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PROGRAMS

Version 1.0

Phase 1 (2013)

To build the first predictive simulation of MS, we have assembled and integrated existing demographic information, brain imaging data, long-term information on disease course and clinical response to treatments, and changes in gene activity before and after treatment from 3 databases comprising 7,000 people with MS.

    De-identified data from the following databases are curated and loaded into a cloud-based data knowledge management system called TranSMART™.
    • Brigham and Women's Hospital (BWH): The Comprehensive Longitudinal Investigation of MS at the Brigham and Women's Hospital (CLIMB) dataset comprising ~2,000 patients with demographic, imaging, treatment and clinical measurements, including:
      • Genome-wide association study (GWAS) data from ~1,200 patients
      • Whole blood pre- and post-treatment gene expression changes from ~400 patients
      • Longitudinal post-treatment gene expression changes from ~100 patients
    • Accelerated Cure Project (ACP): MS Repository with over 50 clinical and covariate measures documenting clinical history and treatment captured using a patient self-reported questionnaire
    • PatientsLikeMe: Deep phenotyping of 2,000 patients derived from their 35,000-patient MS community

Deliverables:

  • A novel alliance business model: we are establishing innovative pro-intellectual property (IP) contracts for a truly cooperative approach for accelerating research
  • Data-Mining:
    • Develop a prognostic model of MS based on clinical data and biomarkers
    • Compare Expanded Disability Status Scale (EDSS) symptom patterns from clinical trials to patient-derived phenotypes from PLM population
  • Model 1.0 Simulation: develop computational methods for improved disease modeling
    • Analyze clinical, molecular, and genetic dataset (BWH) to build causal network models of MS that link clinical -> molecular -> genetic interactions (Hierarchical BioModel™ of MS based on GNS platform)
    • VirtualCell Project

      • What is a virtual cell?
        The VirtualCell project has the goal of creating a computer model of a prokaryotic cell using the totality of knowledge amassed on its components and biophysical processes. The model, which consists of multiple algorithms grouped in 4 major areas (DNA, RNA, metabolites and proteins), aims at predicting phenotype from genotype with unprecedented accuracy. In other words, dynamic simulations of a virtual cell would demonstrate all the behaviors of an actual cell.

        Scientists are convening across disciplines to address the challenges of engineering a virtual cell (i.e. "Towards the 3D virtual cell" at UCSD, December 2012). To date, a whole-cell computational model has been created for a small parasitic bacterium having the smallest known genome that can constitute a cell. This model was able to predict the phenotype from genotype with 80% accuracy (Karr et al)

        This first model is an important building block, from which other cell types can begin to be modeled. Eventually, it will be possible to create models of human cell types that are relevant to disease. A whole-cell model of a neuron will give us fundamental understanding of normal neuronal function, and, even more critically, the ability to probe what goes wrong in disease conditions like multiple sclerosis.

        MetaCell is creating a software specification and analysis document that will defines a development plan and architecture for the Virtual Cell to be developed through Orion Bionetwork's 2.0 program.

    • OpenWorm

      • What is OpenWorm?
        Orion is partnered with MetaCell to support OpenWorm, in order to seed the systems biology simulation approach it is taking in MS.

        OpenWorm is an open-source project that is building a whole-organism computer model of a C. elegans nematode. Construction of OpenWorm is being carried out by a non-incorporated community of volunteers.

        OpenWorm has the ambitious goal of combining a physics simulation with a neuronal activity simulation to connect scientific knowledge about the worm's cellular activity to the understanding of its physical behavior. The community is constantly expanding through publication of journal articles and recruitment of new members.

        The algorithms and simulation approaches developed for the 'simple worm' will provide building blocks for modeling more complex systems such as the human.

    • Geppetto

      • Orion is also funding MetaCell to build the a Systems Biology Open Simulation Platform called Geppetto.

        Geppetto is a multi-algorithm, multi-scale simulation platform engineered to support simulation of complex biological systems and their surrounding environment:

        • Modular
          Geppetto allows separation of functionality into independent, interchangeable modules such that each contains everything necessary to execute a given aspect of desired functionality.
        • Scalable
          Geppetto can handle a growing amount of work in a robust fashion by being intrinsically distributed, scaling up to accomodate growing load.
        • Extensible
          Geppetto allows for future growth by including hooks and mechanisms for expanding/enhancing the system with anticipated capabilities, without having to make ad-hoc changes to the system infrastructure.
        • Generic
          Geppetto is not tied to any specific biological simulation, nor to the model being simulated or the simulation aspects (neuronal, mechanical, etc.) being simulated.
        • Client-Server
          Geppetto is based on a client-server model, where the simulation is controlled by a client through a web interface.
        • Distributed
          Geppetto architecture needs to allow separation of the execution of a simulation into multiple processes which can be executed by different server and which communicate with each other by exchanging messages.
        • Dynamic Driven
          Geppetto components can be deployed, re-deployed, and un-deployed without a system (server) restart.
  • Why build a computational community?
    • The challenges of solving brain disease require collaborative solutions. The complexity of diseases like multiple sclerosis makes it difficult to sort through the myriad facts, data, and biological processes that are uncovered on a daily basis by researchers around the world. Computational models are important tools for making complex problems with large amounts of information ("Big Data") more straightforward to analyze and understand. Because of this, Orion is building a global community around the computational modeling effort; we will bring the best minds together in order to accelerate knowledge discovery in MS.
    • Activities to build a computational community will include workshops, a blog and other social media, journal clubs and creation of an open source code repository on GitHub.

Progress Reports:

Stay tuned!