• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Synergy and Redundancy in a Signaling Cascade with Different Feedback Mechanisms?

    2018-11-24 07:36:00LiFangWang王莉芳YingXu徐瑩JunMa馬軍andYaJia賈亞
    Communications in Theoretical Physics 2018年10期
    關(guān)鍵詞:馬軍

    Li-Fang Wang(王莉芳),Ying Xu(徐瑩),Jun Ma(馬軍),and Ya Jia(賈亞),?

    1Department of Physics and Institute of Biophysics,Central China Normal University,Wuhan 430079,China

    2Department of Physics,Lanzhou University of Technology,Lanzhou 730050,China

    3NAAM-Research Group,Department of Mathematics,Faculty of Science,King Abdulaziz University,P.O.Box 80203,Jeddah 21589,Saudi Arabia

    AbstractFeedback plays an important role in various biological signal transmission systems.In this paper,a signaling cascade system(including three layers:input(S),intermediate(V),output(X)components)is employed to study thefluctuations and net synergy in information transmission,in which the V component is regulated by itself or the X component,and each feedback on V is either positive or negative.The Fano factor,the net synergy,and the signalto-noise ratio(SNR)of signaling cascade with the four possible feedback types are theoretically derived by using linear noise approximation of the master equation,and the ability of information transmission through the signaling cascade is characterized by using the partial information decomposition of information theory.It is found that the signaling cascade exhibits different responses to the four feedback mechanisms,which depend on the relationships between degradation rates of components.Our results not only clarify the dependence of the Fano factor,net synergy,and SNR on the feedback regulations with the varying of degradation rates of components,but also imply that living cells could utilize different feedback mechanisms to adapt to the external fluctuating environments.

    Key words:stochastic theory,biological signaling transmission cascade,feedback

    1 Introduction

    Living cells can respond to variations of intracellular or extracellular signals,which behave as information transmission processes.These biochemical reaction networks consist of numerous regulatory motifs where the signal cascades include various feedback mechanisms,and the biological messages are transmitted in the networks of signal cascades,[1?2]such as the gene regulatory network,[3?5]the neuronal network,[6?7]the protein-protein interaction network[3,8?9]etc.It is well known that each biochemical reaction is highly stochastic,which leads to fluctuations in biochemical networks[10?13]and phenotypic diversity of clonal cells.[11,14?16]

    Recently,the information transmission of biochemical regulatory motifs had been extensively investigated.Shannon’s information theory provides a methodology for quantifying the reliability of information transmission systems.[17]Williams et al.[18]introduced a non-negative decomposition of multivariate mutual information terms,and showed that partial information forms a lattice,which illustrates the general structure of multivariate information.An in-depth theoretical analysis of partial information decomposition(an extension of information theory)was proposed by Barrett et al.[19]It was demonstrated that the approach is applied to the measures of information transfer and information-based measures of complexity in a neuroscience contexts.

    In order to understand how cells deal with the fluctuating environment[20?23]and how cells transduce signals via different network motifs in the presence of noise,Ronde et al.[24?25]analyzed how two binary input signals can be encoded in the concentration of a common signaling protein,and the two output signals can provide reliable information about the inputs in the presence of noise.It was clarified that in the signaling cascade with both autoregulation and feedback,the gain-to-noise ratio dependence on frequency is illustrated using the mathematical model and simulation.Selimkhanov et al.[26]demonstrated that signaling dynamics plays an important role in overcoming extrinsic noise by analyzing the ability of temporal signal modulation,and showed that signaling dynamics can mitigate and potentially eliminate extrinsic noise.It was found that extrinsic noise induces information loss through experimental measurements of information transmission in a signal regulated kinase network.The limits on information transmission in signal pathways was studied through a combination of quantitative experiments and theoretical analysis by Hansen et al.[27]It was demonstrated that the amount of information transmitted by the yeast transcription factor Msn2 to single target genes is limited,but information transmission can be increased by adjusting promoter cis-elements or by integrating in-formation from multiple genes in the presence of noise.

    Although there are a number of theoretical and experimental investigations about information transduction of signal modules,little has been dealt with synergy and redundancy in information transmission in a signaling cascade with different feedbacks in the presence of noise.The synergy and redundancy in information transmission are related to many biological phenomena and processes in biological system,such as the activities of neurons,[28?30]eye color,[31]the behaviors of animals and biological processes responsible for disease.[30?34]However,the fluctuations in the signaling cascade can not be neglected since biochemical reaction networks are always in changing environments.Maity et al.[35]demonstrated that the network motifs with different types of regulation affect the degradation rate dependence of the noise and mutual information differently.Biswas et al.[36]studied the signal transmission in a generic two-step cascade using a mathematical model by varying the degradation rate of the input component.Moreover,Pedraza et al.[37]found that thefluctuations in each component are affected by the mean value of the component and its life time.Some experimental evidences[38?39]suggested that degradation time at the single cell level can be measured and adjusted,and stochastic protein expression in an individual cell was investigated by experiments.[40?41]Furthermore,feedback and autoregulation motifs play very important roles in biochemical systems,[39,42?45]for example,buffering noise,improving sensitivity,enhancing adaptability,and changing the response time of the system.The above investigations indicated that feedback strategy and degradation rate can affect the kinetic characteristics of biochemical reaction networks.However,previous researches did not involve the effects of degradation rates of components on synergy and redundancy of information transmission in the signaling cascade with various feedbacks.

    To study the stochastic nature of biochemical interaction processes,one has to use the mesoscopic approaches,instead of the macroscopic description wherefluctuations are neglected.The first approach is to solve the probability distribution of all the different molecular components[46]by using the chemical master equation.The famous Gillespie algorithm is usually used to perform stochastic simulations to obtain numerical results of chemical master equation.[47]The second technique is the linear noise approximation(LNA)of the chemical master equation,which is used in this paper.By applying this technique,the chemical master equation is simplified to the Fokker-Planck equation via utilizing van Kampen’s expansion,[46]and the statistical features of stochastic systems are characterized[48?49]rapidly.The third approach is the Langevin method that can obtain a solution for small fluctuations around steady state.

    Interesting questions now arise:What are the effects of degradation rate of the signaling cascade with different feedbacks on the fluctuations and information transmission?How do different types of feedback influence synergy and redundancy in the information transmission in the presence of fluctuations? What is the relationship between net synergy and the SNR of the system in information transmission?

    Based on the models[19,25,35?36]of information transmission,in this paper,a signaling cascade model with four feedback types is employed to investigate the fluctuation effects and information transmission by using LNA method.The article is organized as follows.In Sec.2,we present a signaling cascade with different feedback types,and then the Fano factor and net synergy are derived via utilizing the linear noise approximation.In Sec.3,we discuss the performance of the signaling cascade involving in different feedbacks.We end with conclusions of the paper in Sec.4.

