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:
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
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
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
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.
Application for the C4L Program
Codes for Life Student Application
Incoming students should apply by the beginning of their third semester