Title: Method and apparatus for computer modeling a joint
Abstract: The present invention relates to a mathematical and computer model of a joint. The model includes representation of the biological processes related to the synovial tissue and cartilage. In one embodiment, the model represents a human joint afflicted with rheumatoid arthritis.
Patent Number: 6,862,561 Issued on 03/01/2005 to Defranoux, et al.
| Inventors:
|
Defranoux; Nadine A. (San Francisco, CA);
Dubnicoff; Todd B. (Burlingame, CA);
Klinke, II; David J. (San Bruno, CA);
Lewis; Annette K. (Menlo Park, CA);
Paterson; Thomas S. (West Hollywood, CA);
Ramanujan; Saroja (San Mateo, CA);
Shoda; Lisl K. M. (Redwood City, CA);
Soderstrom; Karl Petter (San Francisco, CA);
Struemper; Herbert K. (Menlo Park, CA)
|
| Assignee:
|
Entelos, Inc. (Foster City, CA)
|
| Appl. No.:
|
154123 |
| Filed:
|
May 22, 2002 |
| Current U.S. Class: |
703/11; 703/2 |
| Intern'l Class: |
G06G 007//48; G06F 017//10 |
| Field of Search: |
703/22,2,6,11
607/1
424/756
382/128
|
References Cited [Referenced By]
U.S. Patent Documents
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filtration," J. Biomechanics, 26(10):1151-1160.
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3:53-63.
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Joint", J.Biochem., 16:241-251.
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Locomotor Apparatus for Analysis of Hemiplegic Gait", Proc. 20.sup.th
Annual Conference of IEEE Eng. Med. Bio. Soc., 2407-2410.
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Behavior and Possible Failure of Prosthetic Implants", IEEE 17.sup.th
Annual Conference IEEE Eng. Med. Bio. Soc., 2:1251-1252.
Popovic, M. et al., 2001, "Cloning Biological Synergies Improves Control of
the Elbow Neuroprostheses", IEEE Eng. Med. Bio., 20: 74-81.
Scheiner, A., 1994, "The Effect of Joint Stiffness on Stimulation of the
Complete Gait Cycle", Proc. 16.sup.th Annual Conference of IEEE Eng. Med.
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Engineers: 386-387.
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Mathematical Model of Acute and Chronic Asthma", Proc. First Joint
BMES/EMBS Conf. Serving Humanity, Advancing Technology, Oct. 13-16: 1208.
Yvan, P., et al., 1995, "3D Radiographic Reconstruction of Thoracic Facet
Joints", IEEE 17.sup.th Annual Conference IEEE Eng. Med. Bio. Soc., 1:
397-398.
|
Primary Examiner: Frejd; Russell
Attorney, Agent or Firm: Cooley Godward LLP
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATION
The present invention is related to and claims priority to U.S. Provisional
Patent Application Ser. No. 60/293,533, filed on May 29, 2001, entitled
"Method and Apparatus for Computer Modeling a Joint," which is
incorporated herein by reference.
Claims
What is claimed is:
1. A method for developing a computer model of an animal joint, comprising:
identifying a plurality of biological processes related to the animal
joint, the plurality of biological processes including at least one
biological process related to cartilage metabolism; and
combining the plurality of biological processes to form a simulation of the
animal joint.
2. The method of claim 1, wherein the animal joint is a human joint.
3. The method of claim 1, wherein the plurality of biological processes are
related to a biological state of a normal joint.
4. The method of claim 1, wherein at least one biological process from the
plurality of biological processes is associated with a biological variable
that is a therapeutic agent.
5. The method of claim 1, further comprising:
selecting a therapeutic agent from at least one of methotrexate, steroids,
non-steroidal anti-inflammatory drugs, soluble TNF-alpha receptor,
TNF-alpha antibody, and interleukin-1 receptor antagonists; and
associating the selected therapeutic agent with at least one biological
process from the plurality of biological processes.
6. The method of claim 1, wherein the plurality of biological processes are
related to a biological state of a diseased joint.
7. The method of claim 6, wherein the biological state is the diseased
joint afflicted with at least one of rheumatoid arthritis, osteoporosis,
reactive arthritis and osteoarthritis.
8. The method of claim 6,
wherein identifying the plurality of biological processes includes
identifying a set of biological processes related to changes in biological
attributes of the diseased joint, and
wherein combining the plurality of biological processes includes combining
the set of biological processes to form a simulation of at least one
biological attribute of the diseased joint.
9. The method of claim 1, further comprising:
producing a simulated biological attribute associated with a biological
state of the animal joint;
comparing the simulated biological attribute with a corresponding
biological attribute associated with a reference pattern of the joint; and
identifying the computer model as a valid computer model of the animal
joint if the simulated biological attribute is substantially consistent
with the biological attribute associated with the reference pattern of the
animal joint.
10. The method of claim 1, wherein combining the plurality of biological
processes includes:
forming a first mathematical relation among biological variables associated
with a first biological process from the plurality of biological
processes; and
forming a second mathematical relation among biological variables
associated with the first biological process and a second biological
process from the plurality of biological processes.
11. The method of claim 10, further comprising:
creating a set of parametric changes in the first mathematical relation and
the second mathematical relation; and
producing a simulated biological attribute based on at least one parametric
change from the set of parametric changes, the simulated biological
attribute being substantially consistent with at least one biological
attribute associated with a reference pattern of the animal joint.
