OpenWorm Annual Meeting 2025 (DevoWorm update)

Here are the slides for the DevoWorm group's report to the OpenWorm Annual Meeting (2024). You can watch Bradly Alicea present the talk on YouTube.

Thanks again to all of our contributors over the past year, all of our Github contributors, and all of our Google Summer of Code applicants. If you are interested in participating, join one of our meetings or contribute to our Github repo and organization.

Google Summer of Code 2024

 

Welcome to the new Google Summer of Code scholars for 2024! INCF is sponsoring four students for which I (Bradly Alicea) am acting as mentor: two students for the DevoWorm group (via the OpenWorm Foundation community) and two students for the Orthogonal Research and Education Laboratory. 

D-GNNs (sponsored by the DevoWorm group)

Congratulations to Pakhi Banchalia and Mehul Arora for being accepted to work on the Developmental Graph Neural Networks (D-GNNs) project. Pakhi will be working on incorporating Neural Developmental Programs (NDPs) into GNN models. Mehul will be working on hypergraph techniques for developmental lineage trees and embryogenesis [1]. Himanshu Chougule (Google Summer of Code scholar) is a co-mentor for this project.

Virtual Reality for Research and Open-source Sustainability (sponsored by the Orthogonal Research and Education Lab)

Congratulations to Sarrah Bastawala for being accepted to work on the Open-source Sustainability project [2]. Sarrah is incorporating Large Language Models (LLMs) into the collection of agent-based approaches for this project. Jesse Parent is a co-mentor for this project.

We also have two Open-source Development scholars for Summer 2024: Adama Koita and Shubham Soni. They will be participating in the Orthogonal Lab's open-source weekly meetings in addition to projects based around Virtual Reality and Open-source Sustainability, respectively. 

Google Summer of Code 2023

Welcome to the new Google Summer of Code scholars for 2023! INCF is sponsoring four students for which I (Bradly Alicea) am acting as mentor: two students for the DevoWorm group (via the OpenWorm Foundation community) and two students for the Orthogonal Research and Education Laboratory. These four students are pursuing three projects.

D-GNNs and DevoLearn (sponsored by the DevoWorm group)

Congratulations to Himanshu Chougule and Sushmanth Reddy Mereddy for being accepted to work on the Developmental Graph Neural Networks (D-GNNs) project. Sushmanth has been contributing to the DevoLearn platform for quite some time and will spend this summer working on improving the image segmentation to GNN embedding pipeline. Himanshu was a part of last Summer's GSoC cohort in the Orthogonal Research and Education Laboratory, working on the Open-source Sustainability project. His work resulted in developing Agent-based Model-Reinforcement Learning hybrid models. This year, he will be working on building Topological Data Analytic capabilities into the D-GNN pipeline. 

Mayukh Deb and Jiahang Li are co-mentors for this project.

Virtual Reality for Research and Open-source Sustainability (sponsored by the Orthogonal Research and Education Lab)

Congratulations to Vrushali Nandurkar and R.V. Rajagopalan for being accepted to work on the projects Virtual Reality for Research and Open-source Sustainability, respectively. Vrushali is enthusiastic about working on creating open-source virtual world assets for scientific research and educational initiatives. R.V. will be working on a continuation of the Open-source Sustainability project, helping to augment the existing models and web interface.

Jesse Parent is a co-mentor for both projects, while Brian McCorkle is a co-mentor for the Open-source Sustainability project. 

DevoLearn (Open-source) Maintenance and Evangelism

 2021 has been a busy year for the DevoLearn initiative. Not only has Mayukh Deb been busy maintaining and generating new versions of the DevoLearn pre-trained model, but I (Bradly Alicea) has been engaging in technology evangelism to advance awareness and involvement in the initiative.  The DevoLearn pre-trained model software (for C. elegans embryogenesis) is now at version 0.3.0, and has garnered 12 contributors making 165 commits (largely since January 2021). Our involvement in Google Summer of Code has bolstered many of these contributions. While our popularity is currently limited, we are trying to spread the word.

