Describe Cell Growth Research Articles

Ethanol production by Kluyveromyces marxianus ATCC 36907: Fermentation features and mathematical modeling

The effects of different temperatures (30 °C, 35 °C, 40 °C, and 45 °C) and initial glucose concentrations (50 g/L, 120 g/L, and 190 g/L) on the fermentative metabolism of Kluyveromyces marxianus ATCC 36907 were evaluated to optimize ethanol production. Mathematical models were developed based on experimental data to describe cell growth, substrate consumption, and ethanol production. Various kinetic models integrated with material balances were examined. The Akaike information criterion (AIC) was employed to identify the most accurate model representing the experimental behavior. Interestingly, the ethanol production rate was independent of the growth rate of K. marxianus. The growth kinetics indicated that the growth rate could be controlled by the oxygen concentration, which was influenced by cell concentration rather than glucose consumption. A desirability function was utilized to optimize ethanol productivity, substrate conversion efficiency and temperature, considering the conditions for future applications in simultaneous saccharification and fermentation (SSF) processes. The optimized experimental conditions obtained were validated, resulting in a new model specific to these conditions. This new model analysis confirmed the achievement of the objective to prioritize ethanol production. The optimized conditions for maximum ethanol production were initial glucose concentration of 91 g/L and temperature of 40 °C. Under these conditions, the ethanol concentration reached 12 g/L, with glucose conversion efficiency into ethanol of 0.13 g/g and productivity of 1.09 g/L/h after 11 h. This approach provided valuable insights into the fermentative behavior of K. marxianus, serving as a foundation for the design, optimization, and process control in biorefineries.

Mathematical modeling of bioethanol production from sweet sorghum juice under high gravity fermentation: Applicability of Monod-based, logistic, modified Gompertz and Weibull models

BackgroundMathematical modeling of a fermentation process is crucial in understanding and predicting dynamics of the process, which can be used in process improvement, design and control. The present study aimed to develop Monod-based kinetic models to describe cell growth, substrate consumption and ethanol production by Saccharomyces cerevisiae NP 01 under high gravity (HG) fermentation of sweet sorghum juice (SSJ). ResultsThe fermentation using an initial total sugar (TS) concentration of 240 g/L resulted in 113.3 g/L of ethanol production, with 90.9% TS consumption and a fermentation efficiency of 94.4%. Growth of the yeast in terms of specific growth rate was found to be inhibited at a threshold TS concentration of 65 g/L, and the maximum specific growth rate, Monod constant and inhibition constant were 0.45 1/h, 19.5 g/L and 0.002 L/(g·h), respectively. Monod-based models incorporating substrate and product inhibition terms showed high applicability to describe the changes of cell, TS and ethanol concentrations, based on the values of bias factor, accuracy factor, coefficient of determination and root mean square error. ConclusionsThe Monod-based models fitted the data equally well as compared with the logistic, modified Gompertz, and Weibull models, despite estimating the value of different kinetic parameters. These results demonstrated that all the models tested were applicable in modeling HG ethanol fermentation.How to cite: Salakkam A, Phukoetphim N, Laopaiboon P, et al. Mathematical modeling of bioethanol production from sweet sorghum juice under high gravity fermentation: Applicability of Monod-based, logistic, modified Gompertz and Weibull models. Electron J Biotechnol 2023;64. https://doi.org/10.1016/j.ejbt.2023.03.004.

Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks.

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modeling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch, which includes additive process noise and exhibits bistability. We compare the results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of an in vivo experiment using an agent-based simulator describing cell growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

Unraveling encapsulated growth of Nitrosomonas europaea in alginate: An experimental and modeling study

Encapsulation is a promising technology to retain and protect autotrophs for biological nitrogen removal. One-dimensional biofilm models have been used to describe encapsulated systems; they do not, however, incorporate chemical sorption to the encapsulant nor do they adequately describe cell growth and distribution within the encapsulant. In this research we developed a new model to describe encapsulated growth and activity of Nitrosomonas europaea, incorporating ammonium sorption to the alginate encapsulant. Batch and continuous flow reactors were used to verify the simulation results. Quantitative PCR and cross-section fluorescence in situ hybridization were used to analyze the growth and spatial distribution of the encapsulated cells within alginate. Preferential growth of Nitrosomonas near the surface of the encapsulant was predicted by the model and confirmed by experiments. The modeling and experimental results also suggested that smaller encapsulants with a larger surface area to volume ratio would improve ammonia oxidation. Excessive aeration caused the breakage of the encapsulant, resulting in unpredicted microbial release and washout. Overall, our modeling approach is flexible and can be used to engineer and optimize encapsulated systems for enhanced biological nitrogen removal. Similar modeling approaches can be used to incorporate sorption of additional species within an encapsulant, additional nitrogen-converting microorganisms, and the use of other encapsulation materials.

Shaping the Organ: A Biologist Guide to Quantitative Models of Plant Morphogenesis.

Organ morphogenesis is the process of shape acquisition initiated with a small reservoir of undifferentiated cells. In plants, morphogenesis is a complex endeavor that comprises a large number of interacting elements, including mechanical stimuli, biochemical signaling, and genetic prerequisites. Because of the large body of data being produced by modern laboratories, solving this complexity requires the application of computational techniques and analyses. In the last two decades, computational models combined with wet-lab experiments have advanced our understanding of plant organ morphogenesis. Here, we provide a comprehensive review of the most important achievements in the field of computational plant morphodynamics. We present a brief history from the earliest attempts to describe plant forms using algorithmic pattern generation to the evolution of quantitative cell-based models fueled by increasing computational power. We then provide an overview of the most common types of “digital plant” paradigms, and demonstrate how models benefit from diverse techniques used to describe cell growth mechanics. Finally, we highlight the development of computational frameworks designed to resolve organ shape complexity through integration of mechanical, biochemical, and genetic cues into a quantitative standardized and user-friendly environment.

A differential evolution approach to estimate parameters in a temperature-dependent kinetic model for second generation ethanol production under high cell density with Spathaspora passalidarum

An unstructured–unsegregated temperature-dependent kinetic model was developed and validated to describe ethanol fermentation in a mixture of xylose and glucose for the nonconventional yeast Spathaspora passalidarum NRRL Y-27907 under high cell density in the temperature range of 26–32 °C. The kinetic model consisted of 13 equations and 16 kinetic parameters, describing cell growth, individual xylose and glucose uptake and product formation. Global parametric estimation was executed through a differential evolution algorithm. Temperature-dependent parameters were identified and adjusted to Arrhenius-type equations as a function of temperature. The overall r2 of the process calibration and validation were 0.972 and 0.959, respectively, indicating that the model satisfactorily described the process in the temperature range investigated. Furthermore, using a 3D projection of the model to simulate ethanol production, it was concluded that temperatures between 30 and 32 °C resulted in the highest productivities.

Analytical solution for a hybrid Logistic-Monod cell growth model in batch and continuous stirred tank reactor culture.

Monod and Logistic growth models have been widely used as basic equations to describe cell growth in bioprocess engineering. In the case of the Monod equation, the specific growth rate is governed by a limiting nutrient, with the mathematical form similar to the Michaelis–Menten equation. In the case of the Logistic equation, the specific growth rate is determined by the carrying capacity of the system, which could be growth‐inhibiting factors (i.e., toxic chemical accumulation) other than the nutrient level. Both equations have been found valuable to guide us build unstructured kinetic models to analyze the fermentation process and understand cell physiology. In this work, we present a hybrid Logistic‐Monod growth model, which accounts for multiple growth‐dependent factors including both the limiting nutrient and the carrying capacity of the system. Coupled with substrate consumption and yield coefficient, we present the analytical solutions for this hybrid Logistic‐Monod model in both batch and continuous stirred tank reactor (CSTR) culture. Under high biomass yield (Y x/s) conditions, the analytical solution for this hybrid model is approaching to the Logistic equation; under low biomass yield condition, the analytical solution for this hybrid model converges to the Monod equation. This hybrid Logistic‐Monod equation represents the cell growth transition from substrate‐limiting condition to growth‐inhibiting condition, which could be adopted to accurately describe the multi‐phases of cell growth and may facilitate kinetic model construction, bioprocess optimization, and scale‐up in industrial biotechnology.

Monitoring Caco-2 to enterocyte-like cells differentiation by means of electric impedance analysis on printed sensors

BackgroundColorectal adenocarcinoma cells (Caco-2) are a widely used model of intestinal barrier to study cancer development, toxicological assessments, absorption and metabolism in food science or drug discovery. Caco-2 spontaneously differentiate into a monolayer expressing several specific characteristics, typically showed by mature enterocytes. For in vitro experiments, it is crucial to identify non-invasive and non-destructive techniques able to evaluate the integrity and differentiation of the cells monolayer. Thus, we aimed to assess these properties by analyzing electrical impedance measurements. MethodsCaco-2 cells were differentiated for 21 days. The monolayer integrity and differentiation were primarily evaluated by means of morphological, biochemical and molecular data. Impedance measurements in a range of frequencies from 400 Hz to 50 kHz were performed using a dedicated set up, including customized Aerosol Jet Printed carbon-based sensors. ResultsThe trends of RI observed at three different frequencies were able to describe cell growth and differentiation. In order to evaluate which frequencies better correlate with cell differentiation, Principal Component Analysis have been employed and the concordance analysis between RI magnitude and morphological, biochemical and molecular data, highlighted 40 kHz as the optimal frequency to assess Caco-2 cells differentiation process. ConclusionWe demonstrated the feasibility and reliability of applying impedance-based measurements not only to provide information about the monolayer status, but also for cell differentiation monitoring. General significanceThis study underlined the possibility to use a dedicated sensor to assess the integrity and differentiation of Caco-2 monolayer, as a reliable non-destructive alternative to conventional approaches.

Kinetic modeling, recovery, and molecular characterization of poly-beta-hydroxybutyrate polymer in Acinetobacter baumannii isolate P39

To control the poly-β-hydroxybutyrate (PHB) biopolymer production by Acinetobacter baumannii isolate P39 kinetic modeling of the fermentation process, polymer downstream processing, enzymological analysis, and molecular characterization of PHA synthase, key biosynthetic enzyme, should be addressed. A. baumannii isolate P39 produced 0.15g/L PHB after 24h of incubation with a polymer content of 28% per dry weight. Logistic and Leudeking-Piret models were used for describing cell growth and PHB production, respectively. They showed good agreement with the experimental data describing both cell growth and PHB production (average regression coefficient r2:0.999). The growth-associated production of PHB biopolymer as an electron acceptor was confirmed using Leudeking-Piret model and victim substrate experiment. The best method of recovery of PHB biopolymer was chemical digestion using sodium hypochlorite, since it produced the largest amount of polymer and highest molecular weight (16,000g/mole) in comparison to other recovery methods. DTNB assay showed high activity of PHA synthase enzyme, 600U activity, and 153.8U/mg specific activity. Molecular analysis of PHA synthase enzyme confirmed class III identity. Taken together, micelle model was proposed for polyhydroxybutyrate formation in A. baumannii isolate P39.

Bioprocess development and preservation of functional food from tomato seed isolate fermented by kefir culture mixture.

The aim of this work was to explore the use of protein isolate from tomato seed enriched with the sucrose and the ascorbic acid as a medium for the growth of kefir mixture culture to develop a new non-dairy functional food. Unstructured mathematical and logistic models were proposed to describe cell growth, kefiran production, nutriment consumption and antioxidant activity. It was found that the maximal cell mass in the culture reached 8.38gL-1 after 24h of fermentation. A significant amount of kefiran was also produced (0.65gL-1). The kefir culture growth significantly decreased protein content and enhanced the antioxidant activity during varied fermentation through the production of bio active peptides. After 24h of fermentation, IC50 value for protein isolate was estimated to be about 10.48µgmL-1. The proposed models adequately described the changes during fermentation and as observed as a promising approach for the formulation of tomato seed-based functional foods. The preservation of the isolate was also investigated through a spray-drying process. The effect of spray-drying on the viability of lactic acid bacteria and stability of protein content and the antioxidant activity of the powder was also carried out. Results showed that the spray-drying method has great potential for the synthesis of powder from the fermented isolate that are rich in desirable properties. However, it was appropriate to preserve the powder for 10days at 37°C for the preservation of protein functionality.

Modeling shear-sensitive dinoflagellate microalgae growth in bubble column photobioreactors

The shear-sensitive dinoflagellate microalga Karlodinium veneficum was grown in a sparged bubble column photobioreactor. The influence of mass transfer and shear stress on cell growth and physiology (concentration of reactive oxygen species, membrane fluidity and photosynthetic efficiency) was studied, and a model describing cell growth in term of mass transfer and culture parameters (nozzle sparger diameter, air flow rate, and culture height) was developed. The results show that mass transfer limits cell growth at low air-flow rates, whereas the shear stress produced by the presence of bubbles is critically detrimental for air flow rates above 0.1vvm. The model developed in this paper adequately represents the growth of K. veneficum. Moreover, the parameters of the model indicate that bubble rupture is much more harmful for cells than bubble formation.

Integrated ABE fermentation-gas stripping process for enhanced butanol production from sugarcane-sweet sorghum juices

The biobutanol production by ABE (acetone-butanol-ethanol) fermentation with an integrated product recovery system by gas stripping was studied. A mixture of industrial sugarcane-sweet sorghum juices was fermented using Clostridium acetobutylicum DSM 792. Empirical models were developed to determine an operational condition for the integrated process that contributes both to mitigate the butanol inhibitory effect and to obtain a highly concentrated butanol condensate. A Monod model supplemented with a term describing product inhibition was employed to describe cell growth, butanol formation, and substrate consumption, respectively. Gas stripping was described by a first order model. The models showed satisfactory agreement with the experimental data in terms of cell growth, sugar consumption, and butanol production and extraction. A gas recycle flowrate in the range 0.3–0.6 vvm allowed to maintain butanol concentration below the inhibitory concentration (8 g/L) and to obtain a concentrated butanol condensate after phase separation, which could reduce energy consumption in the final product recovery. In a fed-batch fermentation coupled with in situ gas stripping, total sugar conversion and 18.6 g/L butanol distributed 42% in the fermentation broth and 58% in the condensate, were obtained.

Quantitative assessment of the impact of the type of inoculum on the kinetics of cell growth, substrate consumption and pigment productivity by Penicillium purpurogenum GH2 in liquid culture with an integrated stochastic approach

Natural ingredients are in demand and microbial processes to produce biocolourings are thus being developed. Penicillium spp. is particularly interesting for food applications. While microbial growth would be enhanced by using mycelium as inoculum, studies aimed at optimising pigments yields produced by Penicillium spp. have used spores as inoculum. A quantitative comparison of the pre-culture method for producing pigments by P. purpurogenum GH2 is provided in this work. Data analysis showed that simple growth models do not provide a suitable description of the fermentation process with mycelium as inoculum, which requires accounting for the effect of cell death. Probability density functions were applied to describe cell growth, substrate consumption and pigment production jointly and accurately. Specific production was 51% higher using mycelium as inoculum instead of spores, reaching a highest production of pigments with 10% (v/v) initial concentration being optimal (10.60±0.40 units of Optical Density at 500nm). Productivity of pigments per hour was 55% higher using mycelium as inoculum compared to spores. Three stochastic distributions provided equally good quantifications of all data (normal, gamma and Weibull). The three variables could be described by a same distribution function, with biomass being related to both live cells and accumulated dead residue.

Kinetic Modeling of Ethanol Batch Fermentation by Escherichia Coli FBWHR Using Hot-Water Sugar Maple Wood Extract Hydrolyzate as Substrate

A recombinant strain of Escherichia coli FBWHR was used for ethanol fermentation from hot-water sugar maple wood extract hydrolyzate in batch experiments. Kinetic studies of cell growth, sugar utilization and ethanol production were investigated at different initial total sugar concentrations of wood extract hydrolyzate. The highest ethanol concentration of 24.05 g/L was obtained using an initial total sugar concentration of 70.30 g/L. Unstructured models were developed to describe cell growth, sugar utilization and ethanol production and validated by comparing the predictions of model and experimental data. The results from this study could be expected to provide insights into the process performance, optimize the process and aid in the design of processes for large-scale production of ethanol fermentation from woody biomass.

Modeling of Clostridium tyrobutyricum for Butyric Acid Selectivity in Continuous Fermentation

A mathematical model was developed to describe batch and continuous fermentation of glucose to organic acids with Clostridium tyrobutyricum. A modified Monod equation was used to describe cell growth, and a Luedeking-Piret equation was used to describe the production of butyric and acetic acids. Using the batch fermentation equations, models predicting butyric acid selectivity for continuous fermentation were also developed. The model showed that butyric acid production was a strong function of cell mass, while acetic acid production was a function of cell growth rate. Further, it was found that at high acetic acid concentrations, acetic acid was metabolized to butyric acid and that this conversion could be modeled. In batch fermentation, high butyric acid selectivity occurred at high initial cell or glucose concentrations. In continuous fermentation, decreased dilution rate improved selectivity; at a dilution rate of 0.028 h−1, the selectivity reached 95.8%. The model and experimental data showed that at total cell recycle, the butyric acid selectivity could reach 97.3%. This model could be used to optimize butyric acid production using C. tyrobutyricum in a continuous fermentation scheme. This is the first study that mathematically describes batch, steady state, and dynamic behavior of C. tyrobutyricum for butyric acid production.

Central Upwind Scheme for Solving Multivariate Cell Population Balance Models

Microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins and other constituents. Because of the included level of details, multi-variable cell population balance models (PBMs) offer the most general way to describe the complicated phenomena associated with cell growth, substrate consumption and product formation. For that reason, solving and understanding of such models are essential to predict and control cell growth in the processes of biotechnological interest. Such models typically consist of a partial integro-differential equation for describing cell growth and an ordinary integro-differential equation for representing substrate consumption. However, the involved mathematical complexities make their numerical solutions challenging for the given numerical scheme. In this article, the central upwind scheme is applied to solve the single-variate and bivariate cell population balance models considering equal and unequal partitioning of cellular materials. The validity of the developed algorithms is verified through several case studies. It was found that the suggested scheme is more reliable and effective.

Abstract A10: In silico design of biomarker-optimized drug combinations in ERBB2+ cancers

Abstract Crosstalk and compensatory circuits within cancer signaling networks fundamentally limit the activity of targeted therapies. Combination drug regimens are thus required to fully inhibit an oncogenic network. However, rationally designing optimal combinations, given the large number of targeted agents available remains a challenge. Previously, we developed a multi-scale systems model of ERBB2-high breast cancer to quantitatively interrogate relationships between biomarkers (proxies for the mode of network activation) and combination drug efficacy. We found that targeting the ERBB2-ERBB3 heterodimer with a combination of ERBB2 inhibitors (trastuzumab and lapatinib) and an ERBB3 inhibitor (MM-111) was more effective at inducing tumor regression than the combination of an AKT and MEK inhibitor (MK2206 and GSK1120212), and was significantly better tolerated. However, the model was parameterized based on data collected from one cell line. Here we have performed profiling of an 18-cell line panel to assess whether our model and results are extendable to other ERBB2-high cancers. We monitored cell viability and signaling events upon AKT and MEK inhibitor treatments in the presence or absence of the ERBB3 ligand heregulin in these cell lines. While all cells were ERBB2+, we observed widely variable phenotypic and signaling dependencies on the PI3K/AKT and MAPK/ERK pathway activities across the panel. At the phenotypic level, cells primarily depend on either AKT or ERK signaling in basal conditions. Interestingly, upon heregulin stimulation some cells lines switch pathway dependency from AKT to ERK. Adaptive feedback circuits downstream of ERK and AKT were identified in all cell lines, though the identity and strength vary extensively. ERBB3 signaling and total AKT were consistently up-regulated to various degrees upon AKT inhibitor treatment. In contrast, multiple ERBB receptors as well as other RTKS, AKT signaling, and total ERK were upregulated in the response to MEK inhibition. While it is plausible that signaling pathways beyond AKT and ERK are modulating cell viability, we are able to quantitatively describe cell growth regulation based on AKT and ERK pathway activity using quantitative logic-based modeling framework. Our results will help us better understand how signaling events are decoded by cancer cells into phenotypic responses, and enable in silico drug combination screening across molecularly and functionally heterogeneous cancers. Citation Format: Jinyan Du, Daniel Kirouac, Johanna Lahdenranta, Ryan Overland, Matthew Onsum, Charlotte McDonagh. In silico design of biomarker-optimized drug combinations in ERBB2+ cancers. [abstract]. In: Proceedings of the Third AACR International Conference on Frontiers in Basic Cancer Research; Sep 18-22, 2013; National Harbor, MD. Philadelphia (PA): AACR; Cancer Res 2013;73(19 Suppl):Abstract nr A10.

High Resolution Finite Volume Schemes for Solving Multivariable Biological Cell Population Balance Models

Multivariable cell population balance models are commonly used to explain complicated biological phenomena associated with product formation and cell growth in microbial populations. Such models typically consist of a partial integrodifferential equation for describing cell growth and an ordinary integrodifferential equation for representing substrate consumption. Due to their mathematical complexities, the numerical solutions of such models are hard tasks for numerical schemes. In this article, semidiscrete high resolution flux-limiting finite volume schemes are applied to solve single-variate and bivariate cell population balance models. The schemes have the abilities to achieve narrow peaks and resolve sharp discontinuities in the solutions on coarse meshes. These schemes are cheaper due to their short and reliable computational coding for complex problems. Several case studies are carried out. The numerical results of the schemes are compared with each other in terms of CPU time and accuracy. After consideration of different growth rate functions and incorporating equal and unequal partitioning, the suggested schemes were found to be more reliable and effective.

Presentations from the 10th International Conference on Zebrafish Development and Genetics, Madison, Wisconsin, June 20–24, 2012

Presentations from the 10th International Conference on Zebrafish Development and Genetics, Madison, Wisconsin, June 20–24, 2012

A combined computational/experimental strategy to map phosphatases on growth pathways

Motivations. Large-scale screenings allow linking the function of poorly characterized genes to phenotypic readouts. According to this strategy, genes are associated to a function of interest if the alteration of their expression perturbs the phenotypic readouts. However, given the intricacy of the cell regulatory network, such screenings often provide mostly qualitative results, as it is difficult to identify the molecular mechanisms underlying the observed phenotype. In recent years computational modeling has emerged as a powerful tool to investigate biological signaling. In general these model are necessarily limited in size due to their complexity. Conversely large-scale perturbation screenings provide a higher-level view of a larger number of proteins. In this work we aim to bridge the gap between these two worlds in order to obtain a higher-detail mapping of gene products onto complex pathways on a large scale. This strategy was applied to map the poorly characterized family of human phosphatases on pathways related to cell growth. Methods. The strategy we have developed is based on two complementary datasets, which are conceptually linked by "sentinel proteins". These are defined as a number of molecular readouts that define the state of the cell, given specific experimental conditions. The datasets we used are: 1) A detailed mechanistic model describing a pathway of interest. The model includes as entities the sentinel proteins. This is constructed starting from low/medium-throughput experiments. 2) A high-throughput perturbation screening where the molecular readout is defined by the sentinel proteins. The core of the strategy is to use the signaling model to simulate the result of up/down regulating each protein in the signaling pathway. Each perturbation results in a predicted cell state defined as the calculated activity of the sentinel proteins. By matching this signature with the experimental profile obtained in the high-throughput screening it is possible to infer the target and effect (activating/inhibitory) of each protein screened, thus defining its "entry-point" in the network. For instance, if the silencing of a gene results in the same cell state obtained when protein A is up-regulated in the simulation, we can infer that the gene modulates the activity of protein A. The biological modeling was performed using CellNetOptimizer. This software allows the construction of boolean logic models which are represented as signed direct graphs, representing activating/inhibitory relationship between proteins. CNO then optimizes the topology of the model against a dataset of experimental data in order to remove connections that are not relevant in the specific cell system and to integrate using AND/OR logic gates multiple stimuli acting on the same protein. The optimization procedure is repeated 1000 times both because of its stochastic nature and also because the training data are generally not sufficient to fully constrain the model. By performing subsequent simulations on this family of 1000 models it is possible to average out the inconsistencies present in any single model. Moreover by this approach one obtains quantitative predictions even though a given node can only be on (1) or off (0) in any single model. We also extended this strict boolean approach to allow the simulation of three different states for a protein, i.e. control, up and down regulation. Results. We assembled from the literature a network describing cell growth pathways. This model includes 34 species and 59 stimulatory or inhibitory interactions and was optimized using CNO against a dataset of experimental data obtained in HeLa cells. The model also includes five sentinel proteins, whose activation status defines the "cell state". We used the results of a siRNA screening of the human phosphatase family that yielded 58 proteins whose silencing affect the activity of one of the sentinel proteins. The interference of 35 of the 58 phosphatase hits (60%) results in a profile that matches one of those inferred by inactivating or activating in silico one of the nodes of the model. The correctness of this mapping, and thus the predictive capabilities of the model, was demonstrated in a number of experiments. In particular one experiment confirmed that the over-expression of four phosphatases, which were mapped to different positions of the signaling network (i.e. upstream and downstream of AKT and ERK), differentially affect the activity of two readouts (RAF1 and AKT activation) that were not considered in the mapping procedure. In conclusion in this study we developed a novel strategy to map perturbations screening onto complex pathways. The proposed mapping strategy is general and could be used in combination with the results of such large screenings to achieve a more detailed mechanistic description of the molecular mechanisms by which genes or small molecules determine phenotype modulation.