Current Research Projects

Project 1 (CS): Web interface for the VCell database

VCell (http://vcell.org/; https://github.com/virtualcell/vcell) is an open-source software platform that can model and simulate reaction networks. It is in existence for over 20 years and has several thousand users. It is a client-based architecture with models centrally stored in Oracle database. The models can be accessed via REST API https://vcell.cam.uchc.edu/api/v0/biomodel. The prototype interface for accessing and displaying model information is deployed at http://www.vcelldb.org, available at https://github.com/virtualcell/modelbricks-webapp. It is implemented in Handlebars, with most information retrieved via API, and some information stored locally as JSON.

Coding (mosltly HTML/JS) is required to provide a complete search and filter pages, add static pages as directed, redesign and add missing elements to model pages, make all pages scalable and pretty. Visualization should be updated to look similar to what is implemented in https://bnglviz.github.io/examples.html (https://github.com/bnglViz/bnglViz.github.io). Finally, modify REST API to add retrieval of new elements.

Desired skills:

  • Essential skills: Handlebars, Javascript, CSS, HTML
  • Desired: Java, REST API (to interact with VCell API)

Project 2 (Biology): ModelBricks

We develop a novel web-based knowledgebase of ModelBricks – well-annotated and compact model elements, designed to facilitate the construction of annotated and reusable models. A typical ModelBrick includes details about the interaction mechanism, modeling assumptions, and relevant kinetic data, ensuring comprehensive annotations for model entities linked to public databases. Accessible through the web, fully annotated ModelBricks can be employed to assemble functional models, whether for large systems or more focused cellular physiology models.

Desired skills:

  • Biology background to read modeling papers, extract information, and create short summaries.

Project 3 (Math/CS): Hierarchical Modeling in Biology

Biological systems express combinatorial complexity: the number of molecular complexes and modifications that arise during biosystem evolution is enormously large because biomolecules contain multiple sites of modification and interact with multiple binding partners, forming transient molecular complexes of various sizes. A protein that has 10 sites of modifications (phosphorylated/unphosphorylated) may form 2^{10} = 1,024 molecular forms that have to be considered in a non-reductionist approach to modeling. Rule-based modeling developed by Blinov and co-authors expresses the evolution of a biosystem in terms of molecular sites and their states, bonds between sites of molecules, and transformations governed by rules for changing states of sites and making or breaking bonds between

We extend the rule-based modeling using the mereology theory based on categorical logic. Mereology is the study of wholes and parts. Biological organisms are whole subsets of spacetime, and they consist of parts (organs) that consist of parts (tissues) that consist of parts (cells), etc. The organization of this hierarchical structure may be represented by a special kind of category, a finite partially ordered set with the greatest member. Parts of wholes may be related by bonds. A bond between two parts is a constraint on what behaviors each may perform.

Desired skills:

  • EITHER Math, one of – set theory, polynomial algebra, category theory.
  • OR programming in logical languages, such as PROLOG or theorem prover COQ

Project 4 (CS/ML): Machine learning of flower patterns

We study Monkeyflowers that produce many beautiful and unique pigmentation patterns. The basic mechanisms of pattern production is based on the reaction-diffusion system of a slow activator and a fast inhibitor (a Turing pattern). Using the modeling and simulation software VCell, the reaction-diffusion system can be spatially simulated with a series of partial differential equations. By changing the parameters of the math model, many different patterns emerge, corresponding to phenotypes observed in species of Mimulus. Using machine-learning algorithms we classify flower patterns, so they can be easily distinguished, and identify key parameters and parameter regions that correspond to patterns related to certain mutants in a plant, and trajectories in parameter space describing the lineage of patterns.

Desired skills:

Project 5 (CS/Math): Models Visualization

A large mechanistic model (accounting for many species and activities and interactions among domains of biomolecules) is very difficult to store, visualize, or modify. Rule-based modeling provides a concise way  Integration of rules, MIMs and SBML is one of my projects. We’re using RailRoad diagrams as a way to visualize rule-based models, and also formalize VCell visualization schema. Extra research may be required to map rule-based modeling to railroad diagrams.

Desired skills:

Project 6 (CS/Math): Analysis of molecular assemblies

Multivalency may lead to the formation of large molecular clusters or polymers. A large number of molecules in molecular clusters with rapidly changing composition requires new modeling techniques to simulate the dynamics of cluster composition changes. Rule-based modeling helps to simulate the evolution of such clusters, but the analysis of clusters composition is a non-trivial task. We designed Python tools to analyze and visualize data generated by NFSim and SpringSaLaD rule-based tools. We will look at LAMMPS now.

    Desired skills:

    • Programming in Python.
    • Interest in molecular dynamics.

    Project 7 (CS/Math): Use of AI to process Systems Biology Data

    Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis (e.g. http://sbgn.org, http://sbml.org). These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigate the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We test public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures.

      Desired skills:

      • Familiarity with XML, JSON
      • Familiarity with AI prompts and chatbots
      • (Optional) Open API, REST API