# Feedback-mediated dynamics in a model of a compliant thick ascending limb

## Feedback-mediated dynamics in a model of a compliant thick ascending limb

Mathematical Biosciences 228 (2010) 185–194 Contents lists available at ScienceDirect Mathematical Biosciences journal homepage: www.elsevier.com/lo...
Mathematical Biosciences 228 (2010) 185–194

Contents lists available at ScienceDirect

Mathematical Biosciences journal homepage: www.elsevier.com/locate/mbs

Feedback-mediated dynamics in a model of a compliant thick ascending limb Anita T. Layton Department of Mathematics, Duke University, Durham, NC 27708-0320, USA

a r t i c l e

i n f o

Article history: Received 27 May 2010 Received in revised form 28 September 2010 Accepted 1 October 2010 Available online 8 October 2010 Keywords: Renal hemodynamic control Negative feedback loop Delay differential equation Non-linear dynamics

a b s t r a c t The tubuloglomerular feedback (TGF) system in the kidney, which is a key regulator of ﬁltration rate, has been shown in physiologic experiments in rats to mediate oscillations in tubular ﬂuid pressure and ﬂow, and in NaCl concentration in the tubular ﬂuid of the thick ascending limb (TAL). In this study, we developed a mathematical model of the TGF system that represents NaCl transport along a TAL with compliant walls. The model was used to investigate the dynamic behaviors of the TGF system. A bifurcation analysis of the TGF model equations was performed by deriving and ﬁnding roots of the characteristic equation, which arises from a linearization of the model equations. Numerical simulations of the full model equations were conducted to assist in the interpretation of the bifurcation analysis. These techniques revealed a complex parameter region that allows a variety of qualitatively different model solutions: a regime having one stable, time-independent steady-state solution; regimes having one stable oscillatory solution only; and regimes having multiple possible stable oscillatory solutions. Model results suggest that the compliance of the TAL walls increases the tendency of the model TGF system to oscillate. Ó 2010 Elsevier Inc. All rights reserved.

1. Introduction The mammalian kidneys are vital organs whose many functions include regulation of acid–base and electrolyte balance, excretion of metabolic waste products, gluconeogenesis, secretion of hormones, as well as the regulation of blood pressure and the maintenance of steady whole-organism water volume . The functional unit of the kidney is the nephron, which consists of a bundle of capillaries, called a glomerulus, and a tubule having walls made up of a single layer of epithelial cells, each with its basement membrane. Forced by blood pressure from the glomerulus, a ﬁltrate of blood plasma enters the tubule, which begins the transformation of this ﬁltrate of blood plasma into urine. Along the tubule, the ﬂuid composition is altered by transport processes in the epithelial cells of the renal tubule. In particular, the thick ascending limb (TAL), a water-impermeable portion of the loop of Henle, actively transports NaCl from tubular ﬂuid into the interstitium, thereby diluting the tubular ﬂuid. (In a rat kidney, there are 38,000 loops of Henle .) A negative feedback mechanism, called tubuloglomerular feedback (TGF), attempts to maintain the NaCl concentration of the ﬂuid exiting the TAL near a target level by varying the tubular ﬂuid ﬂow rate in the TAL. The TGF response is mediated by a change in chloride concentration in tubular ﬂuid ﬂowing past the macula densa (MD), a cluster of specialized cells located in the renal tubule wall near the end of the TAL. An elevated chloride concentration in tubular

ﬂuid at the MD reduces ﬁltration rate and decreases TAL ﬂuid ﬂow, which allows more time for dilution of the tubular ﬂuid. Conversely, a low chloride concentration at the MD increases tubular ﬂuid ﬂow and raises the chloride concentration at the MD back to its normal target value. By these means, TGF stabilizes the ﬂuid and solute delivery into the distal portion of the loop of Henle, and prevents large imbalances in the rate of ﬁltration relative to the capacity for tubular absorption. Mathematical models of the TGF system have suggested that, depending on the physical and transport characteristics of the TAL, renal tubular ﬂuid ﬂow and related variables may exhibit regular oscillations or may reach a time-independent steady state after a transient perturbation [3,4]. Our model studies predict that key parameters such as the feedback loop gain, delay, and the luminal volume of the TAL may be crucial bifurcation parameters [3–6]. Our previous mathematical model of the TGF system consists of simple components that have been individually well-characterized in our publications [3,5,7]. In particular, the TAL is represented by a rigid tube with plug ﬂow that carries only the chloride ion. In vivo, however, the TAL likely expands and contracts as luminal ﬂuid ﬂow rate changes, although the wall movements may be hindered by surrounding TALs and connecting tissues. Using a more detailed mathematical model of the nephron that represents hydrodynamic pressure and compliant tubular walls [8–10], Marsh and collaborators predicted that a compliant renal tubule will tend to function as a low-pass ﬁlter with respect to pressure oscillations. Because our previous rigid tubule model does not compute hydrodynamic pressure but instead prescribes ﬂow rate as a function of predicted

186

A.T. Layton / Mathematical Biosciences 228 (2010) 185–194

Nomenclature Parameters a TAL compliance (cm mmHg1) [Cl] at TAL entrance (mM) Co j TAL chloride permeability (cm s1) K1, K2 parameters for TGF response KM Michaelis constant (mM) length of model nephron (cm) L0 L length of TAL (cm) l ﬂuid viscosity (cm s1) P1 pressure at end of nephron (mmHg) base-case steady-state TAL transit time (s) To Vmax maximum transport rate of chloride (nmole cm2 s1)

Speciﬁed b(x) Ce(x) P0(t) Pe(x)

functions unpressurized TAL radius (lm) extratubular [Cl] (mM) pressure at loop bend (mmHg) extratubular pressure (mmHg)

Dependent variables C(x, t) TAL [Cl] (mM) P(x, t) tubular ﬂuid pressure (mmHg) Q(x, t) tubular ﬂuid ﬂow (nl min1) R(x, t) luminal radius (lm)

Independent variables t time (s) x axial position along nephron (cm)

single-nephron glomerular ﬁltration rate (SNGFR) , one may wonder if the high degree of non-linearity exhibited by our model may be an artifact of the rigid tubule formulation and that if, instead, our model represented pressure-driven ﬂow in conjunction with compliant TAL walls, then some of our model’s predictions might be rendered null and void. To address these concerns, we developed a TGF model that represents a TAL having pressure-driven ﬂow and compliant walls. To investigate and predict the impact of tubular wall compliance on TGF-mediated dynamics in vivo, we have analyzed the model by means of linearization and numerical simulations. 2. Mathematical model 2.1. Model equations and parameters We describe a mathematical model of a TGF system that represents a TAL with compliant walls. The model is formulated as a boundary value problem, and predicts tubular ﬂuid, ﬂuid pressure, and tubular radius as functions of time and space. At the entrance to the TAL, tubular ﬂuid pressure is given by the TGF response. At the end of the TAL, pressure is assumed to be known a priori. However, tubular ﬂuid pressure at the MD is not well-characterized. To avoid that uncertainty, we model a longer tubule: the model repre-

sents the portion of a short-loop nephron that extends in space from x = 0 at loop bend to x = L0 at the end of the collecting duct, where ﬂuid pressure in rat can be inferred to be 1–3 mmHg, based on measurements in the interstitia, vessels, and the pelvic space [12–14]. The tubular walls are assumed to be compliant, with a radius that depends on transmural pressure gradient. We represent chloride ion (Cl), the concentration of which alongside the MD is believed to be the principal tubular ﬂuid signaling agent for TGF activation. Cl concentration is represented only along the TAL, which extends in space from x = 0 at loop bend to x = L at the MD (L 6 L0). A schematic diagram for the model TGF system is given in Fig. 1. Fluid motion along the model nephron is modeled as the ﬂow of an incompressible ﬂuid along a narrow compliant tube. Intratubular ﬂuid ﬂow and pressure are described by the coupled partial differential equations

@ 8l Pðx; tÞ ¼  Qðx; tÞ; @x pRðx; tÞ4   @ @R @ Q ðx; tÞ ¼  2pRðx; tÞ Pðx; tÞ; @x @P @t

ð1Þ ð2Þ

where x is axial position along the nephron (0 6 x 6 L0), t is time, P(x, t) is the tubular ﬂuid pressure, Q(x, t) is the tubular volume ﬂow,

Fig. 1. Model represents three essential elements of the TGF pathway: thick ascending limb (cylinder), delay at the MD (box at right), and TGF response function (box at left). Symbols are identiﬁed in the Glossary. Perturbations enter as adjustments to hydrodynamic pressure P(0, t) that drives ﬂow into TAL entrance (x = 0) at time t. Oscillations in pressure result in oscillations in TAL ﬂow Q(x, t), radius R(x, t), and tubular ﬂuid chloride concentration C(x, t).

187

A.T. Layton / Mathematical Biosciences 228 (2010) 185–194

and R(x, t) is the tubular radius. As given by Eq. (1), which represents Poiseuille ﬂow along a narrow tubule in the zero Reynold’s number limit, ﬂuid ﬂow along the model nephron is driven by axial pressure gradient. Eq. (2) represents the incompressibility constraint or continuity of mass. Eqs. (1) and (2) are justiﬁed in the APPENDIX. The inﬂow pressure P0(t) = P(0, t) is given by the TGF response (see below), and the outﬂow pressure P1 = P(L0, t) is considered ﬁxed. Solute conservation in the TAL’s tubular ﬂuid is represented by a hyperbolic partial differential equation

pRðx; tÞ2

@ @ @ Cðx; tÞ ¼ 2pRðx; tÞCðx; tÞ Rðx; tÞ  Q ðx; tÞ Cðx; tÞ @t @t @x   V max Cðx; tÞ  2pRo þ jðCðx; tÞ  C e ðxÞÞ ; ð3Þ K M þ Cðx; tÞ

where 0 6 x 6 L, C(x, t) is TAL tubular ﬂuid chloride concentration, Ce(x) is the extratubular (interstitial) chloride concentration, and Ro is the steady-state TAL radius. The two terms inside the large pair of parentheses on the right-hand side correspond to active solute transport characterized by Michaelis–Menten-like kinetics (characterized by maximum transport rate Vmax and Michaelis constant KM) and transepithelial diffusion (characterized by backleak permeability j). The boundary condition Co = C(0, t) is considered ﬁxed. To represent a compliant tube, the tubular luminal radius is allowed to vary as a function of transmural pressure difference:

Rðx; tÞ ¼ aðPðx; tÞ  Pe ðxÞÞ þ bðxÞ;

ð4Þ

where Pe(x) is the extratubular (interstitial) pressure, a speciﬁes the degree of tubular compliance, and b(x) is the unpressurized tubular radius (see below). The feedback-mediated loop-bend pressure is given by

P0 ðtÞ ¼ P0 þ K 1 tanhðK 2 ðC op  CðL; t  sÞÞÞ;

2.1.1. Parameters Base-case values for model parameters are given in Table 1. The luminal radius parameter b(x) is given (in lm) by the piecewise function:



0 6 x 6 1:5  L;

b0 ;

b1 ðxÞ; 1:5  L 6 x 6 L0 ;

Table 1 Base-case parameter values. Parameter

Dimensional value

Independent parameters

a b0 b2 Co

j KM L0 L

l Pe P0 P1 Qo Ro To Vmax

1.33  105 cm mmHg1 9.380 lm 5.693 lm 275 mM 1.50  105 cm s1 70.0 mM 2.00 cm 0.500 cm 7.2  103 g/(cm s) 5.00 mmHg 10.00 mmHg 2.00 mmHg 6 nl/min 10.0 lm 15.708 s 14.5 nmole cm2 s1

2.1.2. Numerical method To compute an approximation for the tubular ﬂuid motions, we take the spatial derivative of Eq. (1) and use the resulting equation to eliminate the ﬂuid ﬂow gradient term @Q/@ x from Eq. (2). That yields an advection–diffusion equation for the pressure P

@ Rðx; tÞ2 @ @ Rðx; tÞ3 @ 2 Pðx; tÞ; Pðx; tÞ  Rðx; tÞ Pðx; tÞ ¼ @R @t @x @x2 4l @P @x 16l @R @P

ð6Þ

ð7Þ

subject to the boundary conditions P(0,t) = P0(t) and P(L, t) = P1. Eq. (7) was integrated in time by means of a numerical method that is second order in space and time. Let Dx denote the spatial mesh interval, and let Dt denote the time step interval. We denote P(jDx, nDt) by Pnj ; analogous notation is used for other functions of x and t. To advance P by one time step, we discretized Eq. (7), and we solved for Pnþ1 . j The discretized version of Eq. (7), was given by 0 2 1   1 ! !0  n n n Rn P þ DP njþ1  P nj þ DP nj Pjnþ1  Pjn1 B j C Rjþ1  Rj1 @ jþ1 A @ A 2Dt 4la 2Dx Dx 

ð5Þ

where K1 denotes half of the range of pressure variation around its reference value P0; K2 quantiﬁes TGF sensitivity; the target concentration Cop is the time-independent steady-state TAL tubular ﬂuid chloride concentration alongside the MD when P(0, t) = P0; and C(L, t  s) is the chloride concentration alongside the MD at the time t  s (which is s seconds before time t), where s is the TGF delay.

bðxÞ ¼

where b1(x) is a cubic polynomial such that b1(x = 1.5  L) = b0 and b1(x = L0) = b2 . The values b0 and b2 are chosen such that at steady state (i.e., with Q assumed constant) the model yields a target average TAL radius and a target outﬂow pressure P(L0). Extratubular concentration is speciﬁed by Ce(x) = Co(A1 exp (A3(x/L)) + A2), where A1 = (1  Ce(L)/Co)/(1  exp(A3)), A2 = 1  A1, and A3 = 2, and where Ce(L) corresponds to a cortical interstitial concentration of 150 mM. Graphs for Ce(x) and the steady-state proﬁle C(x, ) were given in Fig. 1 of Ref. .

¼

Rnj

3

nþ1 P nþ1 þ P nþ1 jþ1  2j P j1

2  16la

ðDxÞ

2

þ

n1 P n1 þ P n1 jþ1  2j P j1 2

ðDxÞ

! ;

ð8Þ

where

DPnj ¼

    1 sign Pnjþ1  Pnj þ sign Pnj  Pnj1 2     1      min Pnjþ1  Pnj ; P nj  Pnj1  2

ð9Þ

with

 signðxÞ ¼

0;

if x 6 0;

1; otherwise;

ð10Þ

and where @R in Eq. (7) was replaced by a (from Eq. (4)). Eqs. (8)– @P (10) yield a system of coupled linear equations, which were solved at each time step n. Numerical solutions to the solute conservation Eq. (3) were found as previously described [16,17]: a spatially second-order ENO method was used in conjunction with Heun’s method to obtain a method that was second-order in both space and time. A time step of Dt = 1/320 s was applied on a spatial grid of 1280 subintervals, which yielded a space step of Dx = L0/1280 = 2/1280 cm. 3. The characteristic equation Renal blood pressure undergoes almost constant ﬂuctuations, caused by the animal’s respiration, heart beat, or change in posture. Following a perturbation, tubular ﬂuid pressure (or ﬂow) may tend to an approximation of a time-independent steady state, or it may evolve into a regular, sustained, stable oscillation, i.e., a limit-cycle oscillation (LCO). Given a set of model parameters, one may predict the asymptotic behavior of the in vivo tubular ﬂuid dynamics that occurs in response to a perturbation by means of a direct computation of the numerical solution to the model equations Eqs. (3) and (7). However, such computations can be

188

A.T. Layton / Mathematical Biosciences 228 (2010) 185–194

time-consuming, and may not be feasible if one wishes to attain a thorough understanding of the systematic dependence of model behavior on the parameter values that fall within the physiologic range. Thus, as an alternative, we derived and analyzed a characteristic equation from a linearization of the model equations. We ﬁrst non-dimensionalize Eqs. (7) and (3) by letting e¼ ~ ¼ s=T o ; ~cAo ¼ cAo =cAo (where cAo ¼ 2pRo Þ; C ~ x ¼ x=L; ~t ¼ t=T o ; s e e ¼ C e =C o ; Q e ¼ Q =Q ; V e e C=C o ; C ¼ V =ðC Q =ðc LÞÞ; K max max o o Ao M ¼ o e ¼ P=Po ; R e ¼ R=Ro ; l ~ ¼ j=ðQ o =ðcAo LÞÞ; P ~ ¼ l=ðpP o R4o = K M =C o ; j Q o LÞ, where T o ¼ pR2o L=Q o . Then, expressing Eqs. (7) and (3) in terms of non-dimensional variables, simplifying, and dropping the tildes, we obtain