To that end, we have presented two versions of a promotional talk on using DevoLearn for facilitating Computational Developmental Biology research and education. The first presentation (DevoLearn: Engaging learners with Computational Developmental Biology) is a flash talk given to the OSF Education Un-conference on Open Scholarship Practices in Education Research, held in February. The second presentation was a longer (15-minute) presentation to the INCF Assembly (DevoLearn: a platform for open Developmental Data Science, Machine Learning, and Education), held in April.

As for developing the broader platform, Ujjwal Singh and I will be working this Summer to develop algorithms for colony morphogenesis and behavior in the Diatom genus Bacillaria. This will be added to the platform in a manner similar to the DevoLearn pre-trained model. In addition to the software development activities, Mayukh, myself, and Krishna Katyal have been the main contributors to the DevoWorm Onboarding Guide. Looking forward to an exciting future!

Update, 8/19:

All of this work has paid off! From the #devolearn Slack channel (OpenWorm Slack team).

Hacktoberfest now live!

Welcome to Hacktoberfest! Check out our DevoLearn repositories and our DevoLearn AI resources. Contribute from now until the end of October. Make five commits during the course of October, and Github [1] has something for you!

Want to participate in Hacktoberfest @ DevoWorm? Look at our issue board for Group Meetings, or look at the contribution guidelines for DevoLearn and contribute a Data Science demo. 

Select issues on the Group Meetings issue board (DevoWorm) and the Community Board for DevoLearn are also marked with the "Hacktoberfest" label. Once you decide where to contribute, issue a pull request or communicate your interest as a comment in the issue you want to address!

Thanks to Mauyukh Deb and Ujjwal Singh for their administration efforts, and Github users Abtaha, Jainal09, RaviKarri, RudRajit1729, Joel-Hanson, shreyraj2002, Malvi-M, krishnakatyal, and jesparent for their commits so far!

[1] Github offers a T-shirt as incentive for contributing. Offer only applies to labeled repositories (most of the DevoLearn repositories are eligible).

Welcome to DevoLearn!

 

Accelerate data driven developmental biology research with computational learning models

With Google Summer of Code 2020 almost complete, we can debut our latest endeavor: DevoLearn! DevoLearn is a platform that incorporates a computational analysis platform for embryos of different species, with an annotated collection of secondary datasets (DevoZoo) and educational tools. 

While the first part (DevoLearn 0.2.0) is brand new, the other two components (species-specific models and DevoZoo) are revamped versions of resources we have created over the course of the past three years. Thanks to Mayukh Deb and Ujjwal Singh for their efforts this Summer, and Vinay Varma, Siddharth Yadav, Asmit Singh, and Bradly Alicea for their past efforts leading up to this point.

Now let's take a look at the the DevoLearn umbrella:

DevoLearn umbrella.  Click to enlarge.

The entire project is hosted as a Github project. The DevoLearn software is also hosted on PyPi, and is available as an open-source software package there. The species-specific models (which includes the existing OpenDevoCell resource) are hosted as Herokuapps, while the DevoZoo (access to secondary dat and educational resources) is hosted as a set of Github pages. Aside from the educational component, we encourage people to use DevoLearn for conducting their own Machine Learning and Deep Learning analyses. 

Please also feel free to contribute content to DevoLearn! Contributions in the areas of data science tutorials and other types of quantitative analysis are welcome. We are looking for data science tutorials, educational materials that merge ML/DL and biology, and perhaps even novel analytical models. Thanks to Krishna Katyal for pushing a tutorial on Linux command line basics.

Github repository  link  Click to enlarge.

Over the past few years, the DevoWorm group has been trying to develop new ways of quantifying development. Some of this has been sponsored by Google Summer of Code (embryo cell segmentation) over the past few years, and this summer (thanks to Mayukh) we were able to revisit topics such as worm movement and embryo networks. The DevoLearn software package is a pre-trained model that enables the discovery of meta-features, or features that transcend the typical features of biological image processing. 

PyPi project description  link  Click to enlarge.

Ujjwal's contributions to DevoLearn include a new resource called DevoWormAI and a retooling of the DevoZoo and educational web interface that has slowly been developed over the past few years. Ultimately, this will also include two educational endeavors from 2019: OpenWorm/DevoWorm Curriculum and DevoWormML. These educational initiatives tie together Machine Learning, Developmental Biology, and Complex Systems Theory. Future directions include working towards theoretical models of quantitative embryo data, such as a Laplacian description of the embryo. 

DevoWormAI link  Click to enlarge.

DevoZoo 2.0  link  Click to enlarge.

So explore and make the most out of this resource! Please provide feedback; we would be interested in your proposals for additions or next steps.

Welcome, Summer of Coders (2020)!

The Google Summer of Code selections for 2020 have been made! Congratulations to Ujjwal Singh and Mayukh Deb, who will be joining the OpenWorm Foundation and the DevoWorm group for a summer of community engagement and code. Ujjwal's project is called OpenDevoCell Integration, and here is the description:

This project will focus on improving the data science and machine learning infrastructure of the DevoWorm group. The work will focus on an extension of the Summer of Code projects completed in 2017 and 2019. The first two aims are to improve upon the OpenDevoCell web interface and to improve segmentation techniques overall. While the OpenDevoCell interface has been implemented as a Heroku app, we would like to develop a dashboard for interpretation as well as tighter integration with DevoZoo's collection of open-source microscopy data. The third aim is to deploy the code package as a unified Python library, which would be done in concert with the improvement of segmentation techniques.The priority for this Summer is to improve the web interface both in terms of interactivity and functionality. Ideally, we would like to provide users with multiple options for analysis. This includes the ability to incorporate new forms of analysis as well as algorithms for new types of data. Currently, our web app is optimized for microscopy images acquired using the SPIM technique. However, we would also like to segment microscopy images acquired using a wide range of technologies. Feeding into this is the ability to segment and obtain features for the data in our DevoZoo. The ability to extract quantitative data from these movie images is key to conducting the comparative and time-series analysis. The development of a dashboard would ideally enable users to employ various machine learning and simulation techniques in one place.These improvements are meant to increase participation in our open science initiative and make sophisticated analytical techniques more accessible to students and potential collaborators alike. 

Mayukh's project is called Pre-trained Models for Developmental Neuroscience, and is based on previous work done in the group during the DevoWormML course [1]. This project is described thusly:

This project will center around building a pre-trained model for shapes and processes related to Developmental Biology and Neurobiology and extracted from image data. Our organization's Machine Learning interest group (DevoWormML) has published a blog post [1] on the advantages and need for pre-trained models in this area. In short, biological development is characterized by characteristic shapes, movements, changes in shape, and temporal processes that define important features. Pre-trained models are used in NLP and Deep Learning for the domains of sequence discovery in language processing (GPT-2) and bounding box methods for segmenting complex images (DeepLabv3). Models specialized for biology, however, do not exist. A suitable pre-trained model would greatly reduce the need for input data without sacrificing the ability to generalize to different contexts.Our main interest is in extracting spatiotemporal features from image data. We will focus on microscopy data such as that found in the DevoZoo or from more specialized sources [2]. For a typical pre-trained model, the network is pre-trained with non-random weights that approximate the generalized versions of the features we would like to discover. However, we are also interested in a semantic component, particularly the ability to incorporate elements such as meaning assigned to static knowledge (semantics) and multiple meanings for a single feature (polysemy). This will enable relational modeling and the mapping of segmented image data to lineage trees and taxonomies. This will enable relational modeling and the mapping of segmented image data to lineage trees and taxonomies. Our model, tentatively called DevLearningv1, should be applicable to a wide range of neural network and deep learning techniques.

Thanks to INCF for sponsoring our activities once again this year. Thanks also go to Vinay Varma, who will be providing support on all things mentorship this summer. Vinay was a GSoC student last summer, and will be sharing his wisdom with this year's students. I would also like to invite all those who applied for these projects to contribute to the Organization in some other way. Often, interaction with the community now can lead to additional opportunities down the road. 

As for the Orthogonal Research and Education Lab project (Contextual Neurodevelopmental Dynamics), we unfortunately did not get any slots this year. Thanks to Ankit Gupta and Jesse Parent for their excellent proposals. But I would like to continue pursuing the initiative as an open-source effort, hopefully leading to other avenues for development and funding. The same community interaction advice given for OpenWorm applies to Orthogonal Lab as well. 

We are going to be developing in the Meta-Brain repository on Github. Be sure to check out our Saturday Morning NeuroSim meetings for more information on this project (join our mailing list!). And register for the Neuromatch Summer School if you have not already, as it will be quite relevant to what we will be doing.

A sample of Saturday Morning NeuroSim (with a recap of the ICLR conference). Click to enlarge.

UPDATE (5/5): One of our regular meeting attendees (Devansh Batra) has also received a Google Summer of Code position with the OpenCV organization. Congrats!

NOTES:

[1] Alicea, B., Gordon, R., Kohrmann, A., Parent, J., and Varma, V. (2019). Pre-trained Machine Learning Models for Developmental Biology. The Node blog, October 29.

Open Data Day 2020

Welcome to Open Data Day 2020! Sponsored by the Orthogonal Research and Education Laboratory.  Our activities start today, and will continue over the course of the next year. For this iteration of Open Data Day, we are looking for software developers, data scientists, statisticians, and quantitative biologists to work on a host of issues related to open data-related activities in the DevoWorm group. Listed below are a series are series of possible goals for the next year.

1) We would like to construct pseudo-data sets for theory-building and modeling. This involves establishing simulated and resampled data sets that can be used as the input to machine learning, statistical, and functional models. Examples of these would include numeric data generated using statistical distributions, a generative approach using selected features (cells) as inputs, or the energy potentials of kinetic processes in an embryo.

2) There is also a need to build towards metadata standards, particularly with respect to the integration of different data types. Metadata helpful to the DevoWorm group includes (but is not limited to) cell division timing, high-level descriptions, positional and geometric information, and other features. The development of metadata repositories according to a schema data structure would be helpful.

3) Also needed is a focus on DevoZoo maintenance, including the addition of datasets, the integration of data sets, and improvements in presentation style/interface design. Since last year's launch, the resources for each species or computational platform have become outdated. We would not only like to provide links to resources such as new data sets and gene expression atlases, but also provide access to “intermediate” resources such as ontologies, metadata, and models from other research groups. There is also a further desire to make DevoZoo sustainable.

Current iteration of DevoZoo (click to enlarge).

4) As an initiative farther off into the future, we would like to add semantic capabilities to our models and data sets. One such example is a “controlled vocabulary” for developmental microscopy images and molecular data. In concert with this, having the capability to attach meanings and other notes to image and simulation features would increase the interpretability of such data.

5) In conjunction with the Data Reuse Initiative, we would like to provide some application of the FAIR principles. FAIR stands for making data findable, accessible, interoperable, and reusable. There are two opportunities here: a FAIRness evaluation, or how to make data FAIR, and promotion of each component of FAIR. For example, making datasets on DevoZoo more findable by adding tags or other classification tools would help newcomers make the most of our resource.

Google Summer of Docs congratulations!

Congrats to Casper daCosta-Luis (and co-mentors Bradly Alicea and Chee-Wai Lee) for successfully completing the inaugural Google Season of Docs! Casper's project involved automating project documentation (using Continuous Integration) at the OpenWorm Foundation. His final project report can be found here.

Thanks to our sponsor INCF for supporting our application. Speaking of Google Seasons, applications for Google Summer of Code (GSoC) 2020 will be opening soon. Once again, I am hosting two projects: one through the DevoWorm group (OpenWorm Foundation), and the other through Orthogonal Research and Education Laboratory. More information to come.

Summer of Productivity at OREL

An update on what we did with our Summer at the Orthogonal Research and Education Laboratory. We hosted a study group based on an emerging collaboration to understand how Braitenberg Vehicles can be used as a model to study neural development [1].

We hosted one Google Summer of Code student (Stefan Dvoretskii), and mentored another GSoC student through the OpenWorm Foundation (Vinay Varma). Thanks go to INCF as well for their support. We also hosted the activities of several mentees (Ziyi Gong, Jesse Parent, Ankit Gupta, and Hrishikesh Kulkarni) which is topically diverse and will be featured in future blog posts [2]. Below are three tweets from the OREL Twitter account that show highlights from some of this work (congrats again to Stefan Dvoretskii, Vinay Varma, Jesse Parent, and Hrishikesh Kulkarni for their completed milestones).

Click on image for higher resolution. Click here for links.

 Click on image for higher resolution. Click here for links.

Click on image for higher resolution. Click here for links.

Click on image for higher resolution. Click here for links.

NOTES:

[1] our project repository is located here.

