Abstract
So far we have considered one-dimensional and two-dimensional release processes. When the channel can take on two states—open or closed—we have seen that the associated probability density functions are governed by 2 × 2 systems of partial differential equations. When a drug is added to the Markov model, an extra state is introduced associated with either the open or the closed state and we obtain a model for the probability density functions phrased in terms of 3 × 3 systems of partial differential equations. In subsequent chapters, we will study situations involving many states and, to do so without drowning in cumbersome notation, we need mathematical formalism to present such models compactly. The compact form we use here is taken from Huertas and Smith [35]. We will introduce the more compact notation simply by providing a couple of examples. These will, hopefully, clarify how to formulate rather complex models in an expedient manner.
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So far we have considered one-dimensional and two-dimensional release processes. When the channel can take on two states—open or closed—we have seen that the associated probability density functions are governed by 2 × 2 systems of partial differential equations. When a drug is added to the Markov model, an extra state is introduced associated with either the open or the closed state and we obtain a model for the probability density functions phrased in terms of 3 × 3 systems of partial differential equations. In subsequent chapters, we will study situations involving many states and, to do so without drowning in cumbersome notation, we need mathematical formalism to present such models compactly. The compact form we use here is taken from Huertas and Smith [35]. We will introduce the more compact notation simply by providing a couple of examples. These will, hopefully, clarify how to formulate rather complex models in an expedient manner.
7.1 Two-Dimensional Calcium Release Revisited
Let us start by recalling that the two-dimensional process of calcium release illustrated in Fig. 5.2 on page 92 can be modeled as
where \(\bar{\gamma }=\bar{\gamma } (t)\) is a stochastic variable governed by a Markov model represented by a reaction scheme of the form
We have seen (see, e.g., page 102) that the probability density functions of the open state (ρ o ) and the closed state (ρ c ) are governed by the system
where
To prepare ourselves for more complex systems, we number the states in this simple system with i = 1, 2, where i = 1 is for the open state and i = 2 is for the closed state. The system can now be written in the form
where \(\left (K\rho \right )_{i}\) denotes the ith component of the matrix vector product K ρ. Here the vector ρ is given by
and the matrix is given by
Furthermore, we introduce the functions
where γ i is one for the open state (i.e., i = 1) and zero for the closed state (i.e., i = 2).
7.2 Four-State Model
It useful to illustrate this compact notation for a slightly more complex model based on four states. Suppose that the Markov model governing the stochastic variable \(\bar{\gamma }\) in model (7.1) and (7.2) is based on four states: two open states O 1 and O 2 and two closed states C 1 and C 2, as shown in Fig. 7.1.
The probability density system associated with the model (7.1) and (7.2) when the Markov model is given by Fig. 7.1 can now be written in the form
where
By defining the states O 1, O 2, C 1, and C 2 to be the states 1, 2, 3, and 4, respectively, we can write the system (7.6) in the more compact form
for i = 1, 2, 3, 4, where
and ρ = (ρ 1, ρ 2, ρ 3, ρ 4)T. Here γ i is one for the open states (i.e., i = 1 and i = 2) and zero for the closed states (i.e., i = 3 and i = 4). Furthermore, the matrix is given by
which in compact notation is
7.3 Nine-State Model
We have seen how to formulate probability density systems for two-state and four-state Markov models. For even larger Markov models, it is useful to introduce two-dimensional numbering. This will be illustrated using the nine-state model given in Fig. 7.2.
Here S ij , i, j = 1, 2, 3, denotes the states of the Markov model and K ij mn denotesFootnote 1 the reaction rate from the state S ij to the state S mn . The system governing the probability density functions of these states can be written in the form
where
Here ρ ij denotes the probability density function of the state S ij and we use the convention that K ij mn = 0 for \(i,j,m,n\notin \left \{1,2,3\right \}.\) We also have
where γ ij = 1 when the state S ij represents an open state and γ ij = 0 when S ij represents a closed state.
Notes
- 1.
We use K ij as shorthand for K i, j , but we use the comma when an index of the form j + 1 is needed, that is we write K i, j+1.
References
M.A. Huertas, G.D. Smith, The dynamics of luminal depletion and the stochastic gating of Ca2+-activated Ca2+ channels and release sites. J. Theor. Biol. 246(2), 332–354 (2007)
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Tveito, A., Lines, G.T. (2016). Generalized Systems Governing Probability Density Functions. In: Computing Characterizations of Drugs for Ion Channels and Receptors Using Markov Models. Lecture Notes in Computational Science and Engineering, vol 111. Springer, Cham. https://doi.org/10.1007/978-3-319-30030-6_7
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