    2 Model and Method

    We consider the information transmission through a signaling transmission cascade with different feedbacks as shown in Fig.1,where there are three components:the input component S,the intermediate component V,and the output component X.In gene transcription regulatory networks,transcription factors can be regarded as the signals,and regulate its target gene.Therefore,here S can act as an input signal(e.g.,the transcription factor)that regulates the output signal X(e.g.,the target gene)by the intermediate component V.

    In the deterministic case,the kinetic process of the signaling cascade can be described in the following dimensionless form:

    where s,v,and x are the expression level(or concentration)of components S,V,and X,respectively.The two terms on the right side of Eqs.(1)–(3)describe all the reactions involving in the production events and degradation events of S,V,and X,respectively.The parametersμs,μvand μxdenote the degradation rate of S,V,and X,respectively.

    Table 1 Four freedback mechanisms.

    Note that f(α)in Eq.(2)is a function in the form of f(α)=(γβ)/(kv+α),in which the relationships between parameters α and β can represent four feedback mechanisms as listed in Table 1.The positive and negative feedbacks correspond to the scheme(a)in Fig.1,and the self-activation and self-inhibition feedbacks correspond to the scheme(b)in Fig.1,respectively.

    Fig.1 General schematic diagram of the signaling cascade with(a)the feedback(positive or negative)of output component X and(b)the self-feedback(activation or inhibition)of intermediate component V.

    To characterize the fluctuationproperties due to finite molecular numbers of components S,V,and X,we introduce joint probability distribution P(Ns,Nv,Nx,t),where Ns,Nv,and Nxrepresent the numbers of components S,V,and X at time t,and are expressed by Ns=?s,Nv=?v,Nx=?x.The parameter ? denotes the size of system.The chemical master equation[46,50?51]corresponding to Eqs.(1)–(3)obeys the following form

    By utilizing van Kampen’s ?-expansion method,[46]the number of the i-th component(i=s,v,x)is approximated asand the joint probability distribution is given by P(N1,N2,...,NR,t)=??R/2Π(ξ1,ξ2,...,ξR,t).The Fokker-Planck equation is obtained by collecting the terms of ?0in the expansion of Eq.(4)and written as

    in which Aijis the stationary Jacobian matrix of Eqs.(1)–(3),and Bijis the stationary diffusion matrix:

    To obtain the values of variance and covariance of different components of the signaling cascade,the Lyapunov equation at steady state is given by[48,52]

    where the matrix C encompasses the variances and covariances,which represent the stochastic fluctuations in the numbers of components S,V and X and correlation degree of the fluctuations among components of the system,respectively.Substituting Eqs.(6)and(7)into Eq.(8),the matrix C can be obtained as follows

    2.1 Fano Factor

    The Fano factor,which is a measure of the relative size of the internal fluctuations in the system,[53]is defined as the ratio of the variance to the mean.The Fano factors of component S,V,and X are expressed as

    2.2 Net Synergy

    According to Refs.[18–19,27,36],a partial information decomposition(PID)is adopted in the paper.The information I(s;v,x)is composed of four terms:(i)The unique information that V provides about S:U(s;v|x);(ii)The unique information that X provides about S:U(s;x|v);(iii)the redundant information that both V and X provide about S:R(s;v,x);(iv)The synergistic information that the combination of V and X provides about S:S(s;v,x).The mathematical expressions of these quantities are expressed as I(s;v,x)=U(s;v|x)+U(s;x|v)+R(s;v,x)+S(s;v,x),I(s;v)=U(s;v|x)+R(s;v,x),I(s;x)=U(s;x|v)+R(s;v,x),and then the net synergy is written as?I(s;v,x)=I(s;v,x)?I(s;v)? I(s;x).Thus,the net synergy is represented as?I(s;v,x)=S(s;v,x)?R(s;v,x),in which the relationship between synergy and redundancy is given.I(s;v,x),I(s;v),and I(s;x)denote the mutual information between three components S,V,and X,quantifying the amount of information one component includes about another,and characterize the information transfer of the system between the source(S)and the targets(V and X).Consequently,the net synergy can be rewritten as

    where conditional variances of the system are given by

    2.3 Signal-to-Noise Ratio

    To measure the fidelity of information transmission of the signals,signal-to-noise ratio(SNR),which is taken as a measuring method to quantitatively evaluate the fidelity of information transmission in the signaling pathway,[54?55]is defined as

    3 Results

    In order to clearly demonstrate the impacts of feedback regulation on the noise and net synergy,we consider four different settings of the degradation rates of components:(i)μs< μv:μs=0.1,μv=1;(ii)μs? μv:μs=0.1,μv=10;(iii)μs= μv:μs=1,μv=1;and(iv)μs> μv:μs=1,μv=0.1.For these cases,one can study the stochastic fluctuations of components around equilibrium state in the signaling cascade.Thus,the mean expression level of these components S,V,and X are assumed to keep at a certain value,respectively.Similar assumptions were previously made,especially when analyzing some real systems.[56?57]

    In this paper,we set γ =10,ks= μss,and kx=(μxx)/v in the following four cases.The setting of parameter kvis as follows:(i)Positive feedback α=x and β =x:kv=(xγs? xvμv)/(vμv);(ii)Negative feedback α =x and β =kv:kv=(vxμv)/(γs ? vμv);(iii)Selfactivation α =v and β =v:kv=(γs ? vμv)/μv;(iv)Self-inhibition α =v and β =kv:kv=(v2μv)/(γs?vμv).

    Here,the net synergy and mutual information are measured in bits.Note that in the following sections,the case ofμs? μvis not considered in the negative feedback and self-inhibition.This makes sense because the production of the intermediate component V is repressed strongly by the joint interaction between fast degradation rateμvof V and the suppression arising from X or V itself.

    3.1 Effects of Degradation Rates on Fano Factorof Output Component X for DifferentFeedbacks

    By virtue of the theoretical expressions derived in Sec.2,the Fano factor of output component X as a function ofμxis depicted in Fig.2.In most cases,it is found that with the increasing of the degradation rate of output componentμx,the Fano factor of X increases for very slowμx,and saturates to a plateau value for fastμx.However,in the case ofμs? μv,the Fano factor of X is independent of the change ofμxfor self-activation,referring to the red dash dotted line in Fig.2(c).

