Problem 1¶
Get familiar with real genetic gate designs by working through the following code.
Show hint
from urllib.request import urlopen
import json
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
%matplotlib inline
figsize=(6, 4.5)
Obtaining model and experimental data¶
Cello is a genetic circuit compiler.
It compiles circuits written in a hardare description language (a sublanguage of Verilog) into
NOR and NOT gates, to then finally generate DNA with the genetic circuit in it.
Check out the paper by Nielsen et al. for a description of the compiler.
We will use modeling and experimental data from the Cello v2 compiler in the form of a user constraint file (UCF). Let's load it into a variable so that we can work with it.
def github_to_raw(url: str) -> str:
"""
Convert a GitHub file URL to its corresponding raw.githubusercontent.com URL.
Example:
https://github.com/user/repo/blob/branch/path/to/file
-> https://raw.githubusercontent.com/user/repo/branch/path/to/file
"""
if not url.startswith("https://github.com/"):
raise ValueError("Not a valid GitHub URL")
parts = url.replace("https://github.com/", "").split("/")
try:
user, repo, blob, branch = parts[:4]
path = "/".join(parts[4:])
except ValueError:
raise ValueError("Unexpected GitHub URL format")
if blob != "blob":
raise ValueError("URL must contain 'blob' to specify a branch")
raw_url = f"https://raw.githubusercontent.com/{user}/{repo}/{branch}/{path}"
return raw_url
# fetch it form this url
# url = "https://github.com/CIDARLAB/Cello-UCF/blob/develop/files/v2/ucf/Eco/Eco1C1G1T1.UCF.json"
url = "https://github.com/CIDARLAB/Cello-UCF/blob/develop/files/v2/ucf/Eco/Eco1C2G2T2.UCF.json"
# get & parse
response = urlopen(github_to_raw(url))
ucf_data = json.loads(response.read())
# if stored locally use this:
#
# with open('Eco1C1G1T1.UCF.json','r') as f:
# s = f.read()
# ucf_data = json.loads(s)
And check out what is inside. It is a list that starts with a header that includes general information from the used bacteria to experimental conditions under which data was obtained:
from pprint import pprint
pprint(ucf_data[0])
{'author': ['Jonghyeon Shin', 'Shuyi Zhang', 'Timothy Jones'],
'collection': 'header',
'date': 'Mon Jun 17 14:13:20 EDT 2019',
'description': 'TetR homologs: PhlF, SrpR, BM3R1, HlyIIR, BetI, AmtR, AmeR, '
'QacR, IcaRA',
'genome': 'NEB 10 ∆(ara-leu) 7697 araD139 fhuA ∆lacX74 galK16 galE15 e14- '
'φ80dlacZ∆M15 recA1 relA1 endA1 nupG rpsL (StrR) rph spoT1 '
'∆(mrr-hsdRMS-mcrBC)',
'growth': 'Inoculation: Individual colonies into M9 media, 16 hours overnight '
'in plate shaker. Dilution: Next day, cells dilute ~200-fold into '
'M9 media with antibiotics, growth for 3 hours. Induction: Cells '
'diluted ~650-fold into M9 media with antibiotics. Growth: shaking '
'incubator for 5 hours. Arrest protein production: PBS and 2 mg/ml '
'kanamycin. Measurement: flow cytometry, data processing for RPU '
'normalization.',
'media': 'M9 minimal media composed of M9 media salts (6.78 g/L Na2HPO4, 3 '
'g/L KH2PO4, 1 g/L NH4Cl, 0.5 g/L NaCl, 0.34 g/L thiamine '
'hydrochloride, 0.4% D-glucose, 0.2% Casamino acids, 2 mM MgSO4, and '
'0.1 mM CaCl2; kanamycin (50 μg/ml), spectinomycin (50 μg/ml)',
'organism': 'Escherichia coli NEB 10-beta',
'temperature': '37',
'version': 'Eco1C2G2T2'}
To get an overview on all the elements stored in the UCF file, let's see the collection names for all entries. These describe the type of the item. Since the list is quite long, we compact it into a set to remove multiples.
