openworm

[openworm]

Are the Worm neurons modeled as Leaky Integrate/Fire or what system is used?

They're modelled as Hodgkin Huxley! Check out this GitHub repo for the code:

https://github.com/openworm/c302

[openworm]

New OpenWorm Board!

The Board of Directors of OpenWorm has turned over.  Welcome to the new board!  More info here.

[openworm]

Coding Period (Week - 1)

May 25 - May 31

This marks the beginning of the coding period.

What Have I done:

1. Implemented the W-net deep learning model architecture. Started preparing the dataset by using image augmentation techniques. Worked on writing custom loss function (soft cut normalized loss) for training the encoder and reconstruction loss for training the whole autoencoder.

2. Discussed the ideas for phase 3 of the GSoC phase. We have considered the four ideas

2.1 Extending GSoC 2018 project to Geppetto. Requires a lot of engineering effort. So this is not recommended.

2.2 Create a visualization skin for GSoC 2018 project. Focus on networks and 3-D modeling. Significant time overhead for setting up the environment and learning about the technologies

2.3 Work with Morphozoic and CompuCell3D tool to make a visualization of an embryo of C. Elegans. Required Cpp experience and the work is more related to the visualization field.

2.4 Cell tracking and tagging from Brightfield embryogenesis movies. We have a video like this. We have to segment and track the cells as the process moves on.  This idea is related to the computer vision field and this is something I might end up working on for phase 3 of GSoC. 

3. Presented my research work during the community bonding period and explained my approach for the project to the OpenWorm community. You can find the presentation here.

What will I do next:

1. Continue working on the W-net paper implementation. Implement the post-processing techniques  “Conditional Random Field smoothening” and “Hierarchical Segmentation”.

2. Train the W-net model on the available SPIM images and test the results.

[openworm]

Coding Period Phase-1

Hello!! 

So, I have reached the end of the 1st phase of the coding period for my GSoC with INCF. It has been a nice learning experience so far. Over the last three weeks, I ran into a lot of errors and faced some limitations. However, I was able to get past them (a lot of learning) and I’m well on track to produce good results.

By the 24th of June, I will be done with my evaluation. I will update all the progress so far (on this post), list out the errors and limitations I faced, things I have tried, what worked out, what did not work out and more along these lines.

So please check back on Tuesday and I will also be posting some results too.

Thanks!!

[openworm]

Community Bonding (Week 2)

May 17 - May 24

Finally, I’m done with all my semester exams, academic projects, and reviews this week. That gave me a lot of relief. This post is to summarize all the work that I have done this week and my plans for next week.

What Have I done:

1. I started with Jython programming to see if I can do Image Processing with Python code right from the plugin. Basically, pass the image from the plugin code(Java) to Python, do the required processing and then return the processed image to the plugin for the user to see the results. 

I wanted Python because I could build deep learning models easily with the help of the vast Deep Learning framework ecosystem available for Python. However, that did not go well as I found out that Jython is a bit ineffective in this case. See this thread.

Some other potential options are Weka and DeepLearning4J. Weka is more of a plug and play tool. It is useful for beginners to quickly try out basic ML/DL models. If one wants to customize the model and play with the parameters, it is not recommended. Refer this to learn more about the pros and cons of Weka.

I have tried out DeepLearning4J and it looks awesome. I implemented a basic CNN in Java and the training speed is exceptional. The only problem I faced is that the tutorials on their channel are outdated. I plan to stick with DeepLearning4J for this project.

2. I’m supposed to give a slide show presentation to my mentor on May 31st. I have prepared content for that.

3. Participated in the weekly hackathon conducted 17th May.

What will I do next:

1. Derive a solid objective for building the feature analysis tool. I’m a bit unclear about what would be a good thing to do for feature analysis (during phase 3 of GSoC).

2. Present the project. Report my progress and explain my approach for the project.

Cheers!!

[openworm]

Community Bonding (Week 1)

So, the results of GSoC’19 came out on May 6th, 2019 and I’m glad that I have been selected as a GSoC student with INCF. 

To know a bit about my project, please look at my previous post. 

What Have I done:

1. Have gone through the GSoC reading materials given by my mentor.

2. Setup the environment for developing plugins for ImageJ.

3. Downloaded this example plugin, made changes to the code, built the project, packaged it as a JAR file and finally added it as a plugin to ImageJ. I got myself familiar with the workflow.

4. Participated in two weekly hackathons (10th and 15th May). Have given an overview of my project to the OpenWorm community.

What will I do next:

1. Make use of ImageJ’s capability to add python scripts to plugins. Basically, try to manipulate an image with python code placed in the plugin.

2. See if I can add saved Deep Learning Models to ImageJ plugins with the help of Deeplearning4J.

3. Discuss the progress with my mentor and decide on what to do next.

[openworm]

Introduction to the Project (16.2)

Semantic Image Processing for Developmental Data Science

I will be working for the Openworm foundation. Its primary goal is to understand the neural connections of C.Elegan, by creating a virtual replica of the worm. It is believed to be the first step that is to be taken to understand the rather complex connectomes like the human brain. 

Mentor: Bradly Alicea

The primary goal: To accurately segment the cells of C. Elegans from the SPIM images taken during its Embryogenesis process. Once I’m done with this I can focus on creating an automated feature analysis tool (TBD).

Previous Work: In GSoC’17, an ImageJ plugin has been built to segment the cells from SPIM images. Many classical image processing techniques were integrated into the plugin. We can achieve better accuracy by adding Deep Learning methods. However, the main problem is the lack of labeled data.

Solution Approach: Using Semi-Supervised and Unsupervised Deep Learning techniques for image segmentation.

Here is my accepted proposal. You can look into this for a detailed explanation of the whole project. 

I will be uploading all the code that I will write during this summer to this GitHub Repo.

In case you are having some questions or just want to connect, please feel free to contact me via my email.

Cheers!!

[openworm]

A bit about Project 16.1?

The OpenWorm project has a data management tool called PyOpenWorm (POW) that aids in creating, storing, and sharing information about C. elegans and about the evidence that supports that information. Project 16.1 aims to provide POW with a P2P File Sharing Framework to act as a primary source of distribution for this information!

What’s being proposed?

For reduced infrastructure and maintenance costs the Decentralized P2P Protocol Bit-Torrent is being used. We will develop an easy-to-use Bit-Torrent client enabling POW Users to create, distribute and download from torrents! 

This client will be built into a larger framework for access-control and data-integrity management.

I will update this blog with weekly progress on the fronts of Progress, Hurdles to that Progress and how they were overcome.

Reach out to me, we’ll contribute to Free and Open Source Software together!

Git     -    https://github.com/jaideep-seth

Email -    jaideepnseth@gmail.com

[openworm]

Project Overview

OpenWorm is an attempt to build a biophysical simulation of the model organism C. elegans via assimilation of published data from a range of sources. Publication of biological data is often weakly structured and has no standardised format making assimilation challenging. Currently PyOpenWorm acts as a quick data access layer for researchers to query C. elegans anatomy and physiology. It currently incorporates data from the primary literature, WormAtlas and WormBase. This project would aim to expand the data available for model validation and for query in PyOpenWorm. Expanding the data available would aid hypothesis generation in the C.elegans research community, by allowing fast access to past observations and by facilitating the generation of more robust models through model validation tests such as SciUnit. However, ultimately it could also be a poster child to inspire an increase in standardisation and statistical accountability in the publication of biological research as a whole.

[openworm]

Google Summer of Code kick-off

The list of accepted GSoC particpants has been published and students have been notified of their status. Congratulations to Vinay Varma, Emily Thomas, and Jaideep Seth on being selected! We're glad to have you with us and look forward to collaborating with you throughout the summer.

On behalf of all OpenWorm contributors, I offer a sincere thanks to all those who submitted a proposal: your enthusiasm for OpenWorm and for Free and Open Source Software (FOSS) shone through beautifully. To those who were not accepted this year, especially if this is your first time contributing to FOSS, please continue your involvement in free software and in open science: there are so many problems out there, and they become so much more achievable when we work together.

[openworm]

April is Documentation Month!

Based on popular demand, April will be “Docs Month” within the OpenWorm Foundation. The month will be filled with reevaluating our organizational docs, in addition to putting together a proposal (in conjunction with INCF) for the inaugural Google Season of Docs. 

If you have a technical writing background, please contact us or fill out our new contributor form to join our Slack team. We can work on a proposal together, which can include (but are not limited to) the following current organizational needs:

building an OpenWorm Wiki, which might reference special topics and within the subprojects.

website redesign so as to make it easy to find key information (e.g. people, how to get to Slack, Github, Docker and Blender models).

help refine a reference document started by Senior Contributor Gopal Sarma.

an inventory of projects and docs that align with current organizational goals.

a reorganization of all project docs, mainly merging DevoWorm, NeuroML, Geppetto, and other assorted docs into one location.

help with the educational curriculum.

find and write specs for “casual contributor-friendly” contributions.

While you can propose your own idea, these seven points are the easiest to refine by the deadline (April 23). Let’s get working!

[openworm]

March is PyOpenWorm month!

PyOpenWorm is a data-access and data sharing library for the OpenWorm project. It is designed to provide the project with a coordinated means for generically describing data in a variety of formats, tracking the origins of said data, and describing various transformations between formats.

This month should see a release for PyOpenWorm and further integration with c302, the neural network model generation tool set. We're always happy to welcome contributors in this effort! There are, however, a number of other areas where you can help on PyOpenWorm: see the beginner label on PyOpenWorm issue tracker for a listing of issues to start with or else get in touch with me (Mark Watts, github:mwatts15) for suggestions.

Also this summer, OpenWorm is again participating in Google Summer of Code (GSoC) under the banner of the INCF. My personal favorite project idea focuses on file sharing (model files, experimental data, etc.) within PyOpenWorm. The file sharing component will have a major role in PyOpenworm going forward, so this is a great project to get involved with now. You can see the list of all OpenWorm projects for 2019 here. Although applications aren’t open until 25 March, you can get in touch with the GSoC mentors and learn more about the problems they’re trying to solve today -- so don’t be shy! You can learn more about GSoC in general on their website.

This project of the month kicks off with an "office hours" session on the OpenWorm Slack chat this Wednesday, 6 March at 17:00 UTC where several senior contributors (including me) simultaneously make themselves available for Q&A, discussion, and to talk about current and upcoming activities in the project. You can join by filling out this (short) form.  This is also the best starting place if you're interested in contributing to OpenWorm more broadly since it will let us know what you're interested in and what skills you have now so we can help you find which area best fits your interests.

Thanks for reading! Looking forward to hearing from you :-)

Twitter: @OpenWorm

Contributor form

[openworm]

January is DevoWorm Month!

This has been cross-posted from Synthetic Daisies blog.

Welcome to 2019! And welcome to OpenWorm Foundation's project of the month for January, featuring DevoWorm. Here I will briefly go over progress in the DevoWorm group over the last year and a half. If you would like to know more, we have a group Slack channel (#devoworm) in the OpenWorm team, a group website, and a Github repository. 

For the uninitiated, the DevoWorm group has a multifaceted set of interests. We are interested in simulating and analyzing data related to worm development, but have an interest in the development of other model organisms as well. In terms of results, we have focused mostly on publications and open datasets, but as you will see from the website, we have also been involved in the creation of unique demos and software development. 

The DevoWorm group is also interested in education. Our educational efforts have largely spread out over four types of pedagogy: digital badges, tutorials via interactive notebooks, public lectures, and one-on-one mentorship through the Google Summer of Code (GSoC) program. The OpenWorm Foundation has hosted a DevoWorm GSoC student for the past two years (2017 and 2018), and will be offering a third opportunity this year (2019).  

This is the 15th anniversary for the GSoC program, and it is always an excellent experience. The application process begins on February 25th. If you are interested in a mixture of computational biology, image processing, and machine learning, please contact us for more information. 

COURTESY: Image from "One, Two, Three,....GSoC!" by Vipal Gupta

While GSoC is well-compensated opportunity to participate in DevoWorm, there are also less formal ways through which one can collaborate. One of these ways is through a conventional research pathway such as analyzing data, building a simulation, or curating a dataset. Another way to collaborate is to help create new types of educational content. We are particularly interested in creating virtual reality-based offerings in the near future. If you enjoy creating educational content, or simply enjoy learning, please get in touch! 

Another new initiative is called DevoZoo. The DevoZoo site aggregates open datasets, methods, and techniques relevant to computational developmental biology and data science biology. We currently host open datasets for the following model organisms: C. elegans, Drosophila, Zebrafish, Ascidians, and Mouse. DevoZoo also hosts raw microscopy data in the form of movies for many of these model organisms as well as Spiders. As if this were not enough, we also try to engage learners and open scientists with artificial life models. The DevoZoo presents three: Morphozoans, developmental Braitenberg Vehicles, and Multicell Systems. The artificial life models in particular could use some further development. Check out the DevoZoo webpage or ask us if you would like to learn more.

Finally, you can participate by collaborating on a publication. The DevoWorm group has been featured in four publications in the past year. The OpenWorm article in the "Connectome to Behavior" special issue of Royal Society B provides a succinct description of the project and its current course. Some of our members served as editors and contributors to a special issue of BioSystems in honor of Dr. Lev Beloussov. This issue features 32 articles that provide a very broad and innovative look at the topic of morphogenesis. Our set of contributions (peer-reviewed papers) spanned from network models of the embryo to the developmental emergence of the connectome and quantitative approaches to organogenesis in the eye imaginal disc. 

Picture of Dr. Lev Beloussov 

If you are interested in joining in on the discussion, we hold group meetings online every Monday at 9pm UTC. We are also starting to host hackathons on Fridays during the late morning/early afternoon North American time. Check out our scheduling page for more information. Hope to encounter you soon, and have a great month!

[openworm]

October: Geppetto month!

OpenWorm is made up of many sub-projects, “project of the month” is an effort to highlight a different OpenWom sub-project every month. This month is Geppetto’s turn!

What is Geppetto?

Geppetto is a visualisation and simulation web-based platform for building neuroscience applications. The first use case ever of Geppetto was OpenWorm itslef (some lore: the virtual Worm being Pinocchio, a Geppetto was needed to “make it”), but since then many groups have adopted it as their platform of choice. It is basically a set of reusable components for simulation, visualisation and data aggregation that make it easier to develop your neuroscience application, be it a data portal or an entry point to external simulation engines.

Projects that currently make use of Geppetto as a platform:

Openworm uses Geppetto as an integration platform for the output of various of its subprojects, from connectome browsing to replaying of integrated electrophysiology and fluid dynamics simulations 

Open source brain uses Geppetto to share, visualize and simulate neuronal models, both for individual neurons and networks 

Virtual fly brain is an ontology and 3D/2D  morphology browser for drosophila resources built using Geppetto

Netpyne-ui is a user friendly UI to create and run neuronal models using the netpyne library

Open Development

Geppetto development is entirely open source, like anything else that happens under the OpenWorm umbrella. There are open sprint meetings every two weeks that anybody can join, and we keep a public development board showing development activities and progress. You can browse the issues and see if there is anything you might wanna chance your hand on!

Resources

Here are some links if you want to learn more about Geppetto:

Open access paper (Philosophical Transaction of the Royal Society B, 2018)

Geppetto docs

Geppetto live demo

Development board

Geppetto source code on github

Geppetto Blog

Geppetto on Twitter

Get involved!

Getting involved is easy, simply fill out the OpenWorm volunteer application form and we will invite you to the OpenWorm foundation slack, from there you can interact with the community and join the #geppetto channel if you are interested to learn more about Geppetto or get involved as a contributor. 

[openworm]

September: Movement Validation Month @ OpenWorm

Movement Motivations

The behavior of C. Elegans is partially captured by their movement characteristics. We develop and work with a number of tools for recording, characterizing, storing, analyzing, and sharing worm movement data. Our goal is to eventually be able to compare the movement behavior of worms generated via simulation, against real instances of C. Elegans objectively and quantitatively. The month of September 2018 will see some focus on this activity. We hope you will participate and do stuff that’s interesting to you!

Our Tools

The Database - We work with many others who record the movement activities of real live specimens of C. Elegans. That extended community has generously shared their data via our movement database tool (Github Repo here.) A Common Data Format - To facilitate the sharing and exchange of data, we have developed the WCON format and accompanying libraries for manipulating data. We are working to support as many languages as we can. Our Github code repository can be found here. Tools for Movement Analysis - The Github code base for our collection of tools to analyze movement is found here.

Activities for the Month

Planning - We have a range of active tool development issues we would like to review, and create tasks for. We expect to discuss this over the course of the first and second weeks. This should happen in our OpenWorm Slack channel #Movement-Analysis. We expect to also discuss new ideas/projects people might like to do, and lay out goals (e.g. integration of production code in OpenWorm’s Docker image.) We will kick this off with an Office Hours presentation in the OpenWorm Slack channel #office-hours on Sep 5th 4pm UTC. Doing Interesting Stuff - Over the rest of the month, we will break off to work on individual tasks, have discussions, get questions answered. Activities can take many forms over a wide range of skill sets, technical or creative: 1. Create Blog/Art content; 2. Improve code/tool design; 3. Develop new features; 4. Explore new science/analysis techniques; This will culminate in one or more hackathons over the last week of the month to round off the work. We also expect to create a number of open badges (see https://www.badgelist.com/OpenWorm) for tasks/educational activities that can be built around our results for wider community engagement.

Participate and Share

We encourage both new and experienced volunteers to participate! For newcomers, the best way to do so is filling out our volunteer application form. After we have had a chance to look at your application, an invite will sent for you to join us in Slack. From there we can work with you to find stuff that you may be interested to work with, and people in the community you can get in touch with. We look forward to hearing from you!

