2015-03-28
#hasFeature
hasFeature
#isUsedToModel
isUsedToModel
#lacksFeature
?X subclassOf: not (hasFeature some ?Y)
#lacksFeature
An object property to be used in the OBO version of MAMO to express negation.
#lacksFeature
lacksFeature
#owlDef
owlDef
/MAMO_0000003
http://en.wikipedia.org/wiki/Mathematical_model
/MAMO_0000003
Description of a system using mathematical concepts and language. The model is composed of a set of variables and a set of equations that establish relationships between the variables.
/MAMO_0000003
A set of ordinary differential equation describing a physical process.
/MAMO_0000003
mathematical model
/MAMO_0000004
http://en.wikipedia.org/wiki/Statistical_model
/MAMO_0000004
Model that describes how one or more random variables are related to one or more random variables.
/MAMO_0000004
The relationship between the size and the age.
/MAMO_0000004
statistical model
/MAMO_0000008
http://en.wikipedia.org/wiki/Steady_state_%28chemistry%29
/MAMO_0000008
Model which describes a system where the state variable values do not vary.
/MAMO_0000008
steady-state model
/MAMO_0000009
Can be analysed using flux balance analysis [http://identifiers.org/biomodels.kisao/KISAO_0000437].
Reed JL, Palsson BO (2003) Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol, 185(9):2692-9.
/MAMO_0000009
http://identifiers.org/pubmed/12700248
/MAMO_0000009
The constraint-based modeling procedure does not strive to find a single solution but rather finds a collection of all allowable solutions to the governing equations that can be defined (a solution space). The subsequent application of additional constraints further reduces the solution space and, consequently, reduces the number of allowable solutions that a cell can utilize.
/MAMO_0000009
constraint-based model
/MAMO_0000010
http://en.wikipedia.org/wiki/Variable_%28mathematics%29
/MAMO_0000010
Value that may change within the scope of a given model or set of operations.
/MAMO_0000010
variable
/MAMO_0000017
Synonyms: input variable
/MAMO_0000017
https://en.wikipedia.org/wiki/Dependent_and_independent_variables;
/MAMO_0000017
https://www.ncsu.edu/labwrite/po/independentvar.htm
/MAMO_0000017
variable which is controlled in an experiment, and that affects other variables during the experiment. An independent variable does not depend on other variables of the model.
/MAMO_0000017
independent variable
/MAMO_0000018
Synonyms: output variable
/MAMO_0000018
https://en.wikipedia.org/wiki/Dependent_and_independent_variables;
/MAMO_0000018
https://www.ncsu.edu/labwrite/po/dependentvar.htm
/MAMO_0000018
variable which is measured in an experiment, and that is affected during the experiment. A dependent variable depends on other variables
/MAMO_0000018
Example: in an experiment one controls x and measure y, with a relationship y = ax+b, y is depending on x.
/MAMO_0000018
dependent variable
/MAMO_0000019
https://en.wikipedia.org/wiki/Pharmacokinetics
/MAMO_0000019
Model dedicated to the determination of the fate of substances administered externally to a living organism. Pharmacokinetics models are divided into several areas including the extent and rate of absorption, distribution, metabolism and excretion (ADME) to which Liberation is sometimes added (LADME).
/MAMO_0000019
pharmacokinetics model
/MAMO_0000020
https://en.wikipedia.org/wiki/Pharmacodynamics
/MAMO_0000020
Models dedicated to the study of the biochemical and physiological effects of drugs on the body or on microorganisms or parasites within or on the body and the mechanisms of drug action and the relationship between drug concentration and effect.
/MAMO_0000020
pharmacodynamics model
/MAMO_0000021
http://en.wikipedia.org/wiki/Multiphysics
/MAMO_0000021
Multiphysics treats simulations that involve multiple physical models or multiple simultaneous physical phenomena. For example, combining chemical kinetics and fluid mechanics or combining finite elements with molecular dynamics. Multiphysics typically involves solving coupled systems of partial differential equations.
/MAMO_0000021
multiphysics model
/MAMO_0000022
Rule-based modeling is especially effective in cases where the rule-set is significantly simpler than the model it implies, meaning that the model is a repeated manifestation of a limited number of patterns.
/MAMO_0000022
http://en.wikipedia.org/wiki/Rule-based_modeling#For_biochemical_systems
/MAMO_0000022
Model that uses a set of rules used to describe other model instances. The rule-set can be used to create a model, or suitable tools can use a rule-set in place of a model.
