Stochastic dynamics and survival analysis of a cell population model with random perturbations

  • Received: 22 March 2017 Accepted: 06 April 2018 Published: 01 October 2018
  • MSC : Primary: 37H10, 60J70; Secondary: 60H10

  • We consider a model based on the logistic equation and linear kinetics to study the effect of toxicants with various initial concentrations on a cell population. To account for parameter uncertainties, in our model the coefficients of the linear and the quadratic terms of the logistic equation are affected by noise. We show that the stochastic model has a unique positive solution and we find conditions for extinction and persistence of the cell population. In case of persistence we find the stationary distribution. The analytical results are confirmed by Monte Carlo simulations.

    Citation: Cristina Anton, Alan Yong. Stochastic dynamics and survival analysis of a cell population model with random perturbations[J]. Mathematical Biosciences and Engineering, 2018, 15(5): 1077-1098. doi: 10.3934/mbe.2018048

    Related Papers:

    [1] Joseph O. Falkinham . Mycobacterium avium complex: Adherence as a way of life. AIMS Microbiology, 2018, 4(3): 428-438. doi: 10.3934/microbiol.2018.3.428
    [2] Zoya Samoilova, Alexey Tyulenev, Nadezhda Muzyka, Galina Smirnova, Oleg Oktyabrsky . Tannic and gallic acids alter redox-parameters of the medium and modulate biofilm formation. AIMS Microbiology, 2019, 5(4): 379-392. doi: 10.3934/microbiol.2019.4.379
    [3] Yunfeng Xu, Attila Nagy, Gary R. Bauchan, Xiaodong Xia, Xiangwu Nou . Enhanced biofilm formation in dual-species culture of Listeria monocytogenes and Ralstonia insidiosa. AIMS Microbiology, 2017, 3(4): 774-783. doi: 10.3934/microbiol.2017.4.774
    [4] Luciana C. Gomes, Joana M. R. Moreira, José D. P. Araújo, Filipe J. Mergulhão . Surface conditioning with Escherichia coli cell wall components can reduce biofilm formation by decreasing initial adhesion. AIMS Microbiology, 2017, 3(3): 613-628. doi: 10.3934/microbiol.2017.3.613
    [5] Luciana C. Gomes, Filipe J. Mergulhão . Effect of heterologous protein expression on Escherichia coli biofilm formation and biocide susceptibility. AIMS Microbiology, 2016, 2(4): 434-446. doi: 10.3934/microbiol.2016.4.434
    [6] Stephen H. Kasper, Ryan Hart, Magnus Bergkvist, Rabi A. Musah, Nathaniel C. Cady . Zein nanocapsules as a tool for surface passivation, drug delivery and biofilm prevention. AIMS Microbiology, 2016, 2(4): 422-433. doi: 10.3934/microbiol.2016.4.422
    [7] Afraa Said Al-Adawi, Christine C. Gaylarde, Jan Sunner, Iwona B. Beech . Transfer of bacteria between stainless steel and chicken meat: A CLSM and DGGE study of biofilms. AIMS Microbiology, 2016, 2(3): 340-358. doi: 10.3934/microbiol.2016.3.340
    [8] Patrick Di Martino . Extracellular polymeric substances, a key element in understanding biofilm phenotype. AIMS Microbiology, 2018, 4(2): 274-288. doi: 10.3934/microbiol.2018.2.274
    [9] Matthew E. Bechard, Payam Farahani, Dina Greene, Anna Pham, Andrew Orry, Madeline E. Rasche . Purification, kinetic characterization, and site-directed mutagenesis of Methanothermobacter thermautotrophicus RFAP Synthase Produced in Escherichia coli. AIMS Microbiology, 2019, 5(3): 186-204. doi: 10.3934/microbiol.2019.3.186
    [10] Alexandra Soares, Ana Azevedo, Luciana C. Gomes, Filipe J. Mergulhão . Recombinant protein expression in biofilms. AIMS Microbiology, 2019, 5(3): 232-250. doi: 10.3934/microbiol.2019.3.232
  • We consider a model based on the logistic equation and linear kinetics to study the effect of toxicants with various initial concentrations on a cell population. To account for parameter uncertainties, in our model the coefficients of the linear and the quadratic terms of the logistic equation are affected by noise. We show that the stochastic model has a unique positive solution and we find conditions for extinction and persistence of the cell population. In case of persistence we find the stationary distribution. The analytical results are confirmed by Monte Carlo simulations.


