Citation: Constance J. Jeffery. Intracellular/surface moonlighting proteins that aid in the attachment of gut microbiota to the host[J]. AIMS Microbiology, 2019, 5(1): 77-86. doi: 10.3934/microbiol.2019.1.77
[1] | Yan Xie, Zhijun Liu, Ke Qi, Dongchen Shangguan, Qinglong Wang . A stochastic mussel-algae model under regime switching. Mathematical Biosciences and Engineering, 2022, 19(5): 4794-4811. doi: 10.3934/mbe.2022224 |
[2] | Yansong Pei, Bing Liu, Haokun Qi . Extinction and stationary distribution of stochastic predator-prey model with group defense behavior. Mathematical Biosciences and Engineering, 2022, 19(12): 13062-13078. doi: 10.3934/mbe.2022610 |
[3] | Lin Li, Wencai Zhao . Deterministic and stochastic dynamics of a modified Leslie-Gower prey-predator system with simplified Holling-type Ⅳ scheme. Mathematical Biosciences and Engineering, 2021, 18(3): 2813-2831. doi: 10.3934/mbe.2021143 |
[4] | Sanling Yuan, Xuehui Ji, Huaiping Zhu . Asymptotic behavior of a delayed stochastic logistic model with impulsive perturbations. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1477-1498. doi: 10.3934/mbe.2017077 |
[5] | Chun Lu, Bing Li, Limei Zhou, Liwei Zhang . Survival analysis of an impulsive stochastic delay logistic model with Lévy jumps. Mathematical Biosciences and Engineering, 2019, 16(5): 3251-3271. doi: 10.3934/mbe.2019162 |
[6] | Zhiwei Huang, Gang Huang . Mathematical analysis on deterministic and stochastic lake ecosystem models. Mathematical Biosciences and Engineering, 2019, 16(5): 4723-4740. doi: 10.3934/mbe.2019237 |
[7] | Yan Zhang, Shujing Gao, Shihua Chen . Modelling and analysis of a stochastic nonautonomous predator-prey model with impulsive effects and nonlinear functional response. Mathematical Biosciences and Engineering, 2021, 18(2): 1485-1512. doi: 10.3934/mbe.2021077 |
[8] | Xueqing He, Ming Liu, Xiaofeng Xu . Analysis of stochastic disease including predator-prey model with fear factor and Lévy jump. Mathematical Biosciences and Engineering, 2023, 20(2): 1750-1773. doi: 10.3934/mbe.2023080 |
[9] | Yan Wang, Tingting Zhao, Jun Liu . Viral dynamics of an HIV stochastic model with cell-to-cell infection, CTL immune response and distributed delays. Mathematical Biosciences and Engineering, 2019, 16(6): 7126-7154. doi: 10.3934/mbe.2019358 |
[10] | H. J. Alsakaji, F. A. Rihan, K. Udhayakumar, F. El Ktaibi . Stochastic tumor-immune interaction model with external treatments and time delays: An optimal control problem. Mathematical Biosciences and Engineering, 2023, 20(11): 19270-19299. doi: 10.3934/mbe.2023852 |
Cell-based in vitro assays [27] are efficient methods to study the effect of industrial chemicals on environment or human health. Our work is based on the cytotoxicity profiling project carried by Alberta Centre for Toxicology in which initially 63 chemicals were investigated using the xCELLigence Real-Time Cell Analysis High Troughput (RTCA HT) Assay [26]. We consider a mathematical model represented by stochastic differential equations to study cytotoxicity, i.e. the effect of toxicants on human cells, such as the killing of cells or cellular pathological changes.
The cells were seeded into wells of micro-electronic plates (E-Plates), and the test substances with 11 concentrations (1:3 serial dilution from the stock solution) were dissolved in the cell culture medium [20]. The microelectrode electronic impedance value was converted by a software to Cell Index (
The success of clustering and classification methods depends on providing TCRCs that illustrates the cell population evolution from persistence to extinction. In [1] we consider a model represented by a system of ordinary differential equations to determine an appropriate range for the initial concentration of the toxicant. The model's parameters were estimated based on the data included in the TCRCs [1].
Let
$ dn(t)dt=βn(t)−γn2(t)−αCo(t)n(t),
$
|
(1) |
$
dCo(t)dt=λ21Ce(t)−η21Co(t),
$
|
(2) |
$
dCe(t)dt=λ22Co(t)n(t)−η22Ce(t)n(t)
$
|
(3) |
Here
The deterministic model (1)-(3) is a special case of the class of models proposed in [5], and it is related to the models considered in [7, 11, 15]. However, since we consider an acute dose of toxicant instead of a chronic one, the analysis of the survival/death of the cell population is different from the one done in the previously mentioned papers.
