Annotation
literal: 2023-02-03

AnnotationAssertion
annotation:
  literal: Brauer F. Compartmental Models in Epidemiology. Mathematical Epidemiology. 2008;1945:19-79.
IRI: #MAMO_0000206
literal: http://identifiers.org/doi/10.1007/978-3-540-78911-6_2

AnnotationAssertion
IRI: #MAMO_0000206
literal: https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology

AnnotationAssertion
IRI: #MAMO_0000206
literal: Compartmental models simplify the mathematical modelling of infectious diseases. The population is assigned to compartments with labels - for example, S, I, or R, (Susceptible, Infectious, or Recovered). People may progress between compartments. The order of the labels usually shows the flow patterns between the compartments; for example SEIS means susceptible, exposed, infectious, then susceptible again.

AnnotationAssertion
IRI: #MAMO_0000206
literal: epidemiological compartmental model

AnnotationAssertion
annotation:
  literal: Brauer F. Compartmental Models in Epidemiology. Mathematical Epidemiology. 2008;1945:19-79.
IRI: #MAMO_0000207
literal: http://identifiers.org/doi/10.1007/978-3-540-78911-6_2

AnnotationAssertion
annotation:
  literal: Kermack, W. O. and McKendrick, A. G. "A Contribution to the Mathematical Theory of Epidemics." Proc. Roy. Soc. Lond. A 115, 700-721, 1927.
IRI: #MAMO_0000207
literal: http://identifiers.org/doi/10.1098/rspa.1927.0118

AnnotationAssertion
IRI: #MAMO_0000207
literal: SIR

AnnotationAssertion
IRI: #MAMO_0000207
literal: susceptible-infectious-removed

AnnotationAssertion
IRI: #MAMO_0000207
literal: susceptible-infectious-removed model

AnnotationAssertion
IRI: #MAMO_0000207
literal: A compartmental epidemiological model with three compartments: susceptible S, infectious I, and removed R. The passage of individuals is modelled from S to I to R.

AnnotationAssertion
IRI: #MAMO_0000207
literal: SIR model

AnnotationAssertion
annotation:
  literal: Brauer F. Compartmental Models in Epidemiology. Mathematical Epidemiology. 2008;1945:19-79.
IRI: #MAMO_0000208
literal: http://identifiers.org/doi/10.1007/978-3-540-78911-6_2

AnnotationAssertion
IRI: #MAMO_0000208
literal: SIS

AnnotationAssertion
IRI: #MAMO_0000208
literal: susceptible-infectious-susceptible

AnnotationAssertion
IRI: #MAMO_0000208
literal: susceptible-infectious-susceptible model

AnnotationAssertion
IRI: #MAMO_0000208
literal: A compartmental epidemiological model with two compartments: susceptible S and infectious I. The passage of individuals is modelled from S to I and back.

AnnotationAssertion
IRI: #MAMO_0000208
literal: SIS model

AnnotationAssertion
annotation:
  literal: Brauer F. Compartmental Models in Epidemiology. Mathematical Epidemiology. 2008;1945:19-79.
IRI: #MAMO_0000209
literal: http://identifiers.org/doi/10.1007/978-3-540-78911-6_2

AnnotationAssertion
IRI: #MAMO_0000209
literal: SEIR

AnnotationAssertion
IRI: #MAMO_0000209
literal: susceptible-exposed-infectious-recovered

AnnotationAssertion
IRI: #MAMO_0000209
literal: susceptible-exposed-infectious-recovered model

AnnotationAssertion
IRI: #MAMO_0000209
literal: A compartmental epidemiological model with four compartments: susceptible S, exposed E, infectious I, and removed R. The passage of individuals is modelled from S to E to I to R.

AnnotationAssertion
IRI: #MAMO_0000209
literal: SEIR model

AnnotationAssertion
annotation:
  literal: Brauer F. Compartmental Models in Epidemiology. Mathematical Epidemiology. 2008;1945:19-79.
IRI: #MAMO_0000210
literal: http://identifiers.org/doi/10.1007/978-3-540-78911-6_2

AnnotationAssertion
IRI: #MAMO_0000210
literal: SEIS

AnnotationAssertion
IRI: #MAMO_0000210
literal: susceptible-exposed-infectious-susceptible

AnnotationAssertion
IRI: #MAMO_0000210
literal: susceptible-exposed-infectious-susceptible model

AnnotationAssertion
IRI: #MAMO_0000210
literal: A compartmental epidemiological model with three compartments: susceptible S, exposed E, and infectious I. The passage of individuals is modelled from S to E to I to S.

AnnotationAssertion
IRI: #MAMO_0000210
literal: SEIS model

AnnotationAssertion
annotation:
  literal: Derendorf H, Lesko LJ, Chaikin P, Colburn WA, Lee P, Miller R, Powell R, Rhodes G, Stanski D, Venitz J. Pharmacokinetic/pharmacodynamic modeling in drug research and development. J Clin Pharmacol. 2000 Dec;40(12 Pt 2):1399-418.
IRI: #MAMO_0000211
literal: http://identifiers.org/pubmed/11185661

AnnotationAssertion
IRI: #MAMO_0000211
literal: PK-PD model

AnnotationAssertion
IRI: #MAMO_0000211
literal: Pharmacokinetic/pharmacodynamic (PK/PD) relationships and modeling builds the bridge between the two classical disciplines of clinical pharmacology: pharmacokinetics and pharmacodynamics. It links the concentration-time profile as assessed by pharmacokinetics to the intensity of observed response as quantified by pharmacodynamics. The resulting so-called integrated PK/PD-models allow the description of the complete time course of the desired and/or undesired effects in response to a drug therapy.

AnnotationAssertion
IRI: #MAMO_0000211
literal: pharmacokinetic-pharmacodynamic model

AnnotationAssertion
IRI: #continuous_variable
literal: statistical data whose individual element can take any real number value.

