Exact traveling wave solutions of 1D parabolic-parabolic models of chemotaxis
Maria Shubina

TL;DR
This paper derives exact analytical traveling wave solutions for three different one-dimensional parabolic-parabolic chemotaxis models, providing insights into their wave behaviors.
Contribution
It introduces explicit traveling wave solutions for three distinct 1D chemotaxis models, advancing analytical understanding of these systems.
Findings
Exact solutions in terms of traveling wave variables
Analytical expressions for wave profiles
Enhanced understanding of chemotactic wave dynamics
Abstract
In this paper we consider three different 1D parabolic-parabolic systems of chemotaxis. For these systems we obtain the exact analytical solutions in terms of traveling wave variables.
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Exact traveling wave solutions of 1D parabolic-parabolic models of chemotaxis
Maria Shubina
Skobeltsyn Institute of Nuclear Physics
Lomonosov Moscow State University
Leninskie gory, GSP-1, Moscow 119991, Russian Federation
Abstract
In this paper we consider three different 1D parabolic-parabolic systems of chemotaxis. For these systems we obtain the exact analytical solutions in terms of traveling wave variables.
parabolic-parabolic system, exact solution, soliton solution, Patlak-Keller-Segel model
I Introduction
In this paper we consider a number of different systems of nonlinear partial differential equations, which describe a directed cells (bacteria or other organisms) movement up or down a chemical concentration gradient (chemotaxis). The aim of this paper is to obtain exact analytical solutions of these models. For 1D parabolic-parabolic systems under consideration we present these solutions in explicit form in terms of traveling wave variables. Of course, not all of the solutions obtained can have appropriate biological interpretation since the biological functions must be nonnegative in all domain of definition. However some of these solutions are positive and bounded and their analysis requires further investigation.
Chemotaxis plays an important role in many biological and medical fields such as embryogenesis, immunology, cancer growth. The macroscopic classical model of chemotaxis was proposed by Patlak in 1953 P and by Keller and Segel in the 1970s KS1 -KS3 . This model describes the space-time evolution of a cells density and a concentration of a chemical substance . The general form of this model is:
[TABLE]
where and are cells and chemical substance diffusion coefficients respectively, is a chemotaxis coefficient; when this is an attractive chemotaxis (”positive taxis”), and when this is a repulsive (”negative”) one Ni , Li&Wang . The function is the chemosensitivity function and characterizes the chemical growth and degradation. These functions are taken in different forms that corresponds to some variations of the original Keller–Segel model. We follow the reviews of T. Hillen and K. Painter Hillen&Painter and of Z.-A. Wang Wang and consider models presented therein.
This paper is concerned with one-dimensional simplified models when the coefficients , and are positive constants, , , .
II Signal-dependent sensitivity model
Let us start with a model that allows nonnegative bounded solutions that may be of interest from a biological point of view. Now consider the ”logistic” model, one of versions of signal-dependent sensitivity model Hillen&Painter with the chemosensitivity function , and . In the review H1 one can see a mathematical analysis of this model. When and the existence of traveling waves were established in N&I , E&F&N . The replacement , gives , , , . We also set , , as well as . It should be noted that a sign of may affect on mathematical properties of the system. So, corresponds to an increase of a chemical substance, proportional to cells density, whereas corresponds to its decrease. And as we shall see later, various solutions correspond to these two cases.
After above replacements the model reads:
[TABLE]
If we introduce the function , in terms of traveling wave variable , this system has the form:
[TABLE]
where , and is an integration constant.
In this paper we will consider the case of . Then the first equation in () gives
[TABLE]
is a constant and we will examine the following equation for :
[TABLE]
Since is a positive constant we consider two cases: and (3) is linear nonhomogeneous equation, and .
II.1
Let us begin with . We introduce the new variable and the new function :
[TABLE]
and equation (3) becomes:
[TABLE]
where , . Equation (5) is the Lommel differential equation Bateman&Erdelyi , Watson with . For its general solution has the form:
[TABLE]
where , are constants, and are Bessel functions and
[TABLE]
are Lommel functions, is generalized hypergeometric function Bateman&Erdelyi , Watson . Further, substituting of the initial variable and the function (see (4)) into (6) we obtain a formal solution.
II.1.1
We first consider the case . Then and . Equation (5) becomes homogeneous and for its general solution is
[TABLE]
However one can check that the function diverges as for all .
Consider now . For be real let . Then (5) becomes the modified Bessel equation; the analysis of solutions behavior at leads to suitable solutions for and :
[TABLE]
with restrictions and . So on can see that as for all ; for and for as and as for all . The curves of these functions are presented in Fig.1–Fig.2. Thus, the solution obtained may be considered as biologically appropriated one and this requires further investigation.
II.1.2
Let us return to equation (5) with . The analysis of solutions asymptotic forms at Bateman&Erdelyi , Watson gives the following expressions for and :
[TABLE]
with and for . The latter condition leads to the requirement . The and as and , as . Thus, one can see that for , and is satisfied but . These functions are presented in Fig.3–Fig.4. It should be noted that , or because of pole in - function.
II.1.3
Using the analysis of (10) one can see that the condition along with and () leads to the fact that the function has not changed, but becomes positive on all domain of definition. This function is presented in Fig.5.