We have noticed that, for the toxicants considered here, the estimated values of the parameters
1. If
$ \lim\limits_{t\rightarrow\infty} n(t) = K, ~~~~\lim\limits_{t\rightarrow\infty} C_o(t) = \lim\limits_{t\rightarrow\infty} C_e(t) = 0. $ |
2. If
$ \lim\limits_{t\rightarrow\infty} n(t) = 0, ~~~ \lim\limits_{t\rightarrow\infty} C_o(t) = C_e^*\frac{\lambda_1^2}{\eta_1^2}, ~~~ \lim\limits_{t\rightarrow\infty} C_e(t) = C_e^*>\frac{\beta\eta_1^2}{\alpha\lambda_1^2}, $ |
In practice we usually estimate a parameter by an average value plus an error term. To keep the stochastic model as simple as possible, we ignore the relationship between the parameters
$ \tilde{\beta} = \beta+\text{error}_1~~~~, \tilde{\gamma} = \gamma+\text{error}_2 $ | (4) |
By the central limit theorem, the error terms may be approximated by a normal distribution with zero mean. Thus we replace equation (1) by a stochastic differential equation and, together with equations (2) and (3), we get the stochastic model
$
dn(t)=n(t)(β−γn(t)−αCo(t))dt+σ1n(t)dB1(t)−σ2n2(t)dB2(t),
$
|
(5) |
$
dCo(t)=(λ21Ce(t)−η21Co(t))dt,
$
|
(6) |
$
dCe(t)=(λ22Co(t)n(t)−η22Ce(t)n(t))dt,
$
|
(7) |
Here
Several versions of a stochastic logistic equation similar with (5) were considered in [18], [19], [8], [9], [10] and [21]. The system of stochastic differential equations (5)-(7) is closely related with the stochastic models in a polluted environment considered in [15], [16], and [24]. However, for the models considered in these papers, instead of the equations (6) and (7),
In this paper we extend the methods applied in [15] and [16] to find conditions for extinction, weakly persistence, and weakly stochastically permanence for the model (5)-(7). In addition to this we focus on the ergodic properties when the cell population is strongly persistent. The main contribution of this paper is the proof that
In the next section we prove that there is a unique non-negative solution of system (5)-(7) for any non-negative initial value. In section 3 we investigate the asymptotic behavior, and in section 4 we study the weak convergence of
We have to show that system (5)-(7) has a unique global positive solution in order for the stochastic model to be appropriate. Let
Since equations (6) and (7) are linear in
$
Co(t)=Co(0)e−η21t+λ21e−η21t∫t0Ce(s)eη21sds
$
|
(8) |
$
Ce(t)=Ce(0)exp(−η22∫t0n(s)ds)+λ22exp(−η22∫t0n(s)ds)∫t0Co(s)n(s)exp(η22∫s0n(l)dl)ds, t≥0.
$
|
(9) |
Let's define the differential operator
$
L=∂∂t+(βn−γn2−αCon)∂∂n+(λ21Ce−η21Co)∂∂Co+(λ22Con−η22Cen)∂∂Ce+12((σ21n2+σ22n4)∂2∂2n)
$
|
For any function
$ dV(x(t), t) = LV(x(t), t)dt+\frac{\partial V(x(t), t)}{\partial n}\left(\sigma_1 n(t)dB_1(t)-\sigma_2 n^2(t) dB_2(t)\right), \label{ito1} $ | (10) |
where
Theorem 2.1. Let
Proof. The proof is similar with the proof of theorem 3.1 in [29]. Since the coefficients are locally Lipschitz continuous functions, there exists a unique solution on
$
τm=inf{t∈[0,τe):min{n(t),Ce(t)}≤m−1 or max{n(t),Co(t),Ce(t)}≥m},
$
|
(11) |
where
We show that
We define the
$ V(x) = C_o+\frac{\alpha}{4\lambda_2^2}(C_e-\log C_e-1)+\frac{\alpha C_e}{4\lambda_2^2}+\left(\sqrt{n}-\log\sqrt{n}-1\right)+n. $ |
We get
$
LV(x)=(λ21Ce−η21Co)+α4λ22(1−1Ce)(λ22Con−η22Cen)+α4λ22(λ22Con−η22Cen)+(βn−γn2−αCon)(12√n−12n)+12(σ21n2+σ22n4)(−14n√n+12n2)+(βn−γn2−αCon)
$
|
Omitting some of the negative terms, for any
$
LV(x)≤λ21Ce+αCon4+αCon4+αCo2−αCon+f(n),≤λ21Ce+αCo2+f(n),
$
|
where
$
f(n)=−σ22n2√n8+α4λ21η22n+β√n2+γn2+σ214+σ22n24+βn
$
|
Since
Let's define
$ L\tilde{V}(x, t) = -Ce^{-Ct}(1+V(x))+e^{-Ct}LV(x)\le 0. $ |
Using Itô's formula (10) for
$
E[˜V(x(t∧τm),t∧τm)]=˜V(x(0),0)+E[∫t∧τm0L˜V(x(u∧τm),u∧τm)du]≤˜V(x(0),0).