/MAMO_0000022
rule-based model
/MAMO_0000023
http://en.wikipedia.org/wiki/Computational_model
/MAMO_0000023
mathematical model that requires computer simulations to study the behavior of a complex system. The system under study is often a complex nonlinear system for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by adjusting the parameters of the system in the computer, and studying the differences in the outcome of the experiments.
/MAMO_0000023
weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, neural network models.
/MAMO_0000023
computational model
/MAMO_0000024
Synonym: multi-agent model
/MAMO_0000024
http://en.wikipedia.org/wiki/Agent-based_model
/MAMO_0000024
model simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole
/MAMO_0000024
agent-based model
/MAMO_0000025
http://en.wikipedia.org/wiki/Petri_net
/MAMO_0000025
directed bipartite graph, in which the nodes represent transitions (i.e. events that may occur, signified by bars) and places (i.e. conditions, signified by circles). The directed arcs describe which places are pre- and/or postconditions for which transitions (signified by arrows) occurs.
/MAMO_0000025
Petri net
/MAMO_0000026
http://en.wikipedia.org/wiki/Computational_neuroscience
/MAMO_0000026
Computational Neuroscience emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein, and chemical coupling to network oscillations, columnar and topographic architecture, and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.
/MAMO_0000026
computational Neuroscience model
/MAMO_0000027
information obtained about a system by the application of an analysis procedure to a model of this system
/MAMO_0000027
timecourse of a concentation; EC50
/MAMO_0000027
readout
/MAMO_0000028
http://en.wikipedia.org/wiki/Population_models
/MAMO_0000028
type of mathematical model that is applied to the study of population dynamics.
/MAMO_0000028
population model
/MAMO_0000029
http://en.wikipedia.org/wiki/Matrix_population_models
/MAMO_0000029
specific type of population model that uses matrix algebra. Matrix algebra, in turn, is simply a form of algebraic shorthand for summarizing a larger number of often repetitious and tedious algebraic computations.
/MAMO_0000029
matrix population model
/MAMO_0000030
http://en.wikipedia.org/wiki/Algebraic_logic
/MAMO_0000030
algebraic logic model
/MAMO_0000030
logic model
/MAMO_0000030
model where the discrete values of variables (also called levels) is determined by logical combinations of the values of other variables.
/MAMO_0000030
logical model
/MAMO_0000031
evolution of a variable value over time
/MAMO_0000031
timecourse
/MAMO_0000032
model which take into account the spatial distribution or geometric characteristics of the entities described by its variables.
/MAMO_0000032
spatial characteristic
/MAMO_0000033
http://en.wikipedia.org/wiki/Reaction_diffusion_model
/MAMO_0000033
mathematical model which explains how the concentration of one or more substances distributed in space changes under the influence of two processes: local chemical reactions in which the substances are transformed into each other, and diffusion which causes the substances to spread out over a domain in space.
/MAMO_0000033
chemical reaction diffusion model
/MAMO_0000034
http://en.wikipedia.org/wiki/Finite_Element_Method
/MAMO_0000034
model where the space is split into a number of subspaces (sometimes called voxels) that are each considered homegenous and isotropic.
/MAMO_0000034
finite element spatial model
/MAMO_0000035
network model
/MAMO_0000036
http://en.wikipedia.org/wiki/Biochemical_system
/MAMO_0000036
Biochemical network studies chemical processes within and relating to, living organisms.
/MAMO_0000036
biochemical network
/MAMO_0000037
model
/MAMO_0000037
Purposeful simplification of reality, designed to imitate certain phenomena or characteristics of a system while downplaying non-essential aspects. Its value lies in the ability to generalise insights from the model to a broader class of systems.
/MAMO_0000037
model
Albert R, Wang R (2009) Discrete dynamic modeling of cellular signaling networks. Methods Enzymol 467:281–306
/MAMO_0000038
http://identifiers.org/pubmed/19897097
/MAMO_0000038
Signal transduction is a process for cellular communication where the cell receives (and responds to) external stimuli from other cells and from the environment.