[1] two additional students were hosted through the DevoWorm group: Ujjwal Singh and Asmit Singh, who worked on a project called Digital Bacillaria.

Introducing: DevoWormML

This has been cross-posted to The Node blog.

I am pleased to announce a new collaborative interest initiative called DevoWormML, based on work being done in the DevoWorm group. DevoWormML will meet on a weekly basis, and explore the application of machine learning and artificial intelligence to problems in developmental biology. These applications can be geared towards the analysis of imaging data, gaining a better understanding of thought experiments, or anything else relevant to the community.

While "ML" stands for machine learning, participation can include various types of intelligent systems approaches. Our goal is to stimulate interest in new techniques, discover new research domains, and establish new collaborations. Guests are welcome to attend, so if you know an interested colleague, feel free to direct them our way.

Meetings will be Wednesdays at 1pm UTC on Google Meet. Discussions will also take place on the #devowormml channel of OpenWorm Slack (request an invitation). We will discuss organizational details at our first meeting on September 4. If you cannot make this time but are still interested in participating, please contact me. Hope to see you there!

Summer of Working Groups

I am happy to announce that this Summer I will be advising/mentoring two research groups of Google Summer of Code (GSoC) students and applicants. Thanks to INCF for sponsoring our applications and applicants again this year. The first group (DevoWorm), based in the OpenWorm Foundation, is interested in image segmentation and Machine Learning. GSoC applicants Asmit Singh and Ujjwal Singh (currently attending IIT Delhi) are working on extracting quantitative data to construct a digital model for organisms in the diatom genus Bacillaria. The GSoC student (Vinay Varma, currently attending Amrita Vishwa Vidyapeetham University) is working on developing a method for Semantic Segmentation based on microscopy of the embryogenetic process, focusing on the biology of Caenorhabditis elegans.

The other group is based in the Orthogonal Research and Education Laboratory, and is focused on creating a methodology for developmental Braitenberg Vehicles. This involves simulating the formation of a brain (neurons and connectome) in a simple body that continuously interacts with its environment. The GSoC student in this group (Stefan Dvoretskii) is developing such a model using Genetic Algorithms and the open-source SimBrain platform. The GSoC applicants (Ziyi Gong, Jesse Parent, and Ankit Gupta) are working on a variety of unique approaches that will aid in our
understanding of this complex system. These alternative approaches range from biologically-inspired (Ziyi) to a cybernetic architecture based on the Every Good Regulator Theorem (Jesse).

The hybrid education/research working group is something I started with last year's Orthogonal Lab GSoC group. There is a good chance that this Summer's discussions and work periods will produce awesome, cutting-edge science. Follow us on Github (DW group, BV group) and YouTube (DW group, BV group) for more!

Open Leaders 7 is almost finished. Join us for a Sprint!

In a previous post, I mentioned that I was a part of Mozilla's Open Leaders program, which is dedicated to working open and improving the overall health of the internet. Each Open Leader worked on an open project over the duration of the 14 week program, which in my case is an open educational curriculum for the DevoWorm group and the OpenWorm Foundation more generally.

Purpose and Outcomes from the Mozilla's POP (purpose, outcomes, process) standards for project management). Click to enlarge.

The purpose of building this curriculum is twofold: to encourage contributions to the organization, and to create educational opportunities that enrich people's contributions. Therefore, our curriculum combines topical tutorials with course materials focused on interdisciplinary topics (bridging data science, computer science, and biology) and training in niche topical areas. The curriculum has a front-end (managed at Eliademy) and a back-end (managed in a Github repository). You can make contributions to the front-end by either enrolling in the course or making notes in hypothes.is.

Examples of the front-end, back-end, and course content. Click to enlarge.

Another way to contribute is to attend the Sprint for Internet Health. Last year this was referred to as the Global Sprint, and gives people an opportunity to engage in open source creation as well as consumption. Our project already has two contributors (Asmit Singh, Vinay Varma, and Uggwal Singh, who are also Google Summer of Code applicants to the organization) who have been refining the materials by way of pull requests. More details to come!

Details on how to get involved. Click to enlarge.

UPDATE (5/25): An interview with project lead Bradly Alicea (by Robert Schafer) is now available on the Mozilla Open Leaders Medium blog.