Abstract
The biological processes necessary for the development and continued survival of any organism are often strongly influenced by the transport properties of various biologically active species. The transport phenomena involved vary over multiple temporal and spatial scales, from organismlevel behaviors such as the search for food, to systemic processes such as the transport of oxygen from the lungs to distant organs, down to microscopic phenomena such as the stochastic movement of proteins in a cell. Each of these processes is influenced by many interrelated factors. Identifying which factors are the most important, and how they interact to determine the overall result is a problem of great importance and interest. Experimental observations are often fit to relatively simple models, but in reality the observations are the output of complicated functions of the physicochemical, topological, and geometrical properties of a given system. Herein we use multistate continuoustime random walks and generalized master equations to model transport processes involving spatial jumps, immobilization at defined sites, and stochastic internal state changes. The underlying spatial models, which are framed as graphs, may have different classes of nodes, and walkers may have internal states that are governed by a Markov process. A general form of the solutions, using Fourier–Laplace transforms and asymptotic analysis, is developed for several spatially infinite regular lattices in one and two spatial dimensions, and the theory is developed for the analysis of transport and internal state changes on general graphs. The goal in each case is to shed light on how experimentally observable macroscale transport coefficients can be explained in terms of microscale properties of the underlying processes. This work is motivated by problems arising in transport in biological tissues, but the results are applicable to a broad class of problems that arise in other applications.
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Notes
The subscript c indicates the composite process rather than the pure space jump process.
In a finite regular graph boundary vertices have a different degree, but the boundary conditions determine how the degree changes. See (Othmer and Scriven 1971) for the structurally identical problem of diffusion between coupled cells in a finite regular lattice with various boundary conditions.
The subscripts s and e are meant to indicate the start (\(k=1\)) and end (\(k=n_k\)) of an edge. However, this is merely for labeling purposes and does not mean that the transport is directed.
Homogeneous in a lattice as in a continuum, means that T(x, y) and \(\phi (ty)\) do not vary with current position of a particle, as in the continuum case.
Both are formally equivalent if one replaces z by \(e^{i\omega }\) in \(\varvec{g}(z,t)\)). One advantage of the Fourier transform method is that the lattice spacings can be included as multiplying factors in the Fourier transform, e.g., \(e^{i\varDelta X\omega }\) whereas the generating function approach is defined only for integer powers of z. What this means is that the moments defined above must be multiplied by appropriate length factors related to the lattice spacing to obtain the correct results, whereas in the Fourier transform approach, these factors are included automatically.
In this sense, the probability p(y) defined at each lattice point can be thought of as the Fourier series for the function \({\hat{p}}(\nu )\). Thus, the inverse lattice Fourier transform is simply the integral that gives the Fourier coefficients of \(p(\nu )\).
These two quantities technically represent the probabilities associated with particles having reached membrane i (right or left) and not having yet reached a subsequent cell membrane.
If particle immobilization is irreversible the following analysis simplifies somewhat, for Eq. (73) is then independent of \(p_i^{(2,2)}\) and \(k_+\) takes the place of \(k_d\) in the macroscopic parameters.
The reduction of \(\varvec{A}_1\) to \(2n_e\varvec{I}\) is valid only under the restrictions discussed below.
Each junction can still have arbitrarily many internal states. A single type of junction refers to all junctions in a lattice having the same connectivity, via a single edge, to other junctions in that lattice. For example, in a square lattice, each junction is connected to its neighbors to the north, south, east, and west. This differs from the exterior hexagonal lattice where type I junctions are connected to their neighbors to the north, southeast, and southwest; and type II junctions to neighbors to their south, northeast, and northwest.
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Acknowledgements
Supported in part by NIH Grants # GM29123 and 54CA210190 and NSF Grants DMS # 178743 and 185357.
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Dedicated to James D. Murray on his 90th birthday. Jim was a pioneer in Mathematical Biology, and as a mentor to many his leadership and vision have had an enormous impact on the development of the field.
Appendices
Appendix A: Definition of the Kronecker Product
We define the tensor product of vectors as
and if \(\mathbf{R}\) is an \(N \times N\) matrix and \(\mathbf{T}\) an \(n \times n\) one, their tensor product is the \(Nn \times Nn\) matrix
As we have seen throughout, multistate random walks can be conveniently written as matrix–vector problems involving Kronecker products. An important property of matrices and vectors formed as Kronecker products is that one can compute Fourier transforms on the first and second terms of the Kronecker product separately. For instance if \(\varvec{F}\) is a discrete Fourier transform matrix with Hermitian adjoint \(\varvec{F}^{\prime }\),
Kronecker products provide an easy way to describe certain multidimensional problems in terms of simpler onedimensional problems (Othmer and Scriven 1971). Each additional dimension is, roughly speaking, included by appending an additional Kronecker product to the previous transition matrix. This also applies in cases where the dimension of the state space is increased by adding internal state transitions to a spatial jump process.
Appendix B: The Exterior Hexagonal Lattice
In an exterior hexagonal lattice, there are three orientations of edges that occur, and two types of junctions: those centered at upwards facing trijunctions (type I), and those at downwards facing trijunctions (type II), see Fig. 12. Let us assume here that \(\varvec{T}_{SV}\) and \(\varvec{K}_{SV}\) are the same for each edge, and that \(\varvec{K}_J\) is the same for all junctions. Likewise we assume that \(\varvec{D}_s\), \(\varvec{D}_e\), \(\varvec{D}_e^{\prime }\), and \(\varvec{D}_s^{\prime }\) do not vary depending on the edge or junction being considered.