AnnotationAssertion
IRI: #continuous_variable
literal: continuous data variable

AnnotationAssertion
IRI: #hasFeature
literal: hasFeature

AnnotationAssertion
IRI: #isUsedToModel
literal: isUsedToModel

AnnotationAssertion
IRI: #lacksFeature
literal: ?X subclassOf: not (hasFeature some ?Y)

AnnotationAssertion
IRI: #lacksFeature
literal: An object property to be used in the OBO version of MAMO to express negation.

AnnotationAssertion
IRI: #lacksFeature
literal: lacksFeature

AnnotationAssertion
IRI: #ordinal_variable
literal: https://en.wikipedia.org/wiki/Ordinal_data

AnnotationAssertion
IRI: #ordinal_variable
literal: ordinal categorical data variable

AnnotationAssertion
IRI: #ordinal_variable
literal: statistical variable whose elements take values that exist on an ordinal scale, i.e. an arbitrary numerical scale where the exact numerical quantity of a particular value has no significance beyond its ability to establish a ranking over a set of data points.

AnnotationAssertion
IRI: #ordinal_variable
literal: ordinal data variable

AnnotationAssertion
IRI: #owlDef
literal: owlDef

AnnotationAssertion
IRI: /MAMO_0000003
literal: http://en.wikipedia.org/wiki/Mathematical_model

AnnotationAssertion
IRI: /MAMO_0000003
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000003
literal: A set of ordinary differential equation describing a physical process.

AnnotationAssertion
IRI: /MAMO_0000003
literal: mathematical model

AnnotationAssertion
IRI: /MAMO_0000004
literal: http://en.wikipedia.org/wiki/Statistical_model

AnnotationAssertion
IRI: /MAMO_0000004
literal: Model that describes how one or more random variables are related to one or more random variables.

AnnotationAssertion
IRI: /MAMO_0000004
literal: The relationship between the size and the age.

AnnotationAssertion
IRI: /MAMO_0000004
literal: statistical model

AnnotationAssertion
IRI: /MAMO_0000008
literal: http://en.wikipedia.org/wiki/Steady_state_%28chemistry%29

AnnotationAssertion
IRI: /MAMO_0000008
literal: Model which describes a system where the state variable values do not vary.

AnnotationAssertion
IRI: /MAMO_0000008
literal: steady-state model

AnnotationAssertion
IRI: /MAMO_0000009
literal: Can be analysed using flux balance analysis [http://identifiers.org/biomodels.kisao/KISAO_0000437].

AnnotationAssertion
annotation:
  literal: Reed JL, Palsson BO (2003) Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol, 185(9):2692-9.
IRI: /MAMO_0000009
literal: http://identifiers.org/pubmed/12700248

AnnotationAssertion
IRI: /MAMO_0000009
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000009
literal: constraint-based model

AnnotationAssertion
IRI: /MAMO_0000010
literal: http://en.wikipedia.org/wiki/Variable_%28mathematics%29

AnnotationAssertion
IRI: /MAMO_0000010
literal: Value that may change within the scope of a given model or set of operations.

AnnotationAssertion
IRI: /MAMO_0000010
literal: variable

AnnotationAssertion
IRI: /MAMO_0000017
literal: Synonyms: input variable

AnnotationAssertion
IRI: /MAMO_0000017
literal: https://en.wikipedia.org/wiki/Dependent_and_independent_variables

AnnotationAssertion
IRI: /MAMO_0000017
literal: https://www.ncsu.edu/labwrite/po/independentvar.htm

AnnotationAssertion
IRI: /MAMO_0000017
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000017
literal: independent variable

AnnotationAssertion
IRI: /MAMO_0000018
literal: Synonyms: output variable

AnnotationAssertion
IRI: /MAMO_0000018
literal: https://en.wikipedia.org/wiki/Dependent_and_independent_variables

AnnotationAssertion
IRI: /MAMO_0000018
literal: https://www.ncsu.edu/labwrite/po/dependentvar.htm

AnnotationAssertion
IRI: /MAMO_0000018
literal: variable which is measured in an experiment, and that is affected during the experiment. A dependent variable depends on other variables

AnnotationAssertion
IRI: /MAMO_0000018
literal: Example: in an experiment one controls x and measure y, with a relationship y = ax+b, y is depending on x.

AnnotationAssertion
IRI: /MAMO_0000018
literal: dependent variable

AnnotationAssertion
IRI: /MAMO_0000019
literal: https://en.wikipedia.org/wiki/Pharmacokinetics

AnnotationAssertion
IRI: /MAMO_0000019
literal: pharmacokinetics model

AnnotationAssertion
IRI: /MAMO_0000019
literal: 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).

AnnotationAssertion
IRI: /MAMO_0000019
literal: pharmacokinetic model

AnnotationAssertion
IRI: /MAMO_0000020
literal: https://en.wikipedia.org/wiki/Pharmacodynamics

AnnotationAssertion
IRI: /MAMO_0000020
literal: pharmacodynamics model

AnnotationAssertion
IRI: /MAMO_0000020
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000020
literal: pharmacodynamic model

AnnotationAssertion
IRI: /MAMO_0000021
literal: http://en.wikipedia.org/wiki/Multiphysics

AnnotationAssertion
IRI: /MAMO_0000021
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000021
literal: multiphysics model

AnnotationAssertion
IRI: /MAMO_0000022
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000022
literal: http://en.wikipedia.org/wiki/Rule-based_modeling#For_biochemical_systems

AnnotationAssertion
IRI: /MAMO_0000022
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000022
literal: rule-based model

AnnotationAssertion
IRI: /MAMO_0000023
literal: http://en.wikipedia.org/wiki/Computational_model

AnnotationAssertion
IRI: /MAMO_0000023
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000023
literal: weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, neural network models.

