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Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3

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@AMICI-dev

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FFroehlich/README.md

About Me

I am a Early Career Group Leader at the Francis Crick Instute, where I head the Dynamics of Living Systems (https://github.com/frohlich-lab). For more information, you can follow me on twitter or read my papers.

Estimation Pipeline

Over the years, I have developed and contributed to several toolboxes that permit the efficient formulation, simulation and calibration of kinetic models of cellular signaling. All toolboxes are modular and interoperable, allowing the flexible definition of customizable analysis pipelines.

  • Formulate your model using pysb
  • Define your estimation problem using PEtab
  • Solve your estimation problem using pyPESTO
  • Use the efficient optimizers implemented in fides
  • Use the scalable simulate and sensitivity computation methods implemented in AMICI

Pinned Loading

  1. AMICI-dev/AMICIAMICI-dev/AMICIPublic

    High-performance sensitivity analysis for large ordinary differential equation models

    Python 144 34

  2. ICB-DCM/pyPESTOICB-DCM/pyPESTOPublic

    python Parameter EStimation TOolbox

    Python 280 49

  3. fides-dev/fidesfides-dev/fidesPublic

    Trust Region Optimization in Python

    Python 28 5

  4. PEtab-dev/PEtabPEtab-dev/PEtabPublic

    PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology

    64 16

  5. pysb/pysbpysb/pysbPublic

    Python framework for Systems Biology modeling

    Python 199 72

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Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
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  • 
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@AMICI-dev

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FFroehlich/README.md
About Me
I am a Early Career Group Leader at the Francis Crick Instute, where I head the Dynamics of Living Systems (https://github.com/frohlich-lab). For more information, you can follow me on twitter or read my papers.
Estimation Pipeline
Over the years, I have developed and contributed to several toolboxes that permit the efficient formulation, simulation and calibration of kinetic models of cellular signaling. All toolboxes are modular and interoperable, allowing the flexible definition of customizable analysis pipelines.
  • Formulate your model using pysb
  • Define your estimation problem using PEtab
  • Solve your estimation problem using pyPESTO
  • Use the efficient optimizers implemented in fides
  • Use the scalable simulate and sensitivity computation methods implemented in AMICI

Pinned
Loading
  1. AMICI-dev/AMICIAMICI-dev/AMICIPublic
    
High-performance sensitivity analysis for large ordinary differential equation models

    Python
144

34

  2. ICB-DCM/pyPESTOICB-DCM/pyPESTOPublic
    
python Parameter EStimation TOolbox

    Python
280

49

  3. fides-dev/fidesfides-dev/fidesPublic
    
Trust Region Optimization in Python

    Python
28

5

  4. PEtab-dev/PEtabPEtab-dev/PEtabPublic
    
PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology

    
64

16

  5. pysb/pysbpysb/pysbPublic
    
Python framework for Systems Biology modeling

    Python
199

72


, 'i');
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injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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41
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 · 23
following

Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Highlights
  • 
Pro

Organizations
@AMICI-dev

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FFroehlich/README.md
About Me
I am a Early Career Group Leader at the Francis Crick Instute, where I head the Dynamics of Living Systems (https://github.com/frohlich-lab). For more information, you can follow me on twitter or read my papers.
Estimation Pipeline
Over the years, I have developed and contributed to several toolboxes that permit the efficient formulation, simulation and calibration of kinetic models of cellular signaling. All toolboxes are modular and interoperable, allowing the flexible definition of customizable analysis pipelines.
  • Formulate your model using pysb
  • Define your estimation problem using PEtab
  • Solve your estimation problem using pyPESTO
  • Use the efficient optimizers implemented in fides
  • Use the scalable simulate and sensitivity computation methods implemented in AMICI

Pinned
Loading
  1. AMICI-dev/AMICIAMICI-dev/AMICIPublic
    
High-performance sensitivity analysis for large ordinary differential equation models

    Python
144

34

  2. ICB-DCM/pyPESTOICB-DCM/pyPESTOPublic
    
python Parameter EStimation TOolbox

    Python
280

49

  3. fides-dev/fidesfides-dev/fidesPublic
    
Trust Region Optimization in Python

    Python
28

5

  4. PEtab-dev/PEtabPEtab-dev/PEtabPublic
    
PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology

    
64

16

  5. pysb/pysbpysb/pysbPublic
    
Python framework for Systems Biology modeling

    Python
199

72


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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41
followers
 · 23
following

Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Highlights
  • 
Pro

Organizations
@AMICI-dev

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FFroehlich/README.md
About Me
I am a Early Career Group Leader at the Francis Crick Instute, where I head the Dynamics of Living Systems (https://github.com/frohlich-lab). For more information, you can follow me on twitter or read my papers.
Estimation Pipeline
Over the years, I have developed and contributed to several toolboxes that permit the efficient formulation, simulation and calibration of kinetic models of cellular signaling. All toolboxes are modular and interoperable, allowing the flexible definition of customizable analysis pipelines.
  • Formulate your model using pysb
  • Define your estimation problem using PEtab
  • Solve your estimation problem using pyPESTO
  • Use the efficient optimizers implemented in fides
  • Use the scalable simulate and sensitivity computation methods implemented in AMICI

Pinned
Loading
  1. AMICI-dev/AMICIAMICI-dev/AMICIPublic
    
High-performance sensitivity analysis for large ordinary differential equation models

    Python
144

34

  2. ICB-DCM/pyPESTOICB-DCM/pyPESTOPublic
    
python Parameter EStimation TOolbox

    Python
280

49

  3. fides-dev/fidesfides-dev/fidesPublic
    
Trust Region Optimization in Python

    Python
28

5

  4. PEtab-dev/PEtabPEtab-dev/PEtabPublic
    
PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology

    
64

16

  5. pysb/pysbpysb/pysbPublic
    
Python framework for Systems Biology modeling

    Python
199

72


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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41
followers
 · 23
following

Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Highlights
  • 
Pro

Organizations
@AMICI-dev

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FFroehlich/README.md
About Me
I am a Early Career Group Leader at the Francis Crick Instute, where I head the Dynamics of Living Systems (https://github.com/frohlich-lab). For more information, you can follow me on twitter or read my papers.
Estimation Pipeline
Over the years, I have developed and contributed to several toolboxes that permit the efficient formulation, simulation and calibration of kinetic models of cellular signaling. All toolboxes are modular and interoperable, allowing the flexible definition of customizable analysis pipelines.
  • Formulate your model using pysb
  • Define your estimation problem using PEtab
  • Solve your estimation problem using pyPESTO
  • Use the efficient optimizers implemented in fides
  • Use the scalable simulate and sensitivity computation methods implemented in AMICI

Pinned
Loading
  1. AMICI-dev/AMICIAMICI-dev/AMICIPublic
    
High-performance sensitivity analysis for large ordinary differential equation models

    Python
144

34

  2. ICB-DCM/pyPESTOICB-DCM/pyPESTOPublic
    
python Parameter EStimation TOolbox

    Python
280

49

  3. fides-dev/fidesfides-dev/fidesPublic
    
Trust Region Optimization in Python

    Python
28

5

  4. PEtab-dev/PEtabPEtab-dev/PEtabPublic
    
PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology

    
64

16

  5. pysb/pysbpysb/pysbPublic
    
Python framework for Systems Biology modeling

    Python
199

72


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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41
followers
 · 23
following

Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Highlights
  • 
Pro

Organizations
@AMICI-dev

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FFroehlich/README.md
About Me
I am a Early Career Group Leader at the Francis Crick Instute, where I head the Dynamics of Living Systems (https://github.com/frohlich-lab). For more information, you can follow me on twitter or read my papers.
Estimation Pipeline
Over the years, I have developed and contributed to several toolboxes that permit the efficient formulation, simulation and calibration of kinetic models of cellular signaling. All toolboxes are modular and interoperable, allowing the flexible definition of customizable analysis pipelines.
  • Formulate your model using pysb
  • Define your estimation problem using PEtab
  • Solve your estimation problem using pyPESTO
  • Use the efficient optimizers implemented in fides
  • Use the scalable simulate and sensitivity computation methods implemented in AMICI

Pinned
Loading
  1. AMICI-dev/AMICIAMICI-dev/AMICIPublic
    
High-performance sensitivity analysis for large ordinary differential equation models

    Python
144

34

  2. ICB-DCM/pyPESTOICB-DCM/pyPESTOPublic
    
python Parameter EStimation TOolbox

    Python
280

49

  3. fides-dev/fidesfides-dev/fidesPublic
    
Trust Region Optimization in Python

    Python
28

5

  4. PEtab-dev/PEtabPEtab-dev/PEtabPublic
    
PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology

    
64

16

  5. pysb/pysbpysb/pysbPublic
    
Python framework for Systems Biology modeling

    Python
199

72


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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View FFroehlich's full-sized avatar
41
followers
 · 23
following

Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Highlights
  • 
Pro

Organizations
@AMICI-dev

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FFroehlich/README.md
About Me
I am a Early Career Group Leader at the Francis Crick Instute, where I head the Dynamics of Living Systems (https://github.com/frohlich-lab). For more information, you can follow me on twitter or read my papers.
Estimation Pipeline
Over the years, I have developed and contributed to several toolboxes that permit the efficient formulation, simulation and calibration of kinetic models of cellular signaling. All toolboxes are modular and interoperable, allowing the flexible definition of customizable analysis pipelines.
  • Formulate your model using pysb
  • Define your estimation problem using PEtab
  • Solve your estimation problem using pyPESTO
  • Use the efficient optimizers implemented in fides
  • Use the scalable simulate and sensitivity computation methods implemented in AMICI

Pinned
Loading
  1. AMICI-dev/AMICIAMICI-dev/AMICIPublic
    
High-performance sensitivity analysis for large ordinary differential equation models

    Python
144

34

  2. ICB-DCM/pyPESTOICB-DCM/pyPESTOPublic
    
python Parameter EStimation TOolbox

    Python
280

49

  3. fides-dev/fidesfides-dev/fidesPublic
    
Trust Region Optimization in Python

    Python
28

5

  4. PEtab-dev/PEtabPEtab-dev/PEtabPublic
    
PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology

    
64

16

  5. pysb/pysbpysb/pysbPublic
    
Python framework for Systems Biology modeling

    Python
199

72


, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
})();

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View FFroehlich's full-sized avatar
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Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
Achievements
Achievement: Starstruckx2Achievement: QuickdrawAchievement: Pair Extraordinairex4Achievement: YOLOAchievement: Galaxy BrainAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx3
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@AMICI-dev

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FFroehlich/README.md
About Me
I am a Early Career Group Leader at the Francis Crick Instute, where I head the Dynamics of Living Systems (https://github.com/frohlich-lab). For more information, you can follow me on twitter or read my papers.
Estimation Pipeline
Over the years, I have developed and contributed to several toolboxes that permit the efficient formulation, simulation and calibration of kinetic models of cellular signaling. All toolboxes are modular and interoperable, allowing the flexible definition of customizable analysis pipelines.
  • Formulate your model using pysb
  • Define your estimation problem using PEtab
  • Solve your estimation problem using pyPESTO
  • Use the efficient optimizers implemented in fides
  • Use the scalable simulate and sensitivity computation methods implemented in AMICI

Pinned
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  1. AMICI-dev/AMICIAMICI-dev/AMICIPublic
    
High-performance sensitivity analysis for large ordinary differential equation models

    Python
144

34

  2. ICB-DCM/pyPESTOICB-DCM/pyPESTOPublic
    
python Parameter EStimation TOolbox

    Python
280

49

  3. fides-dev/fidesfides-dev/fidesPublic
    
Trust Region Optimization in Python

    Python
28

5

  4. PEtab-dev/PEtabPEtab-dev/PEtabPublic
    
PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology

    
64

16

  5. pysb/pysbpysb/pysbPublic
    
Python framework for Systems Biology modeling

    Python
199

72