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Utility of Modeling

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.

Our Approach to Modeling

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.

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.

To develop enabling methods for improved mammalian tissue modeling, we started with a single cell and proceeded to simulate the essential aspects of fundamental processes –cell growth, division, differentiation, and response– as necessary for development and organization of virtual tissue. “Be the cell” is a simple reminder that living cells are not aware of anything beyond their current state, and interactions among cells and their components are the basis for higher order behavior. We avoid the use of over-arching equations that govern system-wide behavior, because it is antithetical to our modeling philosophy, and because we simply don't think that living systems work that way.

Where is the math?

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.

 

 

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