EP1311972A2 - Systeme informatique permettant la modelisation de l'expression d'une proteine dans un organe - Google Patents

Systeme informatique permettant la modelisation de l'expression d'une proteine dans un organe

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Publication number
EP1311972A2
EP1311972A2 EP01946650A EP01946650A EP1311972A2 EP 1311972 A2 EP1311972 A2 EP 1311972A2 EP 01946650 A EP01946650 A EP 01946650A EP 01946650 A EP01946650 A EP 01946650A EP 1311972 A2 EP1311972 A2 EP 1311972A2
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EP
European Patent Office
Prior art keywords
nodes
node
network
representing
model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP01946650A
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German (de)
English (en)
Inventor
Thomas J. Colatsky
Adam L. Muzikant
Donna Rounds
John Jeremy Rice
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Physiome Sciences Inc
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Physiome Sciences Inc
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Publication date
Application filed by Physiome Sciences Inc filed Critical Physiome Sciences Inc
Publication of EP1311972A2 publication Critical patent/EP1311972A2/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the present invention relates to computer models of organs implemented in software and more particularly to an organ model that enables one to study the effects of protein expression on organ performance and biofunctionality.
  • U.S Patent 5,947,899 shows how to create a computational model of a complete organ. That disclosure is set forth in the context of a whole heart model where the anatomic relationships between cardiac cells are modeled by a set of coupling relationships between nodes of a network. Each node represents a physical subunit of tissue. A separate set of equations define the electrophysiology of the individual cardiac cells and these equations are used to compute an action potential (AP) at each node. The time course of the action potential may be represented graphically and compared with physical measurements made experimentally.
  • AP action potential
  • each node of the model has two computationally separate types of information associated with it.
  • a distinct advantage of the model is the ability to compute the local and global portions of the model simultaneously and independently while allowing the local and global processes to interact.
  • the model paradigm allows for the integration of interaction between the local physiological data and the global coupling relationships.
  • the cellular activity is propagated in a realistic way over the whole organ and whole organ behavior flow directly from the model.
  • This methodology allows one to compute the gross macroscopic biofunctionality at the organ level arising out of discrete populations of single cell models.
  • the gross behavior of the model can be compared with physical experiments for verification and the detailed cellular modeling can be compared with electrophysiologic experiments as well.
  • a partial organ model which can be used to explore and model the impact of genetic and sex linked cellular changes on the organ. Genetic differences in the model are modeled by noting and modeling the changes in protein function or distribution or transportation associated with genetic mutations, sex differences, disease or allele based variations in the pattern of gene expression.
  • This technique is useful for determining the impact of a mutation or sex on the performance of an otherwise equivalent organ.
  • the ability to model some sex based changes and to ignore others within the model domain is a very useful and a feature not easily available in physical models.
  • the utility of this modeling process is the ability to determine mutation induced changes in response to disease, drugs, or other perturbations in transmembrane potential.
  • the organ model is also useful for determining differences in the response to drugs, genetic mutations, or disease expression related to sex and allelic variations in cellular background.
  • the invention is illustrated in the context of a heart wedge model and the node computations take into account sex based differences in protein expression as well as variations in cell type.
  • the model simulates sex based reactions to drugs.
  • the initial conditions are perturbed by a simulated stimulus which is propagated at the network level and which influences the time course of the action potentials computed at each node.
  • the cells types illustrated are excitable cells which signal by changing their transmembrane potentials.
  • the invention may be utilized to model other system with excitable cells and the heart wedge illustration is exemplary and not intended to be limiting.
  • FIG. 1 has a panel 1A that is a simplified flow chart describing the creation and computation of the model, panel IB is an alternate schematic representation of the model and the model building process;
  • FIG. 2 includes panel 2A which is a schematic representation of an organ, and panel 2B which is a schematic representation of a network model showing a lattice of nodes and relationships between nodes;
  • FIG. 3 is graphic representation of action potentials presented as panel 3A and panel 3B;
  • FIG. 4 includes panels 4A, panel 4B, and panel 4C, which are computed action potentials related to sex;
  • FIG. 5 is a computed action potential related to sex based differences in the effect of a drug
  • FIG. 6 is presented as panel 6A that shows experimental data and panel 6B that represents simulation; and FIG. 7 is a series of panels that represent in panel 7A and panel 7B action potentials of mutation based variations in protein expression, panels 7C and 7D show the variation of action potential as a function of cell type.
  • the invention is both a computation model and related methods for modeling a biologic organ, the steps of the process are carried out on a general purpose computer.
  • the computer is not shown in the figures explicitly. However the figures are the result of the computations carried out according to the software processes described.
  • the method .of the invention begins with the collection of experimental data related to the molecular properties and physiology of cells with normal and mutant gene expression.