With two types of junctions that alternate, an easy way of assigning which edges start or end at which junctions is to require that all edges start at a type I junction and end at a type II junction (see the edges labeled “e” and “s” in Fig. 12). It is also helpful to specify some type of labeling system on the junctions and edges. To do so, we consider the combination of three edges around a type I vertex, and the type II junction attached at the end of the vertical edges to be labeled with the same label, (I, J) (see Fig. 12). The lattice position associated with this structure is given as \(\varvec{X}\), the position of the type I vertex. Of course, the positions of each SV on an edge can be found as some displacement, \(\varvec{x}\) from \(\varvec{X}\).
With this description, type I edges are vertical edges that pair type I and type II points with the same lattice index, e.g., \(\varvec{\varDelta I}_1={\varvec{0}}\). Type II edges are diagonal edges that pair type I lattice points and type II lattice points that differ with \(\varvec{\varDelta I}_2 = (0,1)\) from start to end. Finally, type III edges pair type I and type II points with \(\varvec{\varDelta I}_3 = (1,1)\) from start to end.
Since type I points are attached to the start of each edge and type II to the end of each edge, if we structure the probability density vector as \(\varvec{p} = (\varvec{p}_I, \varvec{p}_{II}, \varvec{p}_{e_1}, \varvec{p}_{e_2}, \varvec{p}_{e_3})^T\), we may write the overall transition matrix \(\varvec{T}+\varvec{K}\) as
We apply a lattice Fourier transformation to \(\varvec{X}\) to obtain
where \(\varvec{\varDelta X}_m\) are as defined in Eq. (86). Once the local transitions in \(\varvec{K}_J\), \(\varvec{K}_{SV}\), \(\varvec{T}_{SV}\), \(\varvec{D}_m\), and \(\varvec{D}^{\prime }_m\) are specified, substituting this matrix into Eq. (33) and solving for \(\varvec{q}\) and \(\varvec{p}\) will yield a solution for the spatial distribution of a particle over the hexagonal lattice with possible internal states and SVs between junction points. If we make use of the same definitions for \(\varvec{D}\), \(\varvec{D}^{\prime }\), \(\varvec{T}_{SV}\), \(\varvec{K}_{SV}\) and \(\varvec{K}_J\) from the interior lattice example in Sect. 6, with \(n_k=1\) and \(n_s=2\), we obtain a \(8\times 8\) matrix (after removing the rows and columns associated with immobile states at the junctions as in Sect. 6) that can be inverted to solve for \(\varvec{p}\) in Fourier–Laplace transform space. For instance, with \(1f\) the proportion of particles that are immobilized at the boundary, the steadystate concentration at the junctions can be found as
In closing this section, we note that random walks on exterior hexagonal lattices have been studied several times previously (Montroll 1969; Hughes 1996; Henyey and Seshadri 1982); however, in those cases, no internal states were considered. Thus, our formulation here provides a straightforward way to extend these previous results to more complicated transport processes. We also note that the lattice Green’s function for the exterior hexagonal lattice with no internal states or SVs is known (Henyey and Seshadri 1982; Hughes 1996). In Fourier transform space, the Green’s function for a hexagonal lattice where each edge is taken in one step, and the transition probabilities at each intersection are all 1/3, is of the form
Of course, when we set \(n_k=1\), \(n_s=1\), and replace \(\psi (t)\) with z in Eq. (B.2), we would obtain this result. The \(ij^{\text{ th }}\) element of \(\varvec{p}^H\) gives the occupation probability for a random walk that started on a type j vertex and is currently on a type i vertex.
Our result here appears to differ slightly from the previous results for this Green’s function. This because we have directly included the displacement, \(\varvec{\varDelta X}_i\) into the computation, rather than just using changes in index \(\varvec{\varDelta I}_i\), which yield precisely the result previously reported. In other words, Eq. (B.3) gives the probability of being located at position \(\varvec{X}_{\varvec{I}}\), whereas previous results give the probability of being located at index \(\varvec{I}\).
B.1 The Exterior Hexagonal Lattice with Arbitrary \(n_k\)
Here, we consider the hexagonal lattice with \(n_k\) SVs and \(n_s=1\). The FPT method from Sect. 4.1 becomes essential here as with arbitrary \(n_k\), directly solving the resulting matrices is nontrivial.
From Appendix B, we found that the form of \(\hat{\varvec{T}}(\varvec{\omega })+\hat{\varvec{K}}(\varvec{\omega })\) for the exterior hexagonal lattice. In this example, \(n_s=1\), so \(\varvec{K}_{SV}= \varvec{K}_J={\varvec{0}}\). We also have that
and
Lastly, \(\varvec{T}_{SV}\) is the matrix for a 1D random walk on a segment of length \(n_k\) with absorbing boundaries. This can be written as
Since there is one internal state, \(\varvec{K}_{SV}={\varvec{0}}\). Since each edge has the same \(\varvec{T}_{SV}\), \(\varvec{D}\) and \(\varvec{D}^{\prime }\), we may use the formulation in Appendix D.2 to obtain the effective transition rates, \(\varvec{T}_{2,r}^{\text{ eff }}\).
To do so, start by noting that since \(n_s=1\), \(\varvec{T}_{1,r}^{\text{ eff }}\) and \(\varvec{K}_{r}\) are both equal to 0 and \(\varvec{T}_{2,r}^{\text{ eff }}\) is a scalarvalued function. Since the type I and type II junctions are equivalent in terms of the transitions that occur around them, \(\varvec{T}_{2,r}^{\text{ eff }}\) is found independent of whether the particle is at a type I or type II junction.
This leaves us with the following matrix equation:
for \({\tilde{f}}(s)\). Solving for \({\tilde{f}}_1\) via a Schur complement yields,
and the remaining elements of \(\tilde{\varvec{f}}\) are found as
Finally,
The next step is of course to explicitly solve for this FPT. Since each edge of the lattice is simply a 1D segment with absorbing boundaries, the discussion in Appendix E provides us with an explicit form of the solution to \((\varvec{I}\varvec{T}_{SV})^{1}\). After some algebraic simplifications, we find that
Since \(\varvec{K}=0\) and \(\varvec{T}_{1,r}^{\text{ eff }}=0\), this reduces the problem to a singlestate CTRW on a hexagonal lattice. Thus, we can make use of formula in Eq. (B.3) with z replaced by \(\psi ^{\text{ eff }} = 3\varvec{T}_{2,r}^{\text{ eff }}\). The factor of 3 comes from the fact that \(\varvec{T}_{2,r}^{\text{ eff }}\) is in fact the effective waiting time distribution multiplied by the probability that the particle actually travels down a particular edge. This probability is 1/3 since each junction connects three edges in the exterior hexagonal lattice, and each edge has the same probability of being traveled on.
With this result, it is possible to directly compute the moments for the hexagonal lattice with \(n_K\) SVs. However, assuming each segment has a fixed total length of 1 independent of \(n_k\), the exact formulas become
with \(a=(1\sqrt{1\psi ^2})\psi ^{1}\). For a Poisson distributed waiting time \(\psi =\lambda e^{\lambda t}\), we obtain the following results for the first several \(n_k=1,2,\dots \)
In these results, \(\lambda \) and \(n_k\) are independent. However, if this hexagonal lattice is instead interpreted as a discretization of a continuum problem with 1D diffusion along segments of the lattice, then \(\lambda \sim n_k^2D\) where D is the diffusivity along the segment. In that case, the leadingorder term for any \(n_k\) is
Although we did not specify the direction of diffusion here, it turns out that for the hexagonal lattice, whether diffusion is considered along the x or y direction, the results are equivalent.
Note that the longterm asymptotics may be obtained easily, at least for a single internal state, from the theory developed in Roerdink and Shuler (1985a). However, the full time dependence is not computable under that theory.
Appendix C: General Form of \(\varvec{T}\) and \(\varvec{K}\)
Recall from Eqs. (33)–(36) that the solution \(\varvec{p}(\varvec{X},t\{k_0,\ell _0\})\), to a transport problem depends on the initial concentration distribution, and on the form of the transition matrix. Equation (33) may be written as,
with
We now present a general structure for matrices \(\varvec{T}\) and \(\varvec{K}\) that can include the examples studied, along with other examples that may arise in a variety of applications.
Recall that the number of SVs is \(n_k\), the number of types of edges, \(n_e\), and the number of internal states, \(n_s\). The global matrices \(\varvec{T}\varvec{\phi }\) and \(\varvec{K}\varvec{\varLambda }\) can be written in blockstructured formats, and will be understood here as functions of the Laplace transform variable s. We also will abuse notation here and write \(\varvec{T}\) and \(\varvec{K}\) rather than \(\varvec{T}\varvec{\phi }\) and \(\varvec{K}\varvec{\varLambda }\). Recall also that \(\varvec{K}\) involves only transitions between internal states, and no spatial movement whereas \(\varvec{T}\) involves spatial movement without any change in the internal state. Let \(\varvec{K}_J\in {\mathbb {R}}^{n_s\times n_s},\varvec{T}_{SV},\ \varvec{K}_{SV}\in {\mathbb {R}}^{(n_{k}n_s)\times (n_{k}n_s)}\), \(\varvec{D}^{\prime }_s,\varvec{D}^{\prime }_e\in {\mathbb {R}}^{(n_{k}n_s)\times n_s},\) and \(\varvec{D}_s,\varvec{D}_e\in {\mathbb {R}}^{n_s\times (n_{k}n_s)}\) be various blocks of \(\varvec{T}\) and \(\varvec{K}\). In particular,

\(\varvec{K}_J\) characterizes transitions between the internal states at each junction

\(\varvec{K}_{SV}\) characterizes transitions between internal states associated with the SVs along each edge

\(\varvec{T}_{SV}\) characterizes jumps between SVs on an edge

\(\varvec{D}_s\) and \(\varvec{D}_e\) characterize transitions from the SVs to a vertex

\(\varvec{D}^{\prime }_s\) and \(\varvec{D}^{\prime }_e\) characterize transitions from a junction to an SV
The transitions represented by these matrices are diagrammed in Fig. 5. The indices s and e on \(\varvec{D}\) and \(\varvec{D}^{\prime }\) are used to distinguish whether the transfers occur at the “start” or “end” of each edge (see Fig. 5). This is necessary since when the SVs are labeled from \(k=1\) to \(k=n_k\), different (but closely related) matrices are needed to describe transfers from \(k=1\) (start) and \(k=n_k\) (end). Recall Ex. 2 in Sect. 2.5 where \(\varvec{D}_s\) and \(\varvec{D}_e\) were explicitly constructed. This labeling does not imply that an edge is directed or undirected, but merely serves as a way of specifying the exact positions of SVs along the edge.
Next, define \(\varvec{I}\) as the infinitedimensional identity matrix over the lattice positions, \(\varvec{X}\), and let \(\varvec{A}_{s,m}\) and \(\varvec{A}_{e,m}\) be infinitedimensional adjacency matrices that specify which edges start and end at a given vertex. Similarly, let \(\varvec{A}_{s,m}^{\prime }\) and \(\varvec{A}_{e,m}^{\prime }\) specify the vertices that each edge is attached to. Often, \(\varvec{A}_{e,m}^{\prime }\) is the transpose of \(\varvec{A}_{s,m}\), and likewise for \(\varvec{A}_{s,m}^{\prime }\) and \(\varvec{A}_{e,m}\), so long as the graph is undirected. The index \(m=1,\dots ,n_e\) is used to distinguish between edges of different orientations (e.g., vertical or horizontal), and \(n_e\) is the number of different types of edges. For instance, in a square lattice, horizontal edges connect junctions that differ in their xcoordinate, but the vertical edges connect junctions that differ in their ycoordinate. Even if the same types of transition occur regardless of the edge orientation, distinguishing the orientation of each edge is crucial for obtaining spatial moments of the distribution. Furthermore, this formulation lends itself to extensions where not all edges have the same internal transition matrices.
The overall transition matrix is of the form
The blocks \(\bar{\varvec{D}}_{t,k}\) and \(\bar{\varvec{D}}_{t,k}^{\prime }\) for \(k=1,\dots ,n_e\) and \(t=\{s,e\}\) are themselves block matrices of the form
Note that for each type of edge in \(k=1,\dots ,n_e\), it is often possible to label the lattice so that edge \(\varvec{J}\) of that type connects to junction \(\varvec{J}\) at one terminus. With this in mind, we assume that for each edge, \(\varvec{J}\) of type k, the send of the edge border junction \(\varvec{J}\). Thus, \(\varvec{A}_{s,k}=\varvec{A}^{\prime }_{s,k}=\varvec{I}\), where \(\varvec{I}\) is the identity operator on the lattice. If we further assume that the matrices \(\varvec{D}_k\) are all equal, then \(\varvec{T}\) simplifies nicely,
which resembles the Laplaciantype structure observed previously.
Although \(\varvec{I}\), \(\varvec{A}_{t,m}\), and \(\varvec{A}_{t,m}^{\prime }\) with \(t=\{s,e\}\) were defined as infinitedimensional matrices, they can also be understood as linear operators over the infinite lattice, e.g., \(\varvec{A}_{t,m} = A_{t,m}(\varvec{X},\varvec{Y})\) where \(\varvec{X}\) and \(\varvec{Y}\) are two junctions. In all the cases, we have considered, for each fixed \(\varvec{Y}\), the lattice operators are only nonzero over a few (or one) values of \(\varvec{X}\). The interpretation of \(\varvec{A}_{t,m}\) as lattice operators also gives meaning to the notation \(\varvec{T}(\varvec{X}\varvec{X}^{\prime },s)\) used in Eqs. (33)–(36).
Remark
In some cases there can be more than one type of junction. An example is the exterior hexagonal lattice, which has two types of junctions. When this occurs, additional blocks of rows and columns will augment Eqs. (C.1), (C.2), and the definitions of \(\varvec{D}\) and \(\varvec{D}^{\prime }\) to describe internal transitions at each type of junction, transitions from each type of junction to each type of edge, and transitions from each type of edge to each type of junction, c.f. Eq. (B.1).
We see that the dimension of the statespace associated with each junction, \(\varvec{X}\) is \(n_T = n_s(n_v+n_en_k)\) where \(n_v\) is the number of junction types, and \(n_e\) the number of edge types.
C.1 Computation of Spatial Moments in the General Case
We now turn our attention toward computation of the moments for the general evolution equations in Eq. (33). Many properties of CTRWs are most easily analyzed in Laplace transform space. With lattice random walks, it is also often convenient to apply a lattice Fourier transform over \(\varvec{X}\), the lattice variable.
To compute the spatial moments (as functions of s, the Laplace transform variable), we start by computing the lattice Fourier transform of Eqs. (C.3) and (C.2) over the lattice spatial variable, \(\varvec{X}\), obtaining
where
and \({\mathscr {F}}\) is the Fourier transform operator on the lattice. The vectors \(\varDelta \varvec{X}_{m}\) specify the displacements between adjacent junctions along a type m edge. The functions \(\alpha _e(\varvec{\omega })\) and \(\beta _e(\varvec{\omega })\) are analogous to the functions \((z1)\) and \((z^{1}1)\) discussed in Sect. 3.1.
Since we use a lattice Fourier transform as opposed to a generating function formalism here, we have replaced z by \(e^{i\varvec{\omega }}\). With the lattice Fourier transform, it seems somewhat easier to handle cases where \(\varDelta \varvec{X}_{t,m}\) are not all of the same length for every \(t=\{s,e\}\) or \(m=1,\dots ,n_s\), although these minutiae could likely be included in a generating function approach with only a few modifications. Lastly, the matrix \(\varvec{K}^{\prime }\) corresponds with \(\varvec{K}_0+\varvec{D}^++\varvec{D}^\), and \(\varvec{T}^{\prime }(\varvec{\omega })\) corresponds with \((z1)\varvec{D}^++(1/z1)\varvec{D}^\) from Sect. 3.2.
Due to the translation invariance of the lattice points, we see that the Fourier transform diagonalizes the adjacency matrices, \(\varvec{A}_{e,m}\) and \(\varvec{A}_{e,m}^{\prime }\). This reduces an infinitedimensional matrix problem to an \(n_T\times n_T\) problem parameterized by a variable, \(\varvec{\omega }\). The spatial moments over the junctions are found by considering derivatives of \((\varvec{I}\hat{\varvec{K}}\hat{\varvec{T}})^{1}\) with respect to \(\varvec{\omega }\) in the limit that \(\varvec{\omega }\rightarrow \varvec{0}\). For instance, on an infinite onedimensional lattice, the derivative of the lattice Fourier transform of a function \(f(x_k)\) is
Given \(\hat{\varvec{T}}\) and \(\hat{\varvec{K}}\), in the limit \(\varvec{\omega }\rightarrow \varvec{0}\), we have
where derivatives, \(\frac{\partial }{\partial \omega _j}\), are taken with respect to \(\varvec{\omega }\) and are understood to be evaluated at \(\varvec{\omega }=\varvec{0}\). Since \(\varvec{T}^{\prime }(\varvec{\omega })=\varvec{0}\) as \(\varvec{\omega }\rightarrow \varvec{0}\), the only matrix inverse required is \(\left[ \varvec{I}\varvec{K}^{\prime }\right] ^{1}\). Averaging over the internal states within a cell, we can compute the average spatial moments of a process with transition matrix, \(\hat{\varvec{T}}\) as (c.f. Landman and Shlesinger 1979a)
where \(\varvec{x}=(x_1,x_2,\dots ,x_d)\) describes the position of SVs relative to a junction and \(\left\langle \dots \right\rangle \) is a suitable spatialsummation operator acting over the SVs. In this context, elements of \({\hat{\varPhi }}\) from Eq. (34) are written as
Many of the same points carry through if the transport process is posed as a generalized differential master equation rather than as an integral master equation. In the differential master equation setting, after Fourier and Laplace transformation, we write
with (recall we have set \(\varvec{T}\varvec{\phi }\mapsto \varvec{T}\) and \(\varvec{K}\varvec{\varLambda }\mapsto \varvec{K}\) in this section)
Similar to the results above, we define the matrix \(\varvec{S} = \left( s\varvec{\varGamma }+\varvec{I}\varvec{H}\varvec{W}(\varvec{0})\right) ^{1}\), and then, we may compute the moments using the matrix derivative formulas above as
Appendix D: General FirstPassageTime Problems and Secondary Vertex Reductions
In Sect. 4.1, we discussed how the SVs can be eliminated from some problems to form a CTRW involving only the junctions. Here, we discuss how this is done in the general case using the description in See Appendix C and C.1.
D.1 Reduction of SVs in the General Case
Recall the general integral master equation formulation in Eq. (36) with the \(n_T\times n_T\) matrices \(\varvec{T}\) and \(\varvec{K}\) defined in Eqs. (C.1) and (C.2). What we show in this section is that the effective transition rates derived in the previous examples applies more generally as well. In particular, by obtaining effective transition rates, the evolution of a system that has arbitrarily many SVs can be described by a reduced system that only involves internal states at junctions. We first give the resulting evolution equations and moment formulas, and then proceed to describe how to derive the elements of the effective transition matrices that appear in those equations.
In Laplace transform space, we write the \(n_s\)dimensional integral master equation for the reduced system as
where each nonzero element of the diagonal matrix \(\hat{\varvec{\varPhi }}_0\) is
The \(n_s\times n_s\) matrices \(\varvec{T}_1^{\text{ eff }}\) and \(\varvec{T}_2^{\text{ eff }}\) describe effective transition rates for transitions within a junction, and for travel between adjacent junctions. They will be defined precisely in Appendix D.2. The matrix, \(\varvec{K}_J\) is the same as that in Eq. (C.2), and it also describes internal transitions at a junction. The infinitedimensional adjacency matrices \(\varvec{A}_1\) and \(\varvec{A}_2\) are defined as^{Footnote 10}
where \(n_e\) is the number of different types of edges in the lattice. The adjacency matrices \(\varvec{A}_{e,m}\) and \(\varvec{A}_{s,m}\) are those that are introduced in Appendix C, and m ranges over the various types of edges in the system. As described in Appendix C, these matrices can also be understood as operators over the lattice. This leads to the notation in Eq. (D.1) where \(\varvec{A}_2(\varvec{X}\varvec{X}^{\prime })\) is written as a function of the lattice points.
From Eq. (D.1), the integral master equation for \(\varvec{p}_J\) can be derived as
Normally, each transition in a random jump process implies a change of internal state or position; however, with effective transitions, we must be able to account for paths where a particle, starting from a junction in internal state \(\ell \), makes a number of jumps along an edge and then returns to that same junction in state \(\ell \). The WTDs for such events to occur are given by the diagonal elements of \(\varvec{T}^{\text{ eff }}_1\), and they involve no change in position or internal state. We will later discuss how to reformulate the effective transition rates so that these selfjumps need not be considered as “transitions” in \(\varvec{T}^{\text{ eff }}_1\).