    Membrane fouling during filtration is the main limitation of this type of process [1],[2]. Preventive and curative treatments help to limit fouling and to maintain efficient filtration flows [3][5]. While inorganic fouling is generally controlled, organic fouling and in particular fouling of biological origin is not. Biofilm development on filtration membrane surfaces, also known as biofouling, is the major fouling component of water filtration systems [5][7]. Biofouling is a sequential phenomenon harbouring initial stages of microbial attachment to the membrane and later stages of cell multiplication and extracellular polymeric substances (EPS) production leading to biofilm development [8],[9]. The increase in size of the structure via cell multiplication and the synthesis of matrix corresponds to the stage of maturation of the biofilm. During the maturation process of biofilms formed on nanofiltration (NF) membranes, there is a diversification of the polysaccharide residues of the matrix, development of the polysaccharide network and reinforcement of the cohesion of the matrix by increase of the viscosity and the elasticity [9]. At this stage, shear forces can only tear off a fragment of biofilm when the structure becomes too prominent, limiting the growth of the biofilm in thickness and facilitating the colonization of other sites [10]. Another factor facilitating the geographic expansion of the biofilm is the active detachment of microorganisms that return to the liquid phase [11],[12]. This active detachment involves the production of microbial enzymes to degrade the matrix locally and release sessile bacteria [13].

    The biofilm matrix forms a gel structure composed of EPS, mainly polysaccharides, proteins, and nucleic acids and accounts for up to 90% of the dry mass of the biofilm [14][17]. In a mature biofilm formed on the surface of nanofiltration membranes in a drinking water production plant, galactoside residues and β-glycan bonds are dominant in the polysaccharide part of the foulants [7]. Peanut agglutinin and wheat germ agglutinin, recognizing the motifs Galβ1-3GalNAcα1-Ser/Thr, and GlcNAcβ1-4GlcNAcβ1-4GlcNAc, respectively, bind strongly to the polysaccharides of the NF biofilm matrix. Other residues are present, and there is some variability in the proportions of the different polysaccharides in the biofilm matrix, depending on the season and the stage of maturity [7],[9]. The matrix polysaccharides are located mainly between cells and organized into entangled fibres of different lengths and cloudy zones. EPS serve as an anchoring cement and protective enclosure for attached microorganisms, rendering mechanical treatments, biocidal treatments and physicochemical treatments less effective [18]. Among the physicochemical treatments against membrane fouling, the acid treatments have a certain efficiency for the release of a part of the fixed inorganic foulants [19]. Alkaline and chelator treatments are more effective than acid treatments in restoring an increased filtration flow [4], [20][22]. Alkaline treatments can partially eliminate the biological fouling, fouling associated with natural organic materials and mineral substances. Chelating treatments induce, by the capture of metal ions, the blocking of inorganic, organic and even biological materials. Treatment with anionic surfactants at basic pH shows some cleaning efficiency [23], whereas treatment with cationic surfactants is inconclusive [24]. The anionic surfactant SDS has higher efficiency to remove lipids than polysaccharides and DNA from fouled nanofiltration membranes [25]. Nonionic surfactants reduce the amounts of biofilm and live microorganisms but with limited efficiency [26]. In general, chemical treatments have an interesting efficiency, although partial, but can also induce membrane alterations [27],[28].

