Negative feedback that improves information transmission in yeast signalling

Network motifs in cellular neurophysiology

Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease.

Evidence for Behavioral Autorepression in Covid-19 Epidemiological Dynamics

It has long been hypothesized that behavioral reactions to epidemic severity autoregulate infection dynamics, for example when susceptible individuals self-sequester based on perceived levels of circulating disease. However, evidence for such 'behavioral autorepression' has remained elusive, and its presence could significantly affect epidemic forecasting and interventions. Here, we analyzed early COVID-19 dynamics at 708 locations over three epidemiological scales (96 countries, 50 US states, and 562 US counties). Signatures of behavioral autorepression were identified through: (i) a counterintuitive mobility-death correlation, (ii) fluctuation-magnitude analysis, and (iii) dynamics of SARS-CoV-2 infection waves. These data enabled calculation of the average behavioral-autorepression strength (i.e., negative feedback 'gain') across different populations. Surprisingly, incorporating behavioral autorepression into conventional models was required to accurately forecast COVID-19 mortality. Models also predicted that the strength of behavioral autorepression has the potential to alter the efficacy of non-pharmaceutical interventions. Overall, these results provide evidence for the long-hypothesized existence of behavioral autorepression, which could improve epidemic forecasting and enable more effective application of non-pharmaceutical interventions during future epidemics.

Significance

Challenges with epidemiological forecasting during the COVID-19 pandemic suggested gaps in underlying model architecture. One long-held hypothesis, typically omitted from conventional models due to lack of empirical evidence, is that human behaviors lead to intrinsic negative autoregulation of epidemics (termed 'behavioral autorepression'). This omission substantially alters model forecasts. Here, we provide independent lines of evidence for behavioral autorepression during the COVID-19 pandemic, demonstrate that it is sufficient to explain counterintuitive data on 'shutdowns', and provides a mechanistic explanation of why early shutdowns were more effective than delayed, high-intensity shutdowns. We empirically measure autorepression strength, and show that incorporating autorepression dramatically improves epidemiological forecasting. The autorepression phenomenon suggests that tailoring interventions to specific populations may be warranted.

Cascaded amplifying circuit enables sensitive detection of fungal pathogens

The rapid and accurate detection of fungal pathogens is of utmost importance in the fields of healthcare, food safety, and environmental monitoring. In this study, we implemented a cascaded amplifying circuit in Saccharomyces cerevisiae to improve the G protein-coupled receptor (GPCR) mediated fungal detection. The GPCR signaling pathway was coupled with the galactose-regulated (GAL) system and a positive feedback loop was implemented to enhance the performance of yeast biosensor. We systematically compared four generations of biosensors for detecting the mating pheromone of Candida albicans, and the best biosensor exhibited the limit of detection (LOD) as low as 0.25 pM and the limit of quantification (LOQ) of 1 pM after 2 h incubation. Subsequently, we developed a betaxanthin-based colorimetric module for the easy visualization of signal outputs, and the resulting biosensors can give reliable naked-eye readouts. In summary, we demonstrated that cascaded amplifying circuits could substantially improve the engineered yeast biosensors with a better sensitivity and signal output magnitude, which will pave the way for their real-world applications in public health.

Design patterns of biological cells

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.

Reshaping the yeast galactose regulon via GPCR signaling cascade

Dynamically regulated systems are preferable to control metabolic pathways for an improved strain performance with better productivity. Here, we harnessed to the G protein-coupled receptor (GPCR) signaling pathway to reshape the yeast galactose regulon. The galactose-regulated (GAL) system was coupled with the GPCR signaling pathway for mating pheromone via a synthetic transcription factor. In this study, we refabricated the dynamic range, sensitivity, and response time of the GAL system to α factor by modulating the key components of the GPCR signaling cascade. A series of engineered yeasts with self-secretion of α factor were constructed to achieve quorum-sensing behaviors. In addition, we also repurposed the GAL system to make it responsive to heat shock. Taken together, our work showcases the great potential of synthetic biology in creating user-defined metabolic controls. We envision that the plasticity of our genetic design would be of significant interest for the future fabrication of novel gene expression systems.

Heterogeneous off-target impact of ion-channel deletion on intrinsic properties of hippocampal model neurons that self-regulate calcium

How do neurons that implement cell-autonomous self-regulation of calcium react to knockout of individual ion-channel conductances? To address this question, we used a heterogeneous population of 78 conductance-based models of hippocampal pyramidal neurons that maintained cell-autonomous calcium homeostasis while receiving theta-frequency inputs. At calcium steady-state, we individually deleted each of the 11 active ion-channel conductances from each model. We measured the acute impact of deleting each conductance (one at a time) by comparing intrinsic electrophysiological properties before and immediately after channel deletion. The acute impact of deleting individual conductances on physiological properties (including calcium homeostasis) was heterogeneous, depending on the property, the specific model, and the deleted channel. The underlying many-to-many mapping between ion channels and properties pointed to ion-channel degeneracy. Next, we allowed the other conductances (barring the deleted conductance) to evolve towards achieving calcium homeostasis during theta-frequency activity. When calcium homeostasis was perturbed by ion-channel deletion, post-knockout plasticity in other conductances ensured resilience of calcium homeostasis to ion-channel deletion. These results demonstrate degeneracy in calcium homeostasis, as calcium homeostasis in knockout models was implemented in the absence of a channel that was earlier involved in the homeostatic process. Importantly, in reacquiring homeostasis, ion-channel conductances and physiological properties underwent heterogenous plasticity (dependent on the model, the property, and the deleted channel), even introducing changes in properties that were not directly connected to the deleted channel. Together, post-knockout plasticity geared towards maintaining homeostasis introduced heterogenous off-target effects on several channels and properties, suggesting that extreme caution be exercised in interpreting experimental outcomes involving channel knockouts.

Mitogen-activated protein kinase (MAPK) cascades-A yeast perspective

Discovery of the class of protein kinase now dubbed a mitogen (or messenger)-activated protein kinase (MAPK) is an illustrative example of how disparate lines of investigation can converge and reveal an enzyme family universally conserved among eukaryotes, from single-celled microbes to humans. Moreover, elucidation of the circuitry controlling MAPK function defined a now overarching principle in enzyme regulation-the concept of an activation cascade mediated by sequential phosphorylation events. Particularly ground-breaking for this field of exploration were the contributions of genetic approaches conducted using several model organisms, but especially the budding yeast Saccharomyces cerevisiae. Notably, examination of how haploid yeast cells respond to their secreted peptide mating pheromones was crucial in pinpointing genes encoding MAPKs and their upstream activators. Fully contemporaneous biochemical analysis of the activities elicited upon stimulation of mammalian cells by insulin and other growth- and differentiation-inducing factors lead eventually to the demonstration that components homologous to those in yeast were involved. Continued studies of these pathways in yeast were integral to other foundational discoveries in MAPK signaling, including the roles of tethering, scaffolding and docking interactions.

Design principles of improving the dose-response alignment in coupled GTPase switches

"Dose-response alignment" (DoRA), where the downstream response of cellular signaling pathways closely matches the fraction of activated receptor, can improve the fidelity of dose information transmission. The negative feedback has been experimentally identified as a key component for DoRA, but numerical simulations indicate that negative feedback is not sufficient to achieve perfect DoRA, i.e., perfect match of downstream response and receptor activation level. Thus a natural question is whether there exist design principles for signaling motifs within only negative feedback loops to improve DoRA to near-perfect DoRA. Here, we investigated several model formulations of an experimentally validated circuit that couples two molecular switches-mGTPase (monomeric GTPase) and tGTPase (heterotrimeric GTPases) - with negative feedback loops. In the absence of feedback, the low and intermediate mGTPase activation levels benefit DoRA in mass action and Hill-function models, respectively. Adding negative feedback has versatile roles on DoRA: it may impair DoRA in the mass action model with low mGTPase activation level and Hill-function model with intermediate mGTPase activation level; in other cases, i.e., the mass action model with a high mGTPase activation level or the Hill-function model with a non-intermediate mGTPase activation level, it improves DoRA. Furthermore, we found that DoRA in a longer cascade (i.e., tGTPase) can be obtained using Hill-function kinetics under certain conditions. In summary, we show how ranges of activity of mGTPase, reaction kinetics, the negative feedback, and the cascade length affect DoRA. This work provides a framework for improving the DoRA performance in signaling motifs with negative feedback.

A focus on yeast mating: From pheromone signaling to cell-cell fusion

Cells live in a chemical environment and are able to orient towards chemical cues. Unicellular haploid fungal cells communicate by secreting pheromones to reproduce sexually. In the yeast models Saccharomyces cerevisiae and Schizosaccharomyces pombe, pheromonal communication activates similar pathways composed of cognate G-protein-coupled receptors and downstream small GTPase Cdc42 and MAP kinase cascades. Local pheromone release and sensing, at a mobile surface polarity patch, underlie spatial gradient interpretation to form pairs between two cells of distinct mating types. Concentration of secretion at the point of cell-cell contact then leads to local cell wall digestion for cell fusion, forming a diploid zygote that prevents further fusion attempts. A number of asymmetries between mating types may promote efficiency of the system. In this review, we present our current knowledge of pheromone signaling in the two model yeasts, with an emphasis on how cells decode the pheromone signal spatially and ultimately fuse together. Though overall pathway architectures are similar in the two species, their large evolutionary distance allows to explore how conceptually similar solutions to a general biological problem can arise from divergent molecular components.

Efficient information coding and degeneracy in the nervous system

Efficient information coding (EIC) is a universal biological framework rooted in the fundamental principle that system responses should match their natural stimulus statistics for maximizing environmental information. Quantitatively assessed through information theory, such adaptation to the environment occurs at all biological levels and timescales. The context dependence of environmental stimuli and the need for stable adaptations make EIC a daunting task. We argue that biological complexity is the principal architect that subserves deft execution of stable EIC. Complexity in a system is characterized by several functionally segregated subsystems that show a high degree of functional integration when they interact with each other. Complex biological systems manifest heterogeneities and degeneracy, wherein structurally different subsystems could interact to yield the same functional outcome. We argue that complex systems offer several choices that effectively implement EIC and homeostasis for each of the different contexts encountered by the system.

Mechanism of commitment to a mating partner in Saccharomyces cerevisiae

Many cells detect and follow gradients of chemical signals to perform their functions. Yeast cells use gradients of extracellular pheromones to locate mating partners, providing a tractable model to understand how cells decode the spatial information in gradients. To mate, yeast cells must orient polarity toward the mating partner. Polarity sites are mobile, exploring the cell cortex until they reach the proper position, where they stop moving and "commit" to the partner. A simple model to explain commitment posits that a high concentration of pheromone is only detected upon alignment of partner cells' polarity sites, and causes polarity site movement to stop. Here we explore how yeast cells respond to partners that make different amounts of pheromone. Commitment was surprisingly robust to varying pheromone levels, ruling out the simple model. We also tested whether adaptive pathways were responsible for the robustness of commitment, but our results show that cells lacking those pathways were still able to accommodate changes in pheromone. To explain this robustness, we suggest that the steep pheromone gradients near each mating partner's polarity site trap the polarity site in place.

Quantifying information of intracellular signaling: progress with machine learning

Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.

Disrupting autorepression circuitry generates "open-loop lethality" to yield escape-resistant antiviral agents

Across biological scales, gene-regulatory networks employ autorepression (negative feedback) to maintain homeostasis and minimize failure from aberrant expression. Here, we present a proof of concept that disrupting transcriptional negative feedback dysregulates viral gene expression to therapeutically inhibit replication and confers a high evolutionary barrier to resistance. We find that nucleic-acid decoys mimicking cis-regulatory sites act as "feedback disruptors," break homeostasis, and increase viral transcription factors to cytotoxic levels (termed "open-loop lethality"). Feedback disruptors against herpesviruses reduced viral replication >2-logs without activating innate immunity, showed sub-nM IC50, synergized with standard-of-care antivirals, and inhibited virus replication in mice. In contrast to approved antivirals where resistance rapidly emerged, no feedback-disruptor escape mutants evolved in long-term cultures. For SARS-CoV-2, disruption of a putative feedback circuit also generated open-loop lethality, reducing viral titers by >1-log. These results demonstrate that generating open-loop lethality, via negative-feedback disruption, may yield a class of antimicrobials with a high genetic barrier to resistance.

Live cell microscopy: From image to insight

Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.

Resonating neurons stabilize heterogeneous grid-cell networks

A central theme that governs the functional design of biological networks is their ability to sustain stable function despite widespread parametric variability. Here, we investigated the impact of distinct forms of biological heterogeneities on the stability of a two-dimensional continuous attractor network (CAN) implicated in grid-patterned activity generation. We show that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid-patterned activity and resulted in progressively large perturbations in low-frequency neural activity. We postulated that targeted suppression of low-frequency perturbations could ameliorate heterogeneity-induced disruptions of grid-patterned activity. To test this, we introduced intrinsic resonance, a physiological mechanism to suppress low-frequency activity, either by adding an additional high-pass filter (phenomenological) or by incorporating a slow negative feedback loop (mechanistic) into our model neurons. Strikingly, CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing. We found CAN models with mechanistic resonators to be more effective in targeted suppression of low-frequency activity, with the slow kinetics of the negative feedback loop essential in stabilizing these networks. As low-frequency perturbations (1/f noise) are pervasive across biological systems, our analyses suggest a universal role for mechanisms that suppress low-frequency activity in stabilizing heterogeneous biological networks.

Spatial information transfer in hippocampal place cells depends on trial-to-trial variability, symmetry of place-field firing, and biophysical heterogeneities

The relationship between the feature-tuning curve and information transfer profile of individual neurons provides vital insights about neural encoding. However, the relationship between the spatial tuning curve and spatial information transfer of hippocampal place cells remains unexplored. Here, employing a stochastic search procedure spanning thousands of models, we arrived at 127 conductance-based place-cell models that exhibited signature electrophysiological characteristics and sharp spatial tuning, with parametric values that exhibited neither clustering nor strong pairwise correlations. We introduced trial-to-trial variability in responses and computed model tuning curves and information transfer profiles, using stimulus-specific (SSI) and mutual (MI) information metrics, across locations within the place field. We found spatial information transfer to be heterogeneous across models, but to reduce consistently with increasing levels of variability. Importantly, whereas reliable low-variability responses implied that maximal information transfer occurred at high-slope regions of the tuning curve, increase in variability resulted in maximal transfer occurring at the peak-firing location in a subset of models. Moreover, experience-dependent asymmetry in place-field firing introduced asymmetries in the information transfer computed through MI, but not SSI, and the impact of activity-dependent variability on information transfer was minimal compared to activity-independent variability. We unveiled ion-channel degeneracy in the regulation of spatial information transfer, and demonstrated critical roles for N-methyl-d-aspartate receptors, transient potassium and dendritic sodium channels in regulating information transfer. Our results demonstrate that trial-to-trial variability, tuning-curve shape and biological heterogeneities critically regulate the relationship between the spatial tuning curve and spatial information transfer in hippocampal place cells.

Single-Cell Information Analysis Reveals That Skeletal Muscles Incorporate Cell-to-Cell Variability as Information Not Noise

Cell-to-cell variability in signal transduction in biological systems is often considered noise. However, intercellular variation (i.e., cell-to-cell variability) has the potential to enable individual cells to encode different information. Here, we show that intercellular variation increases information transmission of skeletal muscle. We analyze the responses of multiple cultured myotubes or isolated skeletal muscle fibers as a multiple-cell channel composed of single-cell channels. We find that the multiple-cell channel, which incorporates intercellular variation as information, not noise, transmitted more information in the presence of intercellular variation than in the absence according to the "response diversity effect," increasing in the gradualness of dose response by summing the cell-to-cell variable dose responses. We quantify the information transmission of human facial muscle contraction during intraoperative neurophysiological monitoring and find that information transmission of muscle contraction is comparable to that of a multiple-cell channel. Thus, our data indicate that intercellular variation can increase the information capacity of tissues.

An Improved CRISPR Interference Tool to Engineer Rhodococcus opacus

Rhodococcus opacus is a nonmodel bacterium that is well suited for valorizing lignin. Despite recent advances in our systems-level understanding of its versatile metabolism, studies of its gene functions at a single gene level are still lagging. Elucidating gene functions in nonmodel organisms is challenging due to limited genetic engineering tools that are convenient to use. To address this issue, we developed a simple gene repression system based on CRISPR interference (CRISPRi). This gene repression system uses a T7 RNA polymerase system to express a small guide RNA, demonstrating improved repression compared to the previously demonstrated CRISPRi system (i.e., the maximum repression efficiency improved from 58% to 85%). Additionally, our cloning strategy allows for building multiple CRISPRi plasmids in parallel without any PCR step, facilitating the engineering of this GC-rich organism. Using the improved CRISPRi system, we confirmed the annotated roles of four metabolic pathway genes, which had been identified by our previous transcriptomic analysis to be related to the consumption of benzoate, vanillate, catechol, and acetate. Furthermore, we showed our tool's utility by demonstrating the inducible accumulation of muconate that is a precursor of adipic acid, an important monomer for nylon production. While the maximum muconate yield obtained using our tool was 30% of the yield obtained using gene knockout, our tool showed its inducibility and partial repressibility. Our CRISPRi tool will be useful to facilitate functional studies of this nonmodel organism and engineer this promising microbial chassis for lignin valorization.

Molecular switch architecture determines response properties of signaling pathways

Many intracellular signaling pathways are composed of molecular switches, proteins that transition between two states-on and off Typically, signaling is initiated when an external stimulus activates its cognate receptor that, in turn, causes downstream switches to transition from off to on using one of the following mechanisms: activation, in which the transition rate from the off state to the on state increases; derepression, in which the transition rate from the on state to the off state decreases; and concerted, in which activation and derepression operate simultaneously. We use mathematical modeling to compare these signaling mechanisms in terms of their dose-response curves, response times, and abilities to process upstream fluctuations. Our analysis elucidates several operating principles for molecular switches. First, activation increases the sensitivity of the pathway, whereas derepression decreases sensitivity. Second, activation generates response times that decrease with signal strength, whereas derepression causes response times to increase with signal strength. These opposing features allow the concerted mechanism to not only show dose-response alignment, but also to decouple the response time from stimulus strength. However, these potentially beneficial properties come at the expense of increased susceptibility to upstream fluctuations. We demonstrate that these operating principles also hold when the models are extended to include additional features, