutes
	plt.ylabel("[GFP], nM") # default units = nM		plt.ylabel("[GFP], nM") # default units = nM

	yielding the plot		yielding the plot

	[[Image:gfp_sim-plot.png]]		[[Image:gfp_sim-plot.png]]