/MAMO_0000038
signalling network
Schlitt T, Brazma A (2007) Current approaches to gene regulatory network modelling. BMC Bioinf 8(Suppl 6):S9
/MAMO_0000039
http://identifiers.org/pubmed/17903290
/MAMO_0000039
Gene regulation controls the expression of genes and, consequently, all cellular functions. Gene expression is a process that involves transcription of the gene into mRNA, followed by translation to a protein, which may be subject to post-translational modification. The transcription process is controlled by transcription factors (TFs) that can work as activators or inhibitors. TFs are themselves encoded by genes and subject to regulation, which altogether forms complex regulatory networks.
/MAMO_0000039
gene regulatory network
Palsson B (2006) Systems Biology: Properties of Reconstructed Networks.
Cambridge University Press
/MAMO_0000040
http://identifiers.org/isbn/9780521859035
/MAMO_0000040
Metabolism is a mechanism composed by a set of biochemical reactions, by which the cell sustains its growth and energy requirements. It includes several catabolic and anabolic pathways of enzyme-catalyzed reactions that import substrates from the environment and transform them into energy and building blocks required to build the cellular components. Metabolic pathways are interconnected through intermediate metabolites, forming complex networks.
/MAMO_0000040
metabolic network
/MAMO_0000041
http://en.wikipedia.org/wiki/Bayesian_network
/MAMO_0000041
Bayes model
/MAMO_0000041
Bayes network
/MAMO_0000041
Bayesian network
/MAMO_0000041
belief network
/MAMO_0000041
probabilistic DAG model
/MAMO_0000041
probabilistic directed acyclic graphical model
/MAMO_0000041
Bayesian networks are a special type of probabilistic graphs. Their nodes represent random variables (discrete or continuous) and the edges represent conditional dependencies, forming a directed acyclic graph. Each node contains a probabilistic function that is dependent on the values of its input nodes.
/MAMO_0000041
Bayesian model
Zou M, Conzen S (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21(1):71–79
/MAMO_0000043
http://identifiers.org/pubmed/15308537
/MAMO_0000043
dynamic Bayes model
/MAMO_0000043
dynamic Bayes network
/MAMO_0000043
dynamic Bayesian network
/MAMO_0000043
dynamic belief network
/MAMO_0000043
dynamic probabilistic DAG model
/MAMO_0000043
dynamic probabilistic directed acyclic graphical model
/MAMO_0000043
A dynamic Bayesian network is a Bayesian network that overcomes the inability to model feedback loops. In this case, the variables are replicated for each time step and the feedback is modeled by connecting the nodes at adjacent time steps.
/MAMO_0000043
dynamic Bayesian model
/MAMO_0000044
http://en.wikipedia.org/wiki/Process_calculus
/MAMO_0000044
process calculus
/MAMO_0000044
Process algebras are a family of formal languages for modeling concurrent systems. They generally consist on a set of process primitives, operators for sequential and parallel composition of processes, and communication channels.
/MAMO_0000044
process algebra
/MAMO_0000045
http://en.wikipedia.org/wiki/Differential_equation
/MAMO_0000045
Differential equations describe the rate of change of continuous variables. They are typically used for modeling dynamical systems in several areas.
/MAMO_0000045
differential equation model
/MAMO_0000046
http://en.wikipedia.org/wiki/Ordinary_differential_equation
/MAMO_0000046
ODE model
/MAMO_0000046
model using equations containing a function of one independent variable and its derivatives.
/MAMO_0000046
ordinary differential equation model
/MAMO_0000047
http://en.wikipedia.org/wiki/Stochastic_differential_equation
/MAMO_0000047
SDE model
/MAMO_0000047
model using differential equations in which one or more of the terms is a stochastic process, resulting in a solution which is itself a stochastic process
/MAMO_0000047
stochastic differential equation model
/MAMO_0000048
http://en.wikipedia.org/wiki/Partial_differential_equation
/MAMO_0000048
PDE model
/MAMO_0000048
model using differential equations that contains unknown multivariable functions and their partial derivatives.
/MAMO_0000048
partial differential equation model
/MAMO_0000049
http://en.wikipedia.org/wiki/Finite-state_machine
/MAMO_0000049
Interacting state machines are diagram-based formalisms that describe the temporal behavior of a system based on the changes in the states of its parts. They differ from other approaches as they define a system in terms of its states rather than its components.
/MAMO_0000049
interacting state machine
/MAMO_0000050
http://en.wikipedia.org/wiki/Cellular_automaton
/MAMO_0000050
Cellular automata are discrete dynamic models that consist on a grid of cells with a finite number of states. A cellular automaton has an initial configuration that changes at each time step through a predefined rule that calculates the state of each cell as a function of the state of its neighbors at the previous step.