12. The method of claim 10, further comprising:
converting a first biological variable into a converted biological variable
the value of which changes over time, the first biological variable being
associated with at least one from the first mathematical relation and the
second mathematical relation; and
producing a series of simulated biological attributes based on the
converted biological variable, the series of simulated biological
attributes being substantially consistent with a corresponding series of
biological attribute associated with a reference pattern of the animal
joint, the series of simulated biological attributes representing the
chronological progression of a biological state of the animal joint.
13. The method of claim 10, further comprising:
converting a parameter into a converted biological variable the value of
which changes over time, the parameter being associated with at least one
from the first mathematical relation and the second mathematical relation;
and
producing a series of simulated biological attributes based on the
converted biological variable, the series of simulated biological
attributes being substantially consistent with a corresponding series of
biological attribute associated with a reference pattern of the animal
joint, the series of simulated biological attributes representing the
chronological progression of a biological state of the animal joint.
14. A processor-readable medium comprising code representing instructions
to cause a processor to:
define a plurality of biological processes related to a biological state of
an animal joint, the plurality of biological processes including at least
one of a first set of biological processes related to tissue inflammation
and a second set of biological processes related to cartilage metabolism;
and
define a plurality of mathematical relationships related to interactions
among biological variables associated with the plurality of biological
processes, at least two biological processes from the plurality of
biological processes being associated with the plurality of mathematical
relationships, a combination of the plurality of biological processes and
the plurality of mathematical relationships defining a simulation of the
biological state of the animal joint.
15. The processor-readable medium of claim 14, wherein the first set of
biological processes include at least one biological process related to
synovial, tissue inflammation.
16. The processor-readable medium of claim 14, wherein at least one
biological process from the plurality of biological processes is
associated with a biological variable that is a therapeutic agent.
17. The processor-readable medium of claim 14, further comprising code
representing instructions to cause the processor to:
select a therapeutic agent from at least one group of methotrexate,
steroids, non-steroidal anti-inflammatory drugs, soluble TNF-alpha
receptor, TNF-alpha antibody, and interleukin-1 receptor antagonists; and
associate the selected therapeutic agent with at least one biological
process from the plurality of biological processes.
18. The processor-readable medium of claim 14, further comprising code
representing instructions to cause the processor to:
define a first compartment, the first compartment including the first set
of biological processes, and
define a second compartment, the second compartment including the second
set of biological processes.
19. The processor-readable medium of claim 18, further comprising code
representing instructions to cause the processor to:
define a third set of biological processes related to the interaction of
the first compartment with the second compartment.
20. The processor-readable medium of claim 14, wherein the biological state
is the state of a diseased joint.
21. The processor-readable medium of claim 20, wherein the biological state
is the state is the state of a diseased joint afflicted with at least one
of rheumatoid arthritis, osteoporosis, reactive arthritis or
osteoarthritis, reactive arthritis, and osteoarthristis.
22. The processor-readable medium of claim 20, wherein upon execution of
the code, a simulated biological attribute for the biological state of the
diseased joint is produced, the simulated biological attribute being
substantially consistent with at least one biological attribute associated
with a reference pattern of the diseased joint.
23. A processor-readable medium comprising code representing instructions
to cause a processor to:
define a plurality of compartments including a plurality of biological
processes related to biological state of an animal joint, the plurality of
compartments including at least one of:
a first compartment, the first compartment including a first set of
biological processes related to synovial tissue
a second compartment, the second compartment including a second set of
biological processes related to cartilage tissue,
the plurality of compartments defining a simulation of the biological state
of the animal joint.
24. The processor-readable medium of claim 23, further comprising code
representing instructions to cause the processor to:
define a first set of mathematical relations associated with the first set
of biological processes and associated with interactions among biological
variables associated with the first set of biological processes, and
define a set of mathematical relations associated with the second set of
biological processes and associated with interactions among biological
variables associated with the second set of biological processes.
25. The processor-readable medium of claim 23, further comprising code
representing instructions to cause the processor to:
define a third set of biological processes related to the interaction of
the first compartment with the second compartment.
26. A method for developing a computer model of a diseased joint,
comprising:
receiving a plurality of user-selected indications to define a plurality of
biological processes, each biological process from the plurality of
biological processes being based on data that relates changes in
biological states to biological attributes of the diseased joint, the
plurality of biological processes including a set of biological processes
related to at least one of tissue inflammation and tissue hyperplasia;
producing a simulated biological attribute associated with at least one
biological attribute of the diseased joint based on the combined plurality
of biology processes; and
assessing a validity of the computer model based on a comparison between
the simulated biological attribute and a corresponding biological
attribute associated with a reference pattern of the diseased joint.
27. The method of claim 26, wherein the diseased joint is afflicted with at
least one of rheumatoid arthritis, osteoporosis, reactive arthritis and
osteoarthritis.
28. The method of claim 26, wherein at least one biological process from
the plurality of biological processes is associated with a biological
variable that is a therapeutic agent.
29. The method of claim 26, further comprising:
selecting a therapeutic agent from at least one of methotrexate, steroids,
non-steroidal anti-inflammatory drugs, soluble TNF-alpha receptor,
TNF-alpha antibody, and interleukin-1 receptor antagonists; and
associating the selected therapeutic agent with at least one biological
process from the plurality of biological processes.
