162 CATALYZING INQUIRY
system may consist of esophagus, stomach, and intestines; and so on down to the level of organelles
within cells and molecular functions within organelles), and every unit depends on the coordinated
interaction of its subunits.
Given the complexity of physiological modeling, it makes sense to replicate this natural organiza-
tion. Thus, models of tissue, organs, and even entire organisms are relevant subjects of physiological
modeling. Functional behavior in each of these entities depends on activity at all spatial and temporal
scales associated with structure from protein to cell to tissue to organ to whole organism (Box 5.11) and
requires the integration of interacting physiological processes such as regulation, growth, signaling,
metabolism, excitation, contraction, and transport processes. One term sometimes used for work that
involves such integration is “physiome” (or by analogy to genomics,“physiomics”).^89
Integration of such models presents many intellectual challenges. Following McCulloch and
Huber,^90 it is helpful to consider two different types of integration. Structural integration implies integra-
tion across physical scales of biological organization from protein to whole organism, while functional
integration refers to the integrated representation of interacting physiological processes. Structurally
integrative models (e.g., models of molecular dynamics and other strategies that predict protein func-
tion from structure) are driven by first principles and hence tend to be computation-intensive. Because
they are based on first principles, they impose constraints on the space of possible organismic models.
Functionally integrative models are strongly data-driven and therefore data-intensive, and are needed
to bridge the multiple time and space scales of substructures within an organism without leaving the
problem computationally intractable. Box 5.12 provides a number of examples of intersection between
structurally and functionally integrated models.
Predictive simulations of subcomponents at various levels of the hierarchy of complexity are gener-
ally based on physicochemical first principles. Integrating such simulations, of which micromechanical
tissue models and molecular dynamics models are examples, with each other across scales of biological
organization is highly computationally intensive (and requires a computational infrastructure that
enables distributed and heterogeneous computational resources to participate in the integration and
facilitates the modular addition of new models and levels of organization).
5.4.4.2 Hematology (Leukemia),
Childhood acute lymphoblastic leukemia (ALL) is a lethal but highly treatable disease. However,
successful treatment depends on the ability to deliver the correct intensity of therapy. Improper inten-
sity can result in an excess of deaths caused by toxicity, decreased mental function over the long term,
and undertreatment for high-risk cases.
The appropriate intensity is determined today through an extensive—and expensive—range of
procedures including morphology, immunophenotyping, cytogenetics, and molecular diagnostics.
However, Limsoon Wong has developed a relatively inexpensive single-platform microarray test that
uses gene expression profiling to identify each of the known clinically important subgroups of child-
hood ALL (Figure 5.11) and hence the appropriate intensity of treatment.^91 This is confirmed using
computer-assisted supervised learning algorithms, in which an overall diagnostic accuracy of 96 per-
cent was achieved in a blinded test sample. To determine whether expression profiling at diagnosis
(^89) J.B. Bassingthwaighte, “Toward Modeling the Human Physionome,” pp. 331-339 in Molecular and Subcellular Cardiology:
Effects on Structure and Function, S. Sideman and R. Beyar, eds., Plenum Press, New York, Volume 382 in Advanced Experiments
in Medical Biology, 1995; http://www.physiome.org/.
(^90) A.D. McCulloch and G. Huber, “Integrative Biological Modelling in Silico,” pp. 4-25 in ‘In Silico’ Simulation of Biological
Processes No. 247, Novartis Foundation Symposium, G. Bock and J.A. Goode, eds., John Wiley & Sons Ltd., Chichester, UK, 2002.
(^91) L. Wong, “Diagnosis of Childhood Acute Lymphoblastic Leukemia and Optimization of Risk-Benefit Ratio of Therapy,”
PowerPoint presentation presented at the Institute for Infocomm Research, 2003, Singapore, available at http://sdmc.lit.org.
sg:8080/~limsoon/psZ/wls-aasbi03.ppt.