AnnotationAssertion
IRI: /MAMO_0000023
literal: computational model

AnnotationAssertion
IRI: /MAMO_0000024
literal: Synonym: multi-agent model

AnnotationAssertion
IRI: /MAMO_0000024
literal: http://en.wikipedia.org/wiki/Agent-based_model

AnnotationAssertion
IRI: /MAMO_0000024
literal: 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

AnnotationAssertion
IRI: /MAMO_0000024
literal: agent-based model

AnnotationAssertion
IRI: /MAMO_0000025
literal: http://en.wikipedia.org/wiki/Petri_net

AnnotationAssertion
IRI: /MAMO_0000025
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000025
literal: Petri net

AnnotationAssertion
IRI: /MAMO_0000026
literal: http://en.wikipedia.org/wiki/Computational_neuroscience

AnnotationAssertion
IRI: /MAMO_0000026
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000026
literal: computational Neuroscience model

AnnotationAssertion
IRI: /MAMO_0000027
literal: information obtained about a system by the application of an analysis procedure to a model of this system

AnnotationAssertion
IRI: /MAMO_0000027
literal: timecourse of a concentation; EC50

AnnotationAssertion
IRI: /MAMO_0000027
literal: readout

AnnotationAssertion
IRI: /MAMO_0000028
literal: http://en.wikipedia.org/wiki/Population_models

AnnotationAssertion
IRI: /MAMO_0000028
literal: type of mathematical model that is applied to the study of population dynamics.

AnnotationAssertion
IRI: /MAMO_0000028
literal: population model

AnnotationAssertion
IRI: /MAMO_0000029
literal: http://en.wikipedia.org/wiki/Matrix_population_models

AnnotationAssertion
IRI: /MAMO_0000029
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000029
literal: matrix population model

AnnotationAssertion
IRI: /MAMO_0000030
literal: http://en.wikipedia.org/wiki/Algebraic_logic

AnnotationAssertion
IRI: /MAMO_0000030
literal: algebraic logic model

AnnotationAssertion
IRI: /MAMO_0000030
literal: logic model

AnnotationAssertion
IRI: /MAMO_0000030
literal: model where the discrete values of variables (also called levels) is determined by logical combinations of the values of other variables.

AnnotationAssertion
IRI: /MAMO_0000030
literal: logical model

AnnotationAssertion
IRI: /MAMO_0000031
literal: evolution of a variable value over time

AnnotationAssertion
IRI: /MAMO_0000031
literal: timecourse

AnnotationAssertion
IRI: /MAMO_0000032
literal: model which take into account the spatial distribution or geometric characteristics of the entities described by its variables.

AnnotationAssertion
IRI: /MAMO_0000032
literal: spatial characteristic

AnnotationAssertion
IRI: /MAMO_0000033
literal: http://en.wikipedia.org/wiki/Reaction_diffusion_model

AnnotationAssertion
IRI: /MAMO_0000033
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000033
literal: chemical reaction diffusion model

AnnotationAssertion
IRI: /MAMO_0000034
literal: http://en.wikipedia.org/wiki/Finite_Element_Method

AnnotationAssertion
IRI: /MAMO_0000034
literal: model where the space is split into a number of subspaces (sometimes called voxels) that are each considered homegenous and isotropic.

AnnotationAssertion
IRI: /MAMO_0000034
literal: finite element spatial model

AnnotationAssertion
IRI: /MAMO_0000035
literal: model based on a network of nodes or vertices, linked by edges or arcs. nodes can be physical entities, processes, events or logic operators. Edges can represent fluxs, interactions, influences etc.

AnnotationAssertion
IRI: /MAMO_0000035
literal: network model

AnnotationAssertion
IRI: /MAMO_0000036
literal: http://en.wikipedia.org/wiki/Biochemical_system

AnnotationAssertion
IRI: /MAMO_0000036
literal: Biochemical network studies chemical processes within and relating to, living organisms.

AnnotationAssertion
IRI: /MAMO_0000036
literal: biochemical network

AnnotationAssertion
IRI: /MAMO_0000037
literal: model

AnnotationAssertion
IRI: /MAMO_0000037
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000037
literal: model

AnnotationAssertion
annotation:
  literal: Albert R, Wang R (2009) Discrete dynamic modeling of cellular signaling networks. Methods Enzymol 467:281–306
IRI: /MAMO_0000038
literal: http://identifiers.org/pubmed/19897097

AnnotationAssertion
IRI: /MAMO_0000038
literal: Signal transduction is a process for cellular communication where the cell receives (and responds to) external stimuli from other cells and from the environment.

AnnotationAssertion
IRI: /MAMO_0000038
literal: signalling network

AnnotationAssertion
annotation:
  literal: Schlitt T, Brazma A (2007) Current approaches to gene regulatory network modelling. BMC Bioinf 8(Suppl 6):S9
IRI: /MAMO_0000039
literal: http://identifiers.org/pubmed/17903290

AnnotationAssertion
IRI: /MAMO_0000039
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000039
literal: gene regulatory network

AnnotationAssertion
annotation:
  literal: Palsson B (2006) Systems Biology: Properties of Reconstructed Networks. Cambridge University Press
IRI: /MAMO_0000040
literal: http://identifiers.org/isbn/9780521859035

AnnotationAssertion
IRI: /MAMO_0000040
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000040
literal: metabolic network

AnnotationAssertion
IRI: /MAMO_0000041
literal: http://en.wikipedia.org/wiki/Bayesian_network

AnnotationAssertion
IRI: /MAMO_0000041
literal: Bayes model

AnnotationAssertion
IRI: /MAMO_0000041
literal: Bayes network

AnnotationAssertion
IRI: /MAMO_0000041
literal: Bayesian network

AnnotationAssertion
IRI: /MAMO_0000041
literal: belief network

AnnotationAssertion
IRI: /MAMO_0000041
literal: probabilistic DAG model

AnnotationAssertion
IRI: /MAMO_0000041
literal: probabilistic directed acyclic graphical model