30. A computer model of an animal joint, comprising:
a computer-readable memory storing:
code to define a pluriality of biological processes related to a biological
state of the animal joint, at least one biological process from the
plurality of biological processes being associated with a therapeutic
agent; and
code to define a plurality of mathematical relationships related to
interactions among biological variables associated with the plurality of
biological processes, at least two biological processes from the plurality
of biological processes being associated with the plurality of
mathematical relationships, a combination of the code to define plurality
of biological processes and the code to define the plurality of
mathematical relationships defining a simulation of the biological state
of the animal joint; and
a processor coupled to the computer-readable memory, the processor
configured to execute the codes.
31. The computer model of claim 30, wherein the plurality of biological
processes include a biological process related to at least one of
cartilage metabolism, tissue inflammation, and tissue hyperplasia.
32. The computer model of claim 30, wherein the plurality of biological
process include a biological process related to at least one of
inflammatory mediators, proteases, fibroblast-like synoviocyte population,
macrophage population, T lymphocyte population, B lymphocyte population,
and dendritic cell population.
33. The computer model of claim 30, further comprising:
code to select the therapeutic agent from at least one of methotrexate,
steroids, non-steroidal anti-inflammatory drugs, soluble TNF-alpha
receptor, TNF-alpha antibody, and interleukin-1 receptor; and antagonists;
and
code to associate the selected therapeutic agent with at least one
biological process from the plurality of biological processes.
34. The computer model of claim 30, wherein the plurality of biological
processes include a first set of biological processes related to synovial
tissue and a second set of biological processes related to cartilage
tissue, the computer model further comprising:
code to define a first compartment, the first compartment including the
first set of biological processes, and
code to define a second compartment, the second compartment including the
second set of biological processes.
35. The computer model of claim 34, further comprising:
code to define a third set of biological processes related to the
interaction of the first compartment with the second compartment.
36. The computer model of claim 30, wherein the biological state is the
state of a diseased joint.
37. The computer model of claim 36, wherein the biological state is the
state of the diseased joint afflicted with at least one of rheumatoid
arthritis, osteoporosis, reactive arthritis and osteoarthritis.
38. The computer model of claim 36, wherein upon execution of the computer
model, a simulated biological attribute for the biological state of the
diseased joint is produced, the simulated biological attribute being
substantially consistent with at least one biological attribute associated
with a reference pattern of the diseased joint.
39. A method for developing an analytical model of an animal joint,
comprising:
identifying a plurality of biological processes related to the animal
joint, the plurality of biological processes including at least one
biological process related to a set of inflammatory mediators; and
combining the plurality of biological processes to form an analytical
representation of the animal joint.
40. The method of claim 39, wherein the plurality of biological processes
are related to a biological state of a normal joint.
41. The method of claim 39, wherein the plurality of biological processes
are related to a biological state of a diseased joint.
42. The method of claim 41, wherein the biological state is the state of
the diseased joint afflicted with at least one of rheumatoid arthritis,
osteoporosis, reactive arthritis, and osteoarthritis.
43. The method of claim 41, comprising:
wherein identifying the plurality of biological processes includes
identifying a step of biological processes related to changes in
biological attributes of the diseased joint, and
wherein combining the plurality of biological processes includes combining
the set of biological processes to form an analytical representation of a
least one biological attribute of the diseased joint.
44. The method of claim 39, further comprising:
producing an analytical representation of a biological attribute associated
with a biological state of the animal joint;
producing an analytical representation of the biological attribute with a
corresponding biological attribute associated with a reference pattern of
the animal joint; and
identifying the analytical model as a valid model of the animal joint; and
identifying the analytical model as a valid model of the animal joint if
the analytical representation of the biological attribute is substantially
consistent with the biological attribute associated with the reference
pattern of the animal joint.
45. The method of claim 39, wherein combining the plurality of biological
processes includes:
forming a first mathematical relation among biological variables associated
with a first biological process from the plurality of biological
processes; and
forming a second mathematical relation among biological variables
associated with the first biological process and a second biological
process from the plurality of biological processes.
46. The method of claim 39, wherein the animal joint is a human joint.
Description
COPYRIGHT NOTICE
A portion of the disclosure of the patent document contains material that
is subject to copyright protection. The copyright owner has no objection
to the facsimile reproduction by anyone of the patent document of the
patent disclosure, as it appears in the Patent and Trademark Office patent
file or records, but otherwise reserves all copyright rights whatsoever.
BACKGROUND OF THE INVENTION
The present invention relates generally to a computer model of a joint.
More specifically, the present invention relates to a computer model of a
joint to represent, for example, rheumatoid arthritis, osteoporosis,
osteoarthritis or other inflammatory diseases of the joint.
Synovial inflammation, rapid degradation of cartilage, and erosion of bone
in affected joints are characteristic of, for example, rheumatoid
arthritis (RA). Recent evidence indicates that skeletal tissue degradation
and inflammation are regulated through overlapping but not identical
pathways in the rheumatoid joint and that therapeutic effects on these two
aspects need not be correlated. Furthermore, considerable uncertainty
exists about the relative contributions of the various biological
processes of the joint to the pathogenesis of RA. Thus, a need exists for
a better understanding of the mechanisms regulating joint inflammation and
joint degradation. Such an understanding would be helpful for
strategically designing therapies for protecting the joint.
