owlDef
The range of skos:altLabel is the class of RDF plain literals.
skos:prefLabel, skos:altLabel and skos:hiddenLabel are pairwise disjoint properties.
alternative label
An alternative lexical label for a resource.
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).
definition
A statement or formal explanation of the meaning of a concept.
example
An example of the use of a concept.
hidden label
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.
The range of skos:hiddenLabel is the class of RDF plain literals.
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.
The range of skos:prefLabel is the class of RDF plain literals.
preferred label
The preferred lexical label for a resource, in a given language.
hasFeature
isUsedToModel
?X subclassOf: not (hasFeature some ?Y)
An object property to be used in the OBO version of MAMO to express negation.
lacksFeature
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.
epidemiological compartmental model
Brauer F. Compartmental Models in Epidemiology. Mathematical Epidemiology. 2008;1945:19-79.
SIR
susceptible-infectious-removed
susceptible-infectious-removed model
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.
SIR model
Kermack, W. O. and McKendrick, A. G. "A Contribution to the Mathematical Theory of Epidemics." Proc. Roy. Soc. Lond. A 115, 700-721, 1927.
SIS
susceptible-infectious-susceptible
susceptible-infectious-susceptible model
A compartmental epidemiological model with two compartments: susceptible S and infectious I. The passage of individuals is modelled from S to I and back.
SIS model
SEIR
susceptible-exposed-infectious-recovered
susceptible-exposed-infectious-recovered model
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.
SEIR model
SEIS
susceptible-exposed-infectious-susceptible
susceptible-exposed-infectious-susceptible model
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.
SEIS model
PK-PD model
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.
pharmacokinetic-pharmacodynamic model
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.
statistical data whose individual element can take any real number value.
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.
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.
A set of ordinary differential equation describing a physical process.
mathematical model
Model that describes how one or more random variables are related to one or more random variables.
The relationship between the size and the age.
statistical model
Model which describes a system where the state variable values do not vary.
steady-state model
Can be analysed using flux balance analysis [http://identifiers.org/biomodels.kisao/KISAO_0000437].
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.
constraint-based model
Reed JL, Palsson BO (2003) Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol, 185(9):2692-9.
Value that may change within the scope of a given model or set of operations.
variable
Synonyms: input variable
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.
independent variable
Synonyms: output variable
variable which is measured in an experiment, and that is affected during the experiment. A dependent variable depends on other variables
Example: in an experiment one controls x and measure y, with a relationship y = ax+b, y is depending on x.
dependent variable
pharmacokinetics model
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).
pharmacokinetic model
pharmacodynamics model
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.
pharmacodynamic model
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.
multiphysics model
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.
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.
rule-based model
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.
weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, neural network models.
computational model
Synonym: multi-agent model
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
agent-based model
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.
Petri net
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.
computational Neuroscience model
information obtained about a system by the application of an analysis procedure to a model of this system
timecourse of a concentation; EC50
readout
type of mathematical model that is applied to the study of population dynamics.
population model
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.
matrix population model
algebraic logic model
logic model
model where the discrete values of variables (also called levels) is determined by logical combinations of the values of other variables.
logical model
evolution of a variable value over time
timecourse
model which take into account the spatial distribution or geometric characteristics of the entities described by its variables.
spatial characteristic
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.
chemical reaction diffusion model
model where the space is split into a number of subspaces (sometimes called voxels) that are each considered homegenous and isotropic.
finite element spatial model
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.
network model
Biochemical network studies chemical processes within and relating to, living organisms.
biochemical network
model
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.
Signal transduction is a process for cellular communication where the cell receives (and responds to) external stimuli from other cells and from the environment.
signalling network
Albert R, Wang R (2009) Discrete dynamic modeling of cellular signaling networks. Methods Enzymol 467:281–306
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.
gene regulatory network
Schlitt T, Brazma A (2007) Current approaches to gene regulatory network modelling. BMC Bioinf 8(Suppl 6):S9
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.
metabolic network
Palsson B (2006) Systems Biology: Properties of Reconstructed Networks. Cambridge University Press
Bayes model
Bayes network
Bayesian network
belief network
probabilistic DAG model
probabilistic directed acyclic graphical model
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.
Bayesian model
dynamic Bayes model
dynamic Bayes network
dynamic Bayesian network
dynamic belief network
dynamic probabilistic DAG model
dynamic probabilistic directed acyclic graphical model
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.
dynamic 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
process calculus
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.
process algebra
differential equations model
model based on differential equations
model using differential equations
Differential equations describe the rate of change of continuous variables. They are typically used for modeling dynamical systems in several areas.
differential equation model
ODE model
model based on ordinary differential equations
model using ordinary differential equation
ordinary differential equations model
model using equations containing a function of one independent variable and its derivatives.
ordinary differential equation model
SDE model
model based on stochastic differential equations
model using stochastic differential equations
stochastic differential equations model
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
stochastic differential equation model
PDE model
model based on partial differential equations
model using partial differential equations
partial differential equations model
model using differential equations that contains unknown multivariable functions and their partial derivatives.
partial differential equation model
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.
interacting state machine
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.
cellular automaton
DSA
Dynamic cellular automata are a variation of cellular automata that allows for movement of the cell contents inside the grid, mimicking brownian motion.
dynamic 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.