[openworm]

August is ChannelWorm Month at OpenWorm!

As we make steady progress on our goal of building the world’s first detailed simulation of C. elegans, we’ve been brainstorming ways to systematically draw attention to each of the components of the OpenWorm platform.

Our scientific and outreach committees have decided that each month will be dedicated to a specific repository that will be designated “Project of the Month.”  

This coming month (August 2018) will be dedicated to ChannelWorm, a repository aimed at constructing quantitative models of ion channel behavior.  

Visit our Github repo and take a look for yourself: https://github.com/openworm/ChannelWorm

See also the Heroku App for extensive curated data on ion channels: https://chopen.herokuapp.com

WHAT ARE ION CHANNELS? Ion channels are pore-forming proteins which facilitate the movement of charged particles (ions) between the extracellular space and the cytoplasm.  All cells contain ion channels and play a central role in normal cellular function.  However, their role in the nervous system is much more significant.  In particular, ion channels in the nervous system mediate the generation of action potentials, the fundamental mechanism by which a network of neurons processes information.  

HOW THIS CONTRIBUTES TO THE OPENWORM VISION: Ion channels are the most granular level of detail in OpenWorm.  In other words, we abstract away the many complexities of intra-cellular function and treat the organism as an information processing system which uses electrical signals to mediate body movement.  Incorporating additional levels of biological detail is certainly a possibility but is not currently on our scientific roadmap.

BRIEF OVERVIEW OF CHANNELWORM: The aim of ChannelWorm is to build quantitative models of ion channel function.  To that end, we use the following workflow:

Create a database of information about C. elegans ion channels specifying channel type and location in the nervous system (i.e. name of neuron) where the channel is expressed. 

Identify relevant scientific papers which contain experimental recordings of ion channel behavior.

Extract plots from the corresponding papers and add them to the database of ion channels. (This data is currently available at https://chopen.herokuapp.com)

Digitize plots to extract data points.

Use parameter fitting techniques to determine numerical values of parameters for quantitative models.

Export ion channel models in the NeuroML (NML) format to be stored in PyOpenWorm.

SHARE IN THE COMMUNITY! We very much encourage both new and experienced volunteers to turn their attention to ChannelWorm and find areas that might be of interest to them.  The best way to get involved is to first fill out our volunteer application form.  

In the meantime, we recommend taking a look at the Github repository for ChannelWorm for open issues the project is currently tackling.  Some of the issues are specifically marked as being appropriate for beginners.  We will add to this list over the next few weeks.  

WHAT HAPPENS AFTER THE APPLICATION: After we look at your application, you’ll receive an invitation to join our workspace on Slack.  From there, you can contact other volunteers who can help find a project to match your skills and interests.  

WHAT IF I’M NOT A PROGRAMMER? Even if you are not a computer programmer, you can still contribute! Here are some of the ways:

Author a blog post: we’d love to have informative scientific blog posts on anything relevant to ion channel function and modeling. Submit a draft and we will host it on the OpenWorm blog.

Literature search and data re-evaluation: we’d also like to support basic science in this area. If you have experience with conducting research, please get in touch!

Create educational content: we can support educational initiatives such as the creation of digital badges and video tutorials. Pitch an idea and become part of the conversation!

Something original: if you have an idea for something that no one has thought of, we’d love to hear your proposal!  Artwork, animations, popular scientific articles, etc. are all welcome.  

At the end of August, we’ll be organizing a distributed hackathon for the community to get together and work on tackling open items on our to-do list. We’ll announce more details towards the second week of August.

OW Science Committee is held at 3:00pm UTC, link provided in Slack #science channel. Hackathon Time/Date: TBA. Digital badges are located at badgelist.com/openworm.

We hope you can join us!

Please fill out our volunteer application form and contact us on Slack for more information!

[openworm]

We need your help this month. Will you make a gift to OpenWorm?

We're a non-profit. We progress towards our vision, to build the world's first digital organism, a computer model of a worm, by relying on our volunteer scientists, programmers, donors, and advisers.

With funds raised from our campaign last year, we've been able to support our volunteers with computational resources to advance our simulation of the worm.  We've been able to empower our community with presentations at scientific meetings such as a workshop at the International meeting for worm biology and another one at the Neural Information Processing Systems conference.   We've even started an open source 3D printable robotics initiative for an educational crawling worm robot.  And so much more.

But in 2018 we still have more to do to make progress on our scientific roadmap.

This year, please consider making a donation to keep advancing the vision of open-source artificial life.

Thank you,

- The OpenWorm Team