    In the case ofμs= μv,for positive feedback and selfactivation,the minimum Fano factor value of X is achieved whenμs? μv,and the maximum Fano factor value of X is obtained whenμs>μv,corresponding to Figs.2(a)and 2(c).For negative feedback and self-inhibition,the system exhibits different responses to the variations of the degradation rates of components.For instance,at a given value ofμx,when μs< μv,the Fano factor of X is large and becomes small whenμs> μv,as shown in Figs.2(b)and 2(d).

    The above results indicate that in the case ofμs> μv,for positive feedback and self-activation,significant relative fluctuations in the output component X are generated and the rapid variation of input signal S can be sensed by output component X.For negative feedback and self-inhibition,although the input component S is at a faster degradation rate compared with the intermediate component V,the fluctuations in the output component X are suppressed and X is not sensitive to the change of input signal.These results are in accord with some closely related phenomena in biological systems,such as osmo adaptation,[58]bacterial chemotaxis,[43]and the responses of S.Cerevisiae to the changes in external carbon source.[59]

    In the case ofμs= μv,for positive feedback and self-activation,at a given value ofμx,the fluctuations in the output component X are comparatively smaller than those in the cases ofμs< μvand μs> μv,but larger forμs? μv.By contrast,for negative feedback and self-inhibition,when μs= μv,the fluctuations in X are at an intermediate level between the other two cases,i.e.,μs< μvand μs> μv.The results of Fig.2 suggest that the fluctuations in the output component X can be influenced by the input signal and other intermediate components through correlation intensity of the fluctuations,which can be adjusted by various feedback regulation.Therefore,cells can take advantage of appropriate responses to adapt to changing environments when facing challenges caused by random fluctuations.

    Fig.2 (Color online)Effects of degradation rates on Fano factor of output component X for different feedbacks:(a)Positive feedback α =x and β =x.(b)Negative feedback α =x and β =kv.(c)Self-activation α =v and β =v.(d)Self-inhibition α =v and β =kv.

    3.2 Characteristics of Information Transmission in Signaling Cascade with Different Feedbacks

    (i)The Case of Positive Feedback α =x and β =x

    For positive feedback,the dependence of the net synergy and mutual information on degradation rates of components are plotted in Fig.3.In the case ofμs< μv,the net synergy?I(s;v,x)is a monotonically decreasing function ofμxfor slow μx,and then it tends to a constant value asμxcontinues to increase for fastμx,as illustrated in Fig.3(a).Moreover,the net synergy?I(s;v,x)<0,which demonstrates that redundant property of the target variables is dominant in the signaling cascade with positive feedback.It is observed from Fig.3(e)that I(s;v,x)is almost independent of the change ofμx.Meanwhile,the mutual information I(s;x)displays an opposite tendency with the variation ofμxin contrast with the net synergy,and?I(s;v,x)≈?I(s;x),implying that the net synergy is mainly determined by the mutual information I(s;x)between the input component S and the output component X.

    In the case ofμs? μv,it is found from Fig.3(b)that the change tendency of net synergy?I(s;v,x)with the alteration ofμxis similar to that in the case ofμs< μv,but with?I(s;v,x)>0,which reflects that synergistic feature of the target variables is prevalent over the redundancy in information transmission.According to Fig.3(f),it is seen that I(s;v,x)is decreased by increasingμx.Meanwhile,with the increasing ofμx,the mutual information I(s;x)increases at beginning for very small values ofμx,reaches a peak value,and then decreases to a constant for large values ofμx,showing that there exists an optimal value ofμxat which the maximum value of mutual information I(s;x)can be obtained in such a case.

    In the case ofμs= μv,with the increasing ofμx,the net synergy?I(s;v,x)exhibits an approximately threshold behavior and the threshold is aroundμx=0.83,as shown in Fig.3(c).This signifies that redundant feature of the targets plays a leading role in information transduction forμx>0.83 due to?I(s;v,x)<0,but forμx<0.83,the net synergy?I(s;v,x)>0.Figure 3(g)shows that there is a peak in the curve of I(s;v,x)and I(s;x)is a monotone increasing function ofμx.

    In the case ofμs> μv,Fig.3(d)shows that the net synergy also displays an approximately threshold behavior as a function ofμx,and the threshold is aboutμx=4.66.Forμx>4.66,the net synergy is slightly reduced with increasingμxand then reaches a constant value for fastμx.In Fig.3(h),it is obvious that the variations of mutual information I(s;v,x)and I(s;x)with the change ofμxis analogous to that inμs=μv.However,the change of the net synergy is confined to a smaller range compared with those in the other three cases,since the mutual information is less in this case.

    (ii)The Case of Negative Feedback α =x and β =kv

    For negative feedback,the influence of degradation rates of three components on the net synergy and mutual information are illustrated in Fig.4.It is found that forμs< μv,the changes of the net synergy and mutual information are similar with increasingμxin Figs.3(a)and 4(a).However,the absolute value of net synergy is slightly smaller in the latter than that in the former case at the same value ofμxsince the net synergy?I(s;v,x)is mainly determined by the mutual information I(s;x).The mutual information I(s;x)shown in Fig.3(e)is more than that in Fig.4(d)for a given value ofμx.

    In Fig.4(b),when μs= μv,the net synergy is a monotone decreasing function ofμx,without showing a threshold behavior,and has a faster decline than that in Fig.3(c),which is attributed to the difference in the mutual information between Figs.3(g)and 4(e).That is,the faster the degradation rateμx,the larger the absolute value of net synergy.

    Fig.3 (Color online)Effects of degradation rates on the net synergy and mutual information for positive feedback α =x and β =x.(a)and(e): μs< μv.(b)and(f): μs ? μv.(c)and(g): μs= μv.(d)and(h):μs> μv.

    Fig.4 (Color online)Effects of degradation rates on the net synergy and mutual information for negative feedback α =x and β =kv.(a)and(d): μs< μv.(b)and(e): μs= μv.(c)and(f): μs> μv.

    From Fig.4(c),it can be found that when μs> μv,the net synergy also displays an approximately threshold behavior with the change ofμx,and the threshold is at aboutμx=0.63.Moreover,forμx>0.63,the net synergy is reduced significantly by increasingμx,unlike the case of Fig.3(d).Figure 4(f)shows that the mutual infor-mation I(s;v,x)and I(s;x)are less in this case,leading to the small variation range of net synergy compared with the other two cases of negative feedback.Our results reveal that positive feedback and negative feedback always play different roles in the signaling cascade,and can act as the effective mechanisms of modulating noise and net synergy by varying the degradation rates of components in changing environments.