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.
stochastic cellular automaton
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.
boolean model
Kauffman S (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22(3):437–467
modeling entity feature
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.
modelling entity feature
Characterises the evolution of a modelling entity over time.
temporal quality
Characterises a modelling entity that evolves over time.
dynamical characteristic
Characterises a modelling entity that does not evolve over time.
static characteristic
Characterises the possibility to be measured numerically.
quantitative characteristic
Which values can be enumerated.
discrete characteristic
Which values cannot be enumerated. Whatever two values, there is always another value in between.
continuous characteristic
Which satisfies the principles of superposition and scaling.
linear quality
Characterises the certainty, or lack of, of the modelling entity feature.
uncertainty level
Which value or behaviour is certain.
deterministic nature
stochastic nature
Which can exhibit alternative values or behaviours with differnent probability.
probabilistic nature
CP-net
CPN
colored Petri net
graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role
coloured Petri net
Jensen K, Kristensen LM. Coloured Petri Nets: Modeling and Validation of Concurrent Systems. Berlin: Springer, 2009.
continuous PN
Petri net model in which the number of marks in the places are real numbers instead of integers
continuous Petri net
David R, Alla H. Discrete, continuous, and hybrid Petri Nets. Berlin, London: Springer, 2010.
functional PN
functional PNs
self-modified PN
self-modified PNs
self-modified Petri net
A Petri net that allows the flow relations between places and transitions to depend on the marking.
functional Petri net
Chaouiya C. Petri net modelling of biological networks. Brief Bioinform. 2007;8(4):210-9.
HFPN
HFPNs
Hybrid functional PNs
hybrid functional PN
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.
hybrid 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.
HPN
HPNs
hybrid PN
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).
hybrid Petri net
SPN
stochastic PN
form of Petri net where the transitions fire after a probabilistic delay determined by a random variable.
stochastic Petri net
logit regression
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).
logistic regression
statistical model containing both fixed effects and random effects, that is mixed effects.
mixed model
multinomial logit
softmax regression
regression model which generalizes logistic regression by allowing more than two discrete outcomes.
multinomial logistic regression
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.
Poisson regression
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.
binary data variable
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.
categorical data variable
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.
count data variable
repeated observations of the same variables over time.
longitudinal data variable
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.
phase portrait
value that does not change over time
steady state value
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.
statistical variable
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.
population pharmacokinetics model
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.
mesoscopic model
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.
single particle spatial model
Tolle D., Le Novère N. Particle-based Stochastic Simulation in Systems Biology. Current Bioinformatics (2006), 1: 315-320.
DDE model
delayed differential equations model
model based on delayed differential equations
model using delayed differential equations
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.
delayed differential equation model
DAE model
differential algebraic equations model
model based on differential algebraic equations
model using differential algebraic equations
general form of (systems of) differential equations for vector–valued functions x in one independent variable,
differential algebraic equation model
domain model
model used to describe and quantify interactions between xenobiotics and patients.
pharmacometrics model
logical model in which variables can take more than two levels.
multi-value logic 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
Glazier and Graner model
extended large-q Potts model
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.
cellular Potts 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.
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.
standard Potts model
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,
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.
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.
fuzzy logic 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.
multi-valued logical model with multiple thresholds to assign variable values, logical parameters controlling the assignments, and asynchronous update.
generalized logical model
Snoussi EH. Qualitative dynamics of piecewise-linear differential equations: a discrete mapping approach. Dyn Stab Syst 1989, 4(3-4): 565-583
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.
S system model
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.
pharmacokinetic model expressed as differential equations in which partial derivatives with respect to any of the model parameters are independent of the other parameters
linear pharmacokinetic model
Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 57
linear pharmacokinetic model which contain both fixed and random effects (mixed)
linear mixed effect pharmacokinetic model
Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 181
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
nonlinear pharmacokinetic model
Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 93
nonlinear pharmacokinetic model which contain both fixed and random effects (mixed)
nonlinear mixed effect pharmacokinetic model
Bonate PL (2006) Pharmacokinetic-Pharmacodynamic; Modeling and simulation. p 205
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.
Markov model
HMM
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.
hidden Markov model
DTMC
discrete-time Markov chain
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.
Markov chain
MDP
a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.
Markov decision process
MRF
Markov network
undirected graphical model
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.
Markov random field
model based on piecewise linear differential equations
model using piecewise linear differential equations
piecewise linear differential equations model
model using functions that respond to parameter values via threshold (Heavyside) functions.
piecewise linear differential equation model
Glass L, Kauffman SA. The logical analysis of continuous, non-linear biochemical control networks. J Theor Biol. 1973;39(1):103-29.
model using piecewise linear differential equations in which variables are updated only at certain times.
discrete time 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.
piecewise linear differential equation model in which the variables can only take a discrete number of levels.
qualitative 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.
PIN
PPI
PPI network
PPIN
PPIs
A network representing intentional physical contacts established between two or more proteins as a result of biochemical events and/or electrostatic forces.
protein-protein interaction network
Koh et al. Analyzing protein-protein interaction networks. J Proteome Res. 2012; 11(4):2014-31.
PBPK
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.
physiologically based pharmacokinetic model
HM Jones and K Rowland-Yeo Basic Concepts in Physiologically Based Pharmacokinetic Modeling in Drug Discovery and Development (2013) Pharmacometrics Syst Pharmacol. 2(8): e63.
2D Ising model [http://identifiers.org/mamo/MAMO_0000185] of interacting magnetic spins on a square lattice.
square-lattice Ising model
Computational model that includes all of the molecular components of a biological cell and their interactions.
whole-cell model
Karr et al. A whole-cell computational model predicts phenotype from genotype. Cell. 2012; 150(2):389-401.