Faithful modeling of transient expression and its application to elucidating negative feedback regulation

NF-κB signaling is the major inflammatory pathway for inducing insulin resistance

Insulin resistance is major factor in the development of metabolic syndrome and type 2 diabetes (T2D). We extracted 430 genes from literature associated with both insulin resistance and inflammation. The highly significant pathways were Toll-like receptor signaling, PI3K-Akt signaling, cytokine-cytokine receptor interaction, pathways in cancer, TNF signaling, and NF-kappa B signaling. Among the 297 common genes in all datasets of various T2D patients' tissues including blood, muscle, liver, pancreas, and adipose tissues, 71% and 60% of these genes were differentially expressed in pancreas (GSE25724) and liver (GSE15653), respectively. A total of 169 genes contain highly conserved motifs for various transcription factors involved in immune response, thereby suggesting coordinated expression. Through co-expression analysis, we obtained three modules. The respective modules had 78, 158, and 55 genes, and TRAF2, HMGA1, and RGS5 as hub genes. Further, we used the BioNSi pathways simulation tool and identified the following five KEGG pathways perturbed in four or more tissues, namely Toll-like receptor signaling pathway, RIG-1-like receptor signaling pathway, pathways in cancer, NF-kappa B signaling pathway, and insulin resistance pathway. The genes NFKBIA and IKBKB are common to all these five pathways. In addition, using the NF-κB computational activation model, we identified that the reversal of NF-κB constitutive activation through overexpression of NFKB1 (P50 homodimer), PPARG, PIAS3 could reduce insulin resistance by almost half of its original value. To conclude, co-expression studies, gene expression network simulation, and NF-κB computational modeling substantiate the causal role of NF-κB pathway in insulin resistance. These results taken together with other published evidence suggests that the TNF-TRAF2-IKBKB-NF-κB axis could be explored as a potential target in combination with available metabolic targets in the management of insulin resistance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04202-4.

A Note on GRegNetSim: A Tool for the Discrete Simulation and Analysis of Genetic Regulatory Networks

Discrete simulations of genetic regulatory networks were used to study subsystems of yeast successfully. However, implementations of existing models underlying these simulations do not support a graphic interface, and require computations necessary to analyze their results to be done manually. Furthermore, differences between existing models suggest that an enriched model, encompassing both existing models, is needed. We developed a software tool, GRegNetSim, that allows the end-user to describe genetic regulatory networks graphically. The user can specify various transition functions at different nodes of the network, supporting, for example, threshold and gradient effects, and then apply the network to a variety of inputs. GRegNetSim displays the relationship between the inputs and the mode of behavior of the network in a graphic form that is easy to interpret. Furthermore, it can automatically extract statistical data necessary to analyze the simulations. The discrete simulations performed by GRegNetSim can be used to elucidate and predict the behavior, structure and properties of genetic regulatory networks in a unified manner. GRegNetSim is implemented as a Cytoscape App. Installation files, examples and source code, along with a detailed user guide, are freely available at https://sites.google.com/site/gregnetsim/.

Interactions between the circadian clock and TGF-β signaling pathway in zebrafish

Background

TGF-β signaling is a cellular pathway that functions in most cells and has been shown to play a role in multiple processes, such as the immune response, cell differentiation and proliferation. Recent evidence suggests a possible interaction between TGF-β signaling and the molecular circadian oscillator. The current study aims to characterize this interaction in the zebrafish at the molecular and behavioral levels, taking advantage of the early development of a functional circadian clock and the availability of light-entrainable clock-containing cell lines.

Results

Smad3a, a TGF-β signaling-related gene, exhibited a circadian expression pattern throughout the brain of zebrafish larvae. Both pharmacological inhibition and indirect activation of TGF-β signaling in zebrafish Pac-2 cells caused a concentration dependent disruption of rhythmic promoter activity of the core clock gene Per1b. Inhibition of TGF-β signaling in intact zebrafish larvae caused a phase delay in the rhythmic expression of Per1b mRNA. TGF-β inhibition also reversibly disrupted, phase delayed and increased the period of circadian rhythms of locomotor activity in zebrafish larvae.

Conclusions

The current research provides evidence for an interaction between the TGF-β signaling pathway and the circadian clock system at the molecular and behavioral levels, and points to the importance of TGF-β signaling for normal circadian clock function. Future examination of this interaction should contribute to a better understanding of its underlying mechanisms and its influence on a variety of cellular processes including the cell cycle, with possible implications for cancer development and progression.

Family or career? I want both-the control of meiosis

In this paper I describe my professional and personal journey in science. In the 20th century there were fewer women scientists than man scientists. My personal experience and opinion is that women avoided academic careers. How one can combine family and career is discussed. The interest in science and the interactions I had with prominent leading Yeast Scientists changed my point of view, I matured and developed an academic career. My research focused on how budding yeast cells chose to exit the cell cycle and enter meiosis. My journey started using classical Genetic techniques. The development of Genetic engineering techniques enabled us to verify models and elucidate how entry into meiosis is controlled.

Simulation and visualization of multiple KEGG pathways using BioNSi

Motivation: Many biologists are discouraged from using network simulation tools because these require manual, often tedious network construction. This situation calls for building new tools or extending existing ones with the ability to import biological pathways previously deposited in databases and analyze them, in order to produce novel biological insights at the pathway level.

Results: We have extended a network simulation tool (BioNSi), which now allows merging of multiple pathways from the KEGG pathway database into a single, coherent network, and visualizing its properties. Furthermore, the enhanced tool enables loading experimental expression data into the network and simulating its dynamics under various biological conditions or perturbations. As a proof of concept, we tested two sets of published experimental data, one related to inflammatory bowel disease condition and the other to breast cancer treatment. We predict some of the major observations obtained following these laboratory experiments, and provide new insights that may shed additional light on these results.

Tool requirements: Cytoscape 3.x, JAVA 8

Availability: The tool is freely available at http://bionsi.wix.com/bionsi, where a complete user guide and a step-by-step manual can also be found.

A Computational Approach to Study Gene Expression Networks

We describe a simple computational approach that can be used for the study and simulation of regulatory networks. The advantage of this approach is that it requires neither computational background nor exact quantitative data about the biological system under study. Moreover, it is suitable for examining alternative hypotheses about the structure of a biological network. We used a tool called BioNSi (Biological Network Simulator) that is based on a simple computational model, which can be easily integrated as part of the lab routine, in parallel to experimental work. One benefit of this approach is that it enables the identification of regulatory proteins, which are missing from the experimental work. We describe the general methodology for modeling a network's dynamics in the tool, and then give a point by point example for a specific known network, entry into meiosis in budding yeast.

BioNSi: A Discrete Biological Network Simulator Tool

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

Cell Differentiation and Spatial Organization in Yeast Colonies: Role of Cell-Wall Integrity Pathway

Many microbial communities contain organized patterns of cell types, yet relatively little is known about the mechanism or function of this organization. In colonies of the budding yeast Saccharomyces cerevisiae, sporulation occurs in a highly organized pattern, with a top layer of sporulating cells sharply separated from an underlying layer of nonsporulating cells. A mutant screen identified the Mpk1 and Bck1 kinases of the cell-wall integrity (CWI) pathway as specifically required for sporulation in colonies. The CWI pathway was induced as colonies matured, and a target of this pathway, the Rlm1 transcription factor, was activated specifically in the nonsporulating cell layer, here termed feeder cells. Rlm1 stimulates permeabilization of feeder cells and promotes sporulation in an overlying cell layer through a cell-nonautonomous mechanism. The relative fraction of the colony apportioned to feeder cells depends on nutrient environment, potentially buffering sexual reproduction against suboptimal environments.

The meiotic-mitotic initiation switch in budding yeast maintains its function robustly against sensitive parameter perturbations

Experiments show that the meiotic-mitotic initiation switch in budding yeast functions robustly during the early hours of meiosis initiation. In this study, we explain these experimental observations first by understanding how this switching occurs during the early hours of meiosis by studying the temporal variation of this switch at the gene expression level. Then, we investigate the effects on this meiotic-mitotic switching from the perturbations of the most sensitive parameters in budding yeast meiosis initiation network. We use a mathematical model of meiosis initiation in budding yeast for this task and find the most sensitive group of parameters that influence the expressions of meiosis and mitosis initiators at all stages of the meiotic-mitotic switch. The results indicate that the transition region of the switch, where a double negative feedback loop between meiosis (Ime2) and mitosis (Cdk1/Cln3) initiators plays a major role, shows lower robustness. Feedback loops are frequently observed serving as a major robust adaption mechanism in many biological networks. Consequences of this less robust region appear in the transition region of the resulting switches. Most importantly, despite the differences observed in the transition region, we find that the meiotic-mitotic switch robustly maintains its main function of transition from meiosis to mitosis when the nutrients are re-supplied, against the perturbations in the sensitive parameters.

Single nucleotide variants in transcription factors associate more tightly with phenotype than with gene expression

Mapping the polymorphisms responsible for variation in gene expression, known as Expression Quantitative Trait Loci (eQTL), is a common strategy for investigating the molecular basis of disease. Despite numerous eQTL studies, the relationship between the explanatory power of variants on gene expression versus their power to explain ultimate phenotypes remains to be clarified. We addressed this question using four naturally occurring Quantitative Trait Nucleotides (QTN) in three transcription factors that affect sporulation efficiency in wild strains of the yeast, Saccharomyces cerevisiae. We compared the ability of these QTN to explain the variation in both gene expression and sporulation efficiency. We find that the amount of gene expression variation explained by the sporulation QTN is not predictive of the amount of phenotypic variation explained. The QTN are responsible for 98% of the phenotypic variation in our strains but the median gene expression variation explained is only 49%. The alleles that are responsible for most of the variation in sporulation efficiency do not explain most of the variation in gene expression. The balance between the main effects and gene-gene interactions on gene expression variation is not the same as on sporulation efficiency. Finally, we show that nucleotide variants in the same transcription factor explain the expression variation of different sets of target genes depending on whether the variant alters the level or activity of the transcription factor. Our results suggest that a subset of gene expression changes may be more predictive of ultimate phenotypes than the number of genes affected or the total fraction of variation in gene expression variation explained by causative variants, and that the downstream phenotype is buffered against variation in the gene expression network.

A nutrient dependant switch explains mutually exclusive existence of meiosis and mitosis initiation in budding yeast

Nutrients from living environment are vital for the survival and growth of any organism. Budding yeast diploid cells decide to grow by mitosis type cell division or decide to create unique, stress resistant spores by meiosis type cell division depending on the available nutrient conditions. To gain a molecular systems level understanding of the nutrient dependant switching between meiosis and mitosis initiation in diploid cells of budding yeast, we develop a theoretical model based on ordinary differential equations (ODEs) including the mitosis initiator and its relations to budding yeast meiosis initiation network. Our model accurately and qualitatively predicts the experimentally revealed temporal variations of related proteins under different nutrient conditions as well as the diverse mutant studies related to meiosis and mitosis initiation. Using this model, we show how the meiosis and mitosis initiators form an all-or-none type bistable switch in response to available nutrient level (mainly nitrogen). The transitions to and from meiosis or mitosis initiation states occur via saddle node bifurcation. This bidirectional switch helps the optimal usage of available nutrients and explains the mutually exclusive existence of meiosis and mitosis pathways.

The roles of the catalytic and noncatalytic activities of Rpd3L and Rpd3S in the regulation of gene transcription in yeast

In budding yeasts, the histone deacetylase Rpd3 resides in two different complexes called Rpd3L (large) and Rpd3S (small) that exert opposing effects on the transcription of meiosis-specific genes. By introducing mutations that disrupt the integrity and function of either Rpd3L or Rpd3S, we show here that Rpd3 function is determined by its association with either of these complexes. Specifically, the catalytic activity of Rpd3S activates the transcription of the two major positive regulators of meiosis, IME1 and IME2, under all growth conditions and activates the transcription of NDT80 only during vegetative growth. In contrast, the effects of Rpd3L depends on nutrients; it represses or activates transcription in the presence or absence of a nitrogen source, respectively. Further, we show that transcriptional activation does not correlate with histone H4 deacetylation, suggesting an effect on a nonhistone protein. Comparison of rpd3-null and catalytic-site point mutants revealed an inhibitory activity that is independent of either the catalytic activity of Rpd3 or the integrity of Rpd3L and Rpd3S.

Multiple MAPK cascades regulate the transcription of IME1, the master transcriptional activator of meiosis in Saccharomyces cerevisiae

The choice between alternative developmental pathways is primarily controlled at the level of transcription. Induction of meiosis in budding yeasts in response to nutrient levels provides a system to investigate the molecular basis of cellular decision-making. In Saccharomyces cerevisiae, entry into meiosis depends on multiple signals converging upon IME1, the master transcriptional activator of meiosis. Here we studied the regulation of the cis-acting regulatory element Upstream Activation Signal (UAS)ru, which resides within the IME1 promoter. Guided by our previous data acquired using a powerful high-throughput screening system, here we provide evidence that UASru is regulated by multiple stimuli that trigger distinct signal transduction pathways as follows: (i) The glucose signal inhibited UASru activity through the cyclic AMP (cAMP/protein kinase A (PKA) pathway, targeting the transcription factors (TFs), Com2 and Sko1; (ii) high osmolarity activated UASru through the Hog1/mitogen-activated protein kinase (MAPK) pathway and its corresponding TF Sko1; (iii) elevated temperature increased the activity of UASru through the cell wall integrity pathway and the TFs Swi4/Mpk1 and Swi4/Mlp1; (iv) the nitrogen source repressed UASru activity through Sum1; and (v) the absence of a nitrogen source was detected and transmitted to UASru by the Kss1 and Fus3 MAPK pathways through their respective downstream TFs, Ste12/Tec1 and Ste12/Ste12 as well as by their regulators Dig1/2. These signaling events were specific to UASru; they did not affect the mating and filamentation response elements that are regulated by MAPK pathways. The complex regulation of UASru through all the known vegetative MAPK pathways is unique to S. cerevisiae and is specific for IME1, likely because it is the master regulator of gametogenesis.

The effective application of a discrete transition model to explore cell-cycle regulation in yeast

Background

Bench biologists often do not take part in the development of computational models for their systems, and therefore, they frequently employ them as “black-boxes”. Our aim was to construct and test a model that does not depend on the availability of quantitative data, and can be directly used without a need for intensive computational background.

Results

We present a discrete transition model. We used cell-cycle in budding yeast as a paradigm for a complex network, demonstrating phenomena such as sequential protein expression and activity, and cell-cycle oscillation. The structure of the network was validated by its response to computational perturbations such as mutations, and its response to mating-pheromone or nitrogen depletion. The model has a strong predicative capability, demonstrating how the activity of a specific transcription factor, Hcm1, is regulated, and what determines commitment of cells to enter and complete the cell-cycle.

Conclusion

The model presented herein is intuitive, yet is expressive enough to elucidate the intrinsic structure and qualitative behavior of large and complex regulatory networks. Moreover our model allowed us to examine multiple hypotheses in a simple and intuitive manner, giving rise to testable predictions. This methodology can be easily integrated as a useful approach for the study of networks, enriching experimental biology with computational insights.

Dynamic modeling of yeast meiotic initiation

Background

Meiosis is the sexual reproduction process common to eukaryotes. The diploid yeast Saccharomyces cerevisiae undergoes meiosis in sporulation medium to form four haploid spores. Initiation of the process is tightly controlled by intricate networks of positive and negative feedback loops. Intriguingly, expression of early meiotic proteins occurs within a narrow time window. Further, sporulation efficiency is strikingly different for yeast strains with distinct mutations or genetic backgrounds. To investigate signal transduction pathways that regulate transient protein expression and sporulation efficiency, we develop a mathematical model using ordinary differential equations. The model describes early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signaling molecules for regulating protein activities.

Results

The mathematical model is capable of simulating the orderly and transient dynamics of meiotic proteins including Ime1, the master regulator of meiotic initiation, and Ime2, a kinase encoded by an early gene. The model is validated by quantitative sporulation phenotypes of single-gene knockouts. Thus, we can use the model to make novel predictions on the cooperation between proteins in the signaling pathway. Virtual perturbations on feedback loops suggest that both positive and negative feedback loops are required to terminate expression of early meiotic proteins. Bifurcation analyses on feedback loops indicate that multiple feedback loops are coordinated to modulate sporulation efficiency. In particular, positive auto-regulation of Ime2 produces a bistable system with a normal meiotic state and a more efficient meiotic state.

Conclusions

By systematically scanning through feedback loops in the mathematical model, we demonstrate that, in yeast, the decisions to terminate protein expression and to sporulate at different efficiencies stem from feedback signals toward the master regulator Ime1 and the early meiotic protein Ime2. We argue that the architecture of meiotic initiation pathway generates a robust mechanism that assures a rapid and complete transition into meiosis. This type of systems-level regulation is a commonly used mechanism controlling developmental programs in yeast and other organisms. Our mathematical model uncovers key regulations that can be manipulated to enhance sporulation efficiency, an important first step in the development of new strategies for producing gametes with high quality and quantity.

Qualitative modelling and formal verification of the FLR1 gene mancozeb response in Saccharomyces cerevisiae

BACKGROUND

Qualitative models allow understanding the relation between the structure and the dynamics of gene regulatory networks. The dynamical properties of these models can be automatically analysed by means of formal verification methods, like model checking. This facilitates the model-validation process and the test of new hypotheses to reconcile model predictions with the experimental data.

RESULTS

The authors report in this study the qualitative modelling and simulation of the transcriptional regulatory network controlling the response of the model eukaryote Saccharomyces cerevisiae to the agricultural fungicide mancozeb. The model allowed the analysis of the regulation level and activity of the components of the gene mancozeb-induced network controlling the transcriptional activation of the FLR1 gene, which is proposed to confer multidrug resistance through its putative role as a drug eflux pump. Formal verification analysis of the network allowed us to confront model predictions with the experimental data and to assess the model robustness to parameter ordering and gene deletion.

CONCLUSIONS

This analysis enabled us to better understand the mechanisms regulating the FLR1 gene mancozeb response and confirmed the need of a new transcription factor for the full transcriptional activation of YAP1. The result is a computable model of the FLR1 gene response to mancozeb, permitting a quick and cost-effective test of hypotheses prior to experimental validation.

QUANTITATIVE MODELING OF THE SACCHAROMYCES CEREVISIAE FLR1 REGULATORY NETWORK USING AN S-SYSTEM FORMALISM

In this study we address the problem of finding a quantitative mathematical model for the genetic network regulating the stress response of the yeast Saccharomyces cerevisiae to the agricultural fungicide mancozeb. An S-system formalism was used to model the interactions of a five-gene network encoding four transcription factors (Yap1, Yrr1, Rpn4 and Pdr3) regulating the transcriptional activation of the FLR1 gene. Parameter estimation was accomplished by decoupling the resulting system of nonlinear ordinary differential equations into a larger nonlinear algebraic system, and using the Levenberg–Marquardt algorithm to fit the models predictions to experimental data. The introduction of constraints in the model, related to the putative topology of the network, was explored. The results show that forcing the network connectivity to adhere to this topology did not lead to better results than the ones obtained using an unrestricted network topology. Overall, the modeling approach obtained partial success when trained on the nonmutant datasets, although further work is required if one wishes to obtain more accurate prediction of the time courses.

Ime1 and Ime2 are required for pseudohyphal growth of Saccharomyces cerevisiae on nonfermentable carbon sources

Pseudohyphal growth and meiosis are two differentiation responses to nitrogen starvation of diploid Saccharomyces cerevisiae. Nitrogen starvation in the presence of fermentable carbon sources is thought to induce pseudohyphal growth, whereas nitrogen and sugar starvation induces meiosis. In contrast to the genetic background routinely used to study pseudohyphal growth (Σ1278b), nonfermentable carbon sources stimulate pseudohyphal growth in the efficiently sporulating strain SK1. Pseudohyphal SK1 cells can exit pseudohyphal growth to complete meiosis. Two stimulators of meiosis, Ime1 and Ime2, are required for pseudohyphal growth of SK1 cells in the presence of nonfermentable carbon sources. Epistasis analysis suggests that Ime1 and Ime2 act in the same order in pseudohyphal growth as in meiosis. The different behaviors of strains SK1 and Σ1278b are in part attributable to differences in cyclic AMP (cAMP) signaling. In contrast to Σ1278b cells, hyperactivation of cAMP signaling using constitutively active Ras2(G19V) inhibited pseudohyphal growth in SK1 cells. Our data identify the SK1 genetic background as an alternative genetic background for the study of pseudohyphal growth and suggest an overlap between signaling pathways controlling pseudohyphal growth and meiosis. Based on these findings, we propose to include exit from pseudohyphal growth and entry into meiosis in the life cycle of S. cerevisiae.

Functional dissection of IME1 transcription using quantitative promoter-reporter screening

Transcriptional regulation is a key mechanism that controls the fate and response of cells to diverse signals. Therefore, the identification of the DNA-binding proteins, which mediate these signals, is a crucial step in elucidating how cell fate is regulated. In this report, we applied both bioinformatics and functional genomic approaches to scrutinize the unusually large promoter of the IME1 gene in budding yeast. Using a recently described fluorescent protein-based reporter screen, reporter-synthetic genetic array (R-SGA), we assessed the effect of viable deletion mutants on transcription of various IME1 promoter-reporter genes. We discovered potential transcription factors, many of which have no perfect consensus site within the IME1 promoter. Moreover, most of the cis-regulatory sequences with perfect homology to known transcription factor (TF) consensus were found to be nonfunctional in the R-SGA analysis. In addition, our results suggest that lack of conservation may not discriminate against a TF regulatory role at a specific promoter. We demonstrate that Sum1 and Sok2, which regulate IME1, bind to nonperfect consensuses within nonconserved regions in the sensu stricto Saccharomyces strains. Our analysis supports the view that although comparative analysis can provide a useful guide, functional assays are required for accurate identification of TF-binding site interactions in complex promoters.

A switch from a gradient to a threshold mode in the regulation of a transcriptional cascade promotes robust execution of meiosis in budding yeast

Tight regulation of developmental pathways is of critical importance to all organisms, and is achieved by a transcriptional cascade ensuring the coordinated expression of sets of genes. We aimed to explore whether a strong signal is required to enter and complete a developmental pathway, by using meiosis in budding yeast as a model. We demonstrate that meiosis in budding yeast is insensitive to drastic changes in the levels of its consecutive positive regulators (Ime1, Ime2, and Ndt80). Entry into DNA replication is not correlated with the time of transcription of the early genes that regulate this event. Entry into nuclear division is directly regulated by the time of transcription of the middle genes, as premature transcription of their activator NDT80, leads to a premature entry into the first meiotic division, and loss of coordination between DNA replication and nuclear division. We demonstrate that Cdk1/Cln3 functions as a negative regulator of Ime2, and that ectopic expression of Cln3 delays entry into nuclear division as well as NDT80 transcription. Because Ime2 functions as a positive regulator for premeiotic DNA replication and NDT80 transcription, as well as a negative regulator of Cdk/Cln, we suggest that a double negative feedback loop between Ime2 and Cdk1/Cln3 promotes a bistable switch from the cell cycle to meiosis. Moreover, our results suggest a regulatory mode switch that ensures robust meiosis as the transcription of the early meiosis-specific genes responds in a graded mode to Ime1 levels, whereas that of the middle and late genes as well as initiation of DNA replication, are regulated in a threshold mode.

Toward the dynamic interactome: it's about time

Dynamic molecular interactions play a central role in regulating the functioning of cells and organisms. The availability of experimentally determined large-scale cellular networks, along with other high-throughput experimental data sets that provide snapshots of biological systems at different times and conditions, is increasingly helpful in elucidating interaction dynamics. Here we review the beginnings of a new subfield within computational biology, one focused on the global inference and analysis of the dynamic interactome. This burgeoning research area, which entails a shift from static to dynamic network analysis, promises to be a major step forward in our ability to model and reason about cellular function and behavior.

The combination of positive and negative feedback loops confers exquisite flexibility to biochemical switches

A wide range of cellular processes require molecular regulatory pathways to convert a graded signal into a discrete response. One prevalent switching mechanism relies on the coexistence of two stable states (bistability) caused by positive feedback regulations. Intriguingly, positive feedback is often supplemented with negative feedback, raising the question of whether and how these two types of feedback can cooperate to control discrete cellular responses. To address this issue, we formulate a canonical model of a protein-protein interaction network and analyze the dynamics of a prototypical two-component circuit. The appropriate combination of negative and positive feedback loops can bring a bistable circuit close to the oscillatory regime. Notably, sharply activated negative feedback can give rise to a bistable regime wherein two stable fixed points coexist and may collide pairwise with two saddle points. This specific type of bistability is found to allow for separate and flexible control of switch-on and switch-off events, for example (i) to combine fast and reversible transitions, (ii) to enable transient switching responses and (iii) to display tunable noise-induced transition rates. Finally, we discuss the relevance of such bistable switching behavior, and the circuit topologies considered, to specific biological processes such as adaptive metabolic responses, stochastic fate decisions and cell-cycle transitions. Taken together, our results suggest an efficient mechanism by which positive and negative feedback loops cooperate to drive the flexible and multifaceted switching behaviors arising in biological systems.