  • the most interesting interactions are the protein interactions that involve the transmembrane ionic currents.
  • the impact of mutation on protein function and secondarily, the impact on the transmembrane potentials and other features of cell activity are explored in detail, including biophysical changes in the ion pumps, transmembrane channels, receptors and signaling pathways.
  • Experimental data provide the parameters needed to model a single cell containing proteins having both "normal” and "aberrant" functionality. The profile of cellular functionality is thus developed and the general set of equations is extended to model complex behaviors of the cell.
  • molecular properties of the gene or protein are measured and alterations in expression levels or patterns of expression for a particular gene or protein are noted. For example certain proteins appear to increase the number of channels that are open. Other proteins are responsible for the magnitude of the current across the membrane.
  • the resulting physiological properties of the mutation are characterized by alterations in the time course of the membrane current, transmembrane potential, ion flux, or biochemical reaction rate.
  • the network model is an ensemble of the single cell models that integrates these changes in the biofunctionality of the cells into a whole organ or portion of an organ. Gender may be modeled in a similar fashion in that sex-specific differences associated with protein type, location, and other molecular and physiological properties are used to modify the cellular models.
  • the anatomic detail is required to model disease accurately and the incorporated organ model is required to allow this spatial distribution of cells into nodes and subsequently intact tissue.
  • the model is solved and results displayed. It is a convenient property of the model methodology that the node computations can be performed simultaneously and independently of each other, thus allowing the model to be implemented effectively on parallel computers.
  • Heterogeneity in the spatial distribution of the mutation or mutations can also be modeled, as well as altered gene or protein expression patterns in the organ, by specifying different cellular, subcellular, and molecular properties at individual nodes within the complex model.
  • Various aspects of the modeling process are also presented in U.S. Patent 5,947,899.
  • FIG. 1 is a flowchart that shows a stepwise sequence for both a single cell model and an organ level model.
  • processes 10 through 16 represent the data collection and construction of a single cell model.
  • Processes 24 through 28 involve the creation of the network model of the organ or a part of the organ, while process 20 reflects the computation of a single cell model.
  • Process 30 reflects the output of either simulation and typically both graphic data and data in tabular form are created.
  • process 18 represents a choice between single cell and multi cell models it should be understood that simulations at the cellular level may be used to validate the single cell model against experimental data while network level organ simulations can be used to validate the organ level description against experimental tissue experiments.
  • step 10 those data collected in step 10 are used to modify the system of nonlinear ordinary differential equations (ODEs) defined at each node of the lattice, and to modify the ionic currents of the cellular model.
  • ODEs nonlinear ordinary differential equations
  • this system includes: a) equations defining properties of nonlinear, voltage-gated transmembrane currents; b) equations describing properties of ion pumps, exchangers and other features in the cell; c) equations describing the buffering, uptake, storage, transfer, and release of calcium ions by intracellular organelles; d) equations describing time-varying changes of intracellular ion concentration, and e) equations describing the effects of neurotransmitters, hormones and second messengers on these components.
  • a useful paradigm for modeling cellular dynamics is a battery/resistor/capacitor model where the membrane currents are the sum of a set of voltage sources in parallel with a capacitancence.
  • the voltage sources are modeled as a battery in series with a resistor.
  • the battery represents the driving force for ionic flux through the channel provided by the ionic gradient across the membrane.
  • the resistor represents the resistance to ionic flow through the pore of the channel.
  • Mathematical models for most ionic currents can be taken directly from data collected in step 10. The general form of the equation is where the desired channel current is a function of a voltage dependant open channel current and the state of the pore as opened or closed.
  • Another significant value of this methodology is the ability to validate the cellular model alone. In essence, predictions based upon data and modeled currents can be compared to experimental data to verify the predictive value of the cell model before inclusion into the organ and system model. Fine tuning of the model can be accomplished more easily because of this partitioning and architecture.
  • Process step 14 relates to making the ionic current calculations computationally efficient. Many techniques are available to compute sets of ordinary differential equations. Process 16 relates to the selection of the particular simulation methodology used to analyze the data. For example, some a priori knowledge may allow the user to ignore some variable or make other simplifying assumptions for a particular simulation. In a typical setting a user may have several "single cell" model available and each will "work". The specific choice for a particular study may be simply a user preference.
  • a single cell or network model is selected. Users may begin with a series of single cell simulations and then select a network model without redesigning the single cell model.
  • Processes 24 through 28 relate directly to structuring the network model by defining the location of altered cells in the physical representation of the organ. Typically a finite difference lattice work of nodes are preferred to represent physical structure of the heart and the physical locations for mutated cells. This spatial placement is based upon anatomic knowledge. It is important to note that all or just some cells may have the altered properties.