It also is the case that unlike \(\varvec{T}_J\) and \(\varvec{T}_{SV}\) which did not invoke changes in internal states, \(\varvec{T}^{\text{ eff }}_2\) can include transitions that simultaneously change the position and internal state of a particle. This is because each element of \(\varvec{T}^{\text{ eff }}_{2}\) is the aggregate of a number of different steps on an edge. Even though each individual transition on the edge involves only changes in state (\(\varvec{K}_{SV}\)) or position (\(\varvec{T}_{SV}\)), the aggregate of many transitions required for a particle to travel between junctions can change both.
Each element of \(\varvec{T}_1^{\text{ eff }}\) and \(\varvec{T}_2^{\text{ eff }}\) is found by solving a firstpassagetime problem to obtain the distribution of times for a particle to arrive in internal state \(\ell \) at a junction after having started in state \(\ell _0\) at that junction or an adjacent one without having visited any other junction states in the intervening time. We will discuss the solution to these problems in the next section, but first turn to the computation of the spatial moments for the reduced system.
Note that aside from the exterior hexagonal lattice and a remark in Appendix C, we have only considered lattices with a single type of junction^{Footnote 11} (e.g., the exterior hexagonal lattice does not fall into this category since it has type I and type II junctions). This restriction is mostly done for ease of notation and clarity, as there do not appear to be theoretical difficulties with considering multiple types of junctions. In this section, we will make two additional restrictions, also to ensure clarity of the arguments that follow.
Consider a lattice with a single type of junction and \(n_e\) types of edges, each type distinguished from the others only by its orientation relative to the xaxis. The first additional restriction is that the transport along each edge is undirected. The second restriction is that we now suppose that the lattice is constructed so that at each junction, one edge of each type starts and one edge of each type ends at that junction. Note that this restriction is also used in Appendix C. Aside from the exterior hexagonal lattice and persistent comb random walk, the examples discussed in this paper have these properties. As an example, in a square lattice, at each junction, one vertical edge and one horizontal edge start (have their sterminus) at that junction, and one vertical and one horizontal edge end (have their eterminus) at that junction. Thus, there are \(n_e=2\) types of edges (vertical and horizontal), and each junction is connected to \(4=2n_e\) edges in the square lattice.
Recall that the lattice Fourier transform of a function is
with \(\varvec{\omega }\) defined over a ddimensional cube, \(\left[ \frac{\pi }{\varDelta X},\frac{\pi }{\varDelta X}\right] ^d\). Here, d is the dimension of the space and \(\varvec{\varDelta X}\) the lattice spacing.
Applying the lattice Fourier transform to the matrix,
from Eq. (D.1), and keeping in mind the restrictions above, we obtain
To find the moments, we differentiate in Fourier space as in Appendix C.1. Differentiating \(\tilde{\varvec{p}}_J(\varvec{\omega },s)\), we obtain
with \(j,k=1,2,\dots ,d\). Notice that there is no summation over the \(n_k\) SVs since details regarding the SVs have been condensed into the elements of \(\varvec{T}_2^{\text{ eff }}\) which describes transfers between adjacent junctions. Likewise we do not need any information about how the internal states are situated relative to a junction. We can also write \(\hat{\varvec{\varPhi }}_0\) from above as
Unlike the SVs, the internal states have not been summed over, so the moments are still written as vectors over these states.
Remark 1
It is important to note that since no summation is done over the SVs, the value of \(\varvec{p}_J(\varvec{X},t)\) at junction \(\varvec{X}\) is now the aggregate probability density of all particles that have reached that junction, but not yet arrived at any other junctions. In other words, a particle located on an edge after exiting junction \(\varvec{X}\), but not having yet reached an adjacent junction, would contribute to \(\varvec{p}_J(\varvec{X},t)\). This is a reflection of the fact that in the reduced process, the fine details of the exact location of a particle along an edge have been suppressed. Nonetheless, if one uses the elements of \(\hat{\varvec{\varPhi }}\) associated with junctions from the full system (e.g., Eq. (34)) rather than \(\hat{\varvec{\varPhi }}_0\) for the reduced system, the probabilities at junction \(\varvec{X}\) are obtained (c.f. the difference between \(\hat{\varvec{\varPhi }}\) and \(\hat{\varvec{\varPsi }}\) in Ex. 4).
Remark 2
When the FPT procedure is applied to a Markov process, this result is typically a nonMarkovian process with a reduced number of degrees of freedom (Kampen 1992). The nonMarkovian character arises since even if all the transitions in the original process have Poisson distributed waiting times, the aggregate waitingtime distribution for several sequential Poisson processes to occur is not a Poisson distribution. As noted above, this leads to an overall nonMarkovian process since the only continuous waitingtime distributions which leads to a Markov process are Poisson distributions.
D.2 General FirstPassageTime Problems
It now remains to describe the general firstpassagetime (FPT) problem which must be solved to obtain \(\varvec{T}^{\text{ eff }}_1\) and \(\varvec{T}^{\text{ eff }}_2\) in Eq. (D.1). The elements of \(\varvec{T}^{\text{ eff }}_{1,2}\) are closely related to FPTs for a random walk to reach state \(\ell \) at a junction given that it started in state \(\ell _0\) at one of the junctions bordering that edge and did not leave the edge yet.