    (iii)The Case of Self-Activation α =v and β =v

    According to the above analysis,a natural question is whether these results of feedback regulation still hold in the case of autoregulation.Hence,in the following sections,we will explore how the degradation rates of components affect information transmission in the signaling cascade with self-activation or self-inhibition.The numerical results are plotted in Figs.5 and 6.

    Figure 5 shows that the net synergy and mutual information as a function ofμxfor self-activation.Forμs< μv,the changes of net synergy?I(s;v,x)and mutual information by alteringμxare similar to those in the case of positive feedback,suggesting that redundant property of the targets is dominant in this case once again.Forμs= μv,the variation trend of net synergy and mutual information by varyingμxare analogous to the case of positive feedback.However,the peak value of net synergy is larger compared to positive feedback.

    Fig.5 (Color online)Effects of degradation rates on the net synergy and mutual information for self-activation α =v and β =v.(a)and(e):μs< μv.(b)and(f):μs? μv.(c)and(g):μs= μv.(d)and(h):μs> μv.

    Fig.6 (Color online)Effects of degradation rates on the net synergy and mutual information for self-inhibition α =v and β =kv.(a)and(d):μs< μv.(b)and(e):μs= μv.(c)and(f):μs> μv.

    Forμs? μv,the changes of the net synergy and mutual information differ from those in the case of positive feedback.From Fig.5(b),it is observed that the net synergy is decreased monotonically with an increase inμx.Specifically,the notable distinction between positive feedback and self-activation is the sign of the net synergy?I(s;v,x),i.e.,?I(s;v,x)>0 in Fig.3(b)but?I(s;v,x)<0 in Fig.5(b),implying that synergy is prevalent over redundancy in the former case,but redundancy is remarkable in the latter.By making a comparison between Figs.3(f)and 5(f),it can be seen that for selfactivation,the mutual information I(s;v,x)and I(s;x)are almost independent of the change of degradation rateμx,but just slightly increase for very slow μx.However,for positive feedback,increasingμxcan cause a reduction in I(s;v,x)and a prominent peak appears in the curve of I(s;x).

    Forμs> μv,according to Figs.5(d)and 5(h),the net synergy also exhibits an approximately threshold behavior,and the variation range of net synergy is smaller compared with the other three cases for self-activation.Moreover,the changes of the mutual information with increasingμxare analogous to the case of positive feedback,whereas the maximum value of net synergy?I(s;v,x)is much smaller and I(s;v,x)is less than that in the case of positive feedback.

    (iv)The Case of Self-Inhibition α =v and β =kv

    For self-inhibition,the effects ofμxon the net synergy and mutual information in the information transmission are presented in Fig.6.Whenμs≤ μv,the changes of the net synergy and mutual information with the increasing ofμxare almost the same as those of negative feedback,corresponding to Figs.6(a),6(b),6(d),and 6(e).

    Forμs> μv,the peak value of net synergy?I(s;v,x)shown in Fig.6(c)is larger than that in Fig.4(c).Although the net synergy displays an approximately threshold behavior,the respective threshold values are different in the two cases.Meanwhile,the remarkable peak of I(s;v,x),which appears in Fig.6(f)differs from that in Fig.4(f).However,the changes of mutual information I(s;x)are nearly the same for self-inhibition and negative feedback.

    For each of the four feedback types mentioned above,the most amount of mutual information I(s;x)can be obtained whenμs< μv.By comparison,the least amount of mutual information I(s;x)can be achieved whenμs> μv,which is in agreement with Ref.[35].This indicates that information transmission capacity in the signaling cascade is hindered by slow degradation rate of the intermediate component V in contrast to input signal S.It is important to note that the change of mutual information I(s;x)with the increasing ofμxis related to the correlation degree between the input signal S and output component X.

    According to the above results,it is obvious that different feedback mechanisms in the signaling cascade can give rise to different fluctuation effects,net synergy and mutual information by changing the degradation rates of three components,thus easily generating diverse responses in a genetically identical population.This is the reason why certain types of feedback arises frequently in some conditions,which may be related to their noise behavior and information transmission abilities.In turn,these behaviors are determined by their topologies.Hence,the choice of feedback framework is a mechanism of efficiently modulating stochastic fluctuations and information transduction abilities via varying degradation rates of components in the signaling cascade,which is important for the cells to survive in changing environments.

    3.3 Effects of Degradation Rates on SNR for Different Feedbacks

    To clearly demonstrate the relationship between the net synergy and the SNR in the signaling cascade involving in various feedbacks,in this section,we take into account the dependence of the SNR on degradation rates of components in the signaling cascade with different feedbacks,as shown in Fig.7.

    Whenμs< μv,the response of the SNR to the change ofμxis similar in each of the four figures,i.e.,the SNR of the system increases with the increasing ofμxfor slowμx,and then approaches to a constant for fastμx.Moreover,there is an opposite variation tendency between the net synergy and the SNR in the signaling cascade,which is in accordance with Ref.[36].Meanwhile,in this case,the large values of the SNR of the system are attained for each of the four feedbacks.The reason is that some information is corrupted or lost due to inevitable stochasticfluctuations during the information transmission process.If some important information is corrupted or lost,then the correspond substitution can be provided at the output component as a result of redundant information.Hence redundancy in information transduction can enhance the SNR of the signaling cascade.

    When μs? μv,there is an optimal value ofμxat which the maximum value of the SNR can be obtained for positive feedback.Correspondingly,there is also an optimal value ofμxat which the mutual information I(s;x)can reach its maximum in such a case(See the red dashed line in Fig.3(f)).However,these results of positive feedback are invalid in the case of self-activation.In fact,cells need the signaling cascade that encompasses different feedbacks to carry out some certain functions.It suggests that each possible feedback strategy may have a highfitness under certain environments,and the tunability of information transduction in feedback regimes would be utilized by organisms in order to better adapt to changes in their environments.Thus,the diverse performance of the system might explain why certain network motifs,or their combinations,preferentially occur in biological systems and are retained in evolution.

    When μs= μv,the trend of change in the SNR in the signaling cascade with the alteration ofμxis similar to the case ofμs< μvfor each of the four feedbacks.For positive feedback,there exists a point of intersection(whereμx=3.71)for the SNR curves betweenμs? μvandμs= μv.That is,the SNR of the system in the former case is higher than that in the latter before reaching the intersection point at a given value ofμxand becomes lower after the point of intersection.

    When μs> μv,the low SNR of the signaling cascade is obtained for each of the four feedbacks in Fig.7.Moreover,the SNR of the system is not enhanced even though the Fano factor value of output component X is small(see Figs.2(b)and 2(d))for negative feedback and self-inhibition.This suggests that the slow degradation rate of the intermediate componentμvimpedes information transmission in the signaling cascade,and limits the amount of information transmission from the input component to the output component again.