/MAMO_0000050
cellular automaton
Wishart DS, Yang R, Arndt D, Tang P, Cruz J (2005) Dynamic cellular automata: an alternative approach to cellular simulation. In Silico Biol., 5(2):139-61.
/MAMO_0000051
http://identifiers.org/pubmed/15972011
/MAMO_0000051
DSA
/MAMO_0000051
Dynamic cellular automata are a variation of cellular automata that allows for movement of the cell contents inside the grid, mimicking brownian motion.
/MAMO_0000051
dynamic cellular automaton
/MAMO_0000052
http://en.wikipedia.org/wiki/Stochastic_cellular_automaton
/MAMO_0000052
Cellular automaton whose updating rule is stochastic, which means the new entity's state is not chosen deterministically based on the neighbours' states, but according to some probability distributions depending on the neighbours' states.
/MAMO_0000052
stochastic cellular automaton
/MAMO_0000053
http://en.wikipedia.org/wiki/Boolean_network
Kauffman S (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22(3):437–467
/MAMO_0000053
http://identifiers.org/pubmed/5803332
/MAMO_0000053
Boolean models model networks of genes by boolean variables that represent active and inactive states. At each time step, the state of each gene is determined by a logic rule which is a function of the state of its regulators. The state of all genes forms a global state that changes synchronously.
/MAMO_0000053
boolean model
/MAMO_0000054
modeling entity feature
/MAMO_0000054
Dependent entity which another modelling entity has, i.e., that entity exhibits its property. Other common terms for property in natural language are characteristic, property, quality, etc.
/MAMO_0000054
modelling entity feature
/MAMO_0000055
http://en.wikipedia.org/wiki/Temporality
/MAMO_0000055
Characterises the evolution of a modelling entity over time.
/MAMO_0000055
temporal quality
/MAMO_0000056
http://en.wikipedia.org/wiki/Dynamical
/MAMO_0000056
Characterises a modelling entity that evolves over time.
/MAMO_0000056
dynamical characteristic
/MAMO_0000057
http://en.wiktionary.org/wiki/static
/MAMO_0000057
Characterises a modelling entity that does not evolve over time.
/MAMO_0000057
static characteristic
/MAMO_0000058
http://en.wiktionary.org/wiki/qualitative
/MAMO_0000058
http://en.wiktionary.org/wiki/quantitative
/MAMO_0000058
Characterises the possibility to be measured numerically.
/MAMO_0000058
quantitative characteristic
/MAMO_0000059
http://en.wiktionary.org/wiki/discrete
/MAMO_0000059
Which values can be enumerated.
/MAMO_0000059
discrete characteristic
/MAMO_0000060
http://en.wiktionary.org/wiki/continuous
/MAMO_0000060
Which values cannot be enumerated. Whatever two values, there is always another value in between.
/MAMO_0000060
continuous characteristic
/MAMO_0000061
http://en.wikipedia.org/wiki/Nonlinear
/MAMO_0000061
http://en.wikipedia.org/wiki/Linear_system
/MAMO_0000061
Which satisfies the principles of superposition and scaling.
/MAMO_0000061
linear quality
/MAMO_0000062
http://en.wikipedia.org/wiki/Uncertainty
/MAMO_0000062
Characterises the certainty, or lack of, of the modelling entity feature.
/MAMO_0000062
uncertainty level
/MAMO_0000063
http://en.wikipedia.org/wiki/Deterministic
/MAMO_0000063
Which value or behaviour is certain.
/MAMO_0000063
deterministic nature
/MAMO_0000064
http://en.wikipedia.org/wiki/Probabilistic
/MAMO_0000064
stochastic nature
/MAMO_0000064
Which can exhibit alternative values or behaviours with differnent probability.
/MAMO_0000064
probabilistic nature
/MAMO_0000069
http://en.wikipedia.org/wiki/Coloured_Petri_net
Jensen K, Kristensen LM. Coloured Petri Nets: Modeling and Validation of Concurrent Systems. Berlin: Springer, 2009.
