66 CATALYZING INQUIRY
databases into models of biological activity. As databases become increasingly annotated with func-
tional and other information, they lay the groundwork for model formation.
In the future, such “database models” are envisioned as the basis of informed predictions and
decision making in biomedicine. For example, physicians of the future may use biological information
systems (BISs) that apply known interactions and causal relationships among proteins that regulate cell
division to changes in an individual’s DNA sequence, gene expression, and proteins in an individual
tumor.^18 The physician might use this information together with the BIS to support a decision on
whether the inhibition of a particular protein kinase is likely to be useful for treating that particular
tumor.
Indeed, a major goal in the for-profit sector is to create richly annotated databases that can serve as
testbeds for modeling pharmaceutical applications. For example, Entelos has developed PhysioLab, a
computer model system consisting of a large set (more than 1,000) of ordinary nonlinear differential
equations.^19 The model is a functional representation of human pathophysiology based on current
genomic, proteomic, in vitro, in vivo, and ex vivo data, built using a top-down, disease-specific systems
approach that relates clinical outcomes to human biology and physiology. Starting with major organ
systems, virtual patients are explicit mathematical representations of a particular phenotype, based on
known or hypothesized factors (genetic, life-style, environmental). Each model simulates up to 60
separate responses previously demonstrated in human clinical studies.
In the neuroscience field, Bower and colleagues have developed the Modeler’s Workspace,^20 which
is based on a notion that electronic databases must provide enhanced functionality over traditional
means of distributing information if they are to be fully successful. In particular, Bower et al. believe
that computational models are an inherently more powerful medium for the electronic storage and
retrieval of information than are traditional online databases.
The Modeler’s Workspace is thus designed to enable researchers to search multiple remote data-
bases for model components based on various criteria; visualize the characteristics of the components
retrieved; create new components, either from scratch or derived from existing models; combine com-
ponents into new models; link models to experimental data as well as online publications; and interact
with simulation packages such as GENESIS to simulate the new constructs.
The tools contained in the Workspace enable researchers to work with structurally realistic biologi-
cal models, that is, models that seek to capture what is known about the anatomical structure and
physiological characteristics of a neural system of interest. Because they are faithful to biological
anatomy and physiology, structurally realistic models are a means of storing anatomical and physi-
ological experimental information.
For example, to model a part of the brain, this modeling approach starts with a detailed description
of the relevant neuroanatomy, such as a description of the three-dimensional structure of the neuron
and its dendritic tree. At the single-cell level, the model represents information about neuronal mor-
phology, including such parameters as soma size, length of interbranch segments, diameter of branches,
bifurcation probabilities, and density and size of dendritic spines. At the neuronal network level, the
model represents the cell types found in the network and the connectivity among them. The model must
also incorporate information regarding the basic physiological behavior of the modeled structure—for
example, by tuning the model to replicate neuronal responses to experimentally derived data.
With such a framework in place, a structural model organizes data in ways that make manifestly
obvious how those data are related to neural function. By contrast, for many other kinds of databases it
is not at all obvious how the data contained therein contribute to an understanding of function. Bower
(^18) R. Brent and D. Endy, “Modelling Cellular Behaviour,” Nature 409:391-395, 2001.
(^19) See, for example, http://www.entelos.com/science/physiolabtech.html.
(^20) M. Hucka, K. Shankar, D. Beeman, and J.M. Bower, “The Modeler’s Workspace: Making Model-Based Studies of the Nervous
System More Accessible,” Computational Neuroanatomy: Principles and Methods, G.A. Ascoli, ed., Humana Press, Totowa, NJ, 2002,
pp. 83-103.