    Fig.7(Color online)Effects of degradation rates on the SNR for different feedbacks:(a)Positive feedback α=x and β =x.(b)Negative feedback α =x and β =kv.(c)Self-activation α =v and β =v.(d)Self-inhibition α=v and β=kv.

    3.4 Comparisons the Results with Different Methods

    Fig.8 (Color online)Fano factors and the net synergy in the caseμs< μvas a function of the degradation rate obtained by theoretical computation(lines)and by the Gillespie algorithm(symbols)with the same and different parameter ? for four feedbacks:positive feedback(black solid lines with open circles),negative feedback(red solid lines with open upward triangles),self-activation(blue solid lines with open diamonds),and self-inhibition(dark cyan solid lines with open stars).

    The aim of this paper is to study the fluctuation effects and information transmission in the signaling cascade with different feedbacks.In order to study the fluctuation effects and information transmission in biological systems,there are diverse standard methods as mentioned in the introduction.Thus in this section,we compare numerical results obtained by using LNA method[46,48,52]with those of the Gillespie algorithm(an accurate simulation algorithm).[47]

    In Fig.8,for different feedback types,the Fano factor of X and net synergy,which change by varying the degradation rate of the output componentμxwith the same and different parameter ?,are compared for both approaches,respectively.It is apparent that the results of these two methods are in accordant with each other in the caseμs< μvfor different feedbacks.Hence our theoretical results obtained by the LNA method are valid for our system studied here.These results suggest that the signaling cascade involving in diverse feedback mechanisms can regulate a variety of cellular processes,and exhibit very rich kinetic characteristics.

    4 Conclusions

    In this paper,a signaling cascade with different feedback mechanisms has been investigated,the theoretical formulas of the Fano factor,the net synergy,and the SNR of the system are derived by using the linear noise approximation,and the effects of the degradation rates of components on the fluctuation and information transmission in the signaling cascade with four feedback mechanisms are analyzed and discussed.

    In the case ofμs< μv,the net synergy?I(s;v,x)<0 for each of the four feedback types,but for positive feedback and self-activation,the absolute value of negative net synergy is larger and the mutual information I(s;x)is more than that of negative feedback and self-inhibition at a given value ofμx.In this case,high SNR of the system is obtained in information transmission.This suggests that for the four feedbacks considered in this paper,when μs< μv,redundant feature of the target variables is prevalent over synergistic property in the information transfer process and redundant information can compensate for either damaged or lost information,thus improve the SNR of the signaling cascade.

    In the case ofμs? μv,for positive feedback,the minimum value of Fano factor of X is obtained in the signaling cascade.Particularly,there exists an optimal value ofμxat which the maximum value of the SNR can be achieved in this case.However,the SNR of the system is not higher than that in the caseμs< μv.In contrast,there is no optimal value ofμxfor self-activation.

    In the case ofμs= μv,for positive feedback and self-activation,the net synergy exhibits an approximately threshold behavior with the change ofμx.However,for negative feedback and self-inhibition,the threshold behavior does not occur,and the net synergy is a monotone decreasing function with the variation ofμxin both cases.

    In the case ofμs> μv,for positive feedback and selfactivation,significant relative fluctuations in output component X are obtained,and redundant effect of the target variables is dominant in the information transduction for fastμx.By comparison,for negative feedback and self-inhibition,the minimum value of Fano factor of X is achieved,and redundancy is prior to synergy in the information transduction except for very slowμx.

    Above results are in accord with some closely related phenomena in biological systems,such as the osmo adaptation,[58]the bacterial chemotaxis,[43]and the responses of S.Cerevisiae to the changes in external carbon source.[59]Hence different feedback mechanisms exhibit various properties in the fluctuation effects and information transmission by changing the degradation rates of the components in the signaling cascade.

    The signaling cascade with different feedback mechanisms studied in this paper is a typical regulatory motif in various network systems,therefore,it may be worth to investigate the impacts of time delays of production rates and degradation rates of components in some biological signal systems(e.g.,the intracellular signal pathways,[60?63]the neural signal systems,[64?69]etc.)the synergy and redundancy in information transmission in our future works.Our results not only give an extension to the previous investigations of information transmission in the signaling cascade,but also provide a good measure for understanding how biochemical networks transduce time-varying input signals and have potential applications in various fields such as climate science,financial industry,and computer networks.