  • FIG. 2 address this aspect of the model in more detail.
  • the output data from the simulation are available in process 30.
  • Various figures such as FIG. 2 A, FIG. 3, FIG. 4, FIG. 5, and FIG. 7 represent examples of the output from process 30. Both graphic and tabular data is available.
  • FIG. 2A is schematic representation of three groups of cells or tissues in a portion of a "wedge" 40 of cardiac tissues.
  • the tissue group typified by cell 42 (A) is on the interior of the "wedge” and represents an endocardial surface.
  • Cells typified by cell 44 (B) occupy the interior of the wedge while cells typified by cell 46 (C) lie on the outer surface of the wedge and are representations of the epicardial surface.
  • these autonomous cell layers will have characteristics action potentials depicted in panel 50 for the corresponding tissue type. The wide variation in the duration of the action potentials is demonstrating the dynamic behavior of the organ model when paced at various rates.
  • the dispersion in duration of action potentials mimics or models a similar behavior seen in physical wedge preparations.
  • the longest duration corresponding to trace 43 are the result of pacing at about 5s intervals and the shorter duration typified by trace 41 represents "fatigue" and non linear behaviors at faster pacing rates corresponding to about 500ms intervals.
  • the A, B and C types of tissue can depolarize giving rise to a voltage. Currents associated with this voltage can be communicated to the companion tissue groups.
  • This coupling relationship is the fundamental requirement of the organ model.
  • the duration of the response is extremely sensitive to the time between stimuli.
  • the various displayed durations for the activation potentials seen in panel 2A "B" for example reflect cycle to cycle variations.
  • Each of the different traces of action potential seen in the figure arises at a different cycle length.
  • This figure shows how a simple single cell model buried in a dynamic organ model can exhibit realistic cycle to cycle variation in action potential.
  • FIG. 2b shows the network model used to model the physical specimen of FIG. 2a.
  • the node 54 is receiving activation from the neighbor node 52.
  • the communication is not symmetrical and the coupling relationship is di ⁇ de- like. Symmetrical communication is allowed between node 54 and node 56 as indicated by the double arrow head configuration of coupling relationship 58.
  • the loop 55 represents the iterative calculation of the local biophysical equations used to model the action potential of the tissues represented by the node 54. It is the value of the model that it can solve local equations and compute the interaction of the local data to give rise to overall activity of the organ.
  • FIG. 2b allow the construction of heterogeneous tissue structures using identical cell types. Heterogeneity can also be achieved by varying the cell type in various locations in the model. Anatomic linkages and pathways can be accurately reproduced by selecting the correct coupling relationship.
  • FIG. 3 shows a first panel 3A that represents the action potential 60 of a male cell and an action potential 62 of a female cell.
  • the female trace 62 shows an extended repolarization time.
  • Panel 3B is labeled with the name of the current tracked for sex based differences in protein expression. There are no uniformly followed naming conventions for proteins although the labels in the figure are in common usage.
  • the BJto current is the transient outward current.
  • the C.IKr current is the delayed rectifier current.
  • the DJK1 current is the inward rectifier current.
  • E.L s current is the slow delayed rectifier current.
  • FIG. 4 illustrates the impact of cell type on the cardiac action potential for
  • Figure 5 shows the effects of sex-based differences in the expression of specific ion channels on the computed action potential for "male” and “female” cardiac ventricular cells.
  • the "male" duration is the shorter of the two durations as typified by trace 75.
  • the longer trace 73 is "female”.
  • the figure shows the differential impact of a drug acting on these cells. As the drug dose increases, the percent of channel blockage increases, resulting in a proarrhythmic response as indicated by the appearance of early depolarizations especially in females. The alternans exhibited in the 60% blocked channel shows up in the female model but not the male model.
  • the alternans is noted by comparing the sequence of complexes 79, 81, 83, 85 and 77.
  • the long potentials followed by short potentials is the hallmark of the arrhythmia.
  • this illustration is modeled with d-sotol it is likely valid for all potassium blocking drugs.
  • the figure shows multiple events paced at an interval of approximately 2.5 seconds.
  • the multi-dimensional impact of these gender-based differences, alone and in combination with drug, can be illustrate using a simulated electrocardiogram as the read-out.
  • This illustration shows channel blockages corresponding to an "overdose" of drug.
  • the computer model is substantially more tolerant of overdose than an experimental preparation.
  • the results provide a mechanistic explanation for the prolonged electrocardiographic QT, and for the increased incidence of drug-induced proarrhythmia interval typically observed in females vs. males.
  • Figure 6 shows in panel 6A classic data of the type collected in process step 10. The data show variations in myocytes based on location and based on pacing rate. The simulations of panel 6B are taken under the same conditions and they show good agreement with the experimental data thus validating the model. The ability to test the model and various scales in time and space is an important attribute of the architecture of the model.
  • Figure 7 has a panel 7A which represents a normal (wild) genotype ECG
  • Corresponding panel 7C shows the action potentials of mycoyte at various levels in the simulated wedge epicardial cells are shown by trace 90, midmyocardial cells are shown by trace 91 and endocardial cells are shown at 92.
  • Panel 7B shows a mutation that impacts the surface ECG 97.
  • the individual behavior of the corresponding myocytes seen in trace 93, 94, and 95 in panel 7D shows the corresponding action potentials for the mycoyte locations.