AnnotationAssertion
IRI: /MAMO_0000041
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000041
literal: Bayesian model

AnnotationAssertion
annotation:
  literal: 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
IRI: /MAMO_0000043
literal: http://identifiers.org/pubmed/15308537

AnnotationAssertion
IRI: /MAMO_0000043
literal: dynamic Bayes model

AnnotationAssertion
IRI: /MAMO_0000043
literal: dynamic Bayes network

AnnotationAssertion
IRI: /MAMO_0000043
literal: dynamic Bayesian network

AnnotationAssertion
IRI: /MAMO_0000043
literal: dynamic belief network

AnnotationAssertion
IRI: /MAMO_0000043
literal: dynamic probabilistic DAG model

AnnotationAssertion
IRI: /MAMO_0000043
literal: dynamic probabilistic directed acyclic graphical model

AnnotationAssertion
IRI: /MAMO_0000043
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000043
literal: dynamic Bayesian model

AnnotationAssertion
IRI: /MAMO_0000044
literal: http://en.wikipedia.org/wiki/Process_calculus

AnnotationAssertion
IRI: /MAMO_0000044
literal: process calculus

AnnotationAssertion
IRI: /MAMO_0000044
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000044
literal: process algebra

AnnotationAssertion
IRI: /MAMO_0000045
literal: http://en.wikipedia.org/wiki/Differential_equation

AnnotationAssertion
IRI: /MAMO_0000045
literal: differential equations model

AnnotationAssertion
IRI: /MAMO_0000045
literal: model based on differential equations

AnnotationAssertion
IRI: /MAMO_0000045
literal: model using differential equations

AnnotationAssertion
IRI: /MAMO_0000045
literal: Differential equations describe the rate of change of continuous variables. They are typically used for modeling dynamical systems in several areas.

AnnotationAssertion
IRI: /MAMO_0000045
literal: differential equation model

AnnotationAssertion
IRI: /MAMO_0000046
literal: http://en.wikipedia.org/wiki/Ordinary_differential_equation

AnnotationAssertion
IRI: /MAMO_0000046
literal: ODE model

AnnotationAssertion
IRI: /MAMO_0000046
literal: model based on ordinary differential equations

AnnotationAssertion
IRI: /MAMO_0000046
literal: model using ordinary differential equation

AnnotationAssertion
IRI: /MAMO_0000046
literal: ordinary differential equations model

AnnotationAssertion
IRI: /MAMO_0000046
literal: model using equations containing a function of one independent variable and its derivatives.

AnnotationAssertion
IRI: /MAMO_0000046
literal: ordinary differential equation model

AnnotationAssertion
IRI: /MAMO_0000047
literal: http://en.wikipedia.org/wiki/Stochastic_differential_equation

AnnotationAssertion
IRI: /MAMO_0000047
literal: SDE model

AnnotationAssertion
IRI: /MAMO_0000047
literal: model based on stochastic differential equations

AnnotationAssertion
IRI: /MAMO_0000047
literal: model using stochastic differential equations

AnnotationAssertion
IRI: /MAMO_0000047
literal: stochastic differential equations model

AnnotationAssertion
IRI: /MAMO_0000047
literal: 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

AnnotationAssertion
IRI: /MAMO_0000047
literal: stochastic differential equation model

AnnotationAssertion
IRI: /MAMO_0000048
literal: http://en.wikipedia.org/wiki/Partial_differential_equation

AnnotationAssertion
IRI: /MAMO_0000048
literal: PDE model

AnnotationAssertion
IRI: /MAMO_0000048
literal: model based on partial differential equations

AnnotationAssertion
IRI: /MAMO_0000048
literal: model using partial differential equations

AnnotationAssertion
IRI: /MAMO_0000048
literal: partial differential equations model

AnnotationAssertion
IRI: /MAMO_0000048
literal: model using differential equations that contains unknown multivariable functions and their partial derivatives.

AnnotationAssertion
IRI: /MAMO_0000048
literal: partial differential equation model

AnnotationAssertion
IRI: /MAMO_0000049
literal: http://en.wikipedia.org/wiki/Finite-state_machine

AnnotationAssertion
IRI: /MAMO_0000049
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000049
literal: interacting state machine

AnnotationAssertion
IRI: /MAMO_0000050
literal: http://en.wikipedia.org/wiki/Cellular_automaton

AnnotationAssertion
IRI: /MAMO_0000050
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000050
literal: cellular automaton

AnnotationAssertion
annotation:
  literal: 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.
IRI: /MAMO_0000051
literal: http://identifiers.org/pubmed/15972011

AnnotationAssertion
IRI: /MAMO_0000051
literal: DSA

AnnotationAssertion
IRI: /MAMO_0000051
literal: Dynamic cellular automata are a variation of cellular automata that allows for movement of the cell contents inside the grid, mimicking brownian motion.

AnnotationAssertion
IRI: /MAMO_0000051
literal: dynamic cellular automaton

AnnotationAssertion
IRI: /MAMO_0000052
literal: http://en.wikipedia.org/wiki/Stochastic_cellular_automaton

AnnotationAssertion
IRI: /MAMO_0000052
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000052
literal: stochastic cellular automaton

AnnotationAssertion
IRI: /MAMO_0000053
literal: http://en.wikipedia.org/wiki/Boolean_network

AnnotationAssertion
annotation:
  literal: Kauffman S (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22(3):437–467
IRI: /MAMO_0000053
literal: http://identifiers.org/pubmed/5803332

AnnotationAssertion
IRI: /MAMO_0000053
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000053
literal: boolean model

AnnotationAssertion
IRI: /MAMO_0000054
literal: modeling entity feature

AnnotationAssertion
IRI: /MAMO_0000054
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000054
literal: modelling entity feature

AnnotationAssertion
IRI: /MAMO_0000055
literal: http://en.wikipedia.org/wiki/Temporality

AnnotationAssertion
IRI: /MAMO_0000055
literal: Characterises the evolution of a modelling entity over time.