    Enzyme-containing cleaning solutions can be effective for the treatment of biofilms [29][32]. The advantages of the enzymatic treatments are their specificity for a target, the optimal temperatures of use generally not exceeding 50 °C, the pH of optimal use of the order of the physiological pH, a short duration of action if the enzyme concentration is optimal, the biodegradability of enzymes and their limited life in an industrial or natural environment [33]. Thus, enzymatic treatments do not degrade the filtration membranes, and limit additional costs for the treatment of waste. However, since biofilms are complex and heterogeneous, the use of a cleaning solution containing several enzymes seems necessary [7],[34],[35]. In addition, an effective cleaning protocol is usually an association of different products used simultaneously or sequentially [36],[37]. The temperature, the pH, the ionic strength, the concentration of each of the products, their time and their order of application play a key role in the optimization of cleaning processes [22]. Conventional industrial protocols for cleaning nanofiltration membranes use acidic, basic, and detergent solutions [36],[38]. However, these protocols are partially effective, particularly for the removal of biofilm matrix components [35],[39].

    In order to identify possibilities for improving the efficiency of commercial cleaning solutions used in nanofiltration membrane practice, we compared the in vitro efficiency of different types of treatments on samples from membranes operating in a drinking water production plant. We used commercial cleaning solutions to which we added or not the two polysaccharidases lactase and pectinase. Both enzymes cleave β-glycan bonds that are widely present in NF biofilms. Lactase cleaves β-D-galactopyranosyl (1→4) β-D-glucopyranose into glucose and galactose. Pectinase contains a polygalacturonase activity and a lower proportion of cellulase activity hydrolyzing respectively the bonds between 2 galactoses in galacturonic acid and glucose polymers. The treatments were tested at two stages of formation of the biofouling deposit corresponding to different levels of maturity of the biofilm.

    The flow chart of membrane samples preparation process is shown in Figure 1.

    Figure 1.  Flow chart of membrane samples preparation process.

    The filtration modules containing new NF200 B-400 membranes (DOW, La Plaine Saint Denis, France) were installed in stage 1 of the integrated pilot at the Méry-sur-Oise industrial plant and extracted after 80 and 475 operating days as previously described [9].

    The in vitro cleanings were performed on randomly chosen membrane samples cut of 1 cm2 from an extracted module. Each cleaning protocol was repeated three times on three different membrane coupons. Three static bath cleaning protocols were applied (Table 1). Cleaning protocols were identical for all the steps except a step of application of a different commercial active ingredient. Three types of active ingredients were used. P3-Ultrasil® 110 (Ecolab) is an alkaline detergent treatment (ADT). P3-Ultrasil® 67 (Ecolab) is a neutral liquid detergent containing a combination of stabilized enzymes and surfactants. P3-Ultrasil® 69 (Ecolab) is a mild alkaline liquid detergent containing a combination of organic and inorganic sequestering agents and buffers. The combination of P3-Ultrasil® 67 and P3-Ultrasil® 69 is an alkaline enzymatic detergent treatment (AEDT). Aspergillus oryzae lactase (Sigma-Aldrich, Saint Quentin Fallavier, France), and Aspergillus niger pectinase (Sigma-Aldrich, Saint Quentin Fallavier, France) are two polysaccharidases. Lactase and pectinase associated together to the AEDT treatment was called the multi-enzymatic treatment (MET). The percentages of active products indicated in Table 1 are in volume / volume. Ultrapure water was a bi-distilled water of 18 MΩ quality.

    Table 1.  Static bath cleaning protocols applied to biofouled NF membrane samples.
    Alkaline detergent treatment (ADT) Alkaline enzymatic detergent treatment (AEDT) Multi-enzymatic treatment (MET)
    Rinsing with ultrapure water Rinsing with ultrapure water Rinsing with ultrapure water
    P3-Ultrasil® 110 (0.5%) P3-Ultrasil® 67 (0.5%), P3-Ultrasil® 69 (1%) Lactase (1%), pectinase (1%)
    Incubation 4h at 35 °C Incubation 6h at 39 °C Incubation 6h at 35 °C
    Rinsing with ultrapure water at 30 °C
    P3-Ultrasil® 67 (0.5%), P3-Ultrasil® 69 (1%)
    Incubation 6h at 39 °C
    Rinsing with ultrapure water at 30 °C Rinsing with ultrapure water at 30 °C Rinsing with ultrapure water at 30 °C
    Citric acid (0.6%) Citric acid (0.6%) Citric acid (0.6%)
    Incubation 4h at 30 °C Incubation 4h at 30 °C Incubation 4h at 30 °C
    Rinsing with ultrapure water at 30 °C Rinsing with ultrapure water at 30 °C Rinsing with ultrapure water at 30 °C

     | Show Table
    DownLoad: CSV

    Sample cuts of fouled membranes were air dried for 24h at 40 °C before analysis by ATR-FTIR as previously described [40]. A Tensor 27 IR spectrophotometer with a diamond/ZeSe flat plate crystal (Bruker Optics, Marne la Vallée, France) was used to record IR spectra with air as the background and a resolution of 2 cm−1. Each spectrum presented is the mean of 15 spectra corresponding to different areas of the membrane surface. All the samples were pressed with the same force to obtain equivalent close contact between sample surface and ATR crystal. The membrane IR signal near 700 cm−1 was used to calculate ratio corresponding to the relative IR signals of proteins (band at 1650 cm−1/membrane signal) and polysaccharides (band at 1040 cm−1/membrane signal). Means ± standard deviations of the relative IR signals of proteins and polysaccharides are presented.

    The equal-variance Student's t test, following the Fisher's test was used to determine the statistical significance of differences. P values below 0.05, 0.01, or 0.001 were considered significant, highly significant, or very highly significant, respectively.

    Biofoulants present on the surface of nanofiltration membranes after different filtration times were analysed by ATR-FTIR. IR spectra of fouled membranes are presented in Figure 2 and corresponding relative IR signals of proteins and polysaccharides are presented in Table 2. As observed previously, a certain heterogeneity has been measured between different zones of the membrane, materialized by standard deviations of the relative values of proteins and polysaccharides of the fouling material, which are sometimes high [7],[9]. This emphasizes the importance of collecting fouled membrane samples in different areas and multiplying the IR spectral acquisitions at different points on the surface of each sample. After 80 days of operation (D80), the membrane IR signals were attenuated but the majority of them remained clearly visible. A large quantity of biological macromolecules was accumulated on the surface of the nanofiltration membrane (Figure 2, Table 2). Proteins, materialized by the amide I signal at 1650 cm−1, and polysaccharides materialized by a broad complex region of signals between 1200 and 900 cm−1, were found to be the main foulants as previously described [9],[41]. At D80, the region corresponding to the polysaccharides was composed of 4 peaks around 1080, 1040, 1000 and 970 cm−1. This reveals the diversity of polysaccharide signals at this stage of biofilm development. After 475 days of filtration (D475), the polysaccharide signals became dominant, which shows that at this stage, the biofilm matrix has developed very strongly. The peak at 1040 cm−1 became the major signal among the various polysaccharide signals, as for membranes in operation for several years [35]. Unlike most peaks of the membrane which are largely masked by signals of fouling material at D475, the signal close to 700 cm−1 remains clearly visible, which makes it possible to calculate ratios corresponding to relative IR signals of proteins (1650 cm−1/700 cm−1) and polysaccharides (1040 cm−1/700 cm−1). The means ± standard deviations of the relative IR signals corresponding to the different spectra of membrane samples are presented in Table 2. At D475, compared to D80, the relative values of the IR signals of the proteins did not change significantly, while the relative values of the polysaccharide signals increased significantly. This has already been associated with stagnation of sessile bacterial density and a joint increase of matrix polysaccharides during biofilm growth [9].

    Figure 2.  Comparison of the ATR-FTIR spectra of the new NF200 B-400 membrane and of the corresponding fouled membrane samples after 80 days (D80) and 475 days (D475) of filtration. Each spectrum presented is the mean of 15 spectra corresponding to different areas of the membrane surface. Mb: membrane.
    Table 2.  Relative IR signals of membrane samples before and after cleaning.
    Days of operation (Biofilm age) Cleaning protocol Relative IR biofilm signals
    Proteins Polysaccharides
    D80 - 2.5 ± 0.1 2.2 ± 0.3
    D80 ADT 1.3 ± 0.3*** 1.0 ± 0.4***
    D80 AEDT 1.0 ± 0.3***# 0.8 ± 0.6***
    D80 MET 0.9 ± 0.2***## 0.6 ± 0 .4***#
    D475 - 2.6 ± 1.3 3.9 ± 2.3‡‡
    D475 ADT 1.6 ± 0.6*** 2.7 ± 1.2**
    D475 AEDT 1.5 ± 0.4*** 2.7 ± 0.9**
    D475 MET 1.3 ± 0.5***†† 2.3 ± 1.2**

    ADT: Alkaline detergent treatment; AEDT: Alkaline enzymatic detergent treatment; MET: Multi-enzymatic treatment; Means and standard deviation of relative IR biofilm signals are presented; ***, **, *: Value differs significantly (P < 0.001, P < 0.01, and P < 0.05, respectively) from the value obtained before cleaning; ‡‡: Value differs significantly (P < 0.01) from the corresponding value obtained at D80; ##, #: Value differs significantly (P < 0.01, and P < 0.05) from the value obtained after cleaning with the ADT protocol; ††, : Value differs significantly (P < 0.01, and P < 0.05, respectively) from the value obtained after cleaning with the AEDT protocol.

     | Show Table
    DownLoad: CSV

    Many commercially available cleaning agents can be used for nanofiltration membrane remediation [42]. The inorganic foulants of the deposit accumulated on the filtration membrane is largely eliminated by the current chemical cleanings, which is not the case of the fouling organic material and in particular deposit polysaccharides of the biofilm matrix [35]. It is therefore necessary to carry out efficacy studies of new anti-biofilm cleaning solutions to improve the efficiency of industrial cleaning. Before performing these tests on a large scale, a preliminary in vitro testing step on samples from membranes operating in a water production plant is a good alternative [40]. The effectiveness of three different cleaning protocols according to the use or not of alkaline detergents, surfactants, organic and inorganic sequestering agents, enzymes and in particular polysaccharidases has been evaluated in vitro with the membrane samples described above. The treatments consisted in commercial cleaning solutions, and polysaccharidases.

    Figure 3.  Comparison of the ATR-FTIR spectra of the fouled membrane samples before and after cleaning with the three protocols. D80 and D475: 80 and 475 days of filtration before analysis, respectively. Each spectrum presented is the mean of 15 spectra corresponding to different areas of the membrane surface. ADT: Alkaline detergent treatment; AEDT: Alkaline enzymatic detergent treatment; MET: Multi-enzymatic treatment.

    After 80 days of operation, all cleaning protocols had an effect on the biofilm (Figure 3A). Whatever the type of treatment applied, the comparison of the IR spectra before and after cleaning revealed a decrease of the amide I signal and of the band corresponding to the polysaccharides. All the decreases of the foulant signals were significant (Table 2). When comparing the treatments with each other, the alkaline enzymatic detergent treatment (AEDT) was significantly more effective than the alkaline detergent treatment (ADT) in removing proteins but no significant difference in efficacy between the two treatments was observed towards the polysaccharides. The addition of polysaccharidases to AEDT (MET) provided no significant gain in efficiency at this stage of biofilm development. After 475 days of operation, significant decreases in signals of proteins and polysaccharides were also observed on the spectra corresponding to the three treatments compared to no treatment (Figure 3B, Table 2). At this stage, treatments ADT and AEDT had the same efficiency, but the addition of polysaccharidases to treatment AEDT corresponding to treatment MET significantly increased removal of polysaccharides and proteins from the membrane surface. This suggests a reduction of polysaccharides in biofilm biomass after the action of polysaccharidases. A cocktail of polysaccharide-hydrolysing enzymes has been previously shown to remove bacterial biofilm from different solid substrata in laboratory conditions [34]. Alkaline treatments destabilize the microbial membrane, denature proteins and induce the unfolding of extracellular polymeric substances [43]. On the mature biofilm formed after 475 days, the polysaccharidases have a synergistic action with the alkaline enzymatic detergent treatment. Chelating agents, surfactants and enzymes have been previously shown to act synergistically [31]. This synergistic effect could be related to a better diffusion of enzymes within the biofilm during the action of polysaccharidases since the attack of a gel structure like a biofilm by an enzyme is limited by diffusion phenomena [44]. This particular effect associated to polysaccharidases is consistent with the prevalence of polysaccharides in the matrix of NF biofilms formed after 475 days (Figure 3).

    There is a need to enhance the efficiency of cleaning procedures to remove biofilms on the surface of nanofiltration membranes used for drinking water production. Despite their efficiency to maintain nanofiltration performance over time through flux recovery, commercial cleaning solutions are only partially efficient against the biofouling deposit. The results presented here showed that polysaccharide-hydrolysing enzymes can increase the in vitro efficiency of a commercially available alkaline enzymatic detergent cleaning solution. Further experiments are needed to characterize the mechanism of this polysaccharidase effect and to confirm this increase of cleaning efficiency in an industrial context.

    [1] [ C. Anton,J. Deng,Y. Wong,Y. Zhang,W. Zhang,S. Gabos,D. Huang,C. Jin, Modeling and simulation for toxicity assessment, Math. BioSci. Eng., 14 (2017): 581-606.
    [2] [ G. K. Basak,R. Bhattcharya, Stability in distribution for a class of singular diffusions, Ann. Prob., 20 (1992): 312-321.
    [3] [ A. Friedman, Stochastic Differential Equations and Applications, Dover, New York, 2006.
    [4] [ A. Grey,D. Greenhalgh,L. Hu,X. Mao,J. Pan, A stochastic differential equation SIS epidemic model, SIAM. J. Appl. Math., 71 (2011): 876-902.
    [5] [ T. Hallam,C. Clark,G. Jordan, Effects of toxicants on populations: A qualitative approach Ⅱ. First order kinetics, J. Math. Biology, 18 (1983): 25-37.
    [6] [ R. Z. Hasminskii, Stochastic Stability of Differential Equations, Springer, Berlin, 2012, 2nd ed.
    [7] [ J. He,K. Wang, The survival analysis for a population in a polluted environment, Nonlinear Analysis: Real World Applications, 10 (2009): 1555-1571.
    [8] [ C. Ji,D. Jiang,N. Shi,D. O'Regan, Existence, uniqueness, stochastic persistence and global stability of positive solutions of the logistic equation with random perturbation, Math. Methods in the Appl. Sciences, 30 (2007): 77-89.
    [9] [ D. Jiang,N. Shi, A note on non-autonomous logistic equation with random perturbation, J. Math. Anal. Appl., 303 (2005): 164-172.
    [10] [ D. Jiang,N. Shi,X. Li, Global stability and stochastic permanence of a non-autonomous logistic equation with random perturbation, J. Math. Anal. Appl., 340 (2008): 588-597.
    [11] [ J. Jiao,W. Long,L. Chen, A single stage-structured population model with mature individuals in a polluted environment and pulse input of environmental toxin, Nonlinear Analysis: Real World Applications, 10 (2009): 3073-3081.
    [12] [ P. Kloeden and E. Platen, Numerical Solutions of Stochastic Differential Equations, Springer-Verlag, Berlin, 1992.
    [13] [ Y. A. Kutoyants, Statistical Inference for Ergodic Diffusion Processes, Springer, London, 2004.
    [14] [ V. Lakshmikantham and S. Leela, Differential and Integral Inequalities, Vol. Ⅰ, Academic Press, New York, 1969.
    [15] [ M. Liu,K. Wang, Survival analysis of stochastic single-species population models in polluted environments, Ecological Modelling, 220 (2009): 1347-1357.
    [16] [ M. Liu,K. Wang,Q. Wu, Survival analysis of stochastic competitive models in a polluted environment and stochastic competitive exclusion principle, Bull. Math. Biol., 73 (2011): 1969-2012.
    [17] [ X. Mao, Stochastic Differential Equations and Applications, Woodhead Pubilshing, Philadelphia, 2011, 2nd ed.
    [18] [ X. Mao,G. Marion,E. Renshaw, Environmental brownian noise suppresses explosions in population dynamics, Markov Proc. and Their Appl., 97 (2002): 95-110.
    [19] [ X. Mao,S. Sabanis,E. Renshaw, Asymptotic behaviour of the stochastic Lotka-Volterra model, J. Math. Anal. Appl., 287 (2003): 141-156.
    [20] [ T. Pan,B. Huang,W. Zhang,S. Gabos,D. Huang,V. Devendran, Cytotoxicity assessment based on the AUC50 using multi-concentration time-dependent cellular response curves, Anal. Chim. Acta, 764 (2013): 44-52.
    [21] [ S. Pinheiro, On a logistic growth model with predation and power-type diffusion coefficient: Ⅰ. Existence of solutions and extinction criteria, Math. Meth. Appl. Sci., 38 (2015): 4912-4930.
    [22] [ S. Resnik, A Probability Path, Birkhauser, Boston, 1999.
    [23] [ Z. Teng,L. Wang, Persistence and extinction for a class of stochastic SIS epidemic models with nonlinear incidence rate, Physica A, 451 (2016): 507-518.
    [24] [ F. Wei,L. Chen, Psychological effect on single-species population models in a polluted environment, Math. Biosci., 290 (2017): 22-30.
    [25] [ F. Wei,S. Geritz,J. Cai, A stochastic single-species population model with partial pollution tolerance in a polluted environment, Appl. Math. Letters, 63 (2017): 130-136.
    [26] [ Z. Xi,S. Khare,A. Cheung,B. Huang,T. Pan,W. Zhang,F. Ibrahim,C. Jin,S. Gabos, Mode of action classification of chemicals using multi-concentration time-dependent cellular response profiles, Comp. Biol. Chem., 49 (2014): 23-35.
    [27] [ J. Xing,L. Zhu,S. Gabos,L. Xie, Microelectronic cell sensor assay for detection of cytotoxicity and prediction of acute toxicity, Toxicology in Vitro, 20 (2006): 995-1004.
    [28] [ Q. Yang,D. Jiang,N. Shi,C. Ji, The ergodicity and extinction of stochastically perturbed SIR and SEIR epidemic models with saturated incidence, J. Math. Anal. Appl., 388 (2012): 248-271.
    [29] [ Q. Yang,X. Mao, Stochastic dynamics of SIRS epidemic models with random perturbation, Math. BioSci. Eng., 11 (2014): 1003-1025.
    [30] [ Y. Zhang, Y. Wong, J. Deng, C. Anton, J. Deng, S. Gabos, W. Zhang, D. Huang and C. Jin, Machine learning algorithms for mode-of-action classification in toxicity assessment, BioData Mining, 9 (2016), p19.
  • This article has been cited by:

    1. Yufang Li, Han Wang, Shu Wang, Kang Xiao, Xia Huang, Enzymatic Cleaning Mitigates Polysaccharide-Induced Refouling of RO Membrane: Evidence from Foulant Layer Structure and Microbial Dynamics, 2021, 0013-936X, 10.1021/acs.est.0c04735
    2. Ruly Terán Hilares, Imman Singh, Kevin Tejada Meza, Gilberto J. Colina Andrade, David Alfredo Pacheco Tanaka, Alternative methods for cleaning membranes in water and wastewater treatment, 2022, 94, 1061-4303, 10.1002/wer.10708
    3. Deepti Singh, Surekha K. Satpute, Poonam Ranga, Baljeet Singh Saharan, Neha Mani Tripathi, Gajender Kumar Aseri, Deepansh Sharma, Sanket Joshi, Biofouling in Membrane Bioreactors: Mechanism, Interactions and Possible Mitigation Using Biosurfactants, 2023, 195, 0273-2289, 2114, 10.1007/s12010-022-04261-4
  • Reader Comments
  • © 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3841) PDF downloads(551) Cited by(2)

Figures and Tables

Figures(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog