Patent for Sale:Virtual Modeling of Biological Tissue with Adaptive Emergent Functionality
Primary Application of the Technology
Additional target markets are biotechnology research based activities whether in a strictly commercial sense, purely academic or a combination of the two.
The Problem Solved by the Technology
A computer model can help bridge this gap by accurately representing known data and by predicting the outcome of wet bench experiments. The process of constructing computer models is itself an informative exercise as it uncovers knowledge gaps, biases, and inconsistencies within the knowledge framework. Models that are rooted in the language of the cell are especially useful as a vehicle for collaboration and debate, ultimately driving the scientific process forward.
We have attempted to combine the usefulness of model organisms with the utility of computer modeling to create computer-modeling techniques that enable scientists to integrate their wet-bench biology and modeling efforts.
How the Technology Solves the Problem
Frequently Asked Questions
In constructing a virtual tissue model, a modeler works directly with these base components, setting parameters and interactions to build models with incrementally increasing complexity and fidelity. Initially, the modeler may know little about the details of some underlying process, but can still construct a simple model that abstracts much of the supporting detail yet captures the essential behavior. From this, the modeler can quickly explore feasible pathways and interactions that generate reasonable organization and behaviors. As more data and greater understanding become available, the model can be improved through a process of iterative refinement.
We agree that, rather than simply reproducing data that are already known, one important goal of biological modeling is to predict the outcome of novel wet bench experiments. However, even if this goal is not achieved in a particular instance, there are great scientific benefits that come from the efforts of constructing and refining a model.
These activities require modelers to formalize their understanding of underlying processes, which often leads to important new questions and avenues of research. As the fidelity of the model improves, so does its ability to predict and guide wet bench research, focusing wet bench experiments on hypotheses most likely to prove fruitful. Modeling is thus complementary and synergistic with wet bench research, each guiding and informing the other.
Class 702: Data Processing:Measuring, Calibrating, Or Testing
This class provides for apparatus and corresponding methods wherein the data processing system or calculating computer is designed for or utilized in an environment relating to a specific or generic measurement system, a calibration or correction system, or a testing system.Subclass 19: Biological or biochemical
Subclass 27: Molecular structure or composition determination
Class 703: Data Processing:Structural Design, Modeling, Simulation, And Emulation
This class provides for electrical data processing apparatus and corresponding methods for the following processes or apparatus: 1. for sketching or outlining of layout of a physical object or part. 2. for representing a physical process or system by mathematical expression. 3. for modeling a physical system which includes devices for performing arithmetic and some limited logic operation upon an electrical signal, such as current or voltage, which is a continuously varying representation of physical quantity. 4. for modeling to reproduce a nonelectrical device or system to predict its performance or to obtain a desired performance. 5. for modeling and reproducing an electronic device or electrical system to predict its performance or to obtain a desired performance. 6. that allows the data processing system to interpret and execute programs written for another kind of data processing system.Subclass 11: Biological or biochemical