    猜你喜歡
    馬軍
    Enhance sensitivity to illumination and synchronization in light-dependent neurons?
    Control of firing activities in thermosensitive neuron by activating excitatory autapse?
    Estimation of biophysical properties of cell exposed to electric field
    Interaction of Wave Trains with Defects?
    Dynamics of Spiral Waves Induced by Periodic Mechanical Deformation with Phase Di ff erence?
    Talk about music content and emotion of music movie "The Legend of 1900"
    東方教育(2017年12期)2017-08-23 05:49:54
    宋代兵器鐵連枷淺析
    游戲情感的女大學(xué)生你攤上事了
    前夫中大獎(jiǎng)
    故事林(2010年11期)2010-05-14 17:29:36
    用100元讀完大學(xué)
    意林(2006年2期)2006-05-14 14:47:46
    极品教师在线视频| videossex国产| 精品人妻偷拍中文字幕| 国语自产精品视频在线第100页| 婷婷色综合大香蕉| 自拍偷自拍亚洲精品老妇| 国产精品国产三级专区第一集| 亚洲成色77777| 人人妻人人澡人人爽人人夜夜 | 成人漫画全彩无遮挡| 色吧在线观看| 亚洲精品久久久久久婷婷小说 | 大话2 男鬼变身卡| 亚洲中文字幕日韩| 国产精品乱码一区二三区的特点| 精品久久久久久久人妻蜜臀av| 国产一级毛片在线| 国产精品久久久久久精品电影小说 | 大又大粗又爽又黄少妇毛片口| 少妇熟女欧美另类| 男插女下体视频免费在线播放| 免费观看性生交大片5| 色哟哟·www| 久久这里只有精品中国| 亚洲精品亚洲一区二区| 永久免费av网站大全| 成年版毛片免费区| 不卡视频在线观看欧美| 伊人久久精品亚洲午夜| 高清视频免费观看一区二区 | 女人被狂操c到高潮| 亚洲国产色片| 在现免费观看毛片| 亚洲av免费高清在线观看| 亚洲最大成人手机在线| 国产片特级美女逼逼视频| 国产精品美女特级片免费视频播放器| 久久久久网色| 免费观看的影片在线观看| 国模一区二区三区四区视频| 午夜福利视频1000在线观看| 亚洲高清免费不卡视频| 亚洲婷婷狠狠爱综合网| 好男人在线观看高清免费视频| 18禁裸乳无遮挡免费网站照片| 一边亲一边摸免费视频| 免费av毛片视频| 亚洲精品成人久久久久久| 少妇丰满av| 国产午夜精品久久久久久一区二区三区| 神马国产精品三级电影在线观看| 91久久精品电影网| 人妻夜夜爽99麻豆av| 日韩人妻高清精品专区| 亚洲真实伦在线观看| 日本五十路高清| 日韩中字成人| 国产精品国产三级国产av玫瑰| 日韩精品有码人妻一区| 一本—道久久a久久精品蜜桃钙片 精品乱码久久久久久99久播 | 一级毛片久久久久久久久女| 一区二区三区四区激情视频| 一边摸一边抽搐一进一小说| 性色avwww在线观看| 久99久视频精品免费| 成人av在线播放网站| 成人鲁丝片一二三区免费| 亚洲国产精品久久男人天堂| 国产一级毛片在线| 亚洲av电影不卡..在线观看| 三级毛片av免费| 久久久国产成人精品二区| 国产精品伦人一区二区| 久久久久久久久久久丰满| 成人美女网站在线观看视频| 亚洲av中文字字幕乱码综合| 麻豆成人av视频| 99久久九九国产精品国产免费| 欧美不卡视频在线免费观看| 国语自产精品视频在线第100页| 亚洲成av人片在线播放无| 高清视频免费观看一区二区 | 99久久精品一区二区三区| 午夜免费男女啪啪视频观看| 国产探花极品一区二区| 国产老妇女一区| 中文天堂在线官网| 人人妻人人澡欧美一区二区| 日韩欧美精品v在线| 最新中文字幕久久久久| 一本—道久久a久久精品蜜桃钙片 精品乱码久久久久久99久播 | 午夜爱爱视频在线播放| 国产69精品久久久久777片| 国产单亲对白刺激| 亚洲精品一区蜜桃| 成人综合一区亚洲| 可以在线观看毛片的网站| 国产又色又爽无遮挡免| 搞女人的毛片| 九九在线视频观看精品| 国产精品一区二区性色av| 日本与韩国留学比较| 免费播放大片免费观看视频在线观看 | 国产精品.久久久| 日本免费在线观看一区| 91午夜精品亚洲一区二区三区| 亚洲欧美日韩无卡精品| 日韩中字成人| 国产极品天堂在线| 日韩精品有码人妻一区| 国产一区二区在线av高清观看| 青春草亚洲视频在线观看| av在线播放精品| a级毛片免费高清观看在线播放| 国产高清国产精品国产三级 | 午夜福利视频1000在线观看| 人妻少妇偷人精品九色| 精品无人区乱码1区二区| 韩国av在线不卡| 欧美一级a爱片免费观看看| 国产伦在线观看视频一区| 少妇高潮的动态图| 午夜福利高清视频| 日本黄大片高清| 国产极品天堂在线| 成人国产麻豆网| 变态另类丝袜制服| 久久精品影院6| 搡老妇女老女人老熟妇| 嫩草影院入口| 国产精品久久久久久精品电影小说 | 神马国产精品三级电影在线观看| 国产乱人偷精品视频| 免费av不卡在线播放| 一二三四中文在线观看免费高清| 久久久久久大精品| 午夜日本视频在线| 婷婷色av中文字幕| 亚洲成人精品中文字幕电影| 国产精品电影一区二区三区| 久久久久免费精品人妻一区二区| 日韩,欧美,国产一区二区三区 | 丝袜美腿在线中文| 乱系列少妇在线播放| 亚洲人成网站在线播| 精品国产三级普通话版| 欧美成人一区二区免费高清观看| 日韩 亚洲 欧美在线| 长腿黑丝高跟| 人人妻人人澡人人爽人人夜夜 | 午夜福利高清视频| 亚洲在线观看片| 精品久久久久久久末码| 亚洲av.av天堂| 日产精品乱码卡一卡2卡三| 蜜桃久久精品国产亚洲av| 国产精品熟女久久久久浪| 最近视频中文字幕2019在线8| 蜜臀久久99精品久久宅男| 国产熟女欧美一区二区| 久久久久久久久中文| 一本一本综合久久| 我要看日韩黄色一级片| 亚洲av男天堂| 欧美bdsm另类| 中文亚洲av片在线观看爽| 