@ R2 @ @ R3 @ 2 P; P R P¼ @t 4la @x @x 16la @x2   @ @ @ V max C R2 C ¼ 2RC R  Q C  þ jðC  C e Þ : @t @t @x KM þ C

ð11Þ ð12Þ

In the derivation of the characteristic equation, we make the simplifying assumption that, at steady state, the tubular ﬂuid pressure is linear and the radius parameter b(x) is chosen such that tubular radius is constant. Given these assumptions, we ﬁrst linearize Eq. (11) by assuming inﬁnitesimal perturbations in C, P, and R:

Cðx; tÞ ¼ C SS ðxÞ þ C  ðx; tÞ;

ð13Þ

Pðx; tÞ ¼ PSS ðxÞ þ P ðx; tÞ;

ð14Þ

Rðx; tÞ ¼ 1 þ R ðx; tÞ;

ð15Þ

Assuming that the solution to Eqs. (19)–(21) takes the form

P ðx; tÞ ¼ P0 ðC op Þf ð1ÞgðxÞekðtsÞ ; we obtain that

gðxÞ ¼ cþ ekþ x þ c ek x ;

ð16Þ

in non-dimensional form, with steady-state tubular ﬂow rate normalized to 1. Note that the p factor does not appear in Eq. (16) because it has been incorporated in the non-dimensionalization of l. Also, from the pressure-radius relationship (4), one can show that

R ðx; tÞ ¼ aðPðx; tÞ  PSS ðxÞÞ ¼ aP ðx; tÞ:

ð17Þ

Substituting Eqs. (14) and (15) into Eq. (11), we obtain

@ ð1 þ R Þ2 ðPSS þ P  Þ  @t 4la ¼





 @R @ ðP SS þ P Þ @x @x

ð1 þ R Þ3 @ 2 ðPSS þ P Þ: 16la @x2

ð18Þ

Keeping only the OðÞ terms and simplifying, we obtain the following linear advection–diffusion equation for P

@ @ 1 @2 P þ 2 P ¼ P ; @t @x 16la @x2

k ¼ 16la 

ð25Þ ð26Þ

Note that in the rigid-tube limit, i.e., a ? 0, k+  k ? 0 and g(x) approaches a linear function, thereby approximating the behavior of a rigid tube. Also, when the solution (23) is substituted into (19), the factor f(1) cancels on both sides of the equation; thus, the value of f(1) is not needed in the analysis. We then linearize the solute conservation equation by substituting Eqs. (13)–(15) and the normalized form of Eq. (1) into Eq. (12):

ð1 þ R Þ2

@ @ ðC SS þ C  Þ ¼ 2ð1 þ R ÞðC SS þ C  Þ ð1 þ R Þ @t @t ð1 þ R Þ4 @ @ ðPSS þ P Þ ðC SS þ C  Þ þ @x @x 8l   V max ðC SS þ C  Þ  þ jðC SS þ C   C e Þ ; K M þ C SS þ C  ð27Þ

To simplify notations, we denote the active transport term by K(C) = VmaxC/(KM + C). Thus, at steady state, where the time-derivatives vanish, one obtains that

1 @ @ PSS C SS ¼ KðC SS Þ þ jðC SS  C e Þ: 8l @x @x

ð28Þ

Keeping only the OðÞ terms in (27), and using the relation @R/ @t = [email protected] P/@t from (17), we arrive at the evolution equation for C   @ @ 1 @ @ @ @ @ @ C  ¼ 2aC SS P þ 4R P SS C SS þ P SS C  þ P  C SS @t @t 8l @x @x @x @x @x @x  ðK 0 ðC SS Þ þ jÞC  : ð29Þ Substituting C(x, t) = f(x)ekt, P(x, t) = P0 (Cop)f(1)g(x)ek(ts), R(x, t) = aP(x, t) into the above equation, we obtain

and

1 0 0 C P ðC op Þf ð1Þg 0 ðxÞekðtsÞ þ P0SS f 0 ðxÞekt 8l SS

þ 4aC 0SS P0SS P0 ðC op Þf ð1ÞgðxÞekðtsÞ  2aC SS kP0 ðC op Þf ð1ÞgðxÞekðtsÞ

subject to the boundary conditions:

P ð0; tÞ ¼ P0 ðC op ÞC  ð1; t  sÞ;

ð20Þ

P ðL0 ; tÞ ¼ 0:

ð21Þ

The boundary condition at x = 0 (i.e., Eq. (20)) speciﬁes the change in P0 in response to a deviation in MD Cl concentration; that response has a delay of s. The other boundary condition (Eq. (21)) imposes a ﬁxed pressure value at x = L0. As in previous studies [3,17,18,7], we assume that C(x, t) can be written as C(x, t) = f(x)ekt, for some function f(x) and a constant k. Thus,

P ð0; tÞ ¼ P0 ðC op Þf ð1ÞekðtsÞ :

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ð16laÞ2 þ 16lak;

ekL0 c  ¼  k L : e 0  ekþ L0

kf ðxÞekt ¼ ð19Þ

ð24Þ

where

where   1, and CSS(x) and PSS(x) denote the steady-state Cl concentration and pressure, respectively. Steady-state tubular radius is assumed to be normalized to 1. Note that from Eq. (1),

@ PSS ðxÞ ¼ 8l; @x

ð23Þ

ð22Þ

 ðK 0 ðC SS Þ þ jÞf ðxÞekt :

