C4L – Center for Computational and Integrative Biology

Codes For Life (C4L) is a computationally-focused track within the CCIB Graduate Program, founded in 2022. C4L is also a National Research Traineeship (NRT) funded by the National Science Foundation.

Research Themes

The specific research themes of the Codes For Life NRT lie at the intersection of genomics and proteomics. These are two scientific communities that both rely heavily on computational approaches but which are unfortunately siloed. However, even if this is not your area of research focus, being a C4L trainee might make sense for you! Furthermore, nearly all of the training activities are open to all CCIB students (and even all RU-C STEM Graduate students) so you can also follow along on a voluntary basis.

Why join the C4L track?

There are multiple benefits to joining the C4L track as a CCIB graduate student. These include:

The opportunity to both experience innovative training models and directly contribute to improving them.
The CCIB graduate program provides an impressive breadth of knowledge and flexibility, but this can come at the expense of advanced computational skills. As a C4L graduate, our goal is that your computational skills will be more advanced than what you would have from nearly any other U.S. program in the life or physical sciences.
NRT model programs are competitive, and being an NRT trainee adds a noteworthy element to your CV! You can read more about the NRT model here
If you are a U.S. citizen and a PhD student, you may be eligible for an NRT fellowship that will fund you for one or two years, during which you would not need to do a TA-ship and your advisor would not need to pay you from a grant.

C4L Requirements

C4L students do have more specific requirements than those required of general CCIB students, but enrolling in C4L should not extend your time-to-degree. As a trainee in the C4L track, you would need to participate in the different activities detailed below:

Specific Courses

As a C4L trainee, you will be required to take one course from each of the following clusters:

Proteins
- e.g. Protein Structure & Function (56:115:522, offered in even years during the Spring semester), Molecular Biophysics (56:121:560)
Genomes
- e.g. Genome Informatics(56:121:552, Spring semester), Evolutionary Genetics (offered in even year Fall semesters)
Artificial Intelligence
- e.g. Artificial Intelligence (56:198:514, offered in odd-year Fall semesters), Machine Learning (56:198:554 offered in even year Fall semesters)
Software Development
- e.g. Data Structures and Algorithms (56:198:501 Spring semester) + a software engineering workshop, Software Engineering (50:198:423)
Codes for Life Seminar (56:121:605) Weekly seminar series offered in Fall and Spring semesters. C4L track students will be required to take 2 semester of C4L Seminar. All NRT Fellows are expected to attend seminar.

Any courses that count toward C4L requirements also count toward your CCIB course requirements. Students can request a waiver which will be reviewed by C4L PIs.

Individual Development Plan

As a STEM graduate student, it is easy to feel like your training activities are driven by your advisor’s or program’s whims, with some annual input from your committee. It might be hard to see how they relate to your career goals, or it might feel like you are missing some critical skills that your current training environment doesn’t provide.

Starting Summer 2022, you and your advisor will need to create an Individual Development Plan (IDP) to align your training activities to your needed skills, interests, and values. Your IDP will be based on the structure provided by Science Careers https://myidp.sciencecareers.org/. You and your faculty advisor will also need to annually assess how well you are following your IDP.

Professional Development and Outreach Activities

Beginning in Fall 2022, C4L trainees are required to participate in at least two professional development activities per semester. These include CCIB-hosted workshops as well as many other eligible workshops that are advertised on the #workshop-and-training slack channel. While the CCIB professional development curriculum is designed to be very flexible, as a C4L student you may also have specific workshop requirements outlined in your IDP.

Beginning in Summer 2023, C4L students will also need to attend communal C4L gatherings, such as the monthly C4L seminar, the annual C4L symposia, and periodic sponsored public outreach activities.

Ethics Learning Community

Ethics are important for everyone, but they are especially important for the individuals who are going to be shaping the scientific landscape for generations. As a participant in a National Research Traineeship, NSF expects that you might be one of those people! Wherever your ethics are at the moment, you’ll need to level-up by participating in an ethics learning community during one semester.

Teamwork Assessment

Modern science is a team activity! As a C4L student, like most STEM graduate students, you will naturally need to carry out some of your research aims as part of a team. Teams of C4L trainees, however, will get to do a regular structured self-assessment on how well they are working as a team. Typically you will need to self-assess on six metrics every six months, and the team leader (usually your PI, but not always!) will review the assessment with the other members of the C4L development team.

Industry Mentorship

Writing robust software that is easily useable by other scientists requires professional discipline that most academic scientists aren’t trained in. If you are a post-qualifying C4L Ph.D. student, beginning in Fall 2023 you will have industry professional(s) as part of your training team. More specifically, you will be assigned a mentor experienced in scientific software development. (The C4L PIs are jealous!) Your mentor will work with you on your own code over a period of six months and will give you specific assignments to improve your code to meet field standards for open-source or industry-developed software. Your job is to follow your industry mentors advice and improve your software as they suggest! Interested students may be able to pursue an industry internship instead, but each internship has additional prerequisites.

Survey Participation

C4L is an experiment! And we will need to get regular feedback from students on how we are doing. C4L students are expected to respond to a reasonably-lengthed survey and (very occasionally) do interviews with social scientists who are evaluating C4L.

C4L Personnel

Grace Brannigan, C4L Director grace.brannigan@rutgers.edu

Sarah Johnson, Senior Program Coordinator sarahsm@rutgers.edu

Anthony Geneva, Associate Director for Student Experience

Guillaume Lamoureux, Associate Director for Foundational Training

Andrey Grigoriev, Associate Director for Research

Iman Dehzangi, Associate Director for Technical Training

Benedetto Piccoli, Senior Personnel, Department of Math

Sunil Shende, Senior Personnel, Department of Computer Science

Suneeta Ramaswami, Senior Personnel, Department of Computer Science

Vladyslav Kholodovych, Senior Personnel, Office of Advanced Research Computing (OARC)

Kristin Curtis, Senior Personnel, Senator Walter Rand Institute for Public Affairs

Gili Ad-Marbach, External Evaluator, University of Maryland

C4L Trainees

Aditya Birla

email / homepage

Lamoureux Lab

PhD Student

Alyssa Vanerelli

email / web

Geneva Lab

PhD Student, Evolutionary Genomics

To provide modern genomic resources for Anolis grahami, a widespread species native to Jamaica and introduced to Bermuda, I have assembled and annotated a high-quality reference genome. Using this genome, I am investigating sex chromosome evolution, estimating population sizes through time, and assessing repeat element content in this species. In addition, I am inferring the relationships among squamate reptiles by analyzing this genome with other published genome assemblies.

Anushriya Subedy

email / homepage

Lamoureux Lab

PhD Student, Machine Learning for Protein-Protein Interactions

Predicting protein-protein interactions (PPIs) requires a comprehensive description of specific biological contexts (pH, post-translational modifications, and interaction partner availability), which are not well represented in the Protein Data Bank (PDB) due to the limitation of experimental methods, and this lack of functional data poses a challenge. Our central hypothesis is more biologically meaningful representations of proteins can be extracted from non-structural, in vivo data, using novel machine learning architectures.

Caden Comsa

email / homepage

Geneva Lab

PhD Student, Conservation Genomics

The Conception Bank Silver Boa (Chilabothrus argentum) is a Bahamian snake just recently discovered in 2015 that has an estimated census population of 128 snakes and is listed as Critically Endangered by the IUCN. I am conducting whole genome sequencing of all currently existing DNA samples taken from Silver Boas to assess the species’ population size and genomic health. I am also assembling a high-quality reference genome from the Turks and Caicos Island Boa (Chilabothrus chrysogaster), a species closely related to the Silver Boa, and performing a comparative demographic and conservation genomics study on the Silver and TCI Boas to investigate what factors may lead to small populations having good versus poor genomic health.

Chris Denaro

email / homepage

Piccoli Lab

PhD Student, Mathematical Modeling

My current project examines the modeling of biological networks using mathematical hypergraphs. We focus on simulating metabolic networks with therapeutic interventions to predict the efficacy of potential therapies. We also examine the generalizability of results on traditional graphs to hypergraphs.

Cleo Falvey

email / homepage

Geneva Lab

PhD Student, Evolutionary Genomics, Host-Virus Coevolution, and Herpetology

I am working on assembling a de novo whole genome assembly of the Hispaniolan lizard Anolis distichus. Additionally, I am sequencing and assembling the whole genome of Atadenovirus strains that are found to widely infect anoles. This research will help us better understand the dynamics of host-virus coevolution and viral host switching.

Connor Pitman

email / homepage

Brannigan Lab

PhD Student, Computational Biophysics

I lead the development team for the Blobulator web tool, which allows users to visualize hydrophobic domains in protein sequences. I also use genomic data and simulations to identify pairwise residue interactions and quantify conformational shifts in intrinsically unstructured proteins.

Ezry St. Iago-McRae

email / homepage

ezry_stIago_mcrae

Brannigan Lab, In Collaboration with the Gripenburg and Klein Labs

PhD Student, Molecular Dynamics of Membranes and Membrane Proteins

Membrane proteins are essential sensors and gate keepers that help maintain the narrow distinction between living and non-living cells. My research extends established computational methods (molecular dynamics and free energy perturbation) to understand protein-lipid interactions at the nanoscale under conditions currently inaccessible to wet-lab techniques. Our results and methods will help further our understanding of protein-lipid interactions with implications across several biological and medical fields including cell physiology and anesthesiology.

Jahmal Ennis

email / homepage

Brannigan/Griepenburg Labs

PhD Student, Membrane Biophysics

We use molecular dynamics simulation to study gold nanoparticle – membrane interactions. I am interested in the drives of gold nanoparticle aggregation in lipid membranes.

James Kelley

email / homepage

jim-kelley

Grigoriev Lab

PhD Student, Computational Genomics

Relating deletion distribution in SARS-CoV-2 WGS samples to differences in VOCs. Finding differences in SVs between tumor and normal samples which may point to drug targets. Developing visualization tools with features useful for examining SVs and reducing the need for manual processing of data.

Jesse Sandberg

email / homepage

Brannigan Lab

PhD Student, Membrane-protein interactions

I study the interplay of membrane composition, membrane curvature, and lipid binding to membrane proteins. Recently, I have been focusing on the Envelope protein (E protein) from SARS-CoV-2, the virus that causes Covid-19.

Sayed Mehedi

email / web

Dehzangi Lab, In Colaboration with the Corbett, Grigoriev, and Fu Lab

PhD Student, Machine Learning

We are developing machine learning-based methodologies for the precise identification of regions of interest as well as the segmentation and quantification of cells within tissue images. Furthermore, we are investigating the utilization of generative adversarial networks to augment the quality of tissue images, aiming to optimize downstream analysis efficiency.

Siddharth Bhadra-Lobo

email / homepage

Lamoureux Lab

PhD Student, Machine Learning for Protein Interactions

Voxel based SE(3)-equivariant convolutional models provide a rapid, kernel-based 3D space invariant scanning operation for representation learning at the molecular level. Our work has demonstrated that these convolutional filters have far reaching potential for the rapid docking, binding, and screening of AI generated structures in the field of AI drug design.