Stability analysis of the GAL regulatory network in Saccharomyces cerevisiae and Kluyveromyces lactis

<p>Abstract</p> <p>Background</p> <p>In the yeast <it>Saccharomyces cerevisiae</it>, interactions between galactose, Gal3p, Gal80p, and Gal4p determine the transcriptional status of the genes required for the galactose utilization. Increase in the cellular galactose concentration causes the galactose molecules to bind onto Gal3p which, via Gal80p, activates Gal4p, which induces the GAL3 and GAL80 gene transcription. Recently, a linear time-invariant multi-input multi-output (MIMO) model of this GAL regulatory network has been proposed; the inputs being galactose and Gal4p, and the outputs being the active Gal4p and galactose utilization. Unfortunately, this model assumes the cell culture to be homogeneous, although it is not so in practice. We overcome this drawback by including more biochemical reactions, and derive a quadratic ordinary differential equation (ODE) based model.</p> <p>Results</p> <p>We show that the model, referred to above, does not exhibit bistability. We establish sufficiency conditions for the domain of attraction of an equilibrium point of our ODE model for the special case of full-state feedback controller. We observe that the GAL regulatory system of <it>Kluyveromyces lactis </it>exhibits an aberration of monotone nonlinearity and apply the Rantzer multipliers to establish a class of stabilizing controllers for this system.</p> <p>Conclusion</p> <p>Feedback in a GAL regulatory system can be used to enhance the cellular memory. We show that the system can be modeled as a quadratic nonlinear system for which the effect of feedback on the domain of attraction of the equilibrium point can be characterized using <it>linear matrix inequality </it>(LMI) conditions that are easily implementable in software. The benefit of this result is that a mathematically sound approach to the synthesis of full-state and partial-state feedback controllers to regulate the cellular memory is now possible, irrespective of the number of state-variables or parameters of interest.</p

A steady state model for the transcriptional regulation of filamentous growth in Saccharomyces cerevisiae

Occurrence of multiple Upstream Activation Sites (UASs) is a structural motif that is observed within the promoter of eukaryotic genes for coordinating gene expression. Transcriptional activation depends on the ability of transcriptional activators to bind to its specific UASs, which are kept inaccessible due to the nucleosomal organization of the chromatin. Targeting of chromatin remodeling complexes by a sequence specific transcriptional activator is shown to be detrimental for transcriptional initiation. Here, we analyze such a regulatory structure involving ordered recruitment of transcriptional activators and chromatin remodeling complexes with respect to activation of a flocculin gene, FLO11 involved in the filamentous growth to gain insights into its regulation. We develop a steady state model for the transcriptional regulation of FLO11 by primary transcriptional activators Flo8p, Ste12p, Tec1p and Mss11p, which are under a complex network comprising of cAMP and MAPK pathways. Our analysis predicts that the FLO11 promoter should undergo varying chromatin remodeling activity from partial to complete disassembly depending upon the concentration of Ste12p. This variation should be sensitive and sharply shift to saturate with Ste12p concentration. Overexpression of Ste12p can increase the overall chromatin remodeling activity by increasing the local concentration of remodeling complex through active recruitment. Further, we demonstrate that the chromatin remodeling activity brings about amplification of cAMP and MAPK signal and in absence of either of the signals, the input signal required for the other increases. We also discuss the results obtained from our steady state analysis in respect to other eukaryotic genes

Elementary mode analysis to study the preculturing effect on the metabolic state of Lactobacillus rhamnosus during growth on mixed substrates

Quantification of metabolism through elementary modes offers insights into the working of a metabolic network. We have determined the fluxes of elementary modes through linear optimization using the stoichiometry of the elementary modes as a constraint. We apply this methodology to obtain insights into the effect of preculturing on growth of Lactobacillus rhamnosus on medium containing mixed substrates. L. rhamnosus, a microaerophilic organism, produces flavor compounds such as diacetyl and acetoin during growth on glucose and citrate. The uptake of citrate has been shown to be sensitive to preculturing states of the cells. Elementary modes demonstrated that citrate was utilized by the organism as a sole carbon source. Further, both glucose and citrate was catabolized by this organism through aerobic and anaerobic routes. The flux analysis indicated that only 21 elementary modes were operational during growth of L. rhamnosus on glucose and citrate. Glucose specifically accounted for 6 elementary modes, while the remaining 15 involved citrate as substrate. The modes associated with glucose were mainly operational when cells were precultured on glucose. It was observed that all the 21 modes contributed to the fluxes when the cells were precultured on citrate. The NADH recycling through lactate formation and oxygen uptake were dependent on the preculturing state. The analysis also demonstrated that preculturing on citrate yielded better productivity of diacetyl and acetoin

Constraints based analysis of extended cybernetic models

The cybernetic modeling framework provides an interesting approach to model the regulatory phenomena occurring in microorganisms. In the present work, we adopt a constraints based approach to analyze the nonlinear behavior of the extended equations of the cybernetic model. We first show that the cybernetic model exhibits linear growth behavior under the constraint of no resource allocation for the induction of the key enzyme. We then quantify the maximum achievable specific growth rate of microorganisms on mixtures of substitutable substrates under various kinds of regulation and show its use in gaining an understanding of the regulatory strategies of microorganisms. Finally, we show that Saccharomyces cerevisiae exhibits suboptimal dynamic growth with a long diauxic lag phase when growing on a mixture of glucose and galactose and discuss on its potential to achieve optimal growth with a significantly reduced diauxic lag period. The analysis carried out in the present study illustrates the utility of adopting a constraints based approach to understand the dynamic growth strategies of microorganisms. (C) 2015 Elsevier Ireland Ltd. All rights reserved

Experimental and steady-state analysis of the GAL regulatory system in Kluyveromyces lactis

The galactose uptake mechanism in yeast is a well-studied regulatory network. The regulatory players in the Galactose Regulatory Mechanism (GAL system) are conserved in Saccharomyces cerevisiae and Kluyveromyces lactis, but the molecular mechanisms that occur as a result of the molecular interactions between them are different. The key differences in the GAL system of K. lactis relative to that of S. cerevisiae are: (a) the autoregulation of KlGAL4; (b) the dual role of KlGal1p as a metabolizing enzyme as well as a galactose-sensing protein; (c) the shuttling of KlGal1p between nucleus and cytoplasm; and (d) the nuclear confinement of KlGal80p. A steady-state model was used to elucidate the roles of these molecular mechanisms in the transcriptional response of the GAL system. The steady-state results were validated experimentally using measurements of &#946;-galactosidase to represent the expression for genes having two binding sites. The results showed that the autoregulation of the synthesis of activator KlGal4p is responsible for the leaky expression of GAL genes, even at high glucose concentrations. Furthermore, GAL gene expression in K. lactis shows low expression levels because of the limiting function of the bifunctional protein KlGal1p towards the induction process in order to cope with the need for the metabolism of lactose/galactose. The steady-state model of the GAL system of K. lactis provides an opportunity to compare with the design prevailing in S. cerevisiae. The comparison indicates that the existence of a protein, Gal3p, dedicated to the sensing of galactose in S. cerevisiae as a result of genome duplication has resulted in a system which metabolizes galactose efficiently

An optimal model for representing the kinetics of growth and product formation by Lactobacillus rhamnosus on multiple substrates

A comprehensive model was developed to simulate Lactobacillus rhamnosus growth on a medium containing multiple limiting carbon sources. The strategy of optimizing specific growth rate to predict growth on multiple substrates was demonstrated. The model predictions were based on parameters obtained from L. rhamnosus growth on individual substrates. The model was able to simulate the growth, substrate consumption, product formation and specific growth rate profiles of L. rhamnosus accurately. The model prediction that co-metabolism of glucose and pyruvate enhances growth rate of and flavor production by the bacterium was experimentally verified

Experimental investigation into indole production using passaging of E. coli and B. subtilis along with unstructured modeling and parameter estimation using dynamic optimization: An integrated framework

Greener process synthesis for drug production focuses on the replacement of chemical processes with biological processes. However, optimization of these processes remains challenging due to lack of experimental data, particularly for long term passaging and suitable parameter estimation techniques. In this context, we propose a mixed passaging scheme containing the co-culture of E. coli and B. subtilis that can be used to produce indole. The cell growth and product profiles obtained from fermenter corresponding to pure culture and individual culture obtained after passaging of E. coli and B. subtilis were used for unstructured model selection. Kinetic parameter estimation was performed using a hybrid technique combining classical and evolutionary algorithms. The dynamic interaction between the two organisms in long term passaging was captured by implementing optimal control for estimating time-varying parameters. The proposed unstructured model is capable of emulating the mixed culture effect on cell growth, product formation, and substrate depletion. This is the first instance where an integrated framework combining experimental and computational methodology is used to estimate and analyse model parameters in a mixed culture of E. coli and B. subtilis towards indole production. A similar framework can be used for optimizing the process parameters to improve the indole production

Designing Antibacterial Peptides with Enhanced Killing Kinetics

Antimicrobial peptides (AMPs) are gaining attention as substitutes for antibiotics in order to combat the risk posed by multi-drug resistant pathogens. Several research groups are engaged in design of potent anti-infective agents using natural AMPs as templates. In this study, a library of peptides with high sequence similarity to Myeloid Antimicrobial Peptide (MAP) family were screened using popular online prediction algorithms. These peptide variants were designed in a manner to retain the conserved residues within the MAP family. The prediction algorithms were found to effectively classify peptides based on their antimicrobial nature. In order to improve the activity of the identified peptides, molecular dynamics (MD) simulations, using bilayer and micellar systems could be used to design and predict effect of residue substitution on membranes of microbial and mammalian cells. The inference from MD simulation studies well corroborated with the wet-lab observations indicating that MD-guided rational design could lead to discovery of potent AMPs. The effect of the residue substitution on membrane activity was studied in greater detail using killing kinetic analysis. Killing kinetics studies on Gram-positive, negative and human erythrocytes indicated that a single residue change has a drastic effect on the potency of AMPs. An interesting outcome was a switch from monophasic to biphasic death rate constant of Staphylococcus aureus due to a single residue mutation in the peptide

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<p>Antimicrobial peptides (AMPs) are gaining attention as substitutes for antibiotics in order to combat the risk posed by multi-drug resistant pathogens. Several research groups are engaged in design of potent anti-infective agents using natural AMPs as templates. In this study, a library of peptides with high sequence similarity to Myeloid Antimicrobial Peptide (MAP) family were screened using popular online prediction algorithms. These peptide variants were designed in a manner to retain the conserved residues within the MAP family. The prediction algorithms were found to effectively classify peptides based on their antimicrobial nature. In order to improve the activity of the identified peptides, molecular dynamics (MD) simulations, using bilayer and micellar systems could be used to design and predict effect of residue substitution on membranes of microbial and mammalian cells. The inference from MD simulation studies well corroborated with the wet-lab observations indicating that MD-guided rational design could lead to discovery of potent AMPs. The effect of the residue substitution on membrane activity was studied in greater detail using killing kinetic analysis. Killing kinetics studies on Gram-positive, negative and human erythrocytes indicated that a single residue change has a drastic effect on the potency of AMPs. An interesting outcome was a switch from monophasic to biphasic death rate constant of Staphylococcus aureus due to a single residue mutation in the peptide.</p

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<p>Antimicrobial peptides (AMPs) are gaining attention as substitutes for antibiotics in order to combat the risk posed by multi-drug resistant pathogens. Several research groups are engaged in design of potent anti-infective agents using natural AMPs as templates. In this study, a library of peptides with high sequence similarity to Myeloid Antimicrobial Peptide (MAP) family were screened using popular online prediction algorithms. These peptide variants were designed in a manner to retain the conserved residues within the MAP family. The prediction algorithms were found to effectively classify peptides based on their antimicrobial nature. In order to improve the activity of the identified peptides, molecular dynamics (MD) simulations, using bilayer and micellar systems could be used to design and predict effect of residue substitution on membranes of microbial and mammalian cells. The inference from MD simulation studies well corroborated with the wet-lab observations indicating that MD-guided rational design could lead to discovery of potent AMPs. The effect of the residue substitution on membrane activity was studied in greater detail using killing kinetic analysis. Killing kinetics studies on Gram-positive, negative and human erythrocytes indicated that a single residue change has a drastic effect on the potency of AMPs. An interesting outcome was a switch from monophasic to biphasic death rate constant of Staphylococcus aureus due to a single residue mutation in the peptide.</p