Synthesis Infrastructures - User contributions [en]

Computable Graphs

2022-11-12T18:27:25Z

Akamatsm: Flesh out 'compiling graphs to manuscripts' project

{{Group
|Decription=How to ground knowledge graphs (that can be used for prediction or computational simulation experiments and models) in the discourse and quantitative evidence in scientific literature?
|Topics=Knowledge Graphs
|Projects=Synthesis center for cell biology, Translate Logseq Knowledge Graph to Systems Biology Network Diagrams
|Discord Channel Name=#computable-graphs
|Discord Channel URL=https://discord.com/channels/1029514961782849607/1038983137222467604
|Members=Aakanksha Naik, Akila Wijerathna-Yapa, Deniz Aydemir, Eran Egmon, Joel Chan, Matthew Akamatsu, Michael Gartner, Konrad Hinsen
}}
Facilitator/Point of Contact: [[Has Facilitator::Joel Chan]]

== What ==

How to ground knowledge graphs (that can be used for prediction or computational simulation experiments and models) in the discourse of evidence in scientific literature? How to transition from unstructured literature to knowledge graphs and keep things updated with appropriate provenance for (un)certainty?

== Discussion entry points ==

* [[Matthew Akamatsu]] https://discord.com/channels/1029514961782849607/1033091746139230238/1040212464631029822

== Resources ==

* [[Simularium]] - https://simularium.allencell.org/
* [[Vivarium Collective]] - https://vivarium-collective.github.io/

=== First breakout group session ===
who was present: Joel, Matt, Michael, Sid, Aakanksha, Belinda
* anchoring on [[Synthesis center for cell biology]]
* challenge: students wrapping their heads around the model; get really excited once they do

* Dafna: very much like the old issue-based argument maps!

* Belinda: what is scope of the project in terms of users?
** Matt: hoping this accessible all the way to high school students also!
* AICS is making platform for running simulations, make it easy to run and share with others - [[Simularium]]
* Dafna: are there pain points on the input side? (seems painful!)
* Dafna: how similar are the things that people pull out from the same paper?
** Matt: for our field, pretty similar, esp. for well-written papers
** Belinda: compare what each person is highlighting and extract summary that is representative of all the annotations
* Dafna: how useful are these (bits of) knowledge graphs for others?
** Works well within lab; better than unstructured text, motivating to try to create micropublications to summarize outcome 10-week rotation
* Dafna: "compiling" discourse graph to manuscript seems much easier, esp. if have consistent structure and human-in-the-loop
** Similar to brainstorming discussion/ethics statements for a paper given abstract (via GPT-3
* Belinda: how much variation in paper structure within your field?
** some variations by journal
** some authors (think more highly of themselves), more declarative/general, less clear distinction between claims and evidence
* Aakanksha:
** hypothes.is experiment
** need infra changes
*** help make the case for these changes
**** maybe how much $$ each person would pay for this!!)
**** demonstration of value
**** brainstorming what research projects would be part of this
**

emerging themes/problems:

* idea around changing the reading process somehow (with high hopes for somehting like semantic scholar PD reader that has beginning annotations tuned to what matt is trying to extract, maybe also on the abstract level) --> these could also feed back / forward to other users
** can probably start from this: Scim: Intelligent Faceted Highlights for Interactive, Multi-Pass Skimming of Scientific Papers <nowiki>https://arxiv.org/pdf/2205.04561.pdf</nowiki>
** cross connections to what

* idea of compiling from discourse graph to manuscript seems feasible?
** Belinda could mock something up in OpenAI playground really quickly for a few papers
*** need examples or access to repo
** Could also integrate into Matt's lab via GPT-3 extension
* question: understanding different levels of value of having a knowledge graph for someone else who didn't create it
** --> Joel can add links to ongoing lit review on this question - not resolved yet
* theme/idea: "compiling" from discourse to knowledge graphs
** Aakanksha: often see ontologies in isolation
*** Aakanksha: can look into NLP around adding context to knowledge graphs
** Michael: interesting to think about the user experience on this - how do they interact?
** Grounding abstractions: https://www.susielu.com/data-viz/abstractions

== Contextualizing knowledge graphs ==

== Understanding knowledge graph transfer ==

== Compiling graphs to manuscripts ==

=== Overview ===

We'd like to build a prototype of a tool that starts with discourse units (phrases with the category Question/motivation, Method, Evidence, Claim) and uses NLP to generate a draft of a manuscript paragraph.
[[File:Discourse graph to manuscript draft via NLP.png|900x900px]]

=== Purpose ===

Such a tool would speed up a really time consuming aspect of the academic job: drafting grant proposals and manuscripts from content. It would also encourage researchers to generate structured content (questions/claims/evidence) which will get incorporated into a discourse graph.

The benefit to the user is a structured approach to writing papers and grant proposals. Using the tool will introduce researchers to the concept of discourse units, and generate a repository of discourse units that can be turned into discourse graphs.

=== Ideal outcomes ===

(more to add here)

This tool could also enable the micropublication of mini discourse graphs (one question/method/evidence/claim) by generating a draft of the explanatory text.
* Could also integrate into Roam Research graphs, e.g. in Matt's lab, via the Roam GPT-3 extension

=== What we're doing next ===

* Belinda could mock something up in OpenAI playground really quickly for a few papers
** need examples or access to repo
* Matt and Michael will share some examples of papers or content from the discourse graph to use as source data
* Sid can check out [https://github.com/LayBacc/roam-ai Roam AI extension] for incorporation into our roam graph workflow (fork, make modifications etc)
** ([https://www.reddit.com/r/RoamResearch/comments/yigf6q/im_genuinely_fascinated_with_the_roam_ai/ discussion on its usage])
** [https://www.loom.com/share/d152e7a184f94080b8777f595821f43e usage video]

=== Related conversation ===
* Dafna: "compiling" discourse graph to manuscript seems much easier, esp. if have consistent structure and human-in-the-loop
** Similar to brainstorming discussion/ethics statements for a paper given abstract (via GPT-3
* Belinda: how much variation in paper structure within your field?
** some variations by journal

=== Claims in the conversation that need evidence ===
* the majority of empirical research papers in biology have a similar structure (question/ motivation/ evidence (fig.1a)/ claim for each paragraph & figure panel)
* multiple researchers (or students) asked to highlight the questions/claims/evidence text from a paper will highlight similar/consensus text (part of the NLP-to-highlights project)

== Next Steps ==

* don't know if a joint project makes sense, but perhaps coordinated first prototypes of a bridge?

could use:

* someone with programming skills to implement a POC translation between a discourse graph and one of the specific modeling languages/ontologies

Computable Graphs

2022-11-12T17:52:44Z

Akamatsm: /* Resources */

File:Discourse graph to manuscript draft via NLP.png

2022-11-12T17:50:47Z

Akamatsm:

Schematic of translating from discourse graph to draft of manuscript using NLP

Synthesis center for cell biology

2022-11-11T07:38:54Z

Akamatsm:

{{Project
|Description=Enabling the grassroots generation of conceptual and quantitative models in cell biology through the creation of a Synthesis Center for Cell and Molecular Biology
|Affilitated With=Allen Institute, University of Washington, University of Connecticut
|Contributors=Matthew Akamatsu, Eran Egmon, Michael Gartner
}}
We are putting together a proposal to make a Synthesis Center for the field of cell and molecular biology. Its goal would be to synthesize the vast quantities of available cell and molecular data (protein types, locations, abundances, interactions) into both conceptual and quantitative models, that allow us to explain and predict the remarkable transition from nonliving molecules to living cells. The center (if they choose our proposal) would be funded by the National Science Foundation for 5-10 years and be housed at the Allen Institute for Cell Science in Seattle. During this workshop, I'd love to bounce around ideas for the synthesis center, and to identify points of intersection between this proposed center and your favorite tool or area.

[[File:Conceptual and quantitative models.png|center|500x500px]]

While most of the "big data" cell biology community is focused on creating new data sets, we are proposing to synthesize existing data into quantitative and conceptual models. For one set of quantitative models, we are using large [https://www.proteinatlas.org/humanproteome/subcellular datasets] of the locations and interactions of cellular components to train generative (a la DALL-E-2) models of cells. The goal is for these synthetic cells to behave realistically in novel environments. For those models to be predictive, we need to constrain them with 1) quantitative parameter values from the literature and 2) mechanistic and biophysical information about the underlying processes. We need some help with #1 (an NLP challenge). For #2, we are building a platform to make mechanistic biophysical models of cellular processes that are interoperable, modular, and accessible. But how do we as a field synthesize existing cell biology data into higher-level concepts, models, and theories?

[[File:Quantitative model generation.png|center|350x350px]]

To make conceptual models, we would like to use the power and modularity of the [https://network-goods.notion.site/The-Discourse-Graph-starter-pack-312374c813b24ec6b4d53a054371ee5a discourse graph] schema - Questions, Claims, and Evidence - to structure the state of knowledge for our favorite research question(s). Furthermore, we'll extend the discourse graph schema to guide our ''ongoing'' research contributions to address these questions. We call these [https://youtu.be/P0KUt2yrUkw results graphs]. Our lab has begun to create discourse and results graphs to track our understanding of and contributions to our current research questions. Using Roam Research and Joel Chan's discourse graph extension, we classify a given research Question, collect Evidence from the literature and our lab notebooks, and use them to support Conclusions, which claim to address the research question. It is early days, but this modular schema appears to help students structure their thinking, track their progress, and - most importantly - frame their work less as an individual endeavor and more as a contribution to a collective project (i.e. we are all trying to uncover the answer together).

[[File:Purpose and users of cell biology discourse graphs.png|center|350x350px]]

[[File:Schematic_of_discourse_graphs.png|center|700x700px|Schematic of a discourse graph generated from the literature, and the analogous terms for ongoing research.]]

With the current tooling, paired with some ease-of-use improvements, and a 'captive audience' in the form of initial users who will also be beneficiaries of the synthesis center, we think that discourse and results graphs in cell biology will allow for ''grassroots'' contributions from students, scientists, and community researchers, to build overarching concepts, models, and theories in cell biology.

[[File:Progress to theories.png|center|800x800px]]

Lots of questions about this proposed center!
* What are the major roadblocks for adoption by the cell biology community? What ease-of-use improvements will tip the balance of benefits vs overhead for using these discourse graph tools?
* What is the role of cartoon models in building our conceptual models, and can we interoperate between/merge the cartoons (a la knowledge graphs, and [https://prior.allenai.org/projects/diagram-understanding computer vision])? Or at the very least use them as the visual backdrop for our discourse graphs?
* In practice, what is the relationship between a conceptual model and a quantitative model? Do we need to formalize components in a knowledge graph? Or just make the information (models/claims with related evidence and arguments) available and accessible to quantitative modelers?
* What does a minimum discourse graph micropublication platform look like? Does it involve Obsidian Publish? What are the minimum features (versioning)?
* What is the best way to interoperate between labs' (and researchers') discourse graphs? Federated? Centralized platform with branches and mergers?
* Can we assist users with extracting evidence and claims from the literature, with an NLP tool? Can our 'captive audience' of students and researchers provide the necessary training data?
* Can we use NLP to help convert between a discourse graph and drafting a narrative (i.e. paper or proposal)? Is the structure of a manuscript sufficiently formulaic to pull this off (I think yes)?
* Can we use NLP to help to extract all the instances of a given parameter value from the literature?
* Would you want to be part of this synthesis center? As a tool builder, synthesizer, other?

Synthesis center for cell biology

2022-11-11T07:33:22Z

Akamatsm:

Synthesis center for cell biology

2022-11-11T07:31:28Z

Akamatsm:

{{Project
|Description=Enabling the grassroots generation of conceptual and quantitative models in cell biology through the creation of a Synthesis Center for Cell and Molecular Biology
|Affilitated With=Allen Institute, University of Washington, University of Connecticut
|Contributors=Matt Akamatsu, Eran Egmon, Michael Gartner
}}
We are putting together a proposal to make a Synthesis Center for the field of cell and molecular biology. Its goal would be to synthesize the vast quantities of available cell and molecular data (protein types, locations, abundances, interactions) into both conceptual and quantitative models, that allow us to explain and predict the remarkable transition from nonliving molecules to living cells. The center (if they choose our proposal) would be funded by the National Science Foundation for 5-10 years and be housed at the Allen Institute for Cell Science in Seattle. During this workshop, I'd love to bounce around ideas for the synthesis center, and to identify points of intersection between this proposed center and your favorite tool or area.

[[File:Conceptual and quantitative models.png|center|500x500px]]

While most of the "big data" cell biology community is focused on creating new data sets, we are proposing to synthesize existing data into quantitative and conceptual models. For one set of quantitative models, we are using large [https://www.proteinatlas.org/humanproteome/subcellular datasets] of the locations and interactions of cellular components to train generative (a la DALL-E-2) models of cells. The goal is for these synthetic cells to behave realistically in novel environments. For those models to be predictive, we need to constrain them with 1) quantitative parameter values from the literature and 2) mechanistic and biophysical information about the underlying processes. We need some help with #1 (an NLP challenge). For #2, we are building a platform to make mechanistic biophysical models of cellular processes that are interoperable, modular, and accessible. But how do we as a field synthesize existing cell biology data into higher-level concepts, models, and theories?

[[File:Quantitative model generation.png|center|350x350px]]

To make conceptual models, we would like to use the power and modularity of the [https://network-goods.notion.site/The-Discourse-Graph-starter-pack-312374c813b24ec6b4d53a054371ee5a discourse graph] schema - Questions, Claims, and Evidence - to structure the state of knowledge for our favorite research question(s). Furthermore, we'll extend the discourse graph schema to guide our ''ongoing'' research contributions to address these questions. We call these [https://youtu.be/P0KUt2yrUkw results graphs]. Our lab has begun to create discourse and results graphs to track our understanding of and contributions to our current research questions. Using Roam Research and Joel Chan's discourse graph extension, we classify a given research Question, collect Evidence from the literature and our lab notebooks, and use them to support Conclusions, which claim to address the research question. It is early days, but this modular schema appears to help students structure their thinking, track their progress, and - most importantly - frame their work less as an individual endeavor and more as a contribution to a collective project (i.e. we are all trying to uncover the answer together).

[[File:Purpose and users of cell biology discourse graphs.png|center|350x350px]]

[[File:Schematic_of_discourse_graphs.png|center|700x700px|Schematic of a discourse graph generated from the literature, and the analogous terms for ongoing research.]]

With the current tooling, paired with some ease-of-use improvements, and a 'captive audience' in the form of initial users who will also be beneficiaries of the synthesis center, we think that discourse and results graphs in cell biology will allow for ''grassroots'' contributions from students, scientists, and community researchers, to build overarching concepts, models, and theories in cell biology.

[[File:Progress to theories.png|center|800x800px]]

Lots of questions about this proposed center!
* What are the major roadblocks for adoption by the cell biology community? What ease-of-use improvements will tip the balance of benefits vs overhead for using these discourse graph tools?
* What is the role of cartoon models in building our conceptual models, and can we interoperate between/merge the cartoons (a la knowledge graphs, and [https://prior.allenai.org/projects/diagram-understanding computer vision])? Or at the very least use them as the visual backdrop for our discourse graphs?
* In practice, what is the relationship between a conceptual model and a quantitative model? Do we need to formalize components in a knowledge graph? Or just make the information (models/claims with related evidence and arguments) available and accessible to quantitative modelers?
* What does a minimum discourse graph micropublication platform look like? Does it involve Obsidian Publish? What are the minimum features (versioning)?
* What is the best way to interoperate between labs' (and researchers') discourse graphs? Federated? Centralized platform with branches and mergers?
* Can we assist users with extracting evidence and claims from the literature, with an NLP tool? Can our 'captive audience' of students and researchers provide the necessary training data?
* Can we use NLP to help convert between a discourse graph and drafting a narrative (i.e. paper or proposal)? Is the structure of a manuscript sufficiently formulaic to pull this off (I think yes)?
* Can we use NLP to help to extract all the instances of a given parameter value from the literature?

Synthesis center for cell biology

2022-11-11T07:23:02Z

Akamatsm:

We are putting together a proposal to make a Synthesis Center for the field of cell and molecular biology. Its goal would be to synthesize the vast quantities of available cell and molecular data (protein types, locations, abundances, interactions) into both conceptual and quantitative models, that allow us to explain and predict the remarkable transition from nonliving molecules to living cells. The center (if they choose our proposal) would be funded by the National Science Foundation for 5-10 years and be housed at the Allen Institute for Cell Science in Seattle. During this workshop, I'd love to bounce around ideas for the synthesis center, and to identify points of intersection between this proposed center and your favorite tool or area.

[[File:Conceptual and quantitative models.png|center|500x500px]]

While most of the "big data" cell biology community is focused on creating new data sets, we are proposing to synthesize existing data into quantitative and conceptual models. For one set of quantitative models, we are using large [https://www.proteinatlas.org/humanproteome/subcellular datasets] of the locations and interactions of cellular components to train generative (a la DALL-E-2) models of cells. The goal is for these synthetic cells to behave realistically in novel environments. For those models to be predictive, we need to constrain them with 1) quantitative parameter values from the literature and 2) mechanistic and biophysical information about the underlying processes. We need some help with #1 (an NLP challenge). For #2, we are building a platform to make mechanistic biophysical models of cellular processes that are interoperable, modular, and accessible. But how do we as a field synthesize existing cell biology data into higher-level concepts, models, and theories?

[[File:Quantitative model generation.png|center|350x350px]]

To make conceptual models, we would like to use the power and modularity of the [https://network-goods.notion.site/The-Discourse-Graph-starter-pack-312374c813b24ec6b4d53a054371ee5a discourse graph] schema - Questions, Claims, and Evidence - to structure the state of knowledge for our favorite research question(s). Furthermore, we'll extend the discourse graph schema to guide our ''ongoing'' research contributions to address these questions. We call these [https://youtu.be/P0KUt2yrUkw results graphs]. Our lab has begun to create discourse and results graphs to track our understanding of and contributions to our current research questions. Using Roam Research and Joel Chan's discourse graph extension, we classify a given research Question, collect Evidence from the literature and our lab notebooks, and use them to support Conclusions, which claim to address the research question. It is early days, but this modular schema appears to help students structure their thinking, track their progress, and - most importantly - frame their work less as an individual endeavor and more as a contribution to a collective project (i.e. we are all trying to uncover the answer together).

[[File:Purpose and users of cell biology discourse graphs.png|center|350x350px]]

[[File:Schematic_of_discourse_graphs.png|center|700x700px|Schematic of a discourse graph generated from the literature, and the analogous terms for ongoing research.]]

With the current tooling, paired with some ease-of-use improvements, and a 'captive audience' in the form of initial users who will also be beneficiaries of the synthesis center, we think that discourse and results graphs in cell biology will allow for ''grassroots'' contributions from students, scientists, and community researchers, to build overarching concepts, models, and theories in cell biology.

[[File:Progress to theories.png|center|800x800px]]

Lots of questions about this proposed center!
* What are the major roadblocks for adoption by the cell biology community? What ease-of-use improvements will tip the balance of benefits vs overhead for using these discourse graph tools?
* What is the role of cartoon models in building our conceptual models, and can we interoperate between/merge the cartoons (a la knowledge graphs, and [https://prior.allenai.org/projects/diagram-understanding computer vision])? Or at the very least use them as the visual backdrop for our discourse graphs?
* In practice, what is the relationship between a conceptual model and a quantitative model? Do we need to formalize components in a knowledge graph? Or just make the information (models/claims with related evidence and arguments) available and accessible to quantitative modelers?
* What does a minimum discourse graph micropublication platform look like? Does it involve Obsidian Publish? What are the minimum features (versioning)?
* What is the best way to interoperate between labs' (and researchers') discourse graphs? Federated? Centralized platform with branches and mergers?
* Can we assist users with extracting evidence and claims from the literature, with an NLP tool? Can our 'captive audience' of students and researchers provide the necessary training data?
* Can we use NLP to help convert between a discourse graph and drafting a narrative (i.e. paper or proposal)? Is the structure of a manuscript sufficiently formulaic to pull this off (I think yes)?
* Can we use NLP to help to extract all the instances of a given parameter value from the literature?

Synthesis center for cell biology

2022-11-10T10:18:43Z

Akamatsm: Creating a synthesis center to enable grassroots contributions for conceptual and quantitative models in cell biology.

We are putting together a proposal to make a Synthesis Center for the field of cell and molecular biology. Its goal would be to synthesize the vast quantities of available cell and molecular data (protein types, locations, abundances, interactions) into both conceptual and quantitative models, that allow us to explain and predict the remarkable transition from nonliving molecules to living cells. The center (if they choose our proposal) would be funded by the National Science Foundation for 5-10 years and be housed at the Allen Institute for Cell Science in Seattle. During this workshop, I'd love to bounce around ideas for the synthesis center, and to identify points of intersection between this proposed center and your favorite tool or area.

[[File:Conceptual and quantitative models.png|center|500x500px]]

While most of the "big data" cell biology community is focused on creating new data sets, we are proposing to synthesize existing data into quantitative and conceptual models. For one set of quantitative models, we are using large datasets of the locations and interactions of cellular components to train generative (a la DALL-E-2) models of cells. The goal is for these synthetic cells to behave realistically in novel environments. For those models to be predictive, we need to constrain them with 1) quantitative parameter values from the literature and 2) mechanistic and biophysical information about the underlying processes. We need some help with #1 (an NLP challenge). For #2, we are building a platform to make mechanistic biophysical models of cellular processes that are interoperable, modular, and accessible. But how do we as a field synthesize existing cell biology data into higher-level concepts, models, and theories?

[[File:Quantitative model generation.png|center|350x350px]]

To make conceptual models, we would like to use the power and modularity of the discourse graph schema - Questions, Claims, and Evidence - to structure the state of knowledge for our favorite research question(s). Furthermore, we'll extend the discourse graph schema to guide our ''ongoing'' research contributions to address these questions. We call these [https://youtu.be/P0KUt2yrUkw results graphs]. Our lab has begun to create discourse and results graphs to track our understanding and contributions to our current research questions. Using Roam Research and Joel Chan's discourse graph extension, we classify a given research Question, collect Evidence from the literature and our lab notebooks, and use them to support Conclusions, which claim to address the research question. It is early days, but this schema appears to help students structure their thinking, track their progress, and - most importantly - frame their work less as an individual endeavor and more as a contribution to a collective project (i.e. we are all trying to uncover the answer together).

[[File:Purpose and users of cell biology discourse graphs.png|center|350x350px]]

[[File:Schematic_of_discourse_graphs.png|center|700x700px|Schematic of a discourse graph generated from the literature, and the analogous terms for ongoing research.]]

With the current tooling, paired with some ease-of-use improvements, and a 'captive audience' in the form of initial users who are also beneficiaries of the synthesis center, we think that discourse and results graphs in cell biology will allow for ''grassroots'' contributions from students, scientists, and community researchers, to build overarching concepts, models, and theories in cell biology.

[[File:Progress to theories.png|center|800x800px]]

Lots of questions about this proposed center!
* What are the major roadblocks for adoption by the cell biology community? What ease-of-use improvements will tip the balance of benefits vs overhead for using these discourse graph tools?
* What is the role of cartoon models in building our conceptual models, and can we interoperate between/merge the cartoons (a la knowledge graphs, and [https://prior.allenai.org/projects/diagram-understanding computer vision])? Or at the very least use them as the visual backdrop for our discourse graphs?
* In practice, what is the relationship between a conceptual model and a quantitative model? Do we need to formalize components in a knowledge graph? Or just make the information (models/claims with related evidence and arguments) available and accessible to quantitative modelers?
* What does a minimum discourse graph micropublication platform look like? Does it involve Obsidian Publish? What are the minimum features (versioning)?
* What is the best way to interoperate between labs' (and researchers') discourse graphs? Federated? Centralized platform with branches and mergers?
* Can we assist users with extracting evidence and claims from the literature, with an NLP tool? Can our 'captive audience' of students and researchers provide the necessary training data?
* Can we use NLP to help convert between a discourse graph and drafting a narrative (i.e. paper or proposal)? Is the structure of a manuscript sufficiently formulaic to pull this off (I think yes)?
* Can we use NLP to help to extract all the instances of a given parameter value from the literature?

File:Schematic of discourse graphs.png

2022-11-10T10:10:55Z

Akamatsm: Akamatsm uploaded a new version of File:Schematic of discourse graphs.png

Schematic describing discourse graphs

File:Progress to theories.png

2022-11-10T09:18:07Z

Akamatsm:

Schema of progressing from data to theories. Hypotheses and requests for data guide new experiments.

File:Quantitative model generation.png

2022-11-10T09:07:33Z

Akamatsm:

Diagram of creating generative models from experimental data and informed by parameter values and biophysical simulations. Biophysical models are informed by conceptual models proposed by researchers.

File:Schematic of discourse graphs.png

2022-11-10T08:41:04Z

Akamatsm:

Schematic describing discourse graphs

Synthesis center for cell biology

2022-11-10T08:30:43Z

Akamatsm: Created page with "We are putting together a proposal to make a Synthesis Center for the field of cell and molecular biology. Its goal would be to synthesize the vast quantities of available cell and molecular data (protein types, locations, abundances, interactions) into both conceptual and quantitative models, that allow us to explain and predict the remarkable transition from nonliving molecules to living cells. The center (if they choose our proposal) would be funded by the National Sc..."

File:Purpose and users of cell biology discourse graphs.png

2022-11-10T08:29:17Z

Akamatsm:

Description of the use cases for cell biology discourse graphs

File:Conceptual and quantitative models.png

2022-11-10T07:53:44Z

Akamatsm:

(left) diagram of a conceptual model. (right) cartoon depicting a quantitative model from https://doi.org/10.7554/eLife.49840.