/MAMO_0000069
http://identifiers.org/isbn/978-3-642-00284-7
/MAMO_0000069
CP-net
/MAMO_0000069
CPN
/MAMO_0000069
colored Petri net
/MAMO_0000069
graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role
/MAMO_0000069
coloured Petri net
David R, Alla H. Discrete, continuous, and hybrid Petri Nets. Berlin, London: Springer, 2010.
/MAMO_0000070
http://identifiers.org/isbn/978-3-642-10669-9
/MAMO_0000070
continuous PN
/MAMO_0000070
Petri net model in which the number of marks in the places are real numbers instead of integers
/MAMO_0000070
continuous Petri net
Chaouiya C. Petri net modelling of biological networks. Brief Bioinform. 2007;8(4):210-9.
/MAMO_0000071
http://identifiers.org/pubmed/17626066
/MAMO_0000071
functional PN
/MAMO_0000071
functional PNs
/MAMO_0000071
self-modified PN
/MAMO_0000071
self-modified PNs
/MAMO_0000071
self-modified Petri net
/MAMO_0000071
A Petri net that allows the flow relations between places and transitions to depend on the marking.
/MAMO_0000071
functional Petri net
Matsuno H, Tanaka Y, Aoshima H, Doi A, Matsui M, Miyano S. Biopathways representation and simulation on hybrid functional Petri net. In Silico Biol. 2003;3(3):389-404.
/MAMO_0000072
http://identifiers.org/pubmed/12954096
/MAMO_0000072
HFPN
/MAMO_0000072
HFPNs
/MAMO_0000072
Hybrid functional PNs
/MAMO_0000072
hybrid functional PN
/MAMO_0000072
hybrid Petri nets with additional features: continuous transition firing rates can depend on the values of the input places and the weights of arcs can be defined as a function of the markings of the connected places.
/MAMO_0000072
hybrid functional Petri net
Chaouiya C. Petri net modelling of biological networks. Brief Bioinform. 2007;8(4):210-9.
/MAMO_0000073
http://identifiers.org/pubmed/17626066
/MAMO_0000073
HPN
/MAMO_0000073
HPNs
/MAMO_0000073
hybrid PN
/MAMO_0000073
Petri nets that allow the coexistence of both continuous and discrete processes. They include discrete places (marked with tokens) and continuous places associated with real variables (e.g. concentration levels).
/MAMO_0000073
hybrid Petri net
/MAMO_0000074
http://en.wikipedia.org/wiki/Stochastic_Petri_net
/MAMO_0000074
SPN
/MAMO_0000074
stochastic PN
/MAMO_0000074
form of Petri net where the transitions fire after a probabilistic delay determined by a random variable.
/MAMO_0000074
stochastic Petri net
/MAMO_0000075
https://en.wikipedia.org/wiki/Logistic_regression
/MAMO_0000075
logit regression
/MAMO_0000075
type of probabilistic classification model used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on one or more predictor variables (features).
/MAMO_0000075
logistic regression
/MAMO_0000076
http://en.wikipedia.org/wiki/Mixed_model
/MAMO_0000076
statistical model containing both fixed effects and random effects, that is mixed effects.
/MAMO_0000076
mixed model
/MAMO_0000077
https://en.wikipedia.org/wiki/Multinomial_logistic_regression
/MAMO_0000077
multinomial logit
/MAMO_0000077
softmax regression
/MAMO_0000077
regression model which generalizes logistic regression by allowing more than two discrete outcomes.
/MAMO_0000077
multinomial logistic regression
/MAMO_0000078
https://en.wikipedia.org/wiki/Poisson_regression
/MAMO_0000078
form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters.
/MAMO_0000078
Poisson regression
/MAMO_0000080
https://en.wikipedia.org/wiki/Binary_data
/MAMO_0000080
Binary data is data whose unit can take on only two possible values, traditionally termed 0 and 1 in accordance with the binary numeral system and Boolean algebra.
/MAMO_0000080
binary data
/MAMO_0000081
https://en.wikipedia.org/wiki/Categorical_data
/MAMO_0000081
In statistics, categorical data is a statistical data type consisting of categorical variables, used for observed data whose value is one of a fixed number of nominal categories, or for data that has been converted into that form, for example as grouped data.
/MAMO_0000081
categorical data
/MAMO_0000082
https://en.wikipedia.org/wiki/Count_variable
/MAMO_0000082
In statistics, count data is a statistical data type, a type of data in which the observations can take only the non-negative integer values {0, 1, 2, 3, ...}, and where these integers arise from counting rather than ranking.
/MAMO_0000082
count data
/MAMO_0000083
http://en.wikipedia.org/wiki/Longitudinal_study
/MAMO_0000083
repeated observations of the same variables over time.
/MAMO_0000083
longitudinal data
/MAMO_0000084
https://en.wikipedia.org/wiki/Phase_portrait
/MAMO_0000084
representation of the trajectories of a dynamical system in the phase plane. Each set of initial conditions is represented by a different curve, or point.
/MAMO_0000084
phase portrait
/MAMO_0000085
https://en.wikipedia.org/wiki/Steady-state
/MAMO_0000085
value that does not change over time
/MAMO_0000085
steady state value
/MAMO_0000086
https://en.wikipedia.org/wiki/Statistical_data_type
/MAMO_0000086
In statistics, groups of individual data points may be classified as belonging to any of various statistical data types, e.g. categorical ("red", "blue", "green"), real number (1.68, -5, 1.7e+6), etc.
/MAMO_0000086
statistical data
Guidance for Industry, Population Pharmacokinetics, U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER)February 1999.
/MAMO_0000087
http://www.fda.gov/downloads/Drugs/Guidances/UCM072137.pdf
/MAMO_0000087
Model to study the sources and correlates of variability in drug concentrations among individuals who are the target patient population receiving clinically relevant doses of a drug of interest.
/MAMO_0000087
population pharmacokinetics model
Tolle D., Le Novère N. Particle-based Stochastic Simulation in Systems Biology. Current Bioinformatics (2006), 1: 315-320.
/MAMO_0000088
http://lenoverelab.org/perso/lenov/PUBLIS/Tolle2006.pdf
/MAMO_0000088
mesoscopic model
/MAMO_0000088
model where the reactions and diffusion of each particle in a population is described and followed over time. Sometimes called mesoscopic model, because more abstract than an atomic or molecular dynamic model, but more detailed than a concentration based reaction diffusion model.
/MAMO_0000088
single particle spatial model
/MAMO_0000089
https://en.wikipedia.org/wiki/Delay_differential_equation
/MAMO_0000089
DDE model
/MAMO_0000089
model using a type of differential equation in which the derivative of the unknown function at a certain time is given in terms of the values of the function at previous times.
/MAMO_0000089
delayed differential equation model
/MAMO_0000090
https://en.wikipedia.org/wiki/Differential_algebraic_equation
/MAMO_0000090
DAE mode
/MAMO_0000090
general form of (systems of) differential equations for vector–valued functions x in one independent variable,
/MAMO_0000090
differential algebraic equation model
/MAMO_0000180
domain model
/MAMO_0000181
pharmocometrics model
P Van Ham. How to deal with variables with more than two levels. In Kinetic Logic A Boolean Approach to the Analysis of Complex Regulatory Systems. Lecture Notes in Biomathematics Vol 29, 1979, pp 326-343
/MAMO_0000182
http://identifiers.org/isbn/978-3-540-09556-9
/MAMO_0000182
logical model in which variables can take more than two levels.
/MAMO_0000182
multi-value logic model
Graner F and Glazier JA. Simulation of biological cell sorting using a two-dimensional extended Potts model, Phys. Rev. Lett. 69, 2013 - Published 28 September 1992.
/MAMO_0000183
http://dx.doi.org/10.1103/PhysRevLett.69.2013
/MAMO_0000183
http://en.wikipedia.org/wiki/Cellular_Potts_model
/MAMO_0000183
Glazier and Graner model
/MAMO_0000183
extended large-q Potts model
/MAMO_0000183
generalized version of a Potts model, where the cell associated with a spin can be composed of several elements, called pixels. Simulations progress by updating the pixels.
/MAMO_0000183
cellular Potts model
/MAMO_0000184
http://en.wikipedia.org/wiki/Potts_model
/MAMO_0000184
cellular Potts model in which each cell is made up of only one pixel (q-spin). The standard Potts model is a generalisation of the Ising model where the spin can take a discret number of values regularly distributed.
/MAMO_0000184
standard Potts model
/MAMO_0000185
http://en.wikipedia.org/wiki/Ising_model
Duke TA, Bray D. Heightened sensitivity of a lattice of membrane receptors. Proc Natl Acad Sci U S A. 1999;96(18):10104-8.
/MAMO_0000185
http://identifiers.org/pubmed/10468569
/MAMO_0000185
model that consists of discrete variables, called spins, that can be in one of two states (+1 or −1). The spins are arranged in a graph, usually, a lattice,
/MAMO_0000185
Ising model
Aldridge BB, Saez-Rodriguez J, Muhlich JL, Sorger PK, Lauffenburger DA. Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling. PLoS Comput Biol. 2009;5(4):e1000340.
/MAMO_0000186
http://identifiers.org/pubmed/19343194
/MAMO_0000186
logical model which rely on fuzzy logic for deciding the new values of variables, that is the use of truth values instead of truth table to interpret the inputs and the resulting output.
/MAMO_0000186
fuzzy logic model
Snoussi EH. Qualitative dynamics of piecewise-linear differential equations: a discrete mapping approach. Dyn Stab Syst 1989, 4(3-4): 565-583
/MAMO_0000187
http://identifiers.org/doi/10.1080/02681118908806072
/MAMO_0000187
multi-valued logical model with multiple thresholds to assign variable values, logical parameters controlling the assignments, and asynchronous update.
/MAMO_0000187
generalized logical model
/MAMO_0000188
http://en.wikipedia.org/wiki/Biochemical_systems_theory
Savageau MA. Biochemical systems analysis. II. The steady-state solutions for an n-pool system using a power-law approximation. J Theor Biol. 1969 Dec;25(3):370-9.
/MAMO_0000188
http://identifier.org/pubmed/5387047
/MAMO_0000188
model in which the creation and destruction of a molecular species are represented using two power-law expansions in the species of the system. Reactions are not represented independently, but are subsumed in the apparent global reaction order (not necessarily integer) for the species affecting its rate.
/MAMO_0000188
S system model
Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 57
/MAMO_0000189
http://identifier.org/isbn/978-0387-27197-2
/MAMO_0000189
https://en.wikipedia.org/wiki/Linear_model#Time_series_models
/MAMO_0000189
pharmacokinetic model expressed as differential equations in which partial derivatives with respect to any of the model parameters are independent of the other parameters
/MAMO_0000189
linear pharmacokinetic model
Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 181
/MAMO_0000190
http://identifier.org/isbn/978-0387-27197-2
/MAMO_0000190
linear pharmacokinetic model which contain both fixed and random effects (mixed)
/MAMO_0000190
linear mixed effect pharmacokinetic model
Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 93
/MAMO_0000191
http://identifier.org/isbn/978-0387-27197-2
/MAMO_0000191
pharmacokinetic model expressed as differential equations in which any partial derivatives with respect to any of the model parameters are dependent on any other model parameter or for which the derivatives do not exist or are discontinuous
/MAMO_0000191
nonlinear pharmacokinetic model
Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 205
/MAMO_0000192
http://identifier.org/isbn/978-0387-27197-2
/MAMO_0000192
nonlinear pharmacokinetic model which contain both fixed and random effects (mixed)
/MAMO_0000192
nonlinear mixed effect pharmacokinetic model
/MAMO_0000193
https://en.wikipedia.org/wiki/Markov_model
/MAMO_0000193
a stochastic model, that models a process where the state depends on previous states in a non-deterministic way, and assumes the Markov property: the conditional probability distribution of future states of the process (conditional on both past and present values) depends only upon the present state; that is, given the present, the future does not depend on the past.
/MAMO_0000193
Markov model
/MAMO_0000194
http://en.wikipedia.org/wiki/Hidden_Markov_model
/MAMO_0000194
HMM
/MAMO_0000194
a Markov model in which the system being modeled is autonomous and its state is only partially observable. In other words, observations are related to the state of the system, but they are typically insufficient to precisely determine the state.
/MAMO_0000194
hidden Markov model
/MAMO_0000195
http://en.wikipedia.org/wiki/Markov_chain
/MAMO_0000195
DTMC
/MAMO_0000195
discrete-time Markov chain
/MAMO_0000195
a Markov model that models the state of a system with a random variable that changes through time. In this context, the Markov property suggests that the distribution for this variable depends only on the distribution of the previous state. The system is autonomous and its state is fully observable.
/MAMO_0000195
Markov chain
/MAMO_0000196
http://en.wikipedia.org/wiki/Markov_decision_process
/MAMO_0000196
MDP
/MAMO_0000196
a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.
/MAMO_0000196
Markov decision process
/MAMO_0000197
http://en.wikipedia.org/wiki/Markov_random_field
/MAMO_0000197
MRF
/MAMO_0000197
Markov network
/MAMO_0000197
undirected graphical model
/MAMO_0000197
a set of random variables having a Markov property described by an undirected graph. A Markov random field may be considered to be a generalization of a Markov chain in multiple dimensions. In a Markov chain, state depends only on the previous state in time, whereas in a Markov random field, each state depends on its neighbors in any of multiple directions. A Markov random field may be visualized as a field or graph of random variables, where the distribution of each random variable depends on the neighboring variables with which it is connected.
/MAMO_0000197
Markov random field
/MAMO_0000198
http://en.wikipedia.org/wiki/Piecewise_linear_function
Glass L, Kauffman SA. The logical analysis of continuous, non-linear biochemical control networks. J Theor Biol. 1973;39(1):103-29.
/MAMO_0000198
http://identifiers.org/pubmed/4741704
/MAMO_0000198
model using functions that respond to parameter values via threshold (Heavyside) functions.
/MAMO_0000198
piecewise linear differential equation model
Coutinho R, Fernandez B, Lima R, Meyroneinc A. Discrete time piecewise affine models of genetic regulatory networks. J Math Biol. 2006 Apr;52(4):524-70. Epub 2006 Mar 6.
/MAMO_0000199
http://identifiers.org/pubmed/16521027
/MAMO_0000199
model using piecewise linear differential equations in which variables are updated only at certain times.
/MAMO_0000199
discrete time piecewise linear differential equation model
De Jong H1, Gouze JL, Hernandez C, Page M, Sari T, Geiselmann J. Qualitative simulation of genetic regulatory networks using piecewise-linear models. Bull Math Biol. 2004 Mar;66(2):301-40.
/MAMO_0000200
http://identifiers.org/pubmed/14871568
/MAMO_0000200
piecewise linear differential equation model in which the variables can only take a discrete number of levels.
/MAMO_0000200
Qualitative piecewise linear differential equation model
http://www.w3.org/2004/02/skos/core#altLabel
The range of skos:altLabel is the class of RDF plain literals.
http://www.w3.org/2004/02/skos/core#altLabel
skos:prefLabel, skos:altLabel and skos:hiddenLabel are pairwise disjoint properties.
http://www.w3.org/2004/02/skos/core#altLabel
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#altLabel
alternative label
http://www.w3.org/2004/02/skos/core#altLabel
An alternative lexical label for a resource.
http://www.w3.org/2004/02/skos/core#altLabel
Acronyms, abbreviations, spelling variants, and irregular plural/singular forms may be included among the alternative labels for a concept. Mis-spelled terms are normally included as hidden labels (see skos:hiddenLabel).
http://www.w3.org/2004/02/skos/core#definition
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#definition
definition
http://www.w3.org/2004/02/skos/core#definition
A statement or formal explanation of the meaning of a concept.
http://www.w3.org/2004/02/skos/core#example
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#example
example
http://www.w3.org/2004/02/skos/core#example
An example of the use of a concept.
http://www.w3.org/2004/02/skos/core#hiddenLabel
skos:prefLabel, skos:altLabel and skos:hiddenLabel are pairwise disjoint properties.
http://www.w3.org/2004/02/skos/core#hiddenLabel
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#hiddenLabel
hidden label
http://www.w3.org/2004/02/skos/core#hiddenLabel
A lexical label for a resource that should be hidden when generating visual displays of the resource, but should still be accessible to free text search operations.
http://www.w3.org/2004/02/skos/core#hiddenLabel
The range of skos:hiddenLabel is the class of RDF plain literals.
http://www.w3.org/2004/02/skos/core#prefLabel
A resource has no more than one value of skos:prefLabel per language tag, and no more than one value of skos:prefLabel without language tag.
http://www.w3.org/2004/02/skos/core#prefLabel
The range of skos:prefLabel is the class of RDF plain literals.
http://www.w3.org/2004/02/skos/core#prefLabel
skos:prefLabel, skos:altLabel and skos:hiddenLabel are pairwise
disjoint properties.
http://www.w3.org/2004/02/skos/core#prefLabel
http://www.w3.org/2004/02/skos/core
http://www.w3.org/2004/02/skos/core#prefLabel
preferred label
http://www.w3.org/2004/02/skos/core#prefLabel
The preferred lexical label for a resource, in a given language.