Due to the complexity of the biological processes in the joint,
mathematical and computer models can be used to help better understand the
interactions between the various tissue compartments, cell types,
mediators, and other factors involved in joint disease and healthy
homeostasis. Several researchers have constructed simple models of the
mechanical environment of the joint and compared the results to patterns
of disease and development in cartilage and bone (Wynarsky & Greenwald, J.
Biomech., 16:241-251, 1983; Pollatschek & Nahir, J. Theor. Biol.,
143:497-505, 1990; Beaupre et al., J. Rehabil Res. Dev., 37:145-151, 2000;
Shi et al., Acta Med. Okayama, 17:646-653, 1999). However, these models
are focused on the mechanical aspects of the joint and do not explicitly
include the biological processes related to cells in the synovial membrane
and other joint compartments. For instance, in RA the cells of the
synovial membrane are known to play a major role in driving the disease
(Szekanecz & Koch, Curr. Rheumatol. Rep., 3:53-63, 2001). Hence, a need
exists to develop a computer or mathematical model, which includes
multiple compartments including the synovial membrane and the interactions
of these compartments, to develop a better understanding of joint
diseases.
SUMMARY OF THE INVENTION
Embodiments of the present invention relate to computer modeling of a
joint. For example, one embodiment of the present invention relates to a
computer model of a human joint afflicted with rheumatoid arthritis. The
present invention also includes a method for developing an analytical
model of an animal joint.
In one embodiment, the invention is a method for developing a computer
model of an animal joint. The method comprises the steps of identifying
data relating to a biological state of the joint; identifying biological
processes related to the data, these identified biological processes
defining at least one portion of the biological state of the joint; and
combining the biological processes to form a simulation of the biological
state of the joint. The biological state of the joint can be, for example,
the state of a normal joint or a diseased joint. The joint diseases that
can be modeled include rheumatoid arthritis, osteoporosis, reactive
arthritis or osteoarthritis.
Another embodiment of the invention is a computer model of the biological
state of an animal joint, comprising code to define the biological
processes related to the biological state of the joint, and code to define
the mathematical relationships related to interactions among biological
variables associated with the biological processes. At least two of the
biological processes are associated with the mathematical relationships. A
combination of the code to define the biological processes and the code to
define the mathematical relationships define a simulation of the
biological state of the joint.
Yet another embodiment of the invention is a computer executable software
code comprising of code to define biological processes related to a
biological state of an animal joint including code to define mathematical
relations associated with a first biological process from the biological
processes and associated with interactions among biological variables
associated with the first biological process, and code to define
mathematical relations associated with a second biological process from
the biological processes and associated with interactions among biological
variables associated with the second biological process, the biological
processes being associated with the biological state of the animal joint.
Another embodiment of the invention is a computer model of an animal joint,
comprising a computer-readable memory storing codes and a processor
coupled to the computer-readable memory, the processor configured to
execute the codes. The memory comprises code to define biological
processes related to the biological state of the joint, and code to define
mathematical relationships related to interactions among biological
variables associated with the biological processes. At least two
biological processes from the biological processes are associated with the
mathematical relationships. The combination of the code to define the
biological processes and the code to define the mathematical relationships
define a simulation of the biological state of the joint.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an example of an Effect Diagram, which shows some of the
modeled biological processes of the biological state of a joint affected
with RA.
FIG. 2 illustrates an example of a Summary Diagram from the Effect Diagram
of FIG. 1.
FIG. 3 illustrates an example of a module diagram for one of the anatomical
elements shown in the Summary Diagram of FIG. 2.
FIG. 4 illustrates an alternative for a portion of the module diagram shown
in the FIG. 3.
FIG. 5 illustrates an example of display screen having chart windows and a
browser window, according to an embodiment of the present invention.
FIG. 6 shows an alternative summary diagram having a condensed functional
view and a compartmental view of RA, according to another embodiment of
the present invention.
FIG. 7 is a schematic representation of a computer system within which
software for performing the methods of the invention may reside or be
executed.
FIG. 8 shows an example of a module diagram for the T cell life cycle in
the synovium.
FIG. 9 depicts a flowchart for a method for developing a computer model of
an animal joint according to one embodiment of the invention.
FIG. 10 depicts a flowchart for a method for developing a computer model of
a joint according to another embodiment of the invention.
FIGS. 11-45 show additional examples of user-interface screens for other
modules for anatomical elements shown in the Summary Diagram of FIG. 2.
DETAILED DESCRIPTION
Overview
Embodiments of the present invention relate to computer modeling of an
animal joint. The term "animal" as used herein includes humans. The term
"joint" as used herein comprises the synovial tissue, synovial fluid,
articular cartilage, bone tissues, and their cellular and extracellular
composition, and the soluble mediators they contain. The computer model
can represent the biological processes related to a joint. Typically, the
model includes biological processes related to cartilage metabolism,
tissue inflammation, and tissue hyperplasia in a non-diseased joint. Also,
the computer model can include the representation of a diseased joint. For
example, the computer model can represent a joint with rheumatoid
arthritis, osteoporosis, osteoarthritis, or other inflammatory diseases of
the joint. In addition, the model can represent joints affected with other
arthritic conditions such as monoarticular, oligoarticular, or
polyarticular arthritides of unknown etiology.