$
|
Notice that for any
$ E\left[V(x(\tau_m, \omega))I_{\Theta_m}(\omega)\right]\ge P(\Theta_m)b_m\ge\epsilon b_m\rightarrow \infty $ |
as
Here we focus on the case when
Lemma 2.2. If
Theorem 2.3. If
$ \lim\limits_{t\rightarrow\infty}C_o(t) = \frac{\lambda_1^2}{\eta_1^2}\lim\limits_{t\rightarrow\infty}C_e(t). $ |
In this section we assume that
Definition 3.1. The population
Definition 3.2. The population
Definition 3.3. The population
Definition 3.4. The population
Theorem 3.5. a. If
b. If
Proof. The proof is similar with the proof of Theorem 6 in [16]. We start with some preliminary results. By Itô's formula in (5) we have
$ d \ln n(t) = \left(\beta -\gamma n(t)- \alpha C_o(t)-\frac{\sigma_1^2+\sigma_2^2 n^2(t)}{2}\right)dt+\sigma_1 dB_1(t)-\sigma_2 n(t) dB_2(t).\notag $ |
This means that we have
$
lnn(t)−lnn(0)=(β−σ212)t−γ∫t0n(s)ds−α∫t0Co(s)ds−σ222∫t0n2(s)ds+σ1B1(t)−σ2∫t0n(s)dB2(s), \label{eqex}
$
|
(12) |
Notice that the quadratic variation [17] of
$ \langle M(t), M(t)\rangle = \sigma_2^2\int_0^t n^2(s) ds.\notag $ |
Now we do the proof for part a. Using the exponential martingale inequality (Theorem 7.4 [17]) and Borel-Cantelli lemma ([22], pp. 102), and proceeding as in the proof of Theorem 6 in [16] we can show that for almost all
$ \sup\limits_{0\le t\le n}\left(M(t)-\frac{1}{2}\langle M(t), M(t)\rangle \right)\le 2 \ln n.\notag $ |
Hence, for all
$ -\frac{\sigma_2^2 }{2}\int_0^t n^2(s)ds-\sigma_2 \int_0^t n(s) dB_2(s)\le 2 \ln n \text{ a.s.}.\notag $ |
Substituting the above inequality in (12) we get
$ \frac{\ln n(t)-\ln n(0)}{t} \le \beta-\frac{\sigma_1^2}{2} - \alpha \frac{\int _0^t C_o(s)ds}{t}+\sigma_1\frac{B_1(t)}{t}+2\frac{\ln n}{n-1} \text{ a.s.}, \notag $ |
for all
$ \limsup\limits _{t\rightarrow \infty}\frac{\ln n(t)}{t}\le \beta-\frac{\sigma_1^2}{2} - \alpha \liminf\limits_{t\rightarrow \infty}\frac{\int _0^t C_o(s)ds}{t}<0\text{ a.s.}. $ |
Next we prove part b. Suppose that
$ \limsup\limits _{t\rightarrow \infty} \frac{\ln n(t, \omega)}{t}\le 0\label{contre1} $ | (13) |
Moreover, from the law of large numbers for local martingales (Theorem 3.4 in [17]) there exists a set
$ \lim\limits_{t\rightarrow \infty}\frac{M(t, \omega)}{t} = \lim\limits_{t\rightarrow \infty}\frac{ B_1(t, \omega)}{t} = 0.\notag $ |
From (12) we get:
$
ln(n(t))t=ln(n(0))t+(β−σ212)−α∫t0Co(s)dst−∫t0(γn(s)+σ222n2(s))dst+σ1B1(t)t+M(t,ω)t
$
|
Hence, for any
$ \limsup\limits_{t\rightarrow \infty} \frac{\ln n(t, \omega)}{t} = \left(\beta-\frac{\sigma_1^2}{2}\right) - \alpha \liminf \limits_{t\rightarrow \infty}\frac{\int _0^t C_o(s, \omega)ds}{t}\notag $ |
Since we know that
We have the following result regarding the expectation of
Lemma 3.6. There exists a constant
Proof. Using Itô's formula in (5) we get:
$
d(etn(t))=n(t)et(1+β−αCo(t)−γn(t))dt+σ1n(t)etdB1(t)−σ2n2(t)etdB2(t)≤n(t)et(1+β−γn(t))dt+σ1n(t)etdB1(t)−σ2n2(t)etdB2(t)≤et(1+β)24γdt+σ1n(t)etdB1(t)−σ2n2(t)etdB2(t)
$
|
(14) |
Let
$ \eta_m = \inf\{t\ge 0: n(t)\notin(1/m, m)\}, \label{infi2} $ | (15) |
for any
$ E\left[e^{t\wedge \tau_m} n(t\wedge \tau_m)\right]\le n(0)+E\left[\int_0^{t\wedge \tau_m} e^s\frac{(1+\beta)^2}{4\gamma} ds\right]\le n(0)+\frac{(1+\beta)^2}{4\gamma}(e^t-1).\notag $ |
Letting
$ E\left[n(t)\right]\le \frac{n(0)}{e^t }+\frac{(1+\beta)^2}{4\gamma}(1-e^{-t}). $ |
Thus, there exists a constant
Corollary 1. For any
Proof. For any
$ P\left(n(t)>c_1(\epsilon)\right)\le \frac{E[n(t)]}{c_1(\epsilon)}. $ |
Hence, from Lemma 3.6 we get
$ \limsup\limits_{t\rightarrow \infty}P\left(n(t)>c_1(\epsilon)\right)\le \limsup\limits_{t\rightarrow \infty}\frac{E[n(t)]}{c_1(\epsilon)}\le \epsilon. $ |
Theorem 3.7. If
Proof. First we show that
By Itô's formula in (5) we get for any real constant c:
$
d(ectn(t))=ect(1n(t)(c−β+σ21+αCo(t))+γ+σ22n(t))dt−σ1ectn(t)dB1(t)+σ2ectdB2(t)
$
|
Since
$ d\left(\frac{e^{ct}}{n(t)}\right)\le e^{ct}\biggl(\gamma +\sigma_2^2n(t)\biggl)dt-\frac{\sigma_1e^{ct}}{n(t)}dB_1(t)+\sigma_2 e^{ct}dB_2(t)\label{eqa2} $ | (16) |
Taking expectation in (16) and using Lemma 3.6 we get:
$
E[ec(t∧ηm)n(t∧ηm)]≤1n(0)+E[∫t∧ηm0ecs(γ+σ22n(s))ds]≤1n(0)+(γ+σ22K1)(ect−1)c,
$
|
where
$ E\left[\frac{1}{n(t)}\right]\le \frac{1}{n(0)e^{ct} }+\frac{\left(\gamma+\sigma_2^2 K_1\right)}{c}(1-e^{-ct}), $ |
so
Next we show that for any
For any
$ \mathbb{P}(n(t)< c_2(\epsilon)) = \mathbb{P}\left(\frac{1}{n(t)}>\frac{1}{c_2(\epsilon)}\right)\le c_2(\epsilon)E\left[\frac{1}{n(t)}\right] $ |
Hence
$ \limsup\limits_{t\rightarrow\infty}\mathbb{P}\left(n(t)< c_2(\epsilon)\right)\le \epsilon\limsup\limits_{n\rightarrow \infty}E[1/n(t)]/M_2\le\epsilon. $ |
Thus
The deterministic system (1)-(3) has a maximum capacity equilibrium point
For stochastic differential equations, invariant and stationary distributions play the same role as fixed points for deterministic differential equations. In general, let
$ dX(t) = b(X(t))dt+\sum\limits_{r = 1}^d \sigma_r(X(t))dB_r(t), \label{eqerg1} $ | (17) |
where
$ L = \sum\limits_{i = 1}^l b_i(x)\frac{\partial}{\partial x_i}+\frac{1}{2}\sum\limits_{i, j = 1}^l A_{i, j}(x)\frac{\partial^2}{\partial x_i\partial x_j}, ~~~ A_{i, j}(x) = \sum\limits_{r = 1}^d \sigma_{r, i}(x)\sigma_{r, j}(x).\notag $ |
Let
Definition 4.1. A stationary distribution [6] for
$ \int_E P(t, x, A)\mu(dx) = \mu(A), \text{ for any }t\ge 0, \text{ and any }A\in \mathcal{B}(E).\notag $ |
Definition 4.2. The Markov process
It is clear that the stability in distribution implies the existence of a unique stationary measure, but the converse is not always true [2]. We have the following result (see lemma 2.2 in [29] and the references therein).
Lemma 4.3. Suppose that there exists a bounded domain
$
limt→∞P(t,x,B)=μ(B)Px{limT→∞1T∫T0f(X(t))dt=∫Ef(x)μ(dx)}=1,
$
|
for all
We now study the stochastic system (5)-(7) when
$
dX(t)=(βX(t)−γX2(t))dt+σ1X(t)dB1(t)−σ2X2(t)dB2(t),
$
|
(18) |
$
dXϵ(t)=(βXϵ(t)−γX2ϵ(t)−αϵXϵ(t))dt+σ1Xϵ(t)dB1(t)−σ2X2ϵ(t)dB2(t),
$
|
(19) |
Lemma 4.4. a. For any given initial value
b. For any
c. There exists a constant
Proof. The proofs for a. and b. can be done similarly with the proof of Theorem 2.1, using the
Let
Theorem 4.5. If
$
limt→∞PX(t,x,B)=μ1(B)Px{limT→∞1T∫T0f(X(t))dt=∫Ef(x)μ1(dx)}=1,
$
|
for all
Proof. We consider the
$ LV(x) = -\frac{\sigma_2^2}{8}x^{5/2}+\frac{\sigma_2^2}{4}x^2-\frac{\gamma}{2}x^{3/2}+\frac{\gamma}{2}x+\left(\frac{\beta}{2}-\frac{\sigma_1^2}{8}\right)x^{1/2} +\left(\frac{\sigma_1^2}{4}-\frac{\beta}{2}\right). $ |
Since
Let
Let define the processes
$
dN(t)=(N(t)(σ21−β)+αN(t)Co(t)+γ+σ22N(t))dt−σ1N(t)dB1(t)+σ2dB2(t) a.s.,
$
|
(20) |
$
dY(t)=(Y(t)(σ21−β)+γ+σ22Y(t))dt−σ1Y(t)dB1(t)+σ2dB2(t) a.s.,
$
|
(21) |
$
dYϵ(t)=(Yϵ(t)(σ21−β+αϵ)+γ+σ22Yϵ(t))dt−σ1Yϵ(t)dB1(t)+σ2dB2(t) a.s..
$
|
(22) |
From the proof of Theorem 3.7 we know that if
Lemma 4.6. If
Proof. The proof is based on the results in Lemma 4.4 and it is similar with the first part of the proof of Theorem 3.7. For completeness we have included it in Appendix C.
We use the processes
Theorem 4.7. Let
Proof. We follow the same idea as in the proof of Theorem 2.4 in [28]. From theorem 4.5 we know that
Firstly, let's notice that
$ Y(t)\le N(t) \text{ and } Y(t)\le Y_\epsilon(t) \text{ for any } t\ge 0 \text{ a.s..} \label{fact1} $ | (23) |
Indeed, if we denote
$ d\xi(t) = \left(\xi(t)\left(\sigma_1^2-\beta-\frac{\sigma_2^2}{N(t)Y(t)}\right)+\alpha N(t) C_o(t)\right)dt-\sigma_1\xi(t)dB_1(t)\text{ a.s.}. $ |
The solution of the previous linear equation is given by (see chapter 3, [17])
$ \xi(t) = \Phi(t)\int_0^t \frac{\alpha N(s) C_o(s)}{\Phi(s)}ds\text{ a.s.}, $ |
where
$ \Phi(t) = \exp\left\{ -t\left(\beta-\frac{\sigma_1^2}{2}\right)-\int_0^t\frac{\sigma_2^2}{N(s)Y(s)}ds-\sigma_1 B_1(t) \right\}>0 $ |
Obviously
Secondly we show that for any
$ {\lim\inf}_{t\rightarrow\infty}(Y_\epsilon(t)-N(t))\ge 0\text{ a.s.}.\label{fact2} $ | (24) |
From equations (20) and (22) we get
$
d(Yϵ(t)−N(t))=((Yϵ(t)−N(t))(σ21+αϵ−β−σ22N(t)Yϵ(t))+αN(t)(ϵ−Co(t)))dt−σ1(Yϵ(t)−N(t))dB1(t) a.s..
$
|
The solution of the linear equation is given by
$ Y_\epsilon(t)-N(t) = \Phi_1(t)\int_0^t \frac{\alpha N(s) \left(\epsilon-C_o(s)\right)}{\Phi_1(s)}ds\text{ a.s.}, $ |
where
$
0<Φ1(t)=exp{−t(β−αϵ−σ212)−∫t0σ22N(s)Yϵ(s)ds−σ1B1(t)}≤exp{−t(β−αϵ−σ212+σ1B1(t)t)}
$
|
Since
$
Yϵ(t)−N(t)=Φ1(t)(∫T0αN(s)(ϵ−Co(s))Φ1(s)ds+∫tTαN(s)(ϵ−Co(s))Φ1(s)ds)≥Φ1(t)∫T0αN(s)(ϵ−Co(s))Φ1(s)ds
$
|
Therefore for any
$ \liminf\limits_{t\rightarrow\infty}(Y_\epsilon(t)-N(t))\ge \lim\limits_{t\rightarrow\infty} \Phi_1(t)\int_0^T \frac{\alpha N(s) \left(\epsilon-C_o(s)\right)}{\Phi_1(s)}ds = 0 ~~~a.s.. $ |
Thirdly we prove that
$ \lim\limits_{\epsilon\rightarrow 0}\lim\limits_{t\rightarrow\infty}E[Y_\epsilon(t)-Y(t)] = 0.\label{fact3} $ | (25) |
We know from (23) that
$
d(Yϵ(t)−Y(t))=((Yϵ(t)−Y(t))(σ21+αϵ−β−σ22Y(t)Yϵ(t))+αϵY(t))dt−σ1(Yϵ(t)−Y(t))dB1(t)≤((Yϵ(t)−Y(t))(σ21+αϵ−β)+αϵY(t))dt−σ1(Yϵ(t)−Y(t))dB1(t) a.s..
$
|
From Lemma 4.6 we know that
$
E[Yϵ(t)−Y(t)]≤∫t0E[Yϵ(s)−Y(s)](σ21+αϵ−β)+αϵE[Y(s)]ds≤∫t0E[Yϵ(s)−Y(s)](σ21+αϵ−β)ds+tαϵsupt≥0E[Y(t)] a.s..
$
|
For any
$ 0\le E[Y_\epsilon(t)-Y(t)]\le\frac{\alpha\epsilon \sup\limits_{t\ge 0}E[Y(t)] }{\beta-\sigma_1^2-\alpha\epsilon}\left(1-\exp(-t(\beta-\sigma_1^2-\alpha\epsilon))\right) $ |
Taking limits in the previous inequality we get equation (25).
Finally, using (23), (24), and (25) we obtain that
Corollary 2. Let
a. If
$
p(x)=1G1x4exp(−βσ22(1x−γβ)2),x>0
$
|
(26) |
$
G1=σ22β5/2(Ψ(γ√2ββσ2)√π(σ22β+2γ2)+γσ2β1/2exp(−γ2σ22β))
$
|
(27) |
where
b. If
Proof. We know that
a. If
$ dY(t) = \left(-Y(t)\beta+\gamma+\frac{\sigma_2^2}{Y(t)}\right)dt+\sigma_2dB_2(t)\text{ a.s.}, \label{eqi2new} $ | (28) |
Let define
$
q(y)=exp(−2σ22∫y1(−βu+σ22u+γ)du)=1y2exp(−βσ22(1−γβ)2)exp(βσ22(y−γβ)2)
$
|
It can be easily shown that
$ \int_0^1 q(y)dy = \infty, ~~ \int_1^\infty q(y)dy = \infty, ~~\int_0^\infty\frac{1}{\sigma_2^2 q(y)}dy = \frac{G_1}{\sigma_2^2}\exp\left(\frac{\beta}{\sigma_2^2}\left(1-\frac{\gamma}{\beta}\right)^2\right), \notag $ |
where
$ p_1(x) = \frac{1}{\sigma_2^2q(x)\int_0^\infty\frac{1}{\sigma_2^2 q(y)}dy} = \frac{x^2\exp\left(-\frac{\beta}{\sigma_2^2}\left(x-\frac{\gamma}{\beta}\right)^2\right)}{G_1} $ |
Thus, by Theorem 4.5,
$
limt→∞1t∫t0X(u)du=∫∞0xp(x)dx=σ22β3/2G1(σ2√βexp(−γ2σ22β)+2γ√πΨ(γ√2ββσ2)) a.s..
$
|
b. If
$ dY(t) = \left(\gamma-Y(t)(\beta-\sigma_1^2)\right)dt-\sigma_1Y(t)dB_1(t)\text{ a.s.}. $ |
Proceeding similarly as for a. we can show that
$ \lim\limits_{t\rightarrow \infty}\frac{1}{t}\int_0^tX(u)du = \left(\frac{2(\beta-\sigma_1^2)}{\sigma_1^2}+1\right)\frac{\sigma_1^2}{2\gamma}\text{ a.s..} $ |
Notice that if
On the other hand, if
First we illustrate numerically the results obtained in section 3 regarding survival analysis.We consider a cell population exposed to the toxicant monastrol as in the experiments described in [1]. The parameters' values for this toxicant are estimated in [1]:
One of the applications of the mathematical model is for finding the threshold value for
We illustrate this for the model with initial values
Notice also that the results displayed in Figs. 2 and 3 agree with the conclusion of Theorem 2.3. For the stochastic model with
$ \liminf\limits_{t\rightarrow\infty} n(t, \omega)>0, \lim\limits_{t\rightarrow \infty}C_o(t, \omega) = \lim\limits_{t\rightarrow \infty}C_e(t, \omega) = 0 $ |
Next we use the same parameters values as stated at the beginning of this section and the initial values
When
We present a stochastic model to study the effect of toxicants on human cells. To account for parameter uncertainties, the model is expressed as a system of coupled ordinary stochastic differential equations. The variables are the cell index
We first prove the positivity of the solutions. Then we investigate the influence of noise on the cell population survival. When the noise variances
Moreover, we prove that when the noise variance
Here we illustrate our results for the toxicant monastrol. We have also considered other toxicants from the experiments described in [1] classified in various clusters [30]. We have noticed that the cluster type does not change the type of stationary distribution, nor has an effect on the behavior of the distributions in response to increased noise variances.
Proof. The proof is similar with the proof of Lemma 3.1 in [1]. We define the stopping time
From (8) with
$
0≤Co(t,ω)=λ21e−η22t∫t0Ce(s,ω)eη21sds≤λ21e−η22tCe(0)∫t0eη21sds=λ21Ce(0)η21(1−e−η21t)≤λ21Ce(0)η21
$
|
Moreover, on
$
dCedt|t=τ=λ22Co(τ)n(τ)−η22Ce(τ)n(τ)≤Ce(0)n(τ)(λ21λ22η21−η22)<0
$
|
Thus we have a contradiction with the definition of
Proof. The proof is similar with the proof of Theorem 3.2 in [1]. Let
If
$ \int_0^t C_o(s, \omega) n(s, \omega)\exp{\left(\eta_2 ^2\int_0^s n(l, \omega)dl\right )}ds\le \frac{\lambda_1^2 C_e(0)}{\eta_1^2}\exp{\left(\eta_2 ^2|n|_1(\omega)\right )}|n|_1(\omega). $ |
Thus
$ \lim\limits_{t\rightarrow\infty} C_e(t, \omega) = C_e(0)\exp(-\eta_2^2|n|_1(\omega))+\lambda_2^2 M(\omega)\exp(-\eta_2^2|n|_1(\omega))<\infty. $ |
Consequently, there exists
$
∫t0Ce(s,ω)eη21sds≥∫tT1(ω)Ce(s,ω)eη21sds≥Ce(0)exp(−η22|n|1(ω))/2∫tT1(ω)eη21sds.
$
|
So we can apply L'Hospital's rule in (8), and we get
$ \lim\limits_{t\rightarrow\infty} C_o(t, \omega) = \frac{\lambda_1^2}{\eta_1^2}\lim\limits_{t\rightarrow\infty} C_e(t, \omega)>0. $ |
Thus, on
Next, if
$ 0\le \frac{\lambda_2^2}{\eta_2^2}\liminf\limits_{t\rightarrow\infty}C_o(t, \omega)\le \liminf\limits_{t\rightarrow\infty}C_e(t, \omega)\le \limsup\limits_{t\rightarrow\infty}C_e(t, \omega)\le \frac{\lambda_2^2}{\eta_2^2}\limsup\limits_{t\rightarrow\infty}C_o(t, \omega) $ |
Similarly, from (8) we either get that
$ 0\le \frac{\lambda_1^2}{\eta_1^2}\liminf\limits_{t\rightarrow\infty}C_e(t, \omega)\le \liminf\limits_{t\rightarrow\infty}C_o(t, \omega)\le \limsup\limits_{t\rightarrow\infty}C_o(t, \omega)\le \frac{\lambda_1^2}{\eta_1^2}\limsup\limits_{t\rightarrow\infty}C_e(t, \omega), $ |
(if
$ \lim\limits_{t\rightarrow\infty}C_o(t, \omega) = \lim\limits_{t\rightarrow\infty}C_e(t, \omega) = 0, $ |
because
In conclusion, on
Proof. We choose any
$
d(ectY(t))=ect(Y(t)(c+σ21−β)+γ+σ22X(t))dt−σ1ectY(t)dB1(t)+σ2ectdB2(t)≤ect(γ+σ22X(t))dt−σ1ectY(t)dB1(t)+σ2ectdB2(t)
$
|
(29) |
Let
$
E[ec(t∧τm)Y(t∧τm)]≤1n(0)+E[∫t∧τm0ecs(γ+σ22X(s))ds]≤1n(0)+(γ+σ22C1)(ect−1)c.
$
|
Letting
$ E\left[Y(t)\right]\le \frac{1}{n(0)e^{ct} }+\frac{\left(\gamma+\sigma_2^2 C_1\right)}{c}(1-e^{-ct}). $ |
Thus, there exists a constant
[1] |
Jeffery CJ (1999) Moonlighting proteins. Trends Biochem Sci 24: 8–11. doi: 10.1016/S0968-0004(98)01335-8
![]() |
[2] | Chen C, Zabad S, Liu H, et al. (2018) MoonProt 2.0: an expansion and update of the moonlighting proteins database. Nucleic Acids Research 46: D640–D644. |
[3] |
Kainulainen V, Korhonen TK (2014) Dancing to another tune-adhesive moonlighting proteins in bacteria. Biology 3: 178–204. doi: 10.3390/biology3010178
![]() |
[4] | Jeffery CJ (2018) Intracellular proteins moonlighting as bacterial adhesion factors. AIMS Microbiol 4: 362–376. |
[5] |
Hennequin C, Porcheray F, Waligora-Dupriet A, et al. (2001) GroEL (Hsp60) of Clostridium difficile is involved in cell adherence. Microbiology 147: 87–96. doi: 10.1099/00221287-147-1-87
![]() |
[6] | Yamaguchi H, Osaki T, Kurihara N, et al. (1997) Heat-shock protein 60 homologue of Helicobacter pylori is associated with adhesion of H. pylori to human gastric epithelial cells. J Med Microbiol 46: 825–831. |
[7] | Ensgraber M, Loos M (1992) A 66-kilodalton heat shock protein of Salmonella typhimurium is responsible for binding of the bacterium to intestinal mucus. Infect Immun 60: 3072–3078. |
[8] | Hoffman PS, Garduno RA (1999) Surface-Associated heat shock proteins of Legionella pneumophila and Helicobacter pylori: Roles in pathogenesis and immunity. Infect Dis Obstet Gynecol 7: 58–63. |
[9] |
Wampler JL, Kim KP, Jaradat Z, et al. (2004) Heat shock protein 60 acts as a receptor for the Listeria adhesion protein in Caco-2 cells. Infect Immun 72: 931–936. doi: 10.1128/IAI.72.2.931-936.2004
![]() |
[10] |
Jagadeesan B, Koo OK, Kim KP, et al. (2010) LAP, an alcohol acetaldehyde dehydrogenase enzyme in Listeria, promotes bacterial adhesion to enterocyte-like Caco-2 cells only in pathogenic species. Microbiology 156: 2782–2795. doi: 10.1099/mic.0.036509-0
![]() |
[11] |
Milohanic E, Pron B, Berche P, et al. (2000) Identification of new loci involved in adhesion of Listeria monocytogenes to eukaryotic cells. Microbiology 146: 731–739. doi: 10.1099/00221287-146-3-731
![]() |
[12] |
Castaldo C, Vastano V, Siciliano RA, et al. (2009) Surface displaced alfa-enolase of Lactobacillus plantarum is a fibronectin binding protein. Microb Cell Fact 8: 14. doi: 10.1186/1475-2859-8-14
![]() |
[13] |
Antikainen J, Kuparinen V, Lähteenmäki K, et al. (2007) pH-dependent association of enolase and glyceraldehyde-3-phosphate dehydrogenase of Lactobacillus crispatus with the cell wall and lipoteichoic acids. J Bacteriol 189: 4539–4543. doi: 10.1128/JB.00378-07
![]() |
[14] |
Patel DK, Shah KR, Pappachan A, et al. (2016) Cloning, expression and characterization of a mucin-binding GAPDH from Lactobacillus acidophilus. Int J Biol Macromol 91: 338–346. doi: 10.1016/j.ijbiomac.2016.04.041
![]() |
[15] |
Kainulainen V, Loimaranta V, Pekkala A, et al. (2012) Glutamine synthetase and glucose-6-phosphate isomerase are adhesive moonlighting proteins of Lactobacillus crispatus released by epithelial cathelicidin LL-37. J Bacteriol 194: 2509–2519. doi: 10.1128/JB.06704-11
![]() |
[16] |
Bergonzelli GE, Granato D, Pridmore RD, et al. (2006) GroEL of Lactobacillus johnsonii La1 (NCC 533) is cell surface associated: potential role in interactions with the host and the gastric pathogen Helicobacter pylori. Infect Immun 74: 425–434. doi: 10.1128/IAI.74.1.425-434.2006
![]() |
[17] |
Granato D, Bergonzelli GE, Pridmore RD, et al. (2004) Cell surface-associated elongation factor Tu mediates the attachment of Lactobacillus johnsonii NCC533 (La1) to human intestinal cells and mucins. Infect Immun 72:2160–2169. doi: 10.1128/IAI.72.4.2160-2169.2004
![]() |
[18] |
Kinoshita H, Uchida H, Kawai Y, et al. (2008) Cell surface Lactobacillus plantarum LA 318 glyceraldehyde-3-phosphate dehydrogenase (GAPDH) adheres to human colonic mucin. J Appl Microbiol 104: 1667–1674. doi: 10.1111/j.1365-2672.2007.03679.x
![]() |
[19] |
Candela M, Biagi E, Centanni M, et al. (2009) Bifidobacterial enolase, a cell surface receptor for human plasminogen involved in the interaction with the host. Microbiology 155: 3294–3303. doi: 10.1099/mic.0.028795-0
![]() |
[20] |
Candela M, Bergmann S, Vici M, et al. (2007) Binding of human plasminogen to Bifidobacterium. J Bacteriol 189: 5929–5936. doi: 10.1128/JB.00159-07
![]() |
[21] |
Crowe JD, Sievwright IK, Auld GC, et al. (2003) Candida albicans binds human plasminogen: identification of eight plasminogen-binding proteins. Mol Microbiol 47: 1637–1651. doi: 10.1046/j.1365-2958.2003.03390.x
![]() |
[22] | Gozalbo D, Gil-Navarro I, Azorin I, et al. (1998) The cell wall-associated glyceraldehyde-3-phosphate dehydrogenase of Candida albicans is also a fibronectin and laminin binding protein. Infect Immun 66: 2052–2059. |
[23] |
Jong AY, Chen SH, Stins MF, et al. (2003) Binding of Candida albicans enolase to plasmin(ogen) results in enhanced invasion of human brain microvascular endothelial cells. J Med Microbiol 52: 615–622. doi: 10.1099/jmm.0.05060-0