AnnotationAssertion
IRI: /MAMO_0000055
literal: temporal quality

AnnotationAssertion
IRI: /MAMO_0000056
literal: http://en.wikipedia.org/wiki/Dynamical

AnnotationAssertion
IRI: /MAMO_0000056
literal: Characterises a modelling entity that evolves over time.

AnnotationAssertion
IRI: /MAMO_0000056
literal: dynamical characteristic

AnnotationAssertion
IRI: /MAMO_0000057
literal: http://en.wiktionary.org/wiki/static

AnnotationAssertion
IRI: /MAMO_0000057
literal: Characterises a modelling entity that does not evolve over time.

AnnotationAssertion
IRI: /MAMO_0000057
literal: static characteristic

AnnotationAssertion
IRI: /MAMO_0000058
literal: http://en.wiktionary.org/wiki/qualitative

AnnotationAssertion
IRI: /MAMO_0000058
literal: http://en.wiktionary.org/wiki/quantitative

AnnotationAssertion
IRI: /MAMO_0000058
literal: Characterises the possibility to be measured numerically.

AnnotationAssertion
IRI: /MAMO_0000058
literal: quantitative characteristic

AnnotationAssertion
IRI: /MAMO_0000059
literal: http://en.wiktionary.org/wiki/discrete

AnnotationAssertion
IRI: /MAMO_0000059
literal: Which values can be enumerated.

AnnotationAssertion
IRI: /MAMO_0000059
literal: discrete characteristic

AnnotationAssertion
IRI: /MAMO_0000060
literal: http://en.wiktionary.org/wiki/continuous

AnnotationAssertion
IRI: /MAMO_0000060
literal: Which values cannot be enumerated. Whatever two values, there is always another value in between.

AnnotationAssertion
IRI: /MAMO_0000060
literal: continuous characteristic

AnnotationAssertion
IRI: /MAMO_0000061
literal: http://en.wikipedia.org/wiki/Nonlinear

AnnotationAssertion
IRI: /MAMO_0000061
literal: http://en.wikipedia.org/wiki/Linear_system

AnnotationAssertion
IRI: /MAMO_0000061
literal: Which satisfies the principles of superposition and scaling.

AnnotationAssertion
IRI: /MAMO_0000061
literal: linear quality

AnnotationAssertion
IRI: /MAMO_0000062
literal: http://en.wikipedia.org/wiki/Uncertainty

AnnotationAssertion
IRI: /MAMO_0000062
literal: Characterises the certainty, or lack of, of the modelling entity feature.

AnnotationAssertion
IRI: /MAMO_0000062
literal: uncertainty level

AnnotationAssertion
IRI: /MAMO_0000063
literal: http://en.wikipedia.org/wiki/Deterministic

AnnotationAssertion
IRI: /MAMO_0000063
literal: Which value or behaviour is certain.

AnnotationAssertion
IRI: /MAMO_0000063
literal: deterministic nature

AnnotationAssertion
IRI: /MAMO_0000064
literal: http://en.wikipedia.org/wiki/Probabilistic

AnnotationAssertion
IRI: /MAMO_0000064
literal: stochastic nature

AnnotationAssertion
IRI: /MAMO_0000064
literal: Which can exhibit alternative values or behaviours with differnent probability.

AnnotationAssertion
IRI: /MAMO_0000064
literal: probabilistic nature

AnnotationAssertion
IRI: /MAMO_0000069
literal: http://en.wikipedia.org/wiki/Coloured_Petri_net

AnnotationAssertion
annotation:
  literal: Jensen K, Kristensen LM. Coloured Petri Nets: Modeling and Validation of Concurrent Systems. Berlin: Springer, 2009.
IRI: /MAMO_0000069
literal: http://identifiers.org/isbn/978-3-642-00284-7

AnnotationAssertion
IRI: /MAMO_0000069
literal: CP-net

AnnotationAssertion
IRI: /MAMO_0000069
literal: CPN

AnnotationAssertion
IRI: /MAMO_0000069
literal: colored Petri net

AnnotationAssertion
IRI: /MAMO_0000069
literal: graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role

AnnotationAssertion
IRI: /MAMO_0000069
literal: coloured Petri net

AnnotationAssertion
annotation:
  literal: David R, Alla H. Discrete, continuous, and hybrid Petri Nets. Berlin, London: Springer, 2010.
IRI: /MAMO_0000070
literal: http://identifiers.org/isbn/978-3-642-10669-9

AnnotationAssertion
IRI: /MAMO_0000070
literal: continuous PN

AnnotationAssertion
IRI: /MAMO_0000070
literal: Petri net model in which the number of marks in the places are real numbers instead of integers

AnnotationAssertion
IRI: /MAMO_0000070
literal: continuous Petri net

AnnotationAssertion
annotation:
  literal: Chaouiya C. Petri net modelling of biological networks. Brief Bioinform. 2007;8(4):210-9.
IRI: /MAMO_0000071
literal: http://identifiers.org/pubmed/17626066

AnnotationAssertion
IRI: /MAMO_0000071
literal: functional PN

AnnotationAssertion
IRI: /MAMO_0000071
literal: functional PNs

AnnotationAssertion
IRI: /MAMO_0000071
literal: self-modified PN

AnnotationAssertion
IRI: /MAMO_0000071
literal: self-modified PNs

AnnotationAssertion
IRI: /MAMO_0000071
literal: self-modified Petri net

AnnotationAssertion
IRI: /MAMO_0000071
literal: A Petri net that allows the flow relations between places and transitions to depend on the marking.

AnnotationAssertion
IRI: /MAMO_0000071
literal: functional Petri net

AnnotationAssertion
annotation:
  literal: 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.
IRI: /MAMO_0000072
literal: http://identifiers.org/pubmed/12954096

AnnotationAssertion
IRI: /MAMO_0000072
literal: HFPN

AnnotationAssertion
IRI: /MAMO_0000072
literal: HFPNs

AnnotationAssertion
IRI: /MAMO_0000072
literal: Hybrid functional PNs

AnnotationAssertion
IRI: /MAMO_0000072
literal: hybrid functional PN

AnnotationAssertion
IRI: /MAMO_0000072
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000072
literal: hybrid functional Petri net

AnnotationAssertion
annotation:
  literal: Chaouiya C. Petri net modelling of biological networks. Brief Bioinform. 2007;8(4):210-9.
IRI: /MAMO_0000073
literal: http://identifiers.org/pubmed/17626066

AnnotationAssertion
IRI: /MAMO_0000073
literal: HPN

AnnotationAssertion
IRI: /MAMO_0000073
literal: HPNs

AnnotationAssertion
IRI: /MAMO_0000073
literal: hybrid PN

AnnotationAssertion
IRI: /MAMO_0000073
literal: 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).

AnnotationAssertion
IRI: /MAMO_0000073
literal: hybrid Petri net

AnnotationAssertion
IRI: /MAMO_0000074
literal: http://en.wikipedia.org/wiki/Stochastic_Petri_net

AnnotationAssertion
IRI: /MAMO_0000074
literal: SPN

AnnotationAssertion
IRI: /MAMO_0000074
literal: stochastic PN

AnnotationAssertion
IRI: /MAMO_0000074
literal: form of Petri net where the transitions fire after a probabilistic delay determined by a random variable.

AnnotationAssertion
IRI: /MAMO_0000074
literal: stochastic Petri net

AnnotationAssertion
IRI: /MAMO_0000075
literal: https://en.wikipedia.org/wiki/Logistic_regression

AnnotationAssertion
IRI: /MAMO_0000075
literal: logit regression

AnnotationAssertion
IRI: /MAMO_0000075
literal: 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).

AnnotationAssertion
IRI: /MAMO_0000075
literal: logistic regression

AnnotationAssertion
IRI: /MAMO_0000076
literal: http://en.wikipedia.org/wiki/Mixed_model

AnnotationAssertion
IRI: /MAMO_0000076
literal: statistical model containing both fixed effects and random effects, that is mixed effects.

AnnotationAssertion
IRI: /MAMO_0000076
literal: mixed model

AnnotationAssertion
IRI: /MAMO_0000077
literal: https://en.wikipedia.org/wiki/Multinomial_logistic_regression

AnnotationAssertion
IRI: /MAMO_0000077
literal: multinomial logit

AnnotationAssertion
IRI: /MAMO_0000077
literal: softmax regression

AnnotationAssertion
IRI: /MAMO_0000077
literal: regression model which generalizes logistic regression by allowing more than two discrete outcomes.

AnnotationAssertion
IRI: /MAMO_0000077
literal: multinomial logistic regression

AnnotationAssertion
IRI: /MAMO_0000078
literal: https://en.wikipedia.org/wiki/Poisson_regression

AnnotationAssertion
IRI: /MAMO_0000078
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000078
literal: Poisson regression

AnnotationAssertion
IRI: /MAMO_0000080
literal: https://en.wikipedia.org/wiki/Binary_data

AnnotationAssertion
IRI: /MAMO_0000080
literal: Data whose individual element can take on only two possible values, traditionally termed 0 and 1 in accordance with the binary numeral system and Boolean algebra.

AnnotationAssertion
IRI: /MAMO_0000080
literal: binary data variable

AnnotationAssertion
IRI: /MAMO_0000081
literal: https://en.wikipedia.org/wiki/Categorical_data

AnnotationAssertion
IRI: /MAMO_0000081
literal: nominal categorical data variable

AnnotationAssertion
IRI: /MAMO_0000081
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000081
literal: categorical data variable

AnnotationAssertion
IRI: /MAMO_0000082
literal: https://en.wikipedia.org/wiki/Count_variable

AnnotationAssertion
IRI: /MAMO_0000082
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000082
literal: count data variable

AnnotationAssertion
IRI: /MAMO_0000083
literal: http://en.wikipedia.org/wiki/Longitudinal_study

AnnotationAssertion
IRI: /MAMO_0000083
literal: repeated observations of the same variables over time.

AnnotationAssertion
IRI: /MAMO_0000083
literal: longitudinal data variable

AnnotationAssertion
IRI: /MAMO_0000084
literal: https://en.wikipedia.org/wiki/Phase_portrait

AnnotationAssertion
IRI: /MAMO_0000084
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000084
literal: phase portrait

AnnotationAssertion
IRI: /MAMO_0000085
literal: https://en.wikipedia.org/wiki/Steady-state

AnnotationAssertion
IRI: /MAMO_0000085
literal: value that does not change over time

AnnotationAssertion
IRI: /MAMO_0000085
literal: steady state value

AnnotationAssertion
IRI: /MAMO_0000086
literal: https://en.wikipedia.org/wiki/Statistical_data_type

AnnotationAssertion
IRI: /MAMO_0000086
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000086
literal: statistical variable

AnnotationAssertion
annotation:
  literal: 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.
IRI: /MAMO_0000087
literal: http://www.fda.gov/downloads/Drugs/Guidances/UCM072137.pdf

AnnotationAssertion
IRI: /MAMO_0000087
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000087
literal: population pharmacokinetics model

AnnotationAssertion
annotation:
  literal: Tolle D., Le Novère N. Particle-based Stochastic Simulation in Systems Biology. Current Bioinformatics (2006), 1: 315-320.
IRI: /MAMO_0000088
literal: http://lenoverelab.org/perso/lenov/PUBLIS/Tolle2006.pdf

AnnotationAssertion
IRI: /MAMO_0000088
literal: mesoscopic model

AnnotationAssertion
IRI: /MAMO_0000088
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000088
literal: single particle spatial model

AnnotationAssertion
IRI: /MAMO_0000089
literal: https://en.wikipedia.org/wiki/Delay_differential_equation

AnnotationAssertion
IRI: /MAMO_0000089
literal: DDE model

AnnotationAssertion
IRI: /MAMO_0000089
literal: delayed differential equations model

AnnotationAssertion
IRI: /MAMO_0000089
literal: model based on delayed differential equations

AnnotationAssertion
IRI: /MAMO_0000089
literal: model using delayed differential equations

AnnotationAssertion
IRI: /MAMO_0000089
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000089
literal: delayed differential equation model

AnnotationAssertion
IRI: /MAMO_0000090
literal: https://en.wikipedia.org/wiki/Differential_algebraic_equation

AnnotationAssertion
IRI: /MAMO_0000090
literal: DAE model

AnnotationAssertion
IRI: /MAMO_0000090
literal: differential algebraic equations model

AnnotationAssertion
IRI: /MAMO_0000090
literal: model based on differential algebraic equations

AnnotationAssertion
IRI: /MAMO_0000090
literal: model using differential algebraic equations

AnnotationAssertion
IRI: /MAMO_0000090
literal: general form of (systems of) differential equations for vector–valued functions x in one independent variable,

AnnotationAssertion
IRI: /MAMO_0000090
literal: differential algebraic equation model

AnnotationAssertion
IRI: /MAMO_0000180
literal: domain model

AnnotationAssertion
IRI: /MAMO_0000181
literal: https://en.wikipedia.org/wiki/Pharmacometrics

AnnotationAssertion
IRI: /MAMO_0000181
literal: model used to describe and quantify interactions between xenobiotics and patients.

AnnotationAssertion
IRI: /MAMO_0000181
literal: pharmacometrics model

AnnotationAssertion
annotation:
  literal: 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
IRI: /MAMO_0000182
literal: http://identifiers.org/isbn/978-3-540-09556-9

AnnotationAssertion
IRI: /MAMO_0000182
literal: logical model in which variables can take more than two levels.

AnnotationAssertion
IRI: /MAMO_0000182
literal: multi-value logic model

AnnotationAssertion
annotation:
  literal: 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.
IRI: /MAMO_0000183
literal: http://dx.doi.org/10.1103/PhysRevLett.69.2013

AnnotationAssertion
IRI: /MAMO_0000183
literal: http://en.wikipedia.org/wiki/Cellular_Potts_model

AnnotationAssertion
IRI: /MAMO_0000183
literal: Glazier and Graner model

AnnotationAssertion
IRI: /MAMO_0000183
literal: extended large-q Potts model

AnnotationAssertion
IRI: /MAMO_0000183
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000183
literal: cellular Potts model

AnnotationAssertion
IRI: /MAMO_0000184
literal: http://en.wikipedia.org/wiki/Potts_model

AnnotationAssertion
IRI: /MAMO_0000184
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000184
literal: standard Potts model

AnnotationAssertion
IRI: /MAMO_0000185
literal: http://en.wikipedia.org/wiki/Ising_model

AnnotationAssertion
annotation:
  literal: Duke TA, Bray D. Heightened sensitivity of a lattice of membrane receptors. Proc Natl Acad Sci U S A. 1999;96(18):10104-8.
IRI: /MAMO_0000185
literal: http://identifiers.org/pubmed/10468569

AnnotationAssertion
IRI: /MAMO_0000185
literal: 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,

AnnotationAssertion
IRI: /MAMO_0000185
literal: Ising model

AnnotationAssertion
annotation:
  literal: 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.
IRI: /MAMO_0000186
literal: http://identifiers.org/pubmed/19343194

AnnotationAssertion
IRI: /MAMO_0000186
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000186
literal: fuzzy logic model

AnnotationAssertion
annotation:
  literal: Snoussi EH. Qualitative dynamics of piecewise-linear differential equations: a discrete mapping approach. Dyn Stab Syst 1989, 4(3-4): 565-583
IRI: /MAMO_0000187
literal: http://identifiers.org/doi/10.1080/02681118908806072

AnnotationAssertion
IRI: /MAMO_0000187
literal: multi-valued logical model with multiple thresholds to assign variable values, logical parameters controlling the assignments, and asynchronous update.

AnnotationAssertion
IRI: /MAMO_0000187
literal: generalized logical model

AnnotationAssertion
IRI: /MAMO_0000188
literal: http://en.wikipedia.org/wiki/Biochemical_systems_theory

AnnotationAssertion
annotation:
  literal: 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.
IRI: /MAMO_0000188
literal: http://identifier.org/pubmed/5387047

AnnotationAssertion
IRI: /MAMO_0000188
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000188
literal: S system model

AnnotationAssertion
annotation:
  literal: Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 57
IRI: /MAMO_0000189
literal: http://identifier.org/isbn/978-0387-27197-2

AnnotationAssertion
IRI: /MAMO_0000189
literal: https://en.wikipedia.org/wiki/Linear_model#Time_series_models

AnnotationAssertion
IRI: /MAMO_0000189
literal: pharmacokinetic model expressed as differential equations in which partial derivatives with respect to any of the model parameters are independent of the other parameters

AnnotationAssertion
IRI: /MAMO_0000189
literal: linear pharmacokinetic model

AnnotationAssertion
annotation:
  literal: Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 181
IRI: /MAMO_0000190
literal: http://identifier.org/isbn/978-0387-27197-2

AnnotationAssertion
IRI: /MAMO_0000190
literal: linear pharmacokinetic model which contain both fixed and random effects (mixed)

AnnotationAssertion
IRI: /MAMO_0000190
literal: linear mixed effect pharmacokinetic model

