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Patent for Sale:

Virtual Modeling of Biological Tissue with Adaptive Emergent Functionality    

A technique for modeling biological tissue yielding virtual multicellular individuals that display adaptive emergent functionality as a reaction to environmental stimuli.

Overview

The technology makes possible the prediction of likely biological outcomes by modeling the development of a single virtual cell whose genome can be configured in any manner the researcher chooses. Unlike traditional "top down", mathematically based modeling attempts, the technology approaches the simulation of physiological phenomena from a "bottom up" or "middle out" perspective. Through a series of abstractions, it attempts to follow the same developmental paths found in actual biology by starting with a single virtual cell and allowing the developmental process to unfold in a completely emergent manner.

Primary Application of the Technology

Quite possibly the highest and best use of this technology is its role in facilitating the discovery of new drug therapies and, potentially, drastically reducing the amount of time that may otherwise be required without such modeling insight. Therefore, the pharmaceutical industry generally would appear to be a prime beneficiary of this 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

Model organisms are widely used by biologists because they are tractable systems that are amenable to biochemical, genetic, and physiological manipulations. The utility of this approach is evidenced by the wealth of biological knowledge derived from studies on model organisms, such as bacteria, yeast, worms, flies, and mice. Such organisms allow scientists to develop and test hypotheses in a system of reduced complexity that shares a set of cellular fundamental processes with more complex species, allowing them to translate their findings. However, new technologies have generated a flood of biological data at the molecular level, which has emphasized the gap between the scale at which data are collected and the higher scales at which we seek to understand biological processes.

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

The philosophy of our modeling approach shares a number of ideas with agent based modeling, to enable the emergence of complex behavior through interaction of many, relatively simple, heterogeneous components. Models are created by manipulating a number of different types of basic elements (e.g., virtual genes or molecular resources) through a graphical user interface. Each component operates by its own simple set of rules or functions that define its responses, given its internal state and inputs from its local environment and neighboring elements.

Competitive Advantage

As Sydney Brenner noted, "a proper simulation must be couched in the machine language of the object: in genes, proteins, and cells". Accordingly, we have focused on simulating key functions of cells as basic units of computation, simulating the physiological processes that build, organize, and maintain tissue integrity.

Frequently Asked Questions

The question often arises, “Where is the math?” Any math in our models defines interactions between components at the lowest levels of the simulation. For example, transcription of a gene (a base component) involves a user-specified algebraic function to determine the level of expression caused by a transcription factor (another base component). Likewise, metabolic processes such as enzymatic reactions, translocation and secretion of molecules, or protein interactions are represented as simple algebraic expressions. In contrast to approaches that describe in mathematical terms aggregate behavior from a top-down perspective, aggregate behavior of our models emerges solely from local interaction of base components.

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.

Additional Information

The system - hardware and software has been built and in use at three major US universities. Extensive documentation would be made available to a buyer.

Patent Summary

U.S. Patent Classes & Classifications Covered in this listing:

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