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  • Investigating Or Analyzing Materials Using Thermal Means (AREA)

Abstract

L'invention concerne un modèle informatique d'un organe ainsi qu'un procédé permettant d'évaluer l'impact des différences génétiques, qui se produisent dans les cellules simples dont se compose l'organe, au niveau microscopique ainsi qu'au niveau de l'organe entier. Les différences génétiques dans le modèle reposent sur des changements de fonction ou de distribution de protéines, associés aux mutations génétiques, au sexe, à la maladie ou aux variations occasionnées par des allèles dans la configuration de l'expression des gènes.
EP01946650A 2000-06-22 2001-06-22 Systeme informatique permettant la modelisation de l'expression d'une proteine dans un organe Withdrawn EP1311972A2 (fr)

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US59912800A 2000-06-22 2000-06-22
US599128 2000-06-22
PCT/US2001/019918 WO2001098935A2 (fr) 2000-06-22 2001-06-22 Systeme informatique permettant la modelisation de l'expression d'une proteine dans un organe

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AU (1) AU2001268668A1 (fr)
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AU4466700A (en) 1999-04-16 2000-11-02 Entelos, Inc. Method and apparatus for conducting linked simulation operations utilizing a computer-based system model
US7353152B2 (en) 2001-05-02 2008-04-01 Entelos, Inc. Method and apparatus for computer modeling diabetes
AU2002259258A1 (en) 2001-05-17 2002-11-25 Entelos, Inc. Apparatus and method for validating a computer model
US6862561B2 (en) 2001-05-29 2005-03-01 Entelos, Inc. Method and apparatus for computer modeling a joint
US7853406B2 (en) 2003-06-13 2010-12-14 Entelos, Inc. Predictive toxicology for biological systems
NZ547302A (en) 2003-11-19 2008-07-31 Entelos Inc Apparatus and methods for assessing metabolic substrate utilization
US7069534B2 (en) 2003-12-17 2006-06-27 Sahouria Emile Y Mask creation with hierarchy management using cover cells
US7844431B2 (en) 2004-02-20 2010-11-30 The Mathworks, Inc. Method and apparatus for integrated modeling, simulation and analysis of chemical and biochemical reactions
US8554486B2 (en) 2004-02-20 2013-10-08 The Mathworks, Inc. Method, computer program product, and apparatus for selective memory restoration of a simulation
WO2006022226A1 (fr) * 2004-08-26 2006-03-02 Kyoto University Dispositif et programme de sortie de bioparametre
US9370310B2 (en) 2007-01-18 2016-06-21 General Electric Company Determination of cellular electrical potentials
GB201005625D0 (en) 2010-04-01 2010-05-19 Novartis Ag Immunogenic proteins and compositions

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AU8391091A (en) * 1990-09-21 1992-04-15 Medsim, Inc. System for simulating the physiological response of a living organism

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JP2004508073A (ja) 2004-03-18
WO2001098935A2 (fr) 2001-12-27
IL152962A0 (en) 2003-06-24
NZ522699A (en) 2006-08-31
WO2001098935A3 (fr) 2003-03-13
CA2412375A1 (fr) 2001-12-27
AU2001268668A1 (en) 2002-01-02

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