Recall that we assume that all edges have the same set internal transitions and spatial jump transitions. Also recall that the transition matrices for internal state changes and spatial jumps on an edge are \(\varvec{K}_{SV}\) and \(\varvec{T}_{SV}\), respectively. Since a particle starting out on an edge can eventually jump to a vertex at the “s” or “e” end of that edge, the matrix \(\varvec{T}_{SV}+\varvec{K}_{SV}\) cannot conserve particle number. Thus, the matrix \(\varvec{T}_{SV}+\varvec{K}_{SV}\) is the transition matrix for a random walk confined to an edge and subject to absorbing boundary conditions. Since particles are absorbed at the boundaries, the rate at which particles are absorbed can be defined. In the full matrix system, these absorption rates correspond to the rate at which particles arrive at the vertices at either end of an edge.
In order to define the arrival rates, we consider the nonzero elements of \(\varvec{D}_e\) and \(\varvec{D}_s\) which describe transitions from an edge to a vertex. As depicted in Fig. 5, there are typically at most \(n_s\) distinct transitions from each end of an edge, one from each internal state at \(k=1\) and \(k=n_k\). Thus, there are typically at most \(n_s\) nonzero entries in \(\varvec{D}_s\) and \(\varvec{D}_e\). Considering \( \varvec{D}_s\) first, each nonzero element of \(\varvec{D}_s\) can be written as \(d_{\ell }\psi _{d,\ell } (t)\). Here, \(d_{\ell }\) is the probability for a particle at the first SV (\(k=1\)) in state \(\ell \) to jump to state \(\ell \) at the adjacent junction, and \(\psi _{d,\ell }(t)\) is the WTD for the jump when it does occur. The same description holds for nonzero elements of \( \varvec{D}_e\), except in that case the particle is jumping from the \(n_k\)th SV, \(k=n_k\) rather than \(k=1\).
Then, the Laplace transform of the FPT density for a particle to reach the junction attached to the “s”end of an edge in state \(\ell \) after starting in state \((k_0,\ell _0)\) on the edge is of the form
where \(\varvec{\delta }_{k_0,\ell _0}\) and \(\varvec{\delta }_{1,\ell }\) are Kronecker deltas over the statespace of the edge. But \({\tilde{f}}_{\ell }(s\{k_0,\ell _0\})\) is just the \(\ell ^{\text{ th }}\) component of a vector \(\tilde{\varvec{f}}(s\{k_0,\ell _0\})\) over the internal states at a junction. The FPT densities to reach each of the \(n_s\) internal states at a junction defined easily by decomposing \(\varvec{D}_s\) as a sum of rank one matrices,
and writing
This describes the FPT densities to reach the vertex adjacent to the “s”end of the edge for a particle starting at some SV on edge, and the FPT densities at the “e”end can be found similarly. However, the effective transition rates we seek describe how long it takes a particle to jump between vertices, not from an SV to a vertex.
Let us consider the \(n_k^{\text{ th }}\) SV on an edge. Each internal state associated with this SV is directly connected to the junction at the “e”end of the edge, and jumps from that junction to the edge are governed by \(\varvec{D}^{\prime }_e\). Like \(\varvec{D}_e\), \(\varvec{D}^{\prime }_e\) typically has at most \(n_s\) nonzero entries and we can write \(\varvec{D}^{\prime }_e\) as
where \(d_{\ell }^{\prime }\) is the probability of a particle in state \(\ell \) at the junction jumping to the edge, and \(\psi ^{\prime }_{d,\ell }\) is the WTD when the jump occurs.
With this, the elements of the vector,
are the FPTs for a particle jumping from state \(\ell _0\) at the junction at the “e”end of an edge to reach state \(\ell \) at the junction at the “s”end of the same edge. Elements of \(\varvec{T}_{2}^{\text{ eff }}\) are then found by multiplying these FPTs by \(d_{\ell _0}^{\prime }\) which gives the probability of the jump occurring. Collecting all of these FPTs into a matrix, we obtain,
By similar arguments, we find the elements of \(\varvec{T}_1^{\text{ eff }}\) by replacing \(\varvec{D}_s\) with \(\varvec{D}_e\),
Notice that we could have replaced \(\varvec{D}_s\) and \(\varvec{D}^{\prime }_e\) by \(\varvec{D}_e\) and \(\varvec{D}^{\prime }_s\) in the computation of \(\varvec{T}_2^{\text{ eff }}\). Either combination is valid since we have assumed that transport along each edge is undirected. Note that many useful examples involve just two types of effective transitions (return to origin, and jump to other states) in which case the matrices above will be rank two.
The presence of condensed paths over the SVs that return a particle to its starting state prior to jumping is a peculiarity that arises here. However, as we will now show, these selfjumps can be eliminated by solving an extended firstpassagetime problem which results in a rescaling of the values of \(\varvec{T}_2^{\text{ eff }}\) and the offdiagonal elements of \(\varvec{T}_1^{\text{ eff }}\) to account for the contingency of multiple returns to the starting point. This was done implicitly in Ex. 4.2 when we solved for the FPT to reach \(X_{i\pm 1}\) from \(X_i\) in the a 1D lattice. In that case, no selfjumps were possible since the effective WTD was defined such that it described sojourns on edges that started (at \(X_i\)) and ended (at \(X_{i\pm 1}\)) at different points in space. This same principle holds in general.
It turns out the form of the rescaled transition rates is relatively simple. We first give the result and then discuss its derivation. To obtain the rescaled transition rates, we additively decompose \(\varvec{T}_1^{\text{ eff }}\) into diagonal and offdiagonal components,
The subscripts d and o indicate the diagonal and offdiagonal components of \(\varvec{T}_1^{\text{ eff }}\). The selfjumps are characterized by \(\varvec{T}_{1,d}^{\text{ eff }}\), and jumps that change the state or position of a particle are given by \(\varvec{K}\), \(\varvec{T}_{1,o}^{\text{ eff }}\), and \(\varvec{T}_2^{\text{ eff }} \). The rescaled internal transitions and firstpassagetimes are then of the form
As noted before, this rescaling is equivalent to the solution of an extended firstpassagetime problem that does not terminate when a particle returns to its starting state. Consider a particle starting at state \(\ell \) at vertex \(\varvec{X}\). To exclude selfjumps, consider the transition matrix for a particle in a system that includes state \(\ell \) at vertex \(\varvec{X}\) and all of the SVs and internal states on the edges that are attached to \(\varvec{X}\) (see Fig. 13).
The transition matrices for each \(\ell \) are of the form
where \(t_{k} = \{s,e\}\) specifies which end of edge k connects to vertex, \(\varvec{X}\), see Fig. 13a. We see that a particle starting on \(\varvec{X}\) in state \(\ell \) can exit the system by 1) reaching \(\varvec{X}\) in any internal state except for \(\ell \), or 2) by reaching a junction adjacent to \(\varvec{X}\) in any internal state including \(\ell \). Since the particle cannot exit this system from the same position and state at which it started, no effective selfjumps occur.
The resulting matrix for the FPT problem is much larger in this case than the matrix \(\varvec{T}_e+\varvec{K}_e\) which was needed to find \(\varvec{T}_{1,2}^{\text{ eff }}\). This is disadvantageous since solutions may be more difficult to compute. However, a significant simplification is possible due to assumptions we have made. In the case, we have been considering, all edges have the same set of transitions and WTDs. Given this, we find that since the particle starts at the junction, the probabilities, \({\tilde{p}}_{k,\ell }(s)\) along each edge are equal. This allows us to write
Given this matrix, FPTs to reach an adjacent vertex or return to \(\varvec{X}\) in a state other than \(\ell \) may be found using the techniques discussed above. After simplification, one can show that Eq. D.7 are the resulting solutions.
Likewise, one can substitute \(\varvec{T}_{1,r}^{\text{ eff }}\) and \(\varvec{T}_{2,r}^{\text{ eff }}\) into Eqs. (D.2) and (D.3) for the spatial moments. However, since both approaches end up computing approximate moments for the same initial system which includes SVs and junctions, they must both yield very similar results. The distinction is that in the latter case, the definition of a single jump has been redefined to only include jumps where the position or state of the particle changes. This distinction is important in regard to interpreting the elements of \(\varvec{T}^{\text{ eff }}\). In the former case, they cannot be considered as jump probabilities multiplying waitingtime distributions in the standard sense, but after rescaling, they can.
Appendix E: Random Walks on Segments with Various Boundary Conditions
Consider a random walk on \({\mathbb {Z}}\) where at each \(k\in {\mathbb {Z}}\), there is a 1/2 probability of jumping to the left or right. If the random walk starts at \(\ell \in {\mathbb {Z}}\), the probability generating function for the probability of finding the walker at m is (Hughes1996)
with
and \(p_n(m\ell )\) being the probability of reaching m after starting at \(\ell \) on the nth step of the walk. For a periodic segment with \(m,\ell =0,1,\dots ,N\), the solution is given by using the method of images (Montroll and Greenberg 1964) as
It is also possible to find solutions with other types of boundary conditions (Hughes 1996; Weiss and Rubin 2007). For instance, the image points for a random walk on a segment with two absorbing boundaries are of the form \(\{ 2kN\pm \ell \}\), and the method of images solution yields
Moments of \(u^{(F)}(z,\cdot \cdot )\) can be found by summing over m and using identities related to geometric series:
For finite N, these formulas are valid (if one uses L’Hopitals rule at \(r=1\)) for all r, and in the case that \(N\rightarrow \infty \), the results are valid if \(0\le r<1\). In particular, the variance of \(u^{(F)}\) over the integers is
In the CTRW setting, one can find the arrival probabilities, q(x, ty) by substituting the Laplace transform of \(\psi (t)\) for z in the formulas in this section. Likewise, upon multiplying by \({\hat{\varPhi }}(s)\), the probability, p(x, ty) and timedependent moments can be found. For instance, for an unbiased 1D CTRW with nearest neighbor jumps,
as was found in Eq. (43) by different techniques.
The firstpassagetime probability generating function may also be found by making use of the definition in Eq. (E.1) for \(u^{(F)}\). As derived in Montroll and Weiss (1965),
Furthermore, this result is easily extended to much more general cases such as multistate random walks, and walks on complicated structures (Hughes 1996).
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Stotsky, J.A., Gou, J. & Othmer, H.G. A Random Walk Approach to Transport in Tissues and Complex Media: From Microscale Descriptions to Macroscale Models. Bull Math Biol 83, 92 (2021). https://doi.org/10.1007/s11538021009170
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DOI: https://doi.org/10.1007/s11538021009170
Keywords
 Continuoustime random walk
 Multiscale model
 Drosophila
 Multistate random walk
 First passage time