II.2
Let us return to equation (3) and rewrite it in terms of the variable :
[TABLE]
To integrate this equation we use the Lie group method of infinitesimal transformations Olver . We find a group invariant of a second prolongation of one–parameter symmetry group vector of (11) and with its help we transform equation (11) into an equation of the first order. It turns out that nontrivial symmetry group requires some conditions:
[TABLE]
and we consider the case . Thus, and for
[TABLE]
we obtain the Abel equation of the second kind:
[TABLE]
Then we find solutions of equation (14) in parametric form Z&P ODE with the parameter . Now we consider the case . A combination of substitutions leads to:
[TABLE]
where we take
[TABLE]
and equation (14) becomes an equation for the function . Solving it, for we obtain:
[TABLE]
where , are constants and is the hypergeometric Gauss function. Further we obtain the solutions of initial equations (2)–(3) in parametric form:
[TABLE]
where the constant is chosen so that , what is consistent with (16). Using the asymptotic representation of hypergeometric Gauss function as Bateman&Erdelyi we can take
[TABLE]
in order for and be real. Then one can see that all functions (18) are continuous bounded ones for and are positive. Hence, one may try biologically interpret the functions and and this requires further investigation. In Fig.6 one may see the different curves for and different . Fig.7 demonstrates and for two . Further, for larger values of and it seems more convenient to present curves , and to analyze them, see Fig.8–Fig.10. One can see from (12) that when , and the case of , is presented in Fig.11.
III Logarithmic sensitivity
The model with logarithmic chemosensitivity function is also studied. For the case of , an extensive analysis is performed in Wang . This survey is focused on different aspects of traveling waves solutions. When this model coincides with (1) for . When and the system was studied in Nossal , Rosen . The complete analysis for is performed in Wang . An existence of global solution is established in W .
Now we consider the system with and . Similarly, a replacement , gives , , , , . Then the model has the form:
[TABLE]
Let us rewrite system (20) in terms of function :
[TABLE]
then in terms of traveling wave variable , , (20*′*) has the form:
[TABLE]
where , and is an integration constant. To integrate (20) we tested this system on the Painlevé ODE test. One can show that for it passes this test only if with the additional condition MSh_ArXiv . If we express as from (20), we obtain an equation only for ; for it has the form:
[TABLE]
For this equation can be linearized. It becomes equivalent to the following linear equation for :
[TABLE]
that gives the equation for :
[TABLE]
. If we rewrite (23) in terms of the variable for the function we obtain an equation similar to (11) with zero right-hand side:
[TABLE]
Using the result of the symmetry group analysis of (11) we can write solution for (see (18)):
[TABLE]
where is given in (17) and may be expressed from (20). However one may see that as and this solution is unacceptable as a biological function.
Another possibility to solve this equation exactly is to put equal to zero. When , that means , and equation (24) can be linearized by Z&P ODE . Its solution has three forms according to a sign of the expression . Since should be nonnegative and bounded function as the only suitable solution is
[TABLE]
where are positive constants and . Unfortunately, the corresponding solution for is alternating and has the form:
[TABLE]
It is easy to see what as . These functions are presented in Fig.12–Fig.13.
IV Linear sensitivity
Let us consider the system with linear function . When the system is called the minimal chemotaxis model following the nomenclature of Childress&Percus . This model is often considered with ( and are constants) and it is studied in many papers. As was proved in Osaki&Yagi , Hillen&Potapov the solutions of this system are global and bounded in time for one space dimension. The case of positive and nonnegative is studied in J&L -F . As we noted earlier, a sign of may effect on mathematical properties of the system, what changes its solvability conditions TF . The review article H1 summarizes different mathematical results.
Now we consider the linear chemosensitivity function and . The replacement , , leads to , , , . Then the system has the form:
[TABLE]
This system reduces to system of ODEs in terms of traveling wave variable , :
[TABLE]
where , and is an integration constant. As shown in MSh this system passes the Painlevé ODE test only if and . Consequently, in this case we can solve () and the exact solution has the form MSh :
[TABLE]
, and are arbitrary constants. The functions and are Infeld’s and Macdonald’s functions respectively (Bessel’s functions of imaginary argument). This solution is not satisfactory from the biological point of view, since is an alternating function for any . However it seems interesting because of the following: in the case of and in terms of its form coincides with the well-known Korteweg-de Vries soliton
[TABLE]
For and arbitrary the function is
[TABLE]
One can see that for (an increase of a chemical substance) the cells density for , and that for is the solitary continuous solution vanishing as , whereas for has a point of discontinuity. One can say that when we obtain ”blow up” solution in the sense that it goes to infinity for finite , and this is true for different . The functions (29) for are presented in Fig.14–Fig.15.
V Conclusion
We investigate three different one-dimensional parabolic-parabolic Patlak-Keller-Segel models. For each of them we obtain the exact solutions in terms of traveling wave variables. Not all of these solutions are acceptable for biological interpretation, but there are solutions that require detailed analysis. It seems interesting to consider the latter for the experimental values of the parameters and see their correspondence with experiment. This question requires a further investigations.
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1[1] C. S. Patlak, Bull.Math.Biophys. 15(3), 311 (1953).
- 2[2] E. F. Keller, L. A. Segel, J.Theor.Biol. 26(3), 399 (1970).
- 3[3] E. F. Keller, L. A. Segel, J.Theor.Biol. 30(2), 225 (1971).
- 4[4] E. F. Keller, L. A. Segel, J.Theor.Biol. 30(2), 235 (1971).
- 5[5] W.-M. Ni, Notices Amer.Math.Soc. 45(1), 9 (1998).
- 6[6] T. Li, Z. A. Wang, Math.Models Methods Appl.Sci. 20, 1967 (2010).
- 7[7] T. Hillen, K. J. Painter, J.Math.Biol. 58, 183 (2009).
- 8[8] Z. A. Wang, Discrete Cont.Dyn.B 18(3), 601 (2013).