ð30Þ

Applying (16), canceling out ekt, and rearranging,

f 0 ðxÞ þ k þ K 0 ðC SS Þ þ j f ðxÞ  

1 0 C SS ðxÞg 0 ðxÞ  2agðxÞ kC SS ðxÞ þ 2C 0SS ðxÞ : ¼ eks P0 ðC op Þf ð1Þ 8l ð31Þ Recall that Cl concentration at the loop bend (i.e., entrance to the TAL) is assumed ﬁxed, so C(0) = f(0)ekt = 0, which implies that f(0) = 0. Hence, we obtain the following solution for (31):

A.T. Layton / Mathematical Biosciences 228 (2010) 185–194

 Z s 

f ðsÞ ¼ exp  k þ K 0 ðC SS Þ þ j dy P0 ðC op Þf ð1Þ 0   Z s

1 0 0 ks e C SS g  2ag kC SS þ 2C 0SS  8l 0 Z x 

 exp k þ K 0 ðC SS Þ þ j dy dx:

ð32Þ

0

Setting s = 1 and canceling the factor f(1),

1 ¼ eks P0 ðC o pÞ



1 0 0 C SS g  2ag kC SS þ 2C 0SS 8l 0  Z 1 

k þ K 0 ðC SS Þ þ j dy dx:  exp  Z

1



ð33Þ

x

To facilitate a comparison of (33) with the characteristic equation derived for a rigid TAL , we use the relation

d C SS ðxÞ ¼ KðC SS Þ  jðC SS ðxÞ  C e ðxÞÞ; dx

ð34Þ

which can be obtained from (12) by eliminating all the time-derivatives and setting QSS = 1. Upon differentiation, (33) yields

C 00SS ðxÞ ¼ K 0 ðC SS ÞC 0SS ðxÞ  jðC 0SS ðxÞ  C 0e ðxÞÞ; C 0e C 0SS

j

) K 0 ðC SS Þ þ j ¼



C 00SS C 0SS

ð35Þ ð36Þ

:

Now substituting (36) into (33), we get     Z 1  Z 1 1 0 C SS jC 0e 1 ¼ ceks ekð1xÞ g  2 ag k 0 þ 2 exp  dy dx; 8l C SS C 0SS 0 x

ð37Þ 0

ðC op ÞC 0SS ð1Þ

where c P is the TGF gain. The gain c can be related to the parameters K1 and K2 in the pressure response function (5). Differentiating Eq. (5) with respect to C and setting C to Cop, we obtain that P0 (Cop) = K1K2; thus

c ¼ K 1 K 2 C 0SS ð1Þ:

ð38Þ

In the rigid-tube limit, where a ? 0, g0 ? 1/L0; thus, (37) reduces to

1¼

c ks e 8lL0

Z

1

 Z ekð1xÞ exp 

0

x

1

jC 0e C 0SS

 dy dx:

ð39Þ

From , the characteristic equation for a TGF model with a rigid TAL is given by

1 ¼ crigid eks

Z

1

0

 Z ekð1xÞ exp  x

1

jC 0e C 0SS

 dy dx:

ð40Þ

Comparing (39) and (40), we see that in the limit a ? 0, the gain factors c for the rigid and compliant TAL models are related by

c ¼ 8lL0 crigid :

ð41Þ

4. Model results To better understand the impacts of the compliance of TAL walls on the dynamics of the TGF system, we conducted a bifurcation analysis for parameter regimes having physiologic relevance. We ﬁrst used the model’s characteristic Eq. (37) to predict, based on a linearization of the model, parameter boundaries that separate differing behaviors. We then used numerical solutions of the full (non-linear) model (Eqs. (3)–(7)) to supplement the information provided by the characteristic equation. A solution to the characteristic Eq. (37) is a number in an inﬁnite series k1, k2, . . . , where kn 2 C. The real and imaginary parts