一个人看的www免费观看视频| 精品久久久久久成人av| 欧美一区二区国产精品久久精品| 中国美白少妇内射xxxbb| 视频中文字幕在线观看| 又粗又硬又长又爽又黄的视频| 人人妻人人澡欧美一区二区| 18+在线观看网站| 又爽又黄无遮挡网站| 99久国产av精品| 一区二区三区乱码不卡18| 国产黄片视频在线免费观看| 国产黄a三级三级三级人| 国产精品无大码| 波野结衣二区三区在线| 国产高清三级在线| 又爽又黄无遮挡网站| 春色校园在线视频观看| 水蜜桃什么品种好| 国模一区二区三区四区视频| 精品免费久久久久久久清纯| 有码 亚洲区| 国产午夜精品久久久久久一区二区三区| 欧美不卡视频在线免费观看| 国产色爽女视频免费观看| 国产视频内射| 午夜老司机福利剧场| 噜噜噜噜噜久久久久久91| 日日撸夜夜添| 亚洲av成人精品一二三区| 日本wwww免费看| 婷婷色综合大香蕉| av在线蜜桃| 天堂中文最新版在线下载 | 久久久国产成人精品二区| 色播亚洲综合网| 国产成人精品婷婷| 五月伊人婷婷丁香| 哪个播放器可以免费观看大片| 久久久久久九九精品二区国产| 黄色一级大片看看| 久久久久久久久中文| 亚洲在线自拍视频| 婷婷色av中文字幕| 99热6这里只有精品| 性色avwww在线观看| 一级二级三级毛片免费看| 国产一区二区亚洲精品在线观看| 国产一区二区三区av在线| 91久久精品电影网| 美女脱内裤让男人舔精品视频| 国产淫语在线视频| 亚洲美女搞黄在线观看| 国产亚洲一区二区精品| 精品一区二区三区视频在线| 久久6这里有精品| 国产精品日韩av在线免费观看| 免费播放大片免费观看视频在线观看 | 国产精品日韩av在线免费观看| 人人妻人人澡欧美一区二区| 国产白丝娇喘喷水9色精品| 日韩成人伦理影院| 亚洲色图av天堂| 中国美白少妇内射xxxbb| 国产成人精品一,二区| 亚洲欧美精品综合久久99| 免费搜索国产男女视频| av又黄又爽大尺度在线免费看 | 国产一区二区三区av在线| 欧美日韩一区二区视频在线观看视频在线 | 亚洲国产欧美在线一区| 日韩制服骚丝袜av| 国产 一区精品| 男的添女的下面高潮视频| av天堂中文字幕网| 亚洲成人av在线免费| 不卡视频在线观看欧美| 欧美一级a爱片免费观看看| 国产精品一二三区在线看| 国产老妇伦熟女老妇高清| 18禁在线无遮挡免费观看视频| 97超视频在线观看视频| 国产一区二区在线观看日韩| 中文天堂在线官网| 你懂的网址亚洲精品在线观看 | 色噜噜av男人的天堂激情| 午夜日本视频在线| 日韩,欧美,国产一区二区三区 | 2022亚洲国产成人精品| 日韩一本色道免费dvd| 美女脱内裤让男人舔精品视频| 蜜桃亚洲精品一区二区三区| 日韩欧美国产在线观看| 欧美激情国产日韩精品一区| 日韩欧美精品v在线| 国产黄片视频在线免费观看| 欧美成人精品欧美一级黄| 少妇熟女aⅴ在线视频| 国产av在哪里看| 亚洲精品色激情综合| 18禁在线无遮挡免费观看视频| 欧美成人午夜免费资源| 国产精品国产高清国产av| 亚洲欧美精品专区久久| 噜噜噜噜噜久久久久久91| 国产乱来视频区| 国产成人一区二区在线| 国产 一区精品| 一二三四中文在线观看免费高清| 人妻系列 视频| 久久久久免费精品人妻一区二区| 亚洲精品亚洲一区二区| 我要看日韩黄色一级片| 久久99热6这里只有精品| 91狼人影院| 26uuu在线亚洲综合色| a级毛片免费高清观看在线播放| 欧美色视频一区免费| 欧美97在线视频| 一区二区三区高清视频在线| 成人三级黄色视频| 免费大片18禁| 国产一级毛片在线| 搞女人的毛片| 国产三级在线视频| 欧美一区二区亚洲| 水蜜桃什么品种好| 国产又色又爽无遮挡免| 免费观看a级毛片全部| 两个人视频免费观看高清| 建设人人有责人人尽责人人享有的 | 日本爱情动作片www.在线观看| 日韩一区二区三区影片| 久久久久免费精品人妻一区二区| 成人毛片a级毛片在线播放| 亚洲国产精品专区欧美| 亚洲四区av| 成人一区二区视频在线观看| 午夜福利网站1000一区二区三区| 九色成人免费人妻av| 国产精品一二三区在线看| 九草在线视频观看| 七月丁香在线播放| 亚洲欧美清纯卡通| 国产一区有黄有色的免费视频 | 亚洲av成人精品一区久久| 久久热精品热| 18禁在线播放成人免费| 亚洲av不卡在线观看| 在线观看66精品国产| 亚洲国产欧美在线一区| 国产美女午夜福利| 国产精品99久久久久久久久| 婷婷六月久久综合丁香| 99久久无色码亚洲精品果冻| 亚洲人成网站在线播| 国产一级毛片七仙女欲春2| 亚洲自偷自拍三级| 三级毛片av免费| 亚洲欧美日韩东京热| 亚洲国产精品专区欧美| 99久久精品国产国产毛片| 国产精品电影一区二区三区| 99视频精品全部免费 在线| 有码 亚洲区| 国产单亲对白刺激| 夫妻性生交免费视频一级片| 国产男人的电影天堂91| 99热这里只有是精品在线观看| 成人午夜精彩视频在线观看| 在线天堂最新版资源| 亚洲国产欧美人成| 久久久久性生活片| 亚洲无线观看免费| 搡女人真爽免费视频火全软件| av国产久精品久网站免费入址| 99久久人妻综合| 天堂中文最新版在线下载 | 美女cb高潮喷水在线观看| a级一级毛片免费在线观看| av播播在线观看一区| 一级毛片久久久久久久久女| 精品久久久久久成人av| 99视频精品全部免费 在线| 床上黄色一级片| 国产69精品久久久久777片| 网址你懂的国产日韩在线| 国产欧美另类精品又又久久亚洲欧美| 亚洲欧美中文字幕日韩二区| 可以在线观看毛片的网站| 国产av在哪里看| 深爱激情五月婷婷| 别揉我奶头 嗯啊视频| 寂寞人妻少妇视频99o| 18禁在线播放成人免费| 国产av一区在线观看免费| 国产成人精品久久久久久| 欧美性猛交╳xxx乱大交人| 亚洲av电影不卡..