Embodiments of the present invention can relate to the computer modeling of
rheumatoid arthritis (RA), such as for example, a knee joint afflicted
with RA. The computer can also represent other joints, for example
metacarpophalangeal and hip joints. The computer model can focus on the
direct cytokine-mediated cellular interactions within the synovium and
cartilage. Comparisons with clinical data can be used, for example, in
fine-tuning the core components of the computer model.
In one embodiment, the computer model relates to, for example, diagnosed,
established, early RA (synovial inflammation and hyperplasia, pannus
formation, early stages of cartilage breakdown) in an adult patient with
active progressive disease. This patient can be characterized by, for
example, persistent synovial hyperplasia and inflammation as well as
continuous degradation of the cartilage matrix. This disease state can be
compared to healthy homeostasis where feasible and useful. Alternatively,
other disease states and virtual patients can be represented in the model.
In one embodiment, the computer model can represent a single prototypical
RA joint. The exact location of this prototypical joint need not be
specified. An abstraction can be obtained that is compatible with
available data and best reflects the overall disease process. The main
compartments contained in the computer model can represent synovial tissue
and cartilage at the cartilage-pannus junction of this prototypical RA
joint.
In yet another embodiment, the computer model can be developed based on new
patient types and can be based on both additions of new components and
increased detail in components already modeled. For example, the computer
model can incorporate biological features such as regulated recruitment of
T cells, different T cell populations present in the tissue, or additional
complexity in the mediator network. In another alternative embodiment, the
computer model can involve the addition of new components, such as
angiogenesis, bone metabolism, B cells or neutrophils.
In one aspect of the invention, the computer executable software code
numerically solves the mathematical equations of the model under various
simulated experimental conditions. Furthermore, the computer executable
software code can facilitate visualization and manipulation of the model
equations and their associated parameters to simulate different patients
subject to a variety of stimuli. See, e.g., U.S. Pat. No. 6,078,739,
entitled "Managing objects and parameter values associated with the
objects within a simulation model," the disclosure of which is
incorporated herein by reference. Thus, the computer model can be used to
rapidly test hypotheses and investigate potential drug targets or
therapeutic strategies.
Mathematical Model
The mathematical model of the computer-executable software code represents
the dynamic biological processes related to the biological state of a
joint. The form of the mathematical equations employed may include, for
example partial differential equations, stochastic differential equations,
differential algebraic equations, difference equations, cellular automata,
coupled maps, equations of networks of Boolean or fuzzy logical networks,
etc. In one embodiment, the mathematical equations used in the model are
ordinary differential equations of the form:
dx/dt=f(x,p,t),
where x is an N dimensional vector whose elements represent the biological
variables of the system (for example synovial macrophage number, tumor
necrosis factor alpha concentration, and cartilage collagen II
concentration), t is time, dx/dt is the rate of change of x, p is an M
dimensional set of system parameters (for example baseline macrophage
matrix metalloproteinase-1 (MMP-1) synthesis rate, T cell cycle time,
catalytic constant for degradation of collagen II by MMP-1, and initial
cartilage thickness), and f is a function that represents the complex
interactions among biological variables.
The term "biological variables" refers to the extra-cellular or
intra-cellular constituents that make up a biological process. For
example, the biological variables can include metabolites, DNA, RNA,
proteins, enzymes, hormones, cells, organs, tissues, portions of cells,
tissues, or organs, subcellular organelles, chemically reactive molecules
like H.sup.+, superoxides, ATP, citric acid, protein albumin, as well as
combinations or aggregate representations of these types of biological
variables. In addition, biological variables can include therapeutic
agents such as methotrexate, steroids, non-steroidal anti-inflammatory
drugs, soluble TNF-alpha receptor, TNF-alpha antibody, and interleukin-1
receptor antagonists.
The term "biological process" is used herein to mean an interaction or
series of interactions between biological variables. Thus, the above
function f mathematically represents the biological processes in the
model. Biological processes can include, for example, macrophage
activation, regulation of macrophage protein synthesis, T cell
proliferation, and collagen II degradation. The term "biological process"
can also include a process comprising of one or more therapeutic agents,
for example the process of binding a therapeutic agent to a cellular
mediator. Each biological variable of the biological process can be
influenced, for example, by at least one other biological variable in the
biological process by some biological mechanism, which need not be
specified or even understood.
The term "parameter" is used herein to mean a value that characterizes the
interaction between two or more biological variables. Examples of
parameters include affinity constants, baseline synthesis of a mediator,
EC.sub.50 value of stimulation of a first mediator by a second mediator,
baseline macrophage matrix metalloproteinase-1 (MMP-1) synthesis rate, T
cell cycle time, catalytic constant for degradation of collagen II by
MMP-1, and initial cartilage thickness.
The term "biological state" is used herein to mean the result of the
occurrence of a series of biological processes. As the biological
processes change relative to each other, the biological state also
undergoes changes. One measurement of a biological state, is the level of
activity of biologic variables, parameters, and/or processes at a
specified time and under specified experimental or environmental
conditions.