pprint(set([d['collection'] for d in ucf_data]))
{'circuit_rules',
'device_rules',
'functions',
'gates',
'genetic_locations',
'header',
'logic_constraints',
'measurement_std',
'models',
'motif_library',
'parts',
'structures'}
A NOR gate from the library¶
The library contains gates as can be seen in the collection variables.
gates_lib = [d for d in ucf_data if d['collection'] == 'gates']
gate_names = map(lambda x: x["name"], gates_lib[:100])
print(f"There are {len(gates_lib)} gates. in this library. Their names are:")
pprint(list(gate_names))
There are 18 gates. in this library. Their names are: ['P3_PhlF', 'S2_SrpR', 'B3_BM3R1', 'A1_AmtR', 'H1_HlyIIR', 'E1_BetI', 'F2_AmeR', 'N1_LmrA', 'B1_BM3R1', 'B2_BM3R1', 'P1_PhlF', 'P2_PhlF', 'S1_SrpR', 'S3_SrpR', 'S4_SrpR', 'F1_AmeR', 'C1_CymR', 'V1_VanR']
Let's pick a gate in the list and show some info on it.
gate_name = 'A1_AmtR'
# uncomment to try another gate
# gate_name = 'S1_SrpR'
gate = [g for g in gates_lib if g['name'] == gate_name][0]
pprint(gate)
{'collection': 'gates',
'color': '3BA9E0',
'gate_type': 'NOR',
'group': 'AmtR',
'model': 'A1_AmtR_model',
'name': 'A1_AmtR',
'regulator': 'AmtR',
'structure': 'A1_AmtR_structure',
'system': 'TetR'}
In fact all gates are NOR gates as we can check. This is not too surprising as we discussed in the lecture that NOR gates are particularly nice to implement as genetic components.
# check is all gates are NOR
all([g['gate_type'] == 'NOR' for g in gates_lib])
True
Structure of the gate¶
It's structure, i.e., the genetic design, is obtained as follows:
structure_lib = [d for d in ucf_data if d['collection'] == 'structures']
print(f"There are {len(structure_lib)} structures in the dataset.\n")
# find matching structure for the chosen gate
structure = [s for s in structure_lib if s['name'] == gate['structure']][0]
pprint(structure)
There are 18 structures in the dataset.
{'collection': 'structures',
'devices': [{'components': ['#in2', '#in1', 'A1_AmtR_cassette'],
'name': 'A1_AmtR'},
{'components': ['BydvJ', 'A1', 'AmtR', 'L3S2P55'],
'name': 'A1_AmtR_cassette'}],
'inputs': [{'name': 'in1', 'part_type': 'promoter'},
{'name': 'in2', 'part_type': 'promoter'}],
'name': 'A1_AmtR_structure',
'outputs': ['pAmtR']}
The structure sounds familiar from the lecture:
It is a tandem promoter
NORgate with two input promotersin1andin2(seeinputs) followed by aA1_AmtR_cassette(a sequence of genetic parts). Just mind that the left input promoter that is potentially affected by roadblock is#in2(ourPAfrom the course) and the right one#in1and not vice versa.The gate's output is the
pAmtRpromoter (seeoutputs).
