The growth of spinal cord tissue is a complex biological phenomenon. To gain a deeper understanding of this phenomenon and to be able to make predictions about the outcome of experimental and biomedical interventions, Northeastern researchers have developed a computational model that can simulate with high precision the outcome of the underlying biological processes. This tool has significant potential to make the development of therapeutic strategies (including the design of drugs) aimed at curing spinal cord injury more rational and cost-effective.
Technology Overview
Northeastern researchers have designed an algorithm using a MATLAB script for computational modeling of stem-cell-driven tissue growth of adult spinal cord tissue. The model employs the cellular automata (CA) framework in which time, space, and state are treated as discrete variables. Simulations based on this model can be used for parameter testing and making predictions about the growth dynamics of biological spinal cord tissue. Such predictions include alterations in the growth dynamics and the final properties of the tissue induced by experimental or medical intervention.
- Can improve spinal cord regrowth after injury
- Cost-effective
- Can make predictions about the likely outcomes of experiments that are difficult to perform due to resource constraint
- Biologically realistic modeling of tissue growth and regeneration in the central nervous system
- Applied/pre‑clinical research
- Licensing
- Research collaboration
- Partnering
Patent Information:
1. Life Science
For Information, Contact:
Colin Sullivan
Commercialization Consultant
Northeastern University
Gunther Zupanc
David Lehotzky
Rifat Sipahi
Computational Modeling
Software Development
Spinal cord
Spinal Cord Injury
Tissue Engineering
Tissue Growth
Tissue Repair