AnnotationAssertion
annotation:
  literal: Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 93
IRI: /MAMO_0000191
literal: http://identifier.org/isbn/978-0387-27197-2

AnnotationAssertion
IRI: /MAMO_0000191
literal: 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

AnnotationAssertion
IRI: /MAMO_0000191
literal: nonlinear pharmacokinetic model

AnnotationAssertion
annotation:
  literal: Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 205
IRI: /MAMO_0000192
literal: http://identifier.org/isbn/978-0387-27197-2

AnnotationAssertion
IRI: /MAMO_0000192
literal: nonlinear pharmacokinetic model which contain both fixed and random effects (mixed)

AnnotationAssertion
IRI: /MAMO_0000192
literal: nonlinear mixed effect pharmacokinetic model

AnnotationAssertion
IRI: /MAMO_0000193
literal: https://en.wikipedia.org/wiki/Markov_model

AnnotationAssertion
IRI: /MAMO_0000193
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000193
literal: Markov model

AnnotationAssertion
IRI: /MAMO_0000194
literal: http://en.wikipedia.org/wiki/Hidden_Markov_model

AnnotationAssertion
IRI: /MAMO_0000194
literal: HMM

AnnotationAssertion
IRI: /MAMO_0000194
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000194
literal: hidden Markov model

AnnotationAssertion
IRI: /MAMO_0000195
literal: http://en.wikipedia.org/wiki/Markov_chain

AnnotationAssertion
IRI: /MAMO_0000195
literal: DTMC

AnnotationAssertion
IRI: /MAMO_0000195
literal: discrete-time Markov chain

AnnotationAssertion
IRI: /MAMO_0000195
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000195
literal: Markov chain

AnnotationAssertion
IRI: /MAMO_0000196
literal: http://en.wikipedia.org/wiki/Markov_decision_process

AnnotationAssertion
IRI: /MAMO_0000196
literal: MDP

AnnotationAssertion
IRI: /MAMO_0000196
literal: a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.

AnnotationAssertion
IRI: /MAMO_0000196
literal: Markov decision process

AnnotationAssertion
IRI: /MAMO_0000197
literal: http://en.wikipedia.org/wiki/Markov_random_field

AnnotationAssertion
IRI: /MAMO_0000197
literal: MRF

AnnotationAssertion
IRI: /MAMO_0000197
literal: Markov network

AnnotationAssertion
IRI: /MAMO_0000197
literal: undirected graphical model

AnnotationAssertion
IRI: /MAMO_0000197
literal: 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.

AnnotationAssertion
IRI: /MAMO_0000197
literal: Markov random field

AnnotationAssertion
IRI: /MAMO_0000198
literal: http://en.wikipedia.org/wiki/Piecewise_linear_function

AnnotationAssertion
annotation:
  literal: Glass L, Kauffman SA. The logical analysis of continuous, non-linear biochemical control networks. J Theor Biol. 1973;39(1):103-29.
IRI: /MAMO_0000198
literal: http://identifiers.org/pubmed/4741704

AnnotationAssertion
IRI: /MAMO_0000198
literal: model based on piecewise linear differential equations

AnnotationAssertion
IRI: /MAMO_0000198
literal: model using piecewise linear differential equations

AnnotationAssertion
IRI: /MAMO_0000198
literal: piecewise linear differential equations model

AnnotationAssertion
IRI: /MAMO_0000198
literal: model using functions that respond to parameter values via threshold (Heavyside) functions.

AnnotationAssertion
IRI: /MAMO_0000198
literal: piecewise linear differential equation model

AnnotationAssertion
annotation:
  literal: 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.
IRI: /MAMO_0000199
literal: http://identifiers.org/pubmed/16521027

AnnotationAssertion
IRI: /MAMO_0000199
literal: model using piecewise linear differential equations in which variables are updated only at certain times.

AnnotationAssertion
IRI: /MAMO_0000199
literal: discrete time piecewise linear differential equation model

AnnotationAssertion
annotation:
  literal: 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.
IRI: /MAMO_0000200
literal: http://identifiers.org/pubmed/14871568

AnnotationAssertion
IRI: /MAMO_0000200
literal: piecewise linear differential equation model in which the variables can only take a discrete number of levels.

AnnotationAssertion
IRI: /MAMO_0000200
literal: qualitative piecewise linear differential equation model

AnnotationAssertion
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literal: http://en.wikipedia.org/wiki/Protein%E2%80%93protein_interaction

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IRI: /MAMO_0000202
literal: http://identifiers.org/pubmed/22385417

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literal: PIN

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literal: PPI

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literal: PPI network

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literal: PPIN

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literal: PPIs

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IRI: /MAMO_0000202
literal: A network representing intentional physical contacts established between two or more proteins as a result of biochemical events and/or electrostatic forces.

AnnotationAssertion
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literal: protein-protein interaction network

AnnotationAssertion
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literal: http://en.wikipedia.org/wiki/Physiologically_based_pharmacokinetic_modelling

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IRI: /MAMO_0000203
literal: http://identifiers.org/pubmed/23945604

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literal: PBPK

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literal: A model using a series of differential equations that are parametrized with known physiological variables and represent a quantitative mechanistic framework by which the absorption, distribution, metabolism, and excretion (ADME) of new drugs can be described.

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literal: physiologically based pharmacokinetic model

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literal: https://en.wikipedia.org/wiki/Square-lattice_Ising_model

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literal: 2D Ising model [http://identifiers.org/mamo/MAMO_0000185] of interacting magnetic spins on a square lattice.

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literal: square-lattice Ising model

AnnotationAssertion
annotation:
  literal: Karr et al. A whole-cell computational model predicts phenotype from genotype. Cell. 2012; 150(2):389-401.
IRI: /MAMO_0000205
literal: http://identifiers.org/pubmed/22817898

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literal: Computational model that includes all of the molecular components of a biological cell and their interactions.

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literal: whole-cell model

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