在线观看| 免费av不卡在线播放| 国国产精品蜜臀av免费| 18禁动态无遮挡网站| 高清午夜精品一区二区三区| 久久久成人免费电影| 久久人人爽人人片av| 国产在线男女| 国产精品无大码| 99久久无色码亚洲精品果冻| 日本三级黄在线观看| 午夜视频国产福利| 欧美又色又爽又黄视频| 五月玫瑰六月丁香| 亚洲国产最新在线播放| 亚洲人成网站在线观看播放| 亚洲欧美精品专区久久| 春色校园在线视频观看| 色5月婷婷丁香| 日日撸夜夜添| 国产精品一二三区在线看| av福利片在线观看| 九九久久精品国产亚洲av麻豆| 欧美xxxx黑人xx丫x性爽| 波多野结衣巨乳人妻| 婷婷色综合大香蕉| 性色avwww在线观看| 免费av观看视频| 国产成人91sexporn| 99热全是精品| 九九在线视频观看精品| 男女国产视频网站| 蜜桃久久精品国产亚洲av| 22中文网久久字幕| 男女视频在线观看网站免费| 免费一级毛片在线播放高清视频| 中文欧美无线码| 国产精品嫩草影院av在线观看| 尾随美女入室| 亚洲一级一片aⅴ在线观看| 精品无人区乱码1区二区| 特级一级黄色大片| 国产高清视频在线观看网站| 建设人人有责人人尽责人人享有的 | 成人午夜高清在线视频| 十八禁国产超污无遮挡网站| 黄片无遮挡物在线观看| 国产精华一区二区三区| kizo精华| 亚洲综合色惰| 啦啦啦啦在线视频资源| 免费观看在线日韩| 成人午夜高清在线视频| 少妇的逼水好多| 我的女老师完整版在线观看| 色视频www国产| 免费观看在线日韩| 国产高清有码在线观看视频| 久久精品熟女亚洲av麻豆精品 | 亚洲成av人片在线播放无| 亚洲自偷自拍三级| 亚洲av电影不卡..在线观看| 国产一级毛片七仙女欲春2| 大话2 男鬼变身卡| 国产亚洲91精品色在线| 免费黄网站久久成人精品| 久久久午夜欧美精品| 97热精品久久久久久| 国产伦在线观看视频一区| 亚洲怡红院男人天堂| 亚洲人与动物交配视频| 波野结衣二区三区在线| 亚洲最大成人av| 亚洲精品乱码久久久v下载方式| 日韩av不卡免费在线播放| 日韩高清综合在线| 在线播放无遮挡| 最后的刺客免费高清国语| 女人十人毛片免费观看3o分钟| 欧美成人免费av一区二区三区| 成年女人看的毛片在线观看| 亚洲欧美日韩卡通动漫| 又爽又黄无遮挡网站| 男人舔奶头视频| 亚洲精品影视一区二区三区av| 赤兔流量卡办理| 亚洲av免费在线观看| 国产av一区在线观看免费| 日本与韩国留学比较| 好男人在线观看高清免费视频| 伊人久久精品亚洲午夜| 国产精品人妻久久久影院| 高清在线视频一区二区三区 | 视频中文字幕在线观看| 亚洲在线观看片| 七月丁香在线播放| 国产精品永久免费网站| 自拍偷自拍亚洲精品老妇| 国产亚洲av嫩草精品影院| 少妇熟女aⅴ在线视频| 国产一区二区亚洲精品在线观看| 国产精品久久电影中文字幕| 神马国产精品三级电影在线观看| videossex国产| 白带黄色成豆腐渣| 亚洲最大成人av| 99久久精品一区二区三区| 亚洲精品乱久久久久久| 久久精品国产99精品国产亚洲性色| 日本-黄色视频高清免费观看| 欧美zozozo另类| 赤兔流量卡办理| 国产亚洲91精品色在线| 午夜久久久久精精品| 日韩欧美精品v在线| 在线免费观看不下载黄p国产| 欧美区成人在线视频| 少妇猛男粗大的猛烈进出视频 | 欧美成人a在线观看| 国产成人freesex在线| 综合色丁香网| 91狼人影院| 淫秽高清视频在线观看| 久久久a久久爽久久v久久| 七月丁香在线播放| 99久久成人亚洲精品观看| 国内揄拍国产精品人妻在线| 一级av片app| 黄片无遮挡物在线观看| 中文字幕精品亚洲无线码一区| 国产av码专区亚洲av| 国模一区二区三区四区视频| 国产精品一区二区三区四区免费观看| 久久久精品94久久精品| 国产探花在线观看一区二区| 免费电影在线观看免费观看| 亚洲美女搞黄在线观看| 日韩中字成人| 午夜福利视频1000在线观看| 精品国内亚洲2022精品成人| 亚洲伊人久久精品综合 | 午夜精品国产一区二区电影 | 大香蕉97超碰在线| 人体艺术视频欧美日本| 能在线免费看毛片的网站| 色综合色国产| 亚洲av日韩在线播放| 国语对白做爰xxxⅹ性视频网站| 精品酒店卫生间| 综合色丁香网| 特级一级黄色大片| 成人漫画全彩无遮挡| 色5月婷婷丁香| 偷拍熟女少妇极品色| 亚洲成av人片在线播放无| 欧美性猛交黑人性爽| 亚洲在线自拍视频| 七月丁香在线播放| 国产精品永久免费网站| 国产极品天堂在线| 久久精品国产亚洲网站| 国产极品天堂在线| 麻豆乱淫一区二区| 国产三级在线视频| 亚洲欧美一区二区三区国产| 欧美精品一区二区大全| 中文乱码字字幕精品一区二区三区 | 久久草成人影院| av在线观看视频网站免费| 麻豆精品久久久久久蜜桃| 2022亚洲国产成人精品| 国产69精品久久久久777片| av播播在线观看一区| 国产亚洲一区二区精品| 国产精品无大码| 又爽又黄无遮挡网站| 亚洲欧美日韩无卡精品| 久久精品国产亚洲网站| av在线观看视频网站免费| 免费看av在线观看网站| 日本午夜av视频| 中文字幕制服av| 男人舔女人下体高潮全视频| 一级av片app| 精品国产三级普通话版| 国产av一区在线观看免费| 久久亚洲精品不卡| 成年av动漫网址| 97人妻精品一区二区三区麻豆| 国产乱来视频区| 亚洲成人精品中文字幕电影| 午夜激情欧美在线| 2022亚洲国产成人精品| 三级国产精品片| av国产免费在线观看| 18禁动态无遮挡网站| 男女下面进入的视频免费午夜| 国产亚洲一区二区精品| 国产一区二区三区av在线| 男人狂女人下面高潮的视频| 精华霜和精华液先用哪个| 韩国高清视频一区二区三区| 男的添女的下面高潮视频| 精品人妻熟女av久视频| 亚洲精品乱码久久久久久按摩| 午夜精品在线福利| 小说图片视频综合网站| 欧美激情在线99| 在线观看66精品国产| 丰满少妇做爰视频| 国产午夜福利久久久久久| 天天躁日日操中文字幕| .国产精品久久| 性色avwww在线观看| 亚洲国产高清在线一区二区三| 一个人观看的视频www高清免费观看| 黄色配什么色好看| 18禁在线无遮挡免费观看视频| 国产精品国产高清国产av| 久久韩国三级中文字幕| 99视频精品全部免费 在线| 男插女下体视频免费在线播放| 亚洲国产高清在线一区二区三| 成人无遮挡网站| 国产熟女欧美一区二区| 欧美色视频一区免费| 日本黄大片高清| 国产中年淑女户外野战色| 少妇人妻一区二区三区视频| 日本三级黄在线观看| 精品久久久久久电影网 | 国产精品无大码| 国产精品久久电影中文字幕| 菩萨蛮人人尽说江南好唐韦庄 | 寂寞人妻少妇视频99o| 亚洲人与动物交配视频| 成人三级黄色视频| 成人亚洲精品av一区二区| 亚洲av成人精品一二三区| 久久精品国产鲁丝片午夜精品| 日本wwww免费看| 天堂网av新在线| 中文字幕熟女人妻在线| 成人高潮视频无遮挡免费网站| 一二三四中文在线观看免费高清| 成年版毛片免费区| 免费观看的影片在线观看| 久久热精品热| 欧美成人精品欧美一级黄| 99久久中文字幕三级久久日本| 老师上课跳d突然被开到最大视频|