Let's take a look at the components used in the A1_AmtR_cassette.
parts = ['BydvJ', 'A1', 'AmtR', 'L3S2P55']
parts_lib = [d for d in ucf_data if d['collection'] == 'parts' and d["name"] in parts]
pprint(parts_lib)
[{'collection': 'parts',
'dnasequence': 'agGGTGTCTCAAGGTGCGTACCTTGACTGATGAGTCCGAAAGGACGAAACACCcctctacaaataattttgtttaa',
'name': 'BydvJ',
'type': 'ribozyme'},
{'collection': 'parts',
'dnasequence': 'AATGTTCCCTAATAATCAGCAAAGAGGTTACTAG',
'name': 'A1',
'type': 'rbs'},
{'collection': 'parts',
'dnasequence': 'ATGGCAGGCGCAGTTGGTCGTCCGCGTCGTAGTGCACCGCGTCGTGCAGGTAAAAATCCGCGTGAAGAAATTCTGGATGCAAGCGCAGAACTGTTTACCCGTCAGGGTTTTGCAACCACCAGTACCCATCAGATTGCAGATGCAGTTGGTATTCGTCAGGCAAGCCTGTATTATCATTTTCCGAGCAAAACCGAAATCTTTCTGACCCTGCTGAAAAGCACCGTTGAACCGAGCACCGTTCTGGCAGAAGATCTGAGCACCCTGGATGCAGGTCCGGAAATGCGTCTGTGGGCAATTGTTGCAAGCGAAGTTCGTCTGCTGCTGAGCACCAAATGGAATGTTGGTCGTCTGTATCAGCTGCCGATTGTTGGTAGCGAAGAATTTGCAGAATATCATAGCCAGCGTGAAGCACTGACCAATGTTTTTCGTGATCTGGCAACCGAAATTGTTGGTGATGATCCGCGTGCAGAACTGCCGTTTCATATTACCATGAGCGTTATTGAAATGCGTCGCAATGATGGTAAAATTCCGAGTCCGCTGAGCGCAGATAGCCTGCCGGAAACCGCAATTATGCTGGCAGATGCAAGCCTGGCAGTTCTGGGTGCACCGCTGCCTGCAGATCGTGTTGAAAAAACCCTGGAACTGATTAAACAGGCAGATGCAAAATAA',
'name': 'AmtR',
'type': 'cds'},
{'collection': 'parts',
'dnasequence': 'CTCGGTACCAAAGACGAACAATAAGACGCTGAAAAGCGTCTTTTTTCGTTTTGGTCC',
'name': 'L3S2P55',
'type': 'terminator'}]
The tandem input promoters drive a construct that comprises of: a ribozyme, an RBS, a CDS for the repressor AmtR, and a terminator.
Somewhere, and not necessarily after this first construct, is the output promoter pAmtR that is repressed by AmtR. This promoter can be an input of another gate, or be directly driving a reporter gene, e.g., a CDS that expresses GFP.
Model of the gate¶
We can also obtain a model for the gate from the library.
model_lib = [d for d in ucf_data if d['collection'] == 'models']
model = [m for m in model_lib if m['name'] == gate['model']][0]
pprint(model)
{'collection': 'models',
'functions': {'cytometry': 'A1_AmtR_cytometry',
'input_composition': 'tandem_input_composition',
'response_function': 'Hill_response',
'tandem_interference_factor': 'tandem_interference_factor',
'toxicity': 'A1_AmtR_toxicity'},
'name': 'A1_AmtR_model',
'parameters': [{'description': 'Maximal transcription',
'name': 'ymax',
'value': 3.13661101},
{'description': 'Minimal transcription',
'name': 'ymin',
'value': 0.035221608},
{'description': 'Half-maximum',
'name': 'K',
'value': 0.047427884},
{'description': 'Cooperativety',
'name': 'n',
'value': 1.656171138},
{'description': 'Tandem factor',
'name': 'alpha',
'value': 0.274797631},
{'description': 'Tandem factor', 'name': 'beta', 'value': 1.0},
{'description': 'Kinetics parameter (induction)',
'name': 'tau_on',
'value': 0.9},
{'description': 'Kinetics parameter (relaxation)',
'name': 'tau_off',
'value': 2.5}]}
It says that a Hill response_function is used as a model with the additional specification that the inputs are composed via a tandem_input_composition. We will check this out by looking into the collection of functions. Indeed the first entries are what we where looking for. The next (third) entry looks like experimental data, and we will come back to it later.