![]() |
[24] |
Luo S, Hoffmann R, Skerka C, et al. (2013) Glycerol-3-phosphate dehydrogenase 2 is a novel factor H-, factor H-like protein 1-,and plasminogen binding surface protein of Candida albicans. J Infect Dis 207: 594–603. doi: 10.1093/infdis/jis718
![]() |
[25] |
Lesiak-Markowicz I, Vogl G, Schwarzmuller T, et al. (2011) Candida albicans Hgt1p, a multifunctional evasion molecule: complement inhibitor, CR3 analogue,and human immunodeficiency virus-binding molecule. J Infect Dis 204: 802–809. doi: 10.1093/infdis/jir455
![]() |
[26] |
Yang W, Li E, Kairong T, et al. (1994) Entamoeba histolytica has an alcohol dehydrogenase homologous to themultifunctional adhE gene product of Escherichia coli. Mol Biochem Parasitol 64: 253–60. doi: 10.1016/0166-6851(93)00020-A
![]() |
[27] |
Jin S, Song YC, Emili A, et al. (2003) JlpA of Campylobacter jejuni interacts with surface-exposed heat shock protein 90-alpha and triggers signaling pathways leading to the activation of NF-kappaB and p38 MAP kinase in epithelial cells. Cell Microbiol 5: 165–74. doi: 10.1046/j.1462-5822.2003.00265.x
![]() |
[28] |
Raymond BB, Djordjevic S (2015) Exploitation of plasmin(ogen) by bacterial pathogens of veterinary significance. Vet Microbiol 178: 1–13. doi: 10.1016/j.vetmic.2015.04.008
![]() |
[29] | Collen D, Verstraete M (1975) Molecular biology of human plasminogen II Metabolism in physiological and some pathological conditions in man. Thromb Diath Haemorrh 34: 403–408. |
[30] |
Dano K, Andreasen PA, Grondahl-Hansen J, et al. (1985) Plasminogen activators, tissue degradation, and cancer. Adv Cancer Res 44: 139–266. doi: 10.1016/S0065-230X(08)60028-7
![]() |
[31] |
Wang W, Jeffery CJ (2016) An analysis of surface proteomics results reveals novel candidates for intracellular/surface moonlighting proteins in bacteria. Mol Biosyst 12: 1420–1431. doi: 10.1039/C5MB00550G
![]() |
[32] |
Han MJ, Lee SY, Hong SH (2012) Comparative analysis of envelope proteomes in Escherichia coli B and K-12 strains. J Microbiol Biotechnol 22: 470–478. doi: 10.4014/jmb.1110.10080
![]() |
[33] |
Bøhle LA, Riaz T, Egge-Jacobsen W, et al. (2011) Identification of surface proteins in Enterococcus faecalis V583. BMC Genomics 12: 135. doi: 10.1186/1471-2164-12-135
![]() |
[34] |
Garcia-del Portillo F, Calvo E, D'Orazio V, et al. (2011) Association of ActA to peptidoglycan revealed by cell wall prteomics of intracellular Listeria monocytogenes. J Biol Chem 286: 34675–34689. doi: 10.1074/jbc.M111.230441
![]() |
[35] |
Amblee V, Jeffery CJ (2015) Physical features of intracellular proteins that moonlight on the cell surface. PLoS One 10: e0130575. doi: 10.1371/journal.pone.0130575
![]() |
[36] |
Matsuoka K, Kanai T (2015) The gut microbiota and inflammatory bowel disease. Semin Immunopathol 37: 47–55. doi: 10.1007/s00281-014-0454-4
![]() |
[37] | Dahlhamer JM, Zammitti EP, Ward BW, et al. (2016) Prevalence of inflammatory bowel disease among adults aged ≥18 years-United States. MMWR 65:1166–1169. |
[38] |
Kinoshita H, Ohuchi S, Arakawa K, et al. (2016) Isolation of lactic acid bacteria bound to the porcine intestinal mucosa and an analysis of their moonlighting adhesins. Biosci Microbiota Food Health 35:185–196. doi: 10.12938/bmfh.16-012
![]() |
[39] |
Celebioglu HU, Olesen SV, Prehn K, et al. (2017) Mucin-and carbohydrate-stimulated adhesion and subproteome changes of the probiotic bacterium Lactobacillus acidophilus NCFM. J Proteomics. 163: 102–110. doi: 10.1016/j.jprot.2017.05.015
![]() |
[40] | Zhu D, Sun Y, Liu F, et al. (2016) Identification of surface-associated proteins of Bifidobacterium animalis ssp. Lactis KLDS 2.0603 by enzymatic shaving. J Dairy Sci. 99: 5155–5172. |
[41] | Celebioglu HU, Svensson B (2017) Exo-and surface proteomes of the probiotic bacterium Lactobacillus acidophilus NCFM. Proteomics. 17: 11. |
[42] |
Celebioglu HU, Delsoglio M, Brix S, et al. (2018) Plant polyphenols stimulate adhesion to intestinal mucosa and induce proteome changes in the probiotic Lactobacillus acidophilus NCFM. Mol Nutr Food Res. 62: 1700638. doi: 10.1002/mnfr.201700638
![]() |
[43] |
Celebioglu HU, Ejby M, Majumder A, et al. (2016) Differential proteome and cellular adhesion analyses of the probiotic bacterium Lactobacillus acidophilus NCFM grown on raffinose - an emerging prebiotic. Proteomics. 16: 1361–1375. doi: 10.1002/pmic.201500212
![]() |
[44] |
Pérez Montoro B, Benomar N, Caballero Gómez N, et al. (2018) Proteomic analysis of Lactobacillus pentosus for the identification of potential markers of adhesion and other probiotic features. Food Res Int. 111: 58–66. doi: 10.1016/j.foodres.2018.04.072
![]() |
1. | Chaoqun Xu, Sanling Yuan, Richards Growth Model Driven by Multiplicative and Additive Colored Noises: Steady-State Analysis, 2020, 19, 0219-4775, 2050032, 10.1142/S0219477520500327 | |
2. | Tiantian Ma, Dan Richard, Yongqing Betty Yang, Adam B Kashlak, Cristina Anton, Functional non-parametric mixed effects models for cytotoxicity assessment and clustering, 2023, 13, 2045-2322, 10.1038/s41598-023-31011-1 |