Computational modeling of an early evolutionary stage of the nervous system

The object of this work is to create a computational model that examines the early evolution of the nervous system in relation to adaptive behavior. The main questions are: how did the nervous system and the most primitive forms of intelligence came into being, how a system can be organized during evolution that is able to ensure the adaptive behavior of a being, what are the basic rules of construction that are sufficient to create a workable nervous system without specifying the details of the construction. The biological bases of the model are the phyla Cnidaria and Porifera as they stand at the beginning of the genesis of nervous organization. We found in our model that in a network of homogenous epithelial-like cells, which is considered the starting point of the genesis of the nervous system, the changes that have positive influence on the behavior are those that make the spreading of the electric potential more efficient. It can cause the increase of the effectiveness of the behavior by itself without creating new specific cell-types. There are some alternatives to increasing the effectiveness of spreading of stimuli, for example increasing the value of biophysical parameters of the cells, or increasing the density of nerve cells and the number of synapses. If during the evolution a sort of cell comes into being that is able to conduct electrical stimuli--even in a rudimentary way--it can increase the adaptivity of behavior by itself without the need for specific information of how to organize the construction of this system.     

Functionality of divergence and convergence in a model of the insect olfactory system

Recent studies have shown that the insect olfactory system uses a spatio-temporal encoding of odours in the population of projection neurons in the antennal lobe, and suggest that the information thus coded is spread across a large population of Kenyon cells in the mushroom bodies. At this stage, the temporal part of the code might be transformed into a spatial code, especially via the temporally sensitive mechanisms of paired–pulse facilitation and feedback inhibition with its possible associated rebound. We explore here a simple model of the olfactory system using a three–layer network of formal neurons, comprising a fixed number (three) of projection and inhibitory neurons, but a variable number of Kenyon cells. We show how enlarging the divergence of the network (i.e. the ratio between the number of Kenyon cells to the number of input – projection – neurons) alters the number of different output spatial states in response to a fixed set of spatio-temporal inputs, and may therefore improve its effectiveness in discriminating between these inputs. Such enlarged divergence also reduces the variation of this effectiveness among random realisations of the network connectivity. Our model shows that the discriminative effectiveness first increases with the divergence, and then plateaus for a divergence factor of ∼20. The maximal average number of different outputs was 470.2, which was computed from some simulations with random realisations of connectivity and with a set of 512 possible inputs. The discriminative effectiveness of the network is sensitive to paired-pulse facilitation, and especially to inhibition with rebound. Received: 6 April 2001 / Accepted in revised form: 8 April 2002     

A Hepatitis C Virus Infection Model with Time-Varying Drug Effectiveness: Solution and Analysis

Simple models of therapy for viral diseases such as hepatitis C virus (HCV) or human immunodeficiency virus assume that, once therapy is started, the drug has a constant effectiveness. More realistic models have assumed either that the drug effectiveness depends on the drug concentration or that the effectiveness varies over time. Here a previously introduced varying-effectiveness (VE) model is studied mathematically in the context of HCV infection. We show that while the model is linear, it has no closed-form solution due to the time-varying nature of the effectiveness. We then show that the model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions, which are defined as infinite series, with time-varying arguments. Fitting the solution to data from HCV infected patients under therapy has yielded values for the parameters in the model. We show that for biologically realistic parameters, the predicted viral decay on therapy is generally biphasic and resembles that predicted by constant-effectiveness (CE) models. We introduce a general method for determining the time at which the transition between decay phases occurs based on calculating the point of maximum curvature of the viral decay curve. For the parameter regimes of interest, we also find approximate solutions for the VE model and establish the asymptotic behavior of the system. We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy, whereas the rate of first phase decline depends on multiple parameters including the rate of increase of drug effectiveness with time.     

Whole-cell hollow-fiber reactor: Effectiveness factors

Analytical expressions, which allow the generation of effectiveness factor graphs for a reactor system employing immobilized whole cells a biocatalyst, are presented. In particular hollow-fiber devices (such as dialysis or ultrafiltration units) are considered. Such devices are analogs to a shell-and-tube heat exchanger. Whole cells are entrapped on the shell side: a nutrient solution is circulated through the tubes, substrate diffuses from the tube side, across the fiber, and into the cell mass on the shell side, where it irreversibly reacts to form product. The product back-diffuses into the circulating nutrient solution. The overall substrate mass-transfer process is hypothesized to be either diffusion limited in the hollow-fiber tube wall and/or the shell-side cell suspension and/or reaction limited at the enzyme sites within the whole cells. The first- and zero-order limits of the Michaelis-Menten rate law are used in generating effectiveness factor expressions. The effectiveness factor is a function of reaction order, Thiele modulus, diffusion coefficient ratio (defined as the effective substrate diffusivity in the hollow-fiber membrane wall divided by the effective substrate diffusivity in the cell suspension), partition coefficient, volume of the cell suspension, and hollow-fiber width. Equations for the effectiveness factor are also detailed when the hollow-fiber mass-transfer resistance is far greater than the cell suspension mass-transfer resistance. An effectiveness factor chart is presented specifically for the commercially available C-DAK 4 dialyzer (Cordis Dow Co., Miami, Florida). In general terms the effectiveness factor expressions are applicable for characterizing diffusion and reaction within a catalytically active cylindrical annulus, Whose inner surface offers a diffusional resistance and whose outer surface is impermeable to reactants. Some generalization of the Thiele modulus is undertaken which serves to draw the asymptotes on the effectiveness factor charts together. Comment is made on the variation of the slope of the effectiveness factor graph and its relation to the change in the observed reaction activation energy. Possible application of the model to the catalytic tube wall reactor is discussed.     

Using Caco-2 Cells to Study Lipid Transport by the Intestine

Studies of dietary fat absorption are generally conducted by using an animal model equipped with a lymph cannula. Although this animal model is widely accepted as the in vivo model of dietary fat absorption, the surgical techniques involved are challenging and expensive. Genetic manipulation of the animal model is also costly and time consuming. The alternative in vitro model is arguably more affordable, timesaving, and less challenging. Importantly, the in vitro model allows investigators to examine the enterocytes as an isolated system, reducing the complexity inherent in the whole organism model. This paper describes how human colon carcinoma cells (Caco-2) can serve as an in vitro model to study the enterocyte transport of lipids, and lipid-soluble drugs and vitamins. It explains the proper maintenance of Caco-2 cells and the preparation of their lipid mixture; and it further discusses the valuable option of using the permeable membrane system. Since differentiated Caco-2 cells are polarized, the main advantage of using the permeable membrane system is that it separates the apical from the basolateral compartment. Consequently, the lipid mixture can be added to the apical compartment while the lipoproteins can be collected from the basolateral compartment. In addition, the effectiveness of the lentivirus expression system in upregulating gene expression in Caco-2 cells is discussed. Lastly, this paper describes how to confirm the successful isolation of intestinal lipoproteins by transmission electron microscopy (TEM).     

Study and mathematical modeling of the production of propionic acid by Propionibacterium acidipropionici immobilized in a stirred tank fermentor

A mathematical model was developed that describes production of propionic acid by fermentation of sweet whey with Propionibacterium acidipropionici immobilized in calcium polygalacturonate beads in a fermentor-type stirred tank. This mathematical model is constituted by a partial differential equations system, which fits consumption, production, growth and internal diffusion rates in the support. Fermentation was experimentally studied with free cells and immobilized cells, effective diffusivities of lactose and propionic acid were estimated in the support, and typical parameters of the model were obtained by nonlinear regression of the experimental data. The variance analysis shows that the combination of micro(max) and K(d) parameters is the source of variation most significative, also they were found to be the most sensitive parameters of the model. Finally, an effectiveness factor was calculated in order to assess the effect of mass transfer on the overall reaction rate observed.     

Cellobiose hydrolysis using Pichia etchellsii cells immobilized in calcium alginate

The rate of celluose degradation, limited due to the inhibition by cellobiose, can be increased by the hydrolysis of cellobiose to glucose using immobilized beta-glucosidase. Production of beta-glucosidase in four yeasts was studied and a maximum activity of 1.22 IU/mg cells was obtained in cells of Pichia etchellsii when grown on 3% cellobiose as the sole carbon source. A study of the immobilization of beta-glucosidase containing cells of Pichia etchellsii on various solid supports was conducted and immobilization by entrapment in calcium alginate gel beads was found to be the most simple and efficient method. A retention of 96.5% of initial activity after ten sequential batch uses of the immobilized preparation was observed. The pH and temperature optima for free and immobilized cells were the same, i.e., 6.5 (0.05M Maleate buffer) and 50 degrees C, respectively. Even though the temperature optimum was found to be 50 degrees C, the enzyme exhibits a better thermal stability at 45 degrees C. Beads stored at 4 degrees C for six months retain 80% of their activity. Kinetic studies performed on free and immobilized cells shown that glucose is a noncompetitive product inhibitor.The immobilized preparation was found to be limited by pore diffusion but exhibited no film-diffusion resistance during packed bed column indicated by a low dispersion number of 0.1348. A model for reaction with pore diffusion for a noncompetitive type of inhibited system was developed and applied to the cellobiose hydrolysis system. The rate of reaction with diffusional limitations was determined by using the model and effectiveness factors were calculated for different particle sizes. An effectiveness factor of 0.49 was obtained for a particle diameter of 2.5 mm. The modified rate expression using the effectiveness factor represented batch and packed bed reactor operation satisfactorily. The productivity in the packed bed column was found to fall rapidly with increase in conversion rate indicating that the operating conditions of the column would have to be a compromise between high conversion rates and reasonable productivity. A half-life of over seven days was obtained at the operating temperature of 45 degrees C in continuous operation of the packed bed reactor. However, the half-life in the column was found to be greatly affected by temperature, increasing to over seventeen days at a temperature of 40 degrees C and decreasing to less than two days at 50 degrees C.     

Highly efficient transfection system for functional gene analysis in adult amphibian lens regeneration

The analysis of newt lens regeneration has been an important subject in developmental biology. Recently, it has been reported that the genes involved in the normal eye development are also expressed in the regenerative process of lens regeneration in the adult newt. However, functional analysis of these genes has not been possible, because there is no system to introduce genes efficiently into the cells involved in the regeneration. In the present study, lipofection was used as the method for gene transfer in cultured pigmented iris cells that can transdifferentiate into lens cells in newt lens regeneration. Positive expression of a reporter gene was obtained in more than 70% of cells. In addition, the aggregate derived from gene-transfected cells maintained its expression at a high level for a long time within the host tissue. To verify the effectiveness of this model system with a reporter gene in lens regeneration, Pax6, which is suggested to be involved in normal eye development and lens regeneration, was transfected. Ectopic expression of lens-specific crystallins was obtained in cells that show no such activity in normal lens regeneration. These results made it possible for the first time to analyze the molecular mechanism of lens regeneration in the adult newt.     

A model for computer simulation of P-glycoprotein and transmembrane delta pH-mediated anthracycline transport in multidrug-resistant tumor cells

Anthracycline resistance in multidrug-resistant (MDR) tumor cells is due in part to a reduced cellular drug accumulation. Using a simple kinetic model and numerical computer simulations, we have analyzed mathematically the following possible mechanisms controlling fluxes of the membrane permeable anthracyclines in MDR cells: (1) active outward transport via a specific drug transporter (P-glycoprotein), (2) exocytotic drug export via the endosomal vesicle system, and (3) pH-gradients across the plasma membrane. Model calculations were based on morphometric and kinetic data previously presented in the literature for daunorubicin transport in wild-type Ehrlich ascites tumor cells (EHR2) and the corresponding daunorubicin (DNR)-resistant cell line EHR2/DNR+. The results confirm the possible importance of the cell-surface pH in controlling DNR accumulation in the cells. With P-glycoprotein as the main efflux pump, a catalytic constant of the protein greater than 40 mol DNR transported/mol protein per min is predicted by the model calculations. Changes in the drug binding affinity of P-glycoprotein (Km = 10(-9)-10(-6) M) is of little importance in influencing its effectiveness to reduce DNR accumulation, which could explain the broad substrate specificity of the MDR efflux pump system. The conditions to evaluate unidirectional fluxes of DNR across the plasma membrane in cells with active P-glycoprotein are defined. An efflux mechanism which relies solely on pH-dependent drug trapping in a pH 5 endosomal compartment by a simple diffusion process followed by exocytosis, appears inadequate to account for the high rate of DNR efflux in EHR2/DNR+ cells.     

人免疫缺陷病毒假病毒药物筛选模型的构建及其应用

目的:构建人免疫缺陷病毒(HIV)假病毒模型,用多种HIV逆转录酶和蛋白酶抑制剂作用于该模型,以检测其是否能有效用于HIV抑制药物的筛选。方法:通过载体改造获得最终慢病毒载体puc18-NL4-3-LUC-stop,其中含有萤光素酶基因,将该载体与包膜质粒VSV-G共转染293FT细胞,包装产生HIV假病毒,在假病毒包装和病毒感染293FT细胞的过程中加入蛋白酶和逆转录酶抑制剂,通过检测感染细胞中萤光素酶的表达来检测该模型的有效性,并利用此模型检测药物的抗病毒效果。结果:将HIV逆转录酶和蛋白酶抑制剂作用于该假病毒模型时发现萤光素酶的表达得到很大程度的抑制。结论:建立了HIV假病毒药物筛选模型,该模型以萤光素酶基因作为报告基因,快速灵敏,在抗HIV药物筛选中有一定的应用价值。     

An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts’ uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.     

Modeling and simulation of cancer immunoprevention vaccine

We present an in silico model that simulates the immune system responses to tumor cells in naive and vaccinated mice. We have demonstrated the ability of this model to accurately reproduce the experimental results. MOTIVATION: In vivo experiments on HER-2/neu mice have shown the effectiveness of Triplex vaccine in the protection of mice from mammary carcinoma. Full protection was conferred using chronic (prophylactic) vaccination protocol while therapeutic vaccination was less efficient. Our in silico model was able to closely reproduce the effects of various vaccination protocols. This model is the first step towards the development of in silico experiments searching for optimal vaccination protocols. RESULTS: In silico experiments carried out on two large statistical samples of virtual mice showed very good agreements with in vivo experiments for all experimental vaccination protocols. They also show, as supported by in vivo experiments, that the humoral response is fundamental in controlling the tumor growth and therefore suggest the selection and timing of experiments for measuring the activity of T cells. CONTACT: francesco@dmi.unict.it SUPPLEMENTARY INFORMATION: http://www.dmi.unict.it/CIG/suppdata_bioinf.html.     

Modeling regulation mechanisms in the immune system

We develop a mathematical framework for modeling regulatory mechanisms in the immune system. The model describes dynamics of key components of the immune network within two compartments: lymph node and tissue. We demonstrate using numerical simulations that our system can eliminate virus-infected cells, which are characterized by a tendency to increase without control (in absence of an immune response), while tolerating normal cells, which are characterized by a tendency to approach a stable equilibrium population. We experiment with different combinations of T cell reactivities that lead to effective systems and conclude that slightly self-reactive T cells can exist within the immune system and are controlled by regulatory cells. We observe that CD8+ T cell dynamics has two phases. In the first phase, CD8+ cells remain sequestered within the lymph node during a period of proliferation. In the second phase, the CD8+ population emigrates to the tissue and destroys its target population. We also conclude that a self-tolerant system must have a mechanism of central tolerance to ensure that self-reactive T cells are not too self-reactive. Furthermore, the effectiveness of a system depends on a balance between the reactivities of the effector and regulatory T cell populations, where the effectors are slightly more reactive than the regulatory cells.     

A data-driven predictive approach for drug delivery using machine learning techniques

In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.     

Inhibition of tumor growth in mice by endostatin derived from abdominal transplanted encapsulated cells

Endostatin,a C-terminal fragment of collagen 18a,inhibits the growth of established tumorsand metastases in vivo by inhibiting angiogenesis.However,the purification procedures required for large-scale production and the attendant cost of these processes,together with the low effectiveness in clinicaltests,suggest that alternative delivery methods might be required for efficient therapeutic use of endostatin.In the present study,we transfected Chinese hamster ovary(CHO)cells with a human endostatin geneexpression vector and encapsulated the CHO cells in alginate-poly-L-lysine microcapsules.The release ofbiologically active endostatin was confirmed using the chicken chorioallantoic membrane assay.The encap-sulated endostatin-expressing CHO cells can inhibit the growth of primary tumors in a subcutaneous B 16tumor model when injected into the abdominal cavity of mouse.These results widen the clinical applicationof the microencapsulated cell endostatin delivery system in cancer treatment.     

Oncogenes, anti-oncogenes and the immune response to cancer: a mathematical model.

We develop a mathematical model for the initial growth of a tumour after a mutation in which either an oncogene is expressed or an anti-oncogene (i.e. tumour suppressor gene) is lost. Our model incorporates mitotic control by several biochemicals, with quite different regulatory characteristics, and we consider mutations affecting the cellular response to these control mechanisms. Our mathematical representation of these mutations reflects the current understanding of the roles of oncogenes and anti-oncogenes in controlling cell proliferation. Numerical solutions of our model, for biologically relevant parameter values, show that the different types of mutations have quite different effects. Mutations affecting the cell response to chemical regulators, or resulting in autonomy from such regulators, cause an advancing wave of tumour cells and a receding wave of normal cells. By contrast, mutations affecting the production of a mitotic regulator cause a slow localized increase in the numbers of both normal and mutant cells. We extend our model to investigate the possible effects of an immune response to cancer by including a first order removal of mutant cells. When this removal rate exceeds a critical value, the immune system can suppress tumour growth; we derive an expression for this critical value as a function of the parameters characterizing the mutation. Our results suggest that the effectiveness of the immune response after an oncogenic mutation depends crucially on the way in which the mutation affects the biochemical control of cell division.     

Cancer cell motility: Optimizing spatial search strategies

Aberrantly regulated cell motility is a hallmark of cancer cells. A hybrid agent-based model has been developed to investigate the synergistic and antagonistic cell motility-impacting effects of three microenvironment variables simultaneously: chemoattraction, haptotactic permission, and biomechanical constraint or resistance. Reflecting distinct cell-specific intracellular machinery, the cancer cells are modeled as processing a variety of spatial search strategies that respond to these three influencing factors with differential weights attached to each. While responding exclusively to chemoattraction optimizes cell displacement effectiveness, incorporating permission and resistance components becomes increasingly important with greater distance to the chemoattractant source and/or after reducing the ligand's effective diffusion coefficient. Extending this to a heterogeneous population of cells shows that displacement effectiveness increases with clonal diversity as characterized by the Shannon index. However, the resulting data can be fit best to an exponential function, suggesting that there is a level of population heterogeneity beyond which its added value to the cancer system becomes minimal as directionality ceases to increase. Possible experimental extensions and potential clinical implications are discussed.     

Analysis of Hepatitis C Virus Decline during Treatment with the Protease Inhibitor Danoprevir Using a Multiscale Model

The current paradigm for studying hepatitis C virus (HCV) dynamics in patients utilizes a standard viral dynamic model that keeps track of uninfected (target) cells, infected cells, and virus. The model does not account for the dynamics of intracellular viral replication, which is the major target of direct-acting antiviral agents (DAAs). Here we describe and study a recently developed multiscale age-structured model that explicitly considers the potential effects of DAAs on intracellular viral RNA production, degradation, and secretion as virus into the circulation. We show that when therapy significantly blocks both intracellular viral RNA production and virus secretion, the serum viral load decline has three phases, with slopes reflecting the rate of serum viral clearance, the rate of loss of intracellular viral RNA, and the rate of loss of intracellular replication templates and infected cells, respectively. We also derive analytical approximations of the multiscale model and use one of them to analyze data from patients treated for 14 days with the HCV protease inhibitor danoprevir. Analysis suggests that danoprevir significantly blocks intracellular viral production (with mean effectiveness 99.2%), enhances intracellular viral RNA degradation about 5-fold, and moderately inhibits viral secretion (with mean effectiveness 56%). The multiscale model can be used to study viral dynamics in patients treated with other DAAs and explore their mechanisms of action in treatment of hepatitis C.     

Commitment to erythroid differentiation by friend erythroleukemia cells: a stochastic analysis

A method for the clonal analysis of murine erythroleukemia cells has been developed which allows the precise characterization of the number of progeny produced by each cell and the degree of differentiation of each progeny cell. The potential of almost every cell in the culture can be monitored because a plating efficiency close to 100% has been achieved. The effects of treatment with an inducer of differentiation (DMSO) on the proliferative capacity of the treated cells have been studied with this technique. Cells from a mass culture treated with inducer give rise to colonies of differentiated progeny when subsequently cloned in the absence of inducer. Colonies exhibiting this phenotype represent the progeny of cells committed to the differentiation pathway by treatment with inducer. We observe that the commitment decision limits the subsequent proliferative capacity of the cell to four additional cell divisions. A quantitative analysis suggests that the commitment decision for each cell is made in a stochastic manner. Irreversible commitment to the expression of differentiated functions occurs with discrete probability per cell generation for many cell generations. The value for this probability is a function of the concentration of inducer (DMSO). A correlative biochemical study suggests that an irreversible commitment decision by a significant proportion of the population precedes or accompanies increases in cytoplasmic globin mRNA levels, one of the earliest detectable biochemical markers for erythroid differentiation in this system.A specific kinetic model based on these considerations has been developed to predict clonal phenotypes as a function of time and probability of commitment. Quantitative predictions based on this model are in excellent agreement with experimental observations. The effectiveness of a stochastic model in predicting the behavior of this system is discussed in relation to the stochastic behavior of normal hematopoiesis and the biochemical mechanisms which control these differentiation programs.