virus antibody model #253

colinfrisch · 2025-04-18T10:12:48Z

Virus-Antibody Model

This model is a simulation of immune reaction declined as a confrontation between antibody agents and virus agents. The global idea is to model how the imune system can struggle against new virus but is able to adapt over time and beat a same virus if it comes back. The results are quite interesting as the simulation can go both ways (virus win or antibodies win) with a little tweak in the base parameters (check the readme for some examples).

What it showcases

Usage of memory in agents : divided into a short term memory using a deque to easily add and remove memories in case of a new virus encounter, and a long term memory (here a simple list)
Agent knowledge sharing : the antibodies are able to share short term memory)
Usage of weak referencing to avoid coding errors (antibodies can store viruses in a self.target attribute)
Emergence of completely different outcomes with only small changes in parameters

What it adds to mesa-examples

Will be the first continuous‑space user example
Weak‑referencing and dual memory systems and share knowledge (cf. what it showcases)
Theme is original and quite different from what already exists in examples : shows the diversity of fields in which mesa can be useful

How It Works

Initialization: The model initializes a population of viruses and antibodies in a continuous 2D space.
Agent Behavior:
- Antibodies move randomly until they detect a virus within their sight range (becomes purple), than pursue the virus.
- Antibodies pass on all the virus DNA in their short term memory to the nearest antibodies (cf. example)
- Viruses move randomly and can duplicate or mutate.
Engagement (antibody vs virus): When an antibody encounters a virus:
- If the antibody has the virus's DNA in its memory, it destroys the virus.
- Otherwise, the virus may defeat the antibody, causing it to lose health or become inactive temporarily.
Duplication: Antibodies and viruses can duplicate according to their duplication rate.

coderabbitai · 2025-04-18T10:12:54Z

Important

Review skipped

Auto reviews are disabled on this repository.

Please check the settings in the CodeRabbit UI or the .coderabbit.yaml file in this repository. To trigger a single review, invoke the @coderabbitai review command.

You can disable this status message by setting the reviews.review_status to false in the CodeRabbit configuration file.

Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out.

❤️ Share

🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

Review comments: Directly reply to a review comment made by CodeRabbit. Example:
- I pushed a fix in commit <commit_id>, please review it.
- Generate unit testing code for this file.
- Open a follow-up GitHub issue for this discussion.
Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
- @coderabbitai generate unit testing code for this file.
- @coderabbitai modularize this function.
PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
- @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
- @coderabbitai read src/utils.ts and generate unit testing code.
- @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
- @coderabbitai help me debug CodeRabbit configuration file.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

@coderabbitai pause to pause the reviews on a PR.
@coderabbitai resume to resume the paused reviews.
@coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
@coderabbitai full review to do a full review from scratch and review all the files again.
@coderabbitai summary to regenerate the summary of the PR.
@coderabbitai generate docstrings to generate docstrings for this PR.
@coderabbitai resolve resolve all the CodeRabbit review comments.
@coderabbitai configuration to show the current CodeRabbit configuration for the repository.
@coderabbitai help to get help.

Other keywords and placeholders

Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (`.coderabbit.yaml`)

You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
Please see the configuration documentation for more information.
If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

Visit our Documentation for detailed information on how to use CodeRabbit.
Join our Discord Community to get help, request features, and share feedback.
Follow us on X/Twitter for updates and announcements.

Copilot

Pull Request Overview

This pull request introduces a simulation that models the interaction of viruses and antibodies in continuous space using the Mesa framework. The PR implements the core model logic, defines agent behaviors with dual memory systems and weak referencing, and sets up visualization components with an accompanying README for usage instructions.

Implements the core Virus/Antibody simulation with model parameters and agent scheduling.
Adds visualization components and parameter interfaces in the application module.
Documents the model details, simulation outcomes, and usage instructions in the README.

Reviewed Changes

Copilot reviewed 10 out of 11 changed files in this pull request and generated 4 comments.

File	Description
examples/virus_antibody/model.py	Implements the primary model setup and simulation step behavior.
examples/virus_antibody/app.py	Provides the visualization interface and model parameter configurations.
examples/virus_antibody/agents.py	Defines antibody and virus agent behaviors with memory, duplication, and engagement logic.
examples/virus_antibody/README.md	Provides documentation covering model overview, usage, and simulation examples.

Files not reviewed (1)

examples/virus_antibody/requirements.txt: Language not supported

Copilot · 2025-04-19T04:28:54Z

examples/virus_antibody/model.py

+        )
+
+        # Create and place the Antibody agents
+        antibodies_positions = self.rng.random(


Inconsistent use of random number generators (self.rng vs self.random) may lead to unpredictable behavior; consider unifying these to a single random instance.

examples/virus_antibody/agents.py

examples/virus_antibody/README.md

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

quaquel · 2025-04-19T09:25:00Z

examples/virus_antibody/agents.py

+
+        # Movement & state
+        self.position = initial_position
+        self.speed = 1.5


Would it be possible to make speed and health kwargs on the init? In my view it is good practice to make all attributes explicitly controllable.

I made the choice of limiting the parameter to these ones because otherwise it's a lot for the user in my opinion. If I add all of them, it would mean more than 10 parameters to be decided by the user. Do you think I should still add all of the other parameters ? Or are there one or two more that would be good to add and still leave the others hidden (like ko_timeout, sight_range, and a few non-crucial parameters) ?

Fair enough. One option is to turn the constants that are the same for all agents of a given class into class level attributes. This still makes it easy to change them without having to add them all to the signature of the __init__.

Good idea ! It does allow to delete quite a few lines of code in model.py also.

examples/virus_antibody/agents.py

quaquel · 2025-04-19T09:29:57Z

examples/virus_antibody/agents.py

+
+        # Chase a valid virus target
+        else:
+            if getattr(target, "space", None) is not None:


again, this should not be needed if everything else is implemented correctly.

quaquel

I have made a first pass. I like the idea of the model, but there is ample room for improvements. If you have questions, don't hesitate to ask.

examples/virus_antibody/agents.py

quaquel · 2025-04-19T09:31:19Z

examples/virus_antibody/agents.py

+            self.lt_memory.append(dna)
+            self.ko_steps_left = self.ko_timeout
+            # mark KO state by weak-ref back to self
+            self.target = weakref.ref(self)


I dont understand what is going on here.

When an antibody looses a confrontation with a virus (because the virus DNA is not in it's memory yet), the antibody is ko for a few steps. It can't move during this time and I symbolise this by setting the agent's target to itself.

Ok, clear.

This might be implemented using event scheduling, which would result in a modest speedup of runtime.

examples/virus_antibody/agents.py

examples/virus_antibody/model.py

colinfrisch · 2025-04-19T10:58:47Z

Thanks a lot @quaquel for this first review ! I'm going to see what I can change asap

quaquel · 2025-04-19T11:16:20Z

Thanks a lot @quaquel for this first review ! I'm going to see what I can change asap

I would start with the two sets in the the init of your model. Next, I would remove the if checks you have to see if an agent is still active in the model one at a time (or do one first to confirm the behavior is still correct). Again, don't hesitate to ask if you run into any issues.

for more information, see https://pre-commit.ci

colinfrisch · 2025-04-20T10:28:53Z

I would start with the two sets in the the init of your model. Next, I would remove the if checks you have to see if an agent is still active in the model one at a time (or do one first to confirm the behavior is still correct). Again, don't hesitate to ask if you run into any issues.

@quaquel I removed the checks and did the other fixes that you suggested. I still have one error that I don't understand, and it seems to be still linked to referencing. For certain configurations of parameters, the model runs for a while (even hundreds of steps) before making the error error in step: 'NoneType' object has no attribute 'agent_positions'. Even after debugging, I can't find the origin of it... would you have any idea to why it's happening ?

You can reproduce this error by keeping the base parameters and setting duplication rate to max in the sliders.

Full traceback :

  File "/Users/colinfrisch/Desktop/mesa/mesa/visualization/solara_viz.py", line 275, in step
    do_step()
    ~~~~~~~^^
  File "/Users/colinfrisch/Desktop/mesa/mesa/mesa_logging.py", line 132, in wrapper
    res = func(*args, **kwargs)
  File "/Users/colinfrisch/Desktop/mesa/mesa/visualization/solara_viz.py", line 309, in do_step
    model.value.step()
    ~~~~~~~~~~~~~~~~^^
  File "/Users/colinfrisch/Desktop/mesa/mesa/model.py", line 122, in _wrapped_step
    self._user_step(*args, **kwargs)
    ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
  File "/Users/colinfrisch/Desktop/mesa-examples/examples/virus_antibody/model.py", line 131, in step
    self.agents.shuffle_do("step")
    ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^
  File "/Users/colinfrisch/Desktop/mesa/mesa/agent.py", line 345, in shuffle_do
    getattr(agent, method)(*args, **kwargs)
    ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
  File "/Users/colinfrisch/Desktop/mesa-examples/examples/virus_antibody/agents.py", line 185, in step
    self.move()
    ~~~~~~~~~^^
  File "/Users/colinfrisch/Desktop/mesa-examples/examples/virus_antibody/agents.py", line 221, in move
    self.position += self.direction * self.speed
    ^^^^^^^^^^^^^
  File "/Users/colinfrisch/Desktop/mesa/mesa/experimental/continuous_space/continuous_space_agents.py", line 34, in position
    return self.space.agent_positions[self.space._agent_to_index[self]]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'agent_positions'

quaquel · 2025-04-20T14:07:18Z

I'll try to take a closer look asap. It indeed seems that step is called on an agent that has already been removed.

quaquel · 2025-04-21T06:58:47Z

examples/virus_antibody/agents.py

+        return dna
+
+    def move(self):
+        # Random walk


Both agents have a random walk component. Might it be possible to implement this the same way for both and move it to a new super agent class from which both current agents derive?

It could be a good idea ! At first I thought it could also be used for the duplicate() method. However, there are a lot of parameters to take into account and the code is very short inside, so I don't think that it would really help to make it derive from a parent class.

So I the end, there is just a few lines for the random movement. Isn't a whole parent class a bit overkill ?

If the choice is between repeated code or a parent class, I prefer the parent class. Of course, in python you could also move the relevant code into a helper function (not really appropriate for random walks).

Alright, will do, thank you for the advice !
On another note, did you have time to check out why step was being called on a removed agent ?

Monday was a banking holiday so I had family obligations. I'll try to get to it today, but no promises unfortunately.

Of course, nothing urgent ! Only if you have time :)

for more information, see https://pre-commit.ci

virus antibody model

321a851

colinfrisch mentioned this pull request Apr 18, 2025

Memory module proposition - v2 projectmesa/mesa#2744

Closed

colinfrisch requested review from Copilot, quaquel and EwoutH April 19, 2025 04:28

Copilot AI reviewed Apr 19, 2025

View reviewed changes

colinfrisch and others added 2 commits April 19, 2025 12:32

Update examples/virus_antibody/README.md

07b922f

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Update examples/virus_antibody/agents.py

e6492dc

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

quaquel reviewed Apr 19, 2025

View reviewed changes

examples/virus_antibody/agents.py Outdated Show resolved Hide resolved

quaquel reviewed Apr 19, 2025

View reviewed changes

examples/virus_antibody/agents.py Outdated Show resolved Hide resolved

quaquel reviewed Apr 19, 2025

View reviewed changes

examples/virus_antibody/agents.py Outdated Show resolved Hide resolved

quaquel reviewed Apr 19, 2025

View reviewed changes

quaquel requested changes Apr 19, 2025

View reviewed changes

colinfrisch and others added 2 commits April 20, 2025 18:16

first fixed

f1b2f7f

[pre-commit.ci] auto fixes from pre-commit.com hooks

de3c224

for more information, see https://pre-commit.ci

quaquel closed this Apr 20, 2025

quaquel reopened this Apr 20, 2025

quaquel reviewed Apr 21, 2025

View reviewed changes

colinfrisch and others added 2 commits April 22, 2025 15:11

added parent class CellularAgent with a _random_move() private method

79a7b06

[pre-commit.ci] auto fixes from pre-commit.com hooks

673828a

for more information, see https://pre-commit.ci

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

virus antibody model #253

virus antibody model #253

colinfrisch commented Apr 18, 2025

coderabbitai bot commented Apr 18, 2025

Review skipped

Chat

CodeRabbit Commands (Invoked using PR comments)

Other keywords and placeholders

CodeRabbit Configuration File (`.coderabbit.yaml`)

Documentation and Community

Copilot AI left a comment

Copilot AI Apr 19, 2025

quaquel Apr 19, 2025

colinfrisch Apr 19, 2025

quaquel Apr 19, 2025

colinfrisch Apr 20, 2025

quaquel Apr 19, 2025

quaquel left a comment

quaquel Apr 19, 2025

colinfrisch Apr 20, 2025

quaquel Apr 21, 2025

colinfrisch commented Apr 19, 2025

quaquel commented Apr 19, 2025

colinfrisch commented Apr 20, 2025 •

edited

Loading

quaquel commented Apr 20, 2025

quaquel Apr 21, 2025

colinfrisch Apr 21, 2025

quaquel Apr 21, 2025

colinfrisch Apr 22, 2025

quaquel Apr 22, 2025

colinfrisch Apr 22, 2025

virus antibody model #253

Are you sure you want to change the base?

virus antibody model #253

Conversation

colinfrisch commented Apr 18, 2025

Virus-Antibody Model

What it showcases

What it adds to mesa-examples

How It Works

coderabbitai bot commented Apr 18, 2025

Review skipped

Chat

CodeRabbit Commands (Invoked using PR comments)

Other keywords and placeholders

CodeRabbit Configuration File (.coderabbit.yaml)

Documentation and Community

Copilot AI left a comment

Choose a reason for hiding this comment

Pull Request Overview

Reviewed Changes

Copilot AI Apr 19, 2025

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

quaquel left a comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

colinfrisch commented Apr 19, 2025

quaquel commented Apr 19, 2025

colinfrisch commented Apr 20, 2025 • edited Loading

quaquel commented Apr 20, 2025

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

CodeRabbit Configuration File (`.coderabbit.yaml`)

colinfrisch commented Apr 20, 2025 •

edited

Loading