{"id":24486040,"url":"https://github.com/mauricelanghinrichs/memocell","last_synced_at":"2025-04-13T19:33:37.569Z","repository":{"id":57440809,"uuid":"314007387","full_name":"mauricelanghinrichs/memocell","owner":"mauricelanghinrichs","description":"Bayesian inference of stochastic cellular processes with and without memory in Python.","archived":false,"fork":false,"pushed_at":"2025-03-12T10:13:37.000Z","size":58054,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-27T10:12:45.525Z","etag":null,"topics":["bayesian-inference","biological-data-analysis","biological-modeling","dynamical-systems","gillespie-algorithm","markov-model","master-equation","model-selection","nested-sampling","parameter-estimation","probabilistic-models","python","statistical-inference","stochastic-models","stochastic-processes"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mauricelanghinrichs.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-11-18T17:25:34.000Z","updated_at":"2025-03-12T10:13:40.000Z","dependencies_parsed_at":"2022-09-02T06:43:18.518Z","dependency_job_id":null,"html_url":"https://github.com/mauricelanghinrichs/memocell","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mauricelanghinrichs%2Fmemocell","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mauricelanghinrichs%2Fmemocell/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mauricelanghinrichs%2Fmemocell/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mauricelanghinrichs%2Fmemocell/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mauricelanghinrichs","download_url":"https://codeload.github.com/mauricelanghinrichs/memocell/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248768223,"owners_count":21158602,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-inference","biological-data-analysis","biological-modeling","dynamical-systems","gillespie-algorithm","markov-model","master-equation","model-selection","nested-sampling","parameter-estimation","probabilistic-models","python","statistical-inference","stochastic-models","stochastic-processes"],"created_at":"2025-01-21T14:31:55.766Z","updated_at":"2025-04-13T19:33:37.556Z","avatar_url":"https://github.com/mauricelanghinrichs.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n.. image:: images/MemoCellLogoWhite.svg\n   :width: 200px\n\n.. image:: https://img.shields.io/pypi/v/memocell.svg\n    :target: https://pypi.python.org/pypi/memocell\n    :alt: Latest PyPI version\n\n.. image:: https://github.com/mauricelanghinrichs/memocell/actions/workflows/CI.yml/badge.svg\n   :target: https://github.com/mauricelanghinrichs/memocell/actions/workflows/CI.yml\n   :alt: Latest CI build status\n\n.. image:: https://codecov.io/gh/mauricelanghinrichs/memocell/branch/master/graph/badge.svg?token=feWWjm4Uow\n   :target: https://codecov.io/gh/mauricelanghinrichs/memocell\n\n.. image:: https://readthedocs.org/projects/memocell/badge/?version=latest\n   :target: https://memocell.readthedocs.io/en/latest/?badge=latest\n   :alt: Documentation Status\n\n\nMemoCell - Bayesian inference of stochastic cellular processes with and without memory in Python.\n\n⚠️ IMPORTANT NOTE: MemoCell is in the final stage before\nthe first official release. A biorxiv paper presenting MemoCell will also be\nreleased soon. Once we remove this note you are ready to go. Stay tuned❗ ⚠️\n\n\nGetting Started\n---------------\n\nInstallation\n^^^^^^^^^^^^\n\nMemoCell requires an installation of a recent Python version.\n\nMake sure to have ``graphviz`` and ``pygraphviz`` installed before installing ``memocell``; for\nexample by executing the following in the terminal::\n\n   conda install graphviz\n   conda install pygraphviz\n\nThen ``memocell`` can be installed by running::\n\n   pip install memocell\n\nOther dependencies should be installed automatically during the ``memocell`` installation.\n\n\nWorkflow Example\n^^^^^^^^^^^^^^^^\n\nA small workflow example of MemoCell is introduced (and continued in the code\nexamples). We look at a simple stochastic process of cell divisions,\nwhere each realisation (/experimental repeat) starts from a single cell initially\n(Figure below, left panel). This initial cell and all its daughter cells will\ndivide according to a certain distribution of division times.\nWe want to infer this waiting time distribution which is often hard to measure\ndirectly.\n\nTo do this, MemoCell requires (typically more accessible) cell count level data.\nIn this case, these could be cell numbers observed at one single time point after\nthe start of the experiment/process (Figure below, right panel).\n\n.. image:: images/intro_cell_count_data_white.svg\n    :width: 550px\n\nWith the specification of a prior model space (which we skip\nhere, see code examples), MemoCell will update this prior by the data\nto obtain posterior knowledge. Afterwards, Bayesian-averaged outputs over the\ncomplete posterior model space are computed for faithful inferences, such as an\nestimate for the distribution of cell division times. The estimate by MemoCell\nrecovers the unobserved, ground truth of the test data set (Figure below,\nleft panel).\n\n.. image:: images/intro_inference_white.svg\n    :width: 550px\n\nInternally, MemoCell compares data and stochastic models by summary statistics\nof the so-called moments (mean, variance, covariance of cell counts).\nThis allows exact and relatively fast inferences of the (possibly\nnon-Markovian) stochastic models.\n\nMemoCell is designed for inferences in multi-reaction pathways of multiple\ncell types as well; to learn, for example, reaction rates, general phase-type\nwaiting time distributions or model topologies (Figure above, right panel).\n\nOf course, one may also apply MemoCell to any other discrete-state-space\ntime-continuous data, such as gene expression and mRNA count data; requirement is\nthat the processes of interest can be represented by the set of zero- and\nfirst-order reaction types available in MemoCell.\n\nCode Examples\n^^^^^^^^^^^^^\n\nConcrete code and usage examples can be found in the above folder ``examples``\n(as jupyter notebooks). If they don't render correctly by github,\ncopy-paste the entire URL into `nbviewer \u003chttps://nbviewer.jupyter.org\u003e`_;\nalternatively, you may download and run them yourself.\n\nDocumentation\n-------------\n\nDocumentation can be found at\n`readthedocs \u003chttps://memocell.readthedocs.io/en/latest/getting_started.html\u003e`_.\nIt includes an extensive\n`API \u003chttps://memocell.readthedocs.io/en/latest/api.html\u003e`_ detailing all\nclasses and functions of MemoCell.\n\nLicense\n-------\n\nThis package can be used under the MIT License (MIT), see LICENSE file.\n\nAuthors\n-------\n\nMemoCell was written and developed by\n`Maurice Langhinrichs \u003cm.langhinrichs@icloud.com\u003e`_\nand `Lisa Buchauer \u003clisa.buchauer@posteo.de\u003e`_\n`@TSB \u003chttps://www.dkfz.de/en/theoretical-systems-biology\u003e`_.\n\nCitation\n--------\n\nThe release paper of MemoCell can be found here ``[TODO add link]``.\n\nPlease cite this publication as\n\n``TODO add citation``\n\nThis work is based on many people's previous achievements; please find\nthe complete list of references in our release paper.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmauricelanghinrichs%2Fmemocell","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmauricelanghinrichs%2Fmemocell","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmauricelanghinrichs%2Fmemocell/lists"}