Side note: While we will not use it in this Problem Session, observe that the $\alpha$ and $\beta$ are set to account for static roadblocking only; in this gate at least.
functions_lib = [d for d in ucf_data if d['collection'] == 'functions']
print('The first two elements describe these theoretical models:\n')
pprint(functions_lib[0])
print()
pprint(functions_lib[1])
print()
pprint(functions_lib[2])
The first two elements describe these theoretical models:
{'collection': 'functions',
'equation': 'ymin + (ymax - ymin) / (1.0 + (x / K)^n)',
'name': 'Hill_response',
'parameters': [{'map': '#//model/parameters/ymax', 'name': 'ymax'},
{'map': '#//model/parameters/ymin', 'name': 'ymin'},
{'map': '#//model/parameters/K', 'name': 'K'},
{'map': '#//model/parameters/n', 'name': 'n'}],
'variables': [{'map': '#//model/functions/input_composition', 'name': 'x'}]}
{'collection': 'functions',
'equation': 't1 * x2 + x1',
'name': 'tandem_input_composition',
'variables': [{'map': '#//structure/inputs/in1/model/functions/response_function',
'name': 'x1'},
{'map': '#//structure/inputs/in2/model/functions/response_function',
'name': 'x2'},
{'map': '#//structure/inputs/in1/model/functions/tandem_interference_factor',
'name': 't1'}]}
{'collection': 'functions',
'equation': 'alpha * (K^n + beta * x^n) / (K^n + x^n)',
'name': 'tandem_interference_factor',
'parameters': [{'map': '#//model/parameters/alpha', 'name': 'alpha'},
{'map': '#//model/parameters/beta', 'name': 'beta'},
{'map': '#//model/parameters/K', 'name': 'K'},
{'map': '#//model/parameters/n', 'name': 'n'}],
'variables': [{'map': '#//model/functions/input_composition', 'name': 'x'}]}
Again the functions are familiar from the lecture.
Transfer Characteristics of the Gate¶
params = {}
for p in model['parameters']:
params[p['name']] = np.float64(p['value'])
print(f"{p['name']}: {p['value']}")
def inv(x):
return params['ymin'] + (params['ymax'] - params['ymin']) / (1.0 + (x / params['K'])**params['n'])
def nor(x, y):
inputs = x + y # linear input combination
return inv(inputs)
def contour_fun(x, y, z, title=None):
"""
Make a filled contour plot given x, y, z
in 2D arrays.
"""
plt.figure()
CS = plt.contourf(x, y, z)
cbar = plt.colorbar(CS)
if title is not None:
plt.title(title)
def contour_fun(x, y, z, title=None):
"""
Make a filled contour plot given x, y, z
in 2D arrays.
"""
plt.figure()
CS = plt.contourf(x, y, z)
cbar = plt.colorbar(CS)
if title is not None:
plt.title(title)
# Get x and y values for plotting
x = np.linspace(0, 0.1, 200)
y = np.linspace(0, 0.1, 200)
xx, yy = np.meshgrid(x, y)
# plot it
contour_fun(
xx, yy,
nor(xx, yy),
title=f"{gate_name} gate"
)
ymax: 3.13661101 ymin: 0.035221608 K: 0.047427884 n: 1.656171138 alpha: 0.274797631 beta: 1.0 tau_on: 0.9 tau_off: 2.5
Measurements¶
Intrestingly there are even measurments that give much greater detail. Remember that the steady state input-output behavior is deterministic with Hill functions. In fact, measurments show that there is significant noise on output behavior.
To see this, let's look at measurements: The measurements are (normalized) histograms of steady-state input-output measurements in terms of Relative Promotor Units (RPUs). Details are described in the supplementary material of Nielsen et al.. For example, Figure S30 shows how distributions are probagated and Section VII.C.9. “gate_cytometry” states that RPUs in UCFs are measured on the same RPU scale to allow for propagation between gates.
Let's plot the response distribution for 3 inputs to such a component and to be sure check if it is already normalized to be a distribution.
pprint([f["name"] for f in functions_lib])
['Hill_response', 'tandem_input_composition', 'tandem_interference_factor', 'P3_PhlF_toxicity', 'S2_SrpR_toxicity', 'B3_BM3R1_toxicity', 'A1_AmtR_toxicity', 'H1_HlyIIR_toxicity', 'E1_BetI_toxicity', 'F2_AmeR_toxicity', 'N1_LmrA_toxicity', 'B1_BM3R1_toxicity', 'B2_BM3R1_toxicity', 'P1_PhlF_toxicity', 'P2_PhlF_toxicity', 'S1_SrpR_toxicity', 'S3_SrpR_toxicity', 'S4_SrpR_toxicity', 'F1_AmeR_toxicity', 'C1_CymR_toxicity', 'V1_VanR_toxicity', 'P3_PhlF_cytometry', 'S2_SrpR_cytometry', 'B3_BM3R1_cytometry', 'A1_AmtR_cytometry', 'H1_HlyIIR_cytometry', 'E1_BetI_cytometry', 'F2_AmeR_cytometry', 'N1_LmrA_cytometry', 'B1_BM3R1_cytometry', 'B2_BM3R1_cytometry', 'P1_PhlF_cytometry', 'P2_PhlF_cytometry', 'S1_SrpR_cytometry', 'S3_SrpR_cytometry', 'S4_SrpR_cytometry', 'F1_AmeR_cytometry', 'C1_CymR_cytometry', 'V1_VanR_cytometry']
gate_cytometry_name = f"{gate_name}_cytometry"
gate_cytometry = [f for f in functions_lib if f["name"] == gate_cytometry_name][0]
pprint(gate_cytometry)
name = gate_cytometry['name']
measurements = gate_cytometry['table']
plt.figure(figsize=figsize)
plt.title(name)
plt.xlabel('RPU (consistent over all distributions)')
# Show some distributions
d = measurements[0]
plt.plot(d['bin'], d['output'], 'ro-', label=d['x'])
middle = int( len(measurements) / 2 )
d = measurements[middle]
plt.plot(d['bin'], d['output'], 'go-', label=d['x'])
d = measurements[-1]
plt.plot(d['bin'], d['output'], 'bo-', label=d['x'])
ax = plt.gca()
ax.set_xlim(0, 50)
plt.legend()
# Let's check if the last one was really a distribution
print(f"Checking if distribution. This should be 1: {sum(d['output'])}")
{'collection': 'functions',
'name': 'A1_AmtR_cytometry',
'table': [{'bin': [0.001,
0.0010568175092136585,
0.001116863247780561,
0.0011803206356517297,
0.0012473835142429433,
0.0013182567385564075,
0.0013931568029453036,
0.0014723125024327193,
0.001555965631605075,
0.0016443717232149323,
0.0017378008287493763,
0.0018365383433483473,
0.0019408858775927793,
0.0020511621788255646,
0.002167704104819694,
0.0022908676527677724,
0.0024210290467361778,
0.0025585858869056452,
0.0027039583641088436,
0.0028575905433749467,
0.003019951720402016,
0.0031915378551007614,
0.003372873086588689,
0.0035645113342624426,
0.003767037989839089,
0.003981071705534973,
0.004207266283844442,
0.004446312674691089,
0.004698941086052156,
0.004965923214503363,
0.005248074602497723,
0.005546257129579105,
0.005861381645140285,
0.006194410750767812,
0.006546361740672747,
0.006918309709189363,
0.007311390834834172,
0.007726805850957022,
0.008165823713585922,
0.008629785477669702,
0.009120108393559097,
0.009638290236239706,
0.010185913880541169,
0.010764652136298349,
0.01137627285823431,
0.012022644346174132,
0.012705741052085419,
0.013427649611378642,
0.014190575216890926,
0.014996848355023733,
0.015848931924611134,
0.016749428760264373,
0.01770108958317421,
0.018706821403658005,
0.01976969640111861,
0.020892961308540396,
0.022080047330189003,
0.023334580622810033,
0.0246603933723434,
0.02606153549998895,
0.02754228703338166,
0.029107171180666053,
0.03076096814740708,
0.032508729738543435,
0.034355794789987466,
0.03630780547701014,
0.03837072454922788,
0.040550853544838394,
0.042854852039743964,
0.04528975799036209,
0.047863009232263824,
0.0505824662003114,
0.053456435939697164,
0.05649369748123025,
0.059703528658383694,
0.06309573444801933,
0.06668067692136222,
0.07046930689671471,
0.07447319739059892,
0.07870457896950989,
0.08317637711026708,
0.08790225168308842,
0.09289663867799362,
0.09817479430199844,
0.10375284158180126,
0.1096478196143185,
0.11587773561551262,
0.1224616199265049,
0.12941958414499863,
0.13677288255958495,
0.1445439770745928,
0.15275660582380732,
0.1614358556826487,
0.17060823890031243,
0.180301774085957,
0.19054607179632482,
0.20137242498623895,
0.21281390459827113,
0.22490546058357805,
0.23768402866248758,
0.25118864315095796,
0.2654605561975539,
0.2805433637951713,
0.2964831389524342,
0.31332857243155854,
0.3311311214825911,
0.3499451670283573,
0.3698281797802663,
0.3908408957924021,
0.41304750199016144,
0.4365158322401661,
0.46131757456037953,
0.4875284901033865,
0.5152286445817567,
0.5445026528424215,
0.5754399373371573,
0.6081350012787182,
0.64268771731702,
0.6792036326171843,
0.7177942912713615,
0.7585775750291835,
0.801678063387679,
0.8472274141405963,
0.8953647655495938,
0.946237161365793,
1.0,
1.0568175092136585,
1.1168632477805611,
1.1803206356517297,
1.2473835142429432,
1.3182567385564075,
1.3931568029453036,
1.4723125024327193,
1.555965631605075,
1.6443717232149324,
1.7378008287493762,
1.8365383433483473,
1.9408858775927793,
2.051162178825567,
2.167704104819694,
2.2908676527677723,
2.4210290467361775,
2.5585858869056453,
2.703958364108843,
2.8575905433749464,
3.019951720402016,
3.1915378551007616,
3.372873086588689,
3.5645113342624426,
3.767037989839089,
3.981071705534973,
4.207266283844442,
4.446312674691089,
4.698941086052156,
4.965923214503363,
5.248074602497728,
5.54625712957911,
5.86138164514029,
6.194410750767818,
6.5463617406727534,
6.918309709189363,
7.311390834834173,
7.726805850957021,
8.165823713585922,
8.629785477669701,
9.120108393559097,
9.638290236239705,
10.18591388054117,
10.76465213629835,
11.37627285823431,
12.02264434617413,
12.705741052085418,
13.427649611378643,
14.190575216890926,
14.99684835502374,
15.848931924611142,
16.749428760264383,
17.70108958317422,
18.706821403658015,
19.76969640111862,
20.892961308540407,
22.080047330189014,
23.334580622810044,
24.66039337234341,
26.061535499988977,
27.54228703338169,
29.107171180666082,
30.760968147407112,
32.50872973854347,
34.3557947899875,
36.30780547701018,
38.37072454922792,
40.55085354483843,
42.85485203974392,
45.28975799036204,
47.863009232263806,
50.58246620031137,
53.45643593969714,
56.49369748123023,
59.70352865838366,
63.0957344480193,
66.68067692136219,
70.46930689671467,
74.47319739059888,
78.70457896950985,
83.17637711026708,
87.90225168308842,
92.89663867799364,
98.17479430199843,
103.75284158180126,
109.64781961431851,
115.87773561551262,
122.4616199265049,
129.41958414499862,
136.77288255958496,
144.5439770745928,
152.75660582380732,
161.4358556826487,
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'x': 2.309624}],
'variables': [{'map': '#//model/functions/input_composition', 'name': 'x'},
{'name': 'bin'}]}
Checking if distribution. This should be 1: 1.0
Model based on experimental data¶
Let us plot the transfer characteristics from the distributions. Distributions can be shown in many different ways.
We go for the mean with quantiles in this case.
We will also plot the deterministic fitted Hill model on top to see how well it fits. For some gates the fit is very good, for some a bit less.
from scipy.stats import rv_discrete
x_vals = []
means = []
q10 = []
q90 = []
for d in measurements:
x = d['x']
vals = np.array(d['bin'], dtype=float)
probs = np.array(d['output'], dtype=float)
# Normalize probabilities
probs = probs / probs.sum()
# Create a discrete random variable
dist = rv_discrete(values=(vals, probs))
# Compute mean and quantiles
mean = dist.mean()
q10_val = dist.ppf(0.1)
q90_val = dist.ppf(0.9)
x_vals.append(x)
means.append(mean)
q10.append(q10_val)
q90.append(q90_val)
x_vals = np.array(x_vals)
means = np.array(means)
q10 = np.array(q10)
q90 = np.array(q90)
# We sort the data ascending in x_val (= the input)
# since unsorted data is in the library.
# Get sorting indices
sort_idx = np.argsort(x_vals)
# Apply sorting to all arrays
x_vals = x_vals[sort_idx]
means = means[sort_idx]
q10 = q10[sort_idx]
q90 = q90[sort_idx]
plt.figure(figsize=(6,4))
# Plot mean as a line
plt.plot(x_vals, means, marker='o', label='mean')
# Fill between 10% and 90% quantiles
plt.fill_between(x_vals, q10, q90, color='orange', alpha=0.3, label='10-90% quantile')
# Set log-log scale
plt.xscale('log')
plt.yscale('log')
# also add the deterministic Hill approximation
x = np.linspace(x_vals[0], x_vals[-1], 1000)
plt.plot(x, inv(x), 'red', label = 'Hill model')
plt.title(gate_name)
plt.legend()
<matplotlib.legend.Legend at 0x12d522030>
The graph nicely shows the two transfer characteristics: the measurement and the fit.
In the measurements, one can clearly see an outlier experiment. When computing with the measurement distributions, we have to be careful to correct or clean this.
Problem 2: Gate characteristics¶
Plot all deterministic Hill models for all NOR gates in log-log plots.
You can plot them for a single combined input.
The plot should be nicely arranged so that the viewer can compare them next to each other. For example in a grid. Also think about common axis limits, gate names as titles, etc.
What do you observe in the gate library. What does this mean for composition of gates?
Show hint
For the composition: check out input and resulting output ranges. The output range is the next input range. Further, does a logical value like a 0 get mapped to a 1 and then back to a 0 when being composed? For which values does this hold? Try solving this graphically.
Problem 3: Simulation of Inverter Chain¶
Propagate determinsitic input-output values from inputs to an output.
a) Write a function that allows you to call progagate(circuit: list[str], i: float) -> float.
Here, circuit is a linear circuit of NOR gates but with a single input (INV gates).
The circuit is encoded as a list[str] of NOR names from the library.
Argument i is an input value in RPUs.
The output of the function is the output of the circuit in RPUs.
Show hint
b) Use the result of (a) to plot the transfer function for a linear circuit. Show a circuit that you consider good and one that you consider bad and tell us why.
Show hint
License: © 2025 Matthias Függer and Thomas Nowak. Licensed under CC BY-NC-SA 4.0.