189

of kn are indicators of the strength and frequency, respectively, of a solution to the model equations. We determined parameter regions that correspond to differing combinations of signs of Re(kn) by computing values of c–s pairs that are encompassed within the physiologic range and that correspond to Re(kn) = 0, roots that may indicate a solution bifurcation or transition between stable solution behaviors. These c–s pairs, which form curves in the c–s plane, were obtained for two cases: (1) the rigid TAL case, i.e., a = 0; (2) the compliant TAL case with baseline compliance a = 1.33  105 cm mmHg1. The results are shown in Fig. 2, panels A1 and B1, respectively. For the rigid TAL case, sufﬁciently small values of c or s, i.e., for points (c, s) that fall below all curves Re(kn) = 0, any initial solution, or any transient perturbation of a steady-state solution, results in a solution that converges to the time-independent steady-state solution; that solution, indicated by ‘Steady State’, is the only stable solution. But for s > 0 and for values of c such that the points (c, s) are above the curve Re(kn) = 0 for some n, a perturbation of the steady-state solution results in a LCO. Previously we also noted that the crossing of the root curves gives rise to new parameter regimes, such as one where Re(k2) > 0 and Re(k1) < 0 [5,7], and where a LCO of a frequency corresponding to either Im(k1) or Im(k2) can be elicited. In other words, model solutions can exhibit multiple stable dynamic modes, i.e., multistability . When TAL tubular wall compliance is introduced, oscillatory states become attainable at notably lower gain values, inasmuch as compliance lowers the root curves, as shown by comparison of Figs. 2A1 and 2B1. Indeed, the linearized model predicts that for a TGF delay of 3.5 s, which corresponding to a non-dimensional value of s = 0.2228, a LCO can be obtained for a gain value of as low as c = 1.37, which seems to be substantially lower than most experimental observations . The degree to which the compliant-TAL model agrees with experimental records will be addressed in the DISCUSSION. We then investigated actual model behaviors, without the linearization that was applied to derive the characteristic Eq. (37). Numerical solutions were computed for the full, non-linear model equations (Eqs. (3)–(7)), as a function of selected values of gain c and delay s. The results, which were obtained for the rigid-TAL case and for the baseline-compliance case, are summarized in Fig. 2, panels A2 and B2. In the regions marked ‘Steady State’, qn < 0, where q Re(k), and the only stable solution is the time-independent steady state. In the regions marked ‘fn’, (n = 1, 2, 3 or 4) the only stable solutions elicited by a transient perturbation are LCO-solutions having a frequency f that approximates that corresponding to Im(kn)/(2p). In some regions, two or more stable LCO, of differing frequencies, can be elicited, depending on initial conditions. For example, in the region marked ‘f1, f2’, one of these corresponds to the f1-LCO, and the other to the f2LCO. This parameter region, where the real parts of both k1 and k2 are both positive, and where the amplitude of Re k2 is sufﬁciently large relative to Re k1, exhibits bistability with respect to model solutions. Simulated oscillations in SNGFR and tubular ﬂuid Cl concentration at the MD were computed for four points, labeled W, X, Y, and Z in Fig. 3A. These points correspond to the following pairs of delays and gains: (sW, cW) = (0.2, 1.0), (sX, cX) = (0.4, 3.0), (sY, cY) = (0.2, 3.0), and (sZ, cZ) = (0.1, 5.0). The MD [Cl] proﬁles corresponding to these points are shown in Fig. 3W–Z. Point W, which lies within the ‘Steady State’ regime, corresponds to a time-independent steady state. Points X, Y, and Z, which lie in the f1, f2, and f3 regions, respectively, correspond to oscillatory solutions, with LCO frequencies f1 = 31.7, f2 = 75.6, and f3 = 125.7. In our previous TGF models [7,3], in which the TAL is represented by a rigid tube, the LCO frequencies have been found in good (although not precise) agreement with the frequencies

190

A.T. Layton / Mathematical Biosciences 228 (2010) 185–194

A1

B1

A2

B2

Fig. 2. A1: root loci, for q Re(k) = 0, of the characteristic equation for a rigid TAL, as a function of gain c and delay s. B1: root loci for a TAL with basecase compliance. A2 and B2: behaviors of model solutions, based on numerical simulations using full model equations.

predicted by an approximate formula (derived based, in part, on the assumption that the TAL backleak permeability is zero)

fn

n ; to ð1 þ 2sÞ

ð42Þ

where s is in non-dimensional form and to is the dimensional steady-state transit time. Thus, in those rigid-TAL models, we have, approximately, that f2 2f1 and f3 3f1. In contrast, the compliant TAL used in this study predicted, for the points considered above, ratios of f2/f1 = 2.385 and f3/f1 = 3.902, which deviate substantially from 2 and 3, respectively. That discrepancy is attributable, in most part, to the compliance of the model TAL tubular walls, inasmuch as the TAL was assumed to be rigid in the derivation of the formula (42). Indeed, as suggested by our results below, our model predicts LCO frequencies that are better approximated by Eq. (42) when the TAL compliance a is reduced. A comparison between the root curves obtained for the characterization equation corresponding to the rigid-TAL model (see Fig. 2A1) and the bifurcation curves obtained for the full model (see Fig. 2A2) indicates that, for the rigid-TAL case, the linearized model and the full, non-linear model give approximately identical predictions for the boundary between steady-state solutions (marked ‘Re(kn) < 0’ in Fig. 2A1 and ‘Steady State’ in Fig. 2A2) and oscillatory solutions. This result, obtained for the rigid-tube model, is consistent with the Hartman-Grobman Theorem , which concerns the local behavior of dynamic systems in the neighborhood of a hyperbolic ﬁxed point, and, straightly speaking, does not apply to the rigid-tube model equations which comprise of partial differential equations. Our result is also consistent our previous studies of the rigid-tube TGF system [3,5,7]. However, for the basecase compliant-TAL case, there is substantial difference between the bound-

A.T. Layton / Mathematical Biosciences 228 (2010) 185–194

191

A

W

X

Y

Z

Fig. 3. A: Behaviors of model solutions, based on numerical simulations using full model equations using basecase compliance. Sample solutions for points (W : c = 1, s = 0.2) (X : c = 3, s = 0.4), (Y: c = 3, s = 0.2), and (Z: c = 5, s = 0.1), are shown in panels W, X, Y, and Z, respectively.

predicts oscillatory solutions for c P 1.85, a critical gain that is 33% higher than that predicted by the linearized model. Comparing the rigid- and compliant-TAL results in Fig. 2, we again note that, for a given non-zero s value, oscillatory states are attainable in the compliant-TAL case at substantially lower gain values. These results, which are consistent with the predictions of the linearized models, suggest that TAL compliance increases the tendency of the model TGF system to oscillate. To further assess the effect of TAL compliance on model stability, we computed numerical solutions for the full model equations for selected values of c and s, for the case where TAL compliance is double that of basecase value, i.e., a = 2.67  105 cm mmHg1. The results, summarized in Fig. 5, show that doubling TAL compliance further lowered the gain values at which oscillatory states are attainable. For example, for s = 0.2228, oscillatory states are attainable for c > 1.80 for the basecase compliance a. When a is doubled,

oscillatory states are attainable for a lower critical gain value of c > 1.15. In contrast, for the rigid-TAL case, a much higher TGF gain is required (c > 3.15) for the model to exhibit oscillatory solutions. The base-case TAL tubular wall compliance was used by Marsh and co-workers  and was based on measurements in isolated tubule studies. However, using that compliance value, the basecase model (and to an even larger extent, the model that uses twice that compliance value) predicts oscillatory solutions at gain values that appear to be signiﬁcantly lower than values suggested by experimental measurements . That discrepancy may be attributed to the likelihood that the effective compliance of the TAL tubular walls may be lowered in vivo by the stiffness of the interstitial matrix, and by the resistance exerted by neighboring TALs whose tubular ﬂuid may be oscillating in sync. To estimate the effective TAL compliance in vivo, we computed numerical solutions for the full model using a TAL compliance that is 1/5 of basecase

192

A.T. Layton / Mathematical Biosciences 228 (2010) 185–194

Fig. 4. Behaviors of model solutions, based on numerical simulations using full model equations using basecase compliance and a tube that of 2.9075 times longer than basecase.

value, i.e., a = 2.67  106 cm mmHg1. The results are summarized in Fig. 5B. With this lower compliance value, substantially higher gain values are needed to produce oscillatory solutions (compare Fig. 5B with base-case results in Fig. 2B2). Indeed, the predictions of the lower-compliance model approximate those of the rigid-tube model (Fig. 2A2), although the former predicts higher-mode oscillations (i.e., f2 and f3-LCO) at lower gain values. (Note, for example, that f3-LCO can be obtained in the lower-compliance model for c as low as 7.8, whereas f3-LCO cannot be obtained in the rigid-tube model for c lower than 9.) 5. Discussion We have developed a mathematical model of the TGF system in the rat kidney. The model represents hydrodynamic pressure in TAL tubular ﬂuid and compliant TAL walls. Using that model, we have identiﬁed a variety of dynamic behaviors that can arise for parameters within normal and pathophysiologic parameter regimes. These behaviors, which include limit-cycle oscillations and solution multistability, were identiﬁed and characterized by means of an analysis of the characteristic equation that arises from a linearization of the model equations and of extensive numerical simulations of the solutions for the full model equations,

A

B

Fig. 5. Behaviors of model solutions, based on numerical simulations using full model equations using TAL compliance that is (A) double that of basecase, and (B) one-ﬁfth that of basecase.

193

A.T. Layton / Mathematical Biosciences 228 (2010) 185–194

is not an artifact of the rigid-tubule formulation. In fact, some of those phenomena are more marked in the more inclusive model than in the rigid-tubule model. The compliant-tube TGF model predicts oscillatory states for very low TGF gain values. It also predicts that oscillations with frequency as high as ﬁve times that of the fundamental frequency can be attained at physiologic delay and gain values. These predictions appear to be inconsistent with micropuncture experiments, which suggest that nephron ﬂow and related variables may be in an approximate steady state, or may exhibit regular oscillations with a period of about 30 s , which corresponds to the fundamental frequency. To our knowledge, oscillations more than twice the fundamental frequency have not be reported. Moreover, for the TGF gain of a normotensive rat, which is 3.5 , the compliant-tube TGF model predicts that nephron ﬂow is always in a oscillatory state, which is inconsistent with experimental observations . To reconcile the above discrepancy, we note that the in situ compliance of the TAL walls depends on a number of factors: the inherent elasticity of the epithelium; possible tethering to other tubules via the interstitial matrix; possible synchronization of the oscillations in tubular ﬂuid pressure and ﬂows, and thus of the distension of the tubular walls, among neighboring tubules; and the renal capsule, which contains the kidney and may limit the distension of tubules and vessels. The basecase TAL compliance of our model was based on measurements in isolated tubules, which, owing to the above factors, may be substantially larger than the in situ compliance of the TAL. Indeed, the agreement of the predictions of our previous rigid-TAL model with experimental measurements [3,5] suggests that the in situ compliance of the TAL may be sufﬁciently low that the rigid-tube formulation does not render the model’s predictions null and void. Acknowledgments This research was supported by the National Science Foundation, grant DMS-0715021, and by the National Institutes of Health: National Institute of Diabetes and Digestive and Kidney Diseases, grant DK089066. Appendix A. Derivation of the ﬂuid equations The model TAL and contiguous resistance tubule is represented as a cylindrical, low-compliance tube. Fluid ﬂow within the tube is assumed to be laminar ﬂow. Because the Reynolds number for the model tubule is sufﬁciently low, tubular ﬂow can be approximated by Poiseuille ﬂow , which describes the laminar ﬂow of a viscous, incompressible, Newtonian ﬂuid through a cylindrical tube:

@ 8l Pðx; tÞ ¼  4 Q ðx; tÞ: @x pR ðx; tÞ

ðA1Þ

(In this equation, the nature of the dependence of R on space and time remains unspeciﬁed.) To maintain mass conservation, the radial and axial ﬂuid velocities, denoted Vr and Vx, respectively, are related  by

1 @ @ ðrV r ðr; x; tÞÞ þ V x ðr; x; tÞ ¼ 0: r @r @x

ðA2Þ

Because we assume cylindrical symmetry, Vr and Vx do not depend on the angular coordinate h. To relate Eq. (A2) to ﬂuid pressure P and volumetric ﬂow Q, we observe that Q ðx; tÞ ¼ Aðx; tÞV x ðr; x; tÞ, where A(x, t) = pR2(x, t) is the cross-sectional area of the tubule,  x ðx; tÞ is the axial velocity averaged over that cross-sectional and V area, i.e.,

V x ðx; tÞ ¼

1 Aðx; tÞ

Z 2p Z

Rðx;tÞ

V x ðr; x; tÞrdrdh:

ðA3Þ

0

0

By integrating Eq. (A2) over the cross-sectional area, one obtains

2pRðx; tÞV r ðR; x; tÞ þ

@ Qðx; tÞ ¼ 0: @x

ðA4Þ

At r = R, Vr is the rate at which the tubular radius R changes, i.e.,

V r ðR; x; tÞ ¼

@ Rðx; tÞ: @t

ðA5Þ

Because the radius R(x, t) is assumed in our model to be a function of tubular ﬂuid pressure P(x, t) (i.e., R(x,t) = F(P(x, t)), for an appropriate F; see Eq. (4), we replace the notation R(x, t) with R(P(x, t)), so that the dependence of R on x and t is made explicit through Eq. (4). By means of the chain rule, we can write

@ RðPðx; tÞÞ ¼ @t



  @R @ ðx; tÞÞ Pðx; tÞ : @P @t

ðA6Þ

Therefore, Eq. (A4) can be expressed as

2pRðx; tÞ

   @R @ @ ðx; tÞ Pðx; tÞ þ Q ðx; tÞ ¼ 0; @P @t @x

ðA7Þ

which is equivalent to Eq. (2). References  D. Eaton, J. Pooler, Vander’s Renal Physiology, sixth ed., McGraw-Hill Medical, New York, 2004.  J. Han, K. Thompson, C. Chou, M. Knepper, Experimental tests of threedimensional model of urinary concentrating mechanism, J. Am. Soc. Nephrol. 2 (1992) 1677.  H. Layton, E. Pitman, L. Moore, Bifurcation analysis of TGF-mediated oscillations in SNGFR, Am. J. Physiol. (Renal Fluid Electrolyte Physiol) 261 (1991) F904.  H. Layton, E. Pitman, L. Moore, Instantaneous and steady-state gains in the tubuloglomerular feedback system, Am. J. Physiol. Renal Physiol. 268 (1995) F163.  A. Layton, L. Moore, H. Layton, Multistability in tubuloglomerular feedback and spectral complexity in spontaneously hypertensive rats, Am. J. Physiol. Renal Physiol. 291 (2006) F79.  P. Budu-Grajdeanu, L. Moore, H. Layton, Effect of tubular inhomogeneities on ﬁlter properties of thick ascending limb of Henle’s loop, Math. Biosci. 209 (2) (2007) 564.  A. Layton, L. Moore, H. Layton, Multistable dynamics mediated by tubuloglomerular feedback in a model of coupled nephrons, Bull. Math. Biol. 71 (2009) 515.  N. Holstein-Rathlou, D. Marsh, A dynamic model of the tubuloglomerular feedback mechanism, Am. J. Physiol. (Renal Fluid Electrolyte Physiol 27) 258 (1990) F1448.  T. Sakai, D. Craig, A. Wexler, D. Marsh, Fluid waves in renal tubules, Biophys. J. 50 (1986) 805.  D. Young, D. Marsh, Pulse wave propagation in rat renal tubules: implications for GFR autoregulation, Am. J. Physiol. (Renal Fluid Electrolyte Physiol 9) 240 (1981) F446.  H. Layton, E. Pitman, M. Knepper, A dynamic numerical method for models of the urine concentrating mechanism, SIAM J. Appl. Math. 55 (5) (1995) 1390.  S. Angell, R. Pruthi, L. Shortliffe, The urodynamic relationship of renal pelvic bladder presures and urinary ﬂow rate in rats with congenital vesicoureteral reﬂux, J. Urology 160 (1998) 150.  C. Gottschalk, A comparable study of renal interstitial pressure, Am. J. Physiol. 169 (1952) 180.  C. Gottschalk, M. Mylle, Micropuncture study of pressures in proximal and distal tubules and peritubular capillaries of the rat kidney during osmotic diuresis, Am. J. Physiol. 189 (1957) 323.  A. Layton, H. Layton, A region-based model framework for the rat urine concentrating mechanism, Bull. Math. Biol. 65 (5) (2003) 859.  H. Layton, E. Pitman, A dynamic numerical method for models of renal tubules, Bull. Math. Biol. 56 (3) (1994) 547.  H. Layton, E. Pitman, L. Moore, Spectral properties of the tubuloglomerular feedback system, Am. J. Physiol. (Renal Fluid Electrolyte Physiol 42) 273 (1997) F635.  E. Pitman, H. Layton, L. Moore, Dynamic ﬂow in the nephron: ﬁltered delay in the TGF pathway, Contemp. Math. 114 (1993) 317.

194

A.T. Layton / Mathematical Biosciences 228 (2010) 185–194

 P. Leyssac, L. Baumbach, An oscillating intratubular pressure response to alterations in Henle loop ﬂow in the rat kidney, Acta Physiol. Scand. 117 (1983) 415.  P. Hartman, On local homeomorphisms of euclidean spaces, Bol. Soc. Math. Mexicana 5 (1960) 220.  H. Layton, Mathematical models of the mammalian urine concentrating mechanism, in: L. HE, W. AM (Eds.), Membrane Transport and Renal Physiology, The IMA Volumes in Mathematics and Its Applications, vol. 129, Springer, New York, 2002, p. 233.

 A. Layton, L. Moore, H. Layton, Waveform distortion in TGF-mediated limitcycle oscillations: Effects of TAL ﬂow, FASEB J. 23 (2009) 804.1.  H. Layton, E. Pitman, L. Moore, Limit-cycle oscillations and tubuloglomerular feedback regulation of distal sodium delivery, Am. J. Physiol. Renal Physiol. 278 (2000) F287.  W. Lai, D. Rubin, E. Krempl, Introduction to Continuum Mechanics, third ed., Pergamon LTD, Oxford UK, 1993.