In one embodiment the biological state can be mathematically defined by the
values of x and p at a given time. Once a biological state of the model is
mathematically specified, numerical integration of the above equation
using a computer determines, for example, the time evolution of the
biological variables x(t) and hence the evolution of the biological state
over time.
The term "simulation" is used herein to mean the numerical or analytical
integration of a mathematical model. For example, simulation can mean the
numerical integration of the mathematical model of the biological state
defined by the above equation, i.e., dx/dt=f( x, p, t).
A biological state can include, for example, the state of an individual
cell, an organ, a tissue, and/or a multi-cellular organism. A biological
state can also include the state of a mediator concentration in the
plasma, interstitial fluid, intracellular fluid; e.g., the states of
synovial inflammation and synovial hyperplasia are characterized by high
concentrations of inflammatory mediators and large numbers of cells,
respectively, in the synovium. These conditions can be imposed
experimentally, or may be conditions present in a patient type. For
example, a biological state of the cartilage can include the chondrocyte
concentration for a patient with a certain age and disease duration. In
another example, the biological states of the collection of synovial
tissue mediators can include the state in which a patient with a certain
disease undergoes a specific treatment.
The term "disease state" is used herein to mean a biological state where
one or more biological processes are related to the cause or the clinical
signs of the disease. For example, a disease state can be the state of a
diseased cell, a diseased organ, a diseased tissue, or a diseased
multi-cellular organism. Such diseases can include, for example, diabetes,
asthma, obesity, and rheumatoid arthritis. A diseased multi-cellular
organism can be, for example, an individual human patient, a specific
group of human patients, or the general human population as a whole. A
diseased state could also include, for example, a diseased protein or a
diseased process, such as defects in matrix synthesis, matrix degradation,
cell apoptosis, and cell signaling, which may occur in several different
organs.
The term "biological attribute" is used herein to mean biological
characteristics of a biological state, including a disease state. For
example, biological attributes of a particular disease state include
clinical signs and diagnostic criteria associated with the disease. The
biological attributes of a biological state, including a disease state,
can be measurements of biological variables, parameters, and/or processes.
For example, for the disease state of rheumatoid arthritis, the biological
attributes can include measurements of synovial hyperplasia, markers of
inflammation, or cartilage thickness.
The term "reference pattern" is used herein to mean a set of biological
attributes that are measured in a normal or diseased biological system.
For example, the measurements may be performed on blood samples, on biopsy
samples, or cell cultures derived from a normal or diseased human or
animal. Examples of diseased biological systems include cellular or animal
models of rheumatoid arthritis, including a human rheumatoid arthritis
patient.
Computer System
FIG. 7 shows a system block diagram of a computer system within which the
methods described above can operate via software code, according to an
embodiment of the present invention. The computer system 700 includes a
processor 702, a main memory 703 and a static memory 704, which are
coupled by bus 706. The computer system 700 can further include a video
display unit 708 (e.g., a liquid crystal display (LCD) or cathode ray tube
(CRT)) on which a user interface can be displayed). The computer system
700 can also include an alpha-numeric input device 710 (e.g., a keyboard),
a cursor control device 712 (e.g., a mouse), a disk drive unit 714, a
signal generation device 716 (e.g., a speaker) and a network interface
device medium 718. The disk drive unit 714 includes a computer-readable
medium 715 on which software 720 can be stored. The software can also
reside, completely or partially, within the main memory 703 and/or within
the processor 702. The software 720 can also be transmitted or received
via the network interface device 718.
The term "computer-readable medium" is used herein to include any medium
which is capable of storing or encoding a sequence of instructions for
performing the methods described herein and can include, but not limited
to, optical and/or magnetic storage devices and/or disks, and carrier wave
signals.
The Computer Model
The computer model can begin with a representation of a normal biological
state, for example, as represented by the biological state of a single
prototypical knee joint. A normal biological state is modeled through a
series of user-interface screens that define the elements, including
biological variables and biological processes, of the biological state
being modeled. These elements of the biological state have dynamic
relationships among themselves. An Effect Diagram can illustrate the
dynamic relationships among the elements of the biological state and can
include a Summary Diagram. This Summary Diagram can provide links to
individual modules of the model; these modules, or functional areas, when
grouped together, represent the large complex physiology of the biological
state being modeled.
The modules model the relevant components of the biological state through
the use of state and function nodes whose relations are defined through
the use of diagrammatic arrow symbols. Thus, the complex and dynamic
mathematical relationships for the various elements of the biological
state are easily represented in a user-friendly manner. In this manner, a
normal biological state can be represented.
Effect Diagram and Summary Diagram
FIG. 1 illustrates an example of an Effect Diagram, which shows some of the
modeled biological processes of the biological state of a joint affected
with RA. The Effect Diagram is organized into modules, or functional
areas, which when grouped together represent the large complex physiology
of the biological state being modeled.
The Effect Diagram includes a Summary Diagram in the upper left corner of
the Effect Diagram. The Effect Diagram can include the Summary Diagram in
the upper most left portion. In addition, the Effect Diagram can include
the modules for the various biological processes of the biological state
being modeled. From the Effect Diagram, a user can select any of these
related user-interface screens by selecting such a screen from the Effect
Diagram (e.g., by clicking a hyperlink to a related user-interface
screen).
FIG. 2 illustrates an example of a Summary Diagram from the Effect Diagram
of FIG. 1. As shown in FIG. 2, the Summary Diagram can provide an overview
of the contents of the Effect Diagram and can contain nodes that link to
modules in the Effect Diagram. These modules can be based on, for example,
the anatomical elements of the biological state being modeled, such as
chondrocytes, cytokines and other soluble factors and cartilage
metabolism.
FIG. 3 illustrates an example of a module diagram for one of the anatomical
elements shown in the Summary Diagram of FIG. 2. More specifically, FIG. 3
illustrates a module diagram for the cartilage metabolism. FIG. 4
illustrates an alternative for a portion of the module diagram shown in
the FIG. 3. FIGS. 11 through 45 list additional examples of user-interface
screens for other modules for anatomical elements shown in the Summary
Diagram of FIG. 2. FIGS 11 through 45 depict Appendix A depicts some of
the modules of FIG. 1.
As FIG. 3 illustrates, the relevant biological variables and biological
processes for the cartilage metabolism are represented through the use of
state and function nodes whose relations are defined through the use of
diagrammatic arrow symbols. Through the use of these state nodes, function
nodes and arrows, the complex and dynamic mathematical relationships for
the various elements of the physiologic system are easily represented in a
user-friendly manner. In this manner, a biological state can be
represented. The nodes and arrows are discussed below in the context of
the mathematical relationship that underlie these diagrammatic
representations.
Mathematical Equations Encoded in the Effect Diagram
As mentioned above, the Effect Diagram is a visual representation of the
model equations. This section describes how the diagram encodes a set of
ordinary differential equations. Note that although the discussion below
regarding state and function nodes refers to biological variables for
consistency, the discussion also relates to variables of any appropriate
type and need not be limited to just biological variables.
State and Function Nodes
State and function nodes show the names of the variables they represent and
their location in the model. Their arrows and modifiers show their
relation to other nodes within the model. State and function nodes also
contain the parameters and equations that are used to compute the values
or their variables in simulated experiments. In one embodiment of the
computer model, the state and function nodes are represented according to
the method described in U.S. Pat. No. 6,051,029 and co-pending U.S.
application Ser. No. 09/588,855, both of which are entitled "Method of
generating a display for a dynamic simulation model utilizing node and
link representations," and both of which are incorporated herein by
reference. Further examples of state and function nodes are further
discussed below.
##STR1##
State node values are defined by differential equations. The predefined
parameters for a state node include its initial value (S.sub.o) and its
status. State nodes that have a half-life have the additional parameter of
a half-life (h) and are labeled with a half-life {character pullout}
symbol.
##STR2##
Function nodes are defined by algebraic functions of their inputs. The
predefined parameters for a function node include its initial value
(F.sub.o) and its status.
Setting the status of a node effects how the value of the node is
determined. The status of a state or function node can be
Computed--the value is calculated as a result of its inputs
Specified-Locked--the value is held constant over time
Specified Data--the value varies with time according to predefined data
points.
State and function nodes can appear more than once in the Effect Diagram as
alias nodes. Alias nodes are indicated by one or more dots, as in the
state node illustration above. All nodes are also defined by their
position, with respect to arrows and other nodes, as being either source
nodes (S) or target nodes (T). Source nodes are located at the tails of
arrows, and target nodes are located at the heads of arrows. Nodes can be
active or inactive. Active nodes are white. Inactive nodes match the
background color of the Effect Diagram.
State Node Equations
The computational status of a state node can be Computed, Specified-Locked,
or Specified Data.
State Node Computed
##EQU1##
Where S is the node value, t is time, S(t) is the node value at time, t,
and h is the half-life. The three dots at the end of the equation indicate
there are additional terms in the equation resulting from any effect
arrows leading into it and by any conversion arrows that lead out of it.
If h is equal to 0, then the half-life calculation is not performed and
dS/dt is determined solely by the arrows attached to the node.
State Node Specified-Locked S(t) = S.sub.0 for all t
State Node Specified Data S(t) is defined by specified data entered
for the state node.
State node values can be limited to a minimum value of zero and a maximum
value of one. If limited at zero, S can never be less than zero and the
value for S is reset to zero if it goes negative. If limited at one, S
cannot be greater than one and is reset to one if it exceeds one.
Function Node Equations
Function node equations are computed by evaluating the specified function
of the values of the nodes with arrows pointing into the function node
(arguments), plus any object and Effect Diagram parameters used in the
function expression. To view the specified function, click the Evaluation
tab in the function node Object window.
The Effect Diagram--Arrows
Arrows link source nodes to target nodes and represent the mathematical
relationship between the nodes. Arrows can be labeled with circles that
indicate the activity of the arrow. A key to the annotations in the
circles is located in the upper left corner of each module in the Effect
Diagram. If an arrowhead is solid, the effect is positive. If the
arrowhead is hollow, the effect is negative.
Arrow Types
##STR3##
Arrow Characteristics
Effect or conversion arrows can be constant, proportional, or interactive.
##STR4##
Arrow Properties can be displayed in an Object window (not shown). The
window may also include tabs for displaying Notes and Arguments associated
with the arrow. If Notes are available in the Object window, the arrow is
labeled with a red dot (.multidot.).
Arrow Equations: Effect Arrows
Proportional Effect Arrow: The rate of change of target tracks source node
value.
##EQU2##
Where T is the target node, C is a coefficient, S is the source node, and a
is an exponent.
Constant Effect Arrow: The rate of change of the target is constant.
##EQU3##
Where T is the target node and K is a constant.
Interaction Effect Arrow: The rate of change of the target depends on both
the source node and target node values.
##EQU4##
Where T is the target node, S is the source node, and a and b are
exponents. This equation can vary depending on the operation selected in
the Object window. The operations available are S+T, S-T, S*T, T/S, and
S/T.
Arrow Equations: Conversion Arrows
Proportional Conversion Arrow: The rate of change of the target tracks the
value of source node.
##EQU5##
Where T is the target node, S is the source node, C is a coefficient, R is
a conversion ratio, and a is an exponent.
Constant Conversion Arrow: The rates of change of target and source are
constant such that an increase in target corresponds to a decrease in
source.
##EQU6##
Where T is the target node, S is the source node, K is a constant, and R is
a conversion ratio.
Interaction Conversion Arrow: The rates of change of the target and source
depend on both source and target node values such that an increase in
target corresponds to a decrease in source.
##EQU7##
Where T is the target node, S is the source node, a and b are exponents,
and R is a conversion ratio. This equation can vary depending on the
operation selected in the Object window. The operations available are S+T,
S-T, S*T, T/S, and S/T.
The Effect Diagram--Modifiers
Modifiers indicate the effects nodes have on the arrows to which they are
connected. The type of modification is qualitatively indicated by a symbol
in the box. For example, a node can allow {character pullout}, block
{character pullout}, regulate {character pullout}, inhibit {character
pullout}, or stimulate {character pullout} an arrow rate.
A key to the modifier annotations is located in the upper left corner of
each module.
Modifier Properties can be displayed in the Object Window. The window may
also include tabs for displaying the notes, arguments, and specified data
associated with the modifier. If notes are available in the Object window,
the modifier is labeled with a red dot (.multidot.).
##EQU8##
Effect Arrow, Modifier Equation:
Where T is the target node, M is a multiplier constant, N is a
normalization constant, .function.( ) is a function (either linear or
specified by a transform curve), and arrow term is an equation fragment
from the attached arrow.
Modifier Effect
By default, conversion arrow modifiers affect both the source and target
arrow terms. However, in some cases, a unilateral, modifier is used. Such
modifier will affect either a source arrow term or on target arrow term;
it does not affect both arrow terms.
Conversion arrow, Source Only Modifier Equation:
##EQU9##
Conversion arrow, Target Only Modifier Equation:
##EQU10##
The equation for a source and target modifier uses both the Source Only
equation and the Target Only equation.
When multiplicative and additive modifiers are combined, effect is given
precedence. For example, if the following modifiers are on an arrow,
a1,a2: Additive, Source and Target
m1,m2: Multiplicative, Source and Target
A1,A2: Additive, Target Only
M1,M2: Multiplicative, Target Only then the rates are modified by
Target node: (a1+a2+A1+A2)*(m1*m2)*(M1*M2)
Source node: (a1+a2)*(m1*m2)
Embodiments of the Invention
FIG. 9 depicts a flowchart for a method for developing a computer model of
an animal joint according to one embodiment of the invention. At step 910,
data relating to a biological state of the joint is identified. At step
920, biological processes related to the data are identified. These
biological processes define at least one portion of the biological state
of the joint. At step 930, the biological processes are combined to form a
simulation of the biological state of the joint.
The method for developing a computer model of an animal joint can further
comprise the optional steps of 940, 950, 960, and 970 for validating the
computer model, as depicted in FIG. 9. In the validation process, at step
940 a simulated biological attribute associated with the biological state
of the joint is produced. At step 950, the simulated biological attribute
is compared with a corresponding biological attribute in a reference
pattern of the joint. At steps 960 and 970, the validity of the computer
model is identified. At step 960, it is determined whether the simulated
biological attribute is substantially consistent with the biological
attribute associated with the reference pattern of the joint. At step 970,
if the simulated biological attribute is substantially consistent with the
biological attribute associated with the reference pattern of the joint
the computer model is identified as a valid computer model of an animal
joint.
FIG. 10 depicts a flowchart for a method for developing a computer model of
a joint according to another embodiment of the invention. At step 1010,
data relating to a biological state of the joint is identified. At step
1020, biological processes related to the data are identified. These
biological processes define at least one portion of the biological state
of the joint. At step 1030, a first mathematical relation among biological
variables associated with a first biological process from the biological
processes is formed. At step 1040, a second mathematical relation among
biological variables associated with the first biological process and a
second biological process associated with the biological processes is
formed. The biological state of the joint can be, for example, the state
of a normal joint or a diseased joint.
Steps 1050, 1060, and 1070 can be optionally performed to produce a
simulated biological attribute that is substantially consistent with at
least one biological attribute associated with a reference pattern of the
joint. At conditional step 1050, a determination is made as to whether a
simulated biological attribute or a series of simulated biological
attributes is to be produced. If a simulated biological attribute is to be
produced, the process continues to step 1060. At step 1060, a set of
parametric changes in the first mathematical relation and the second
mathematical relation is created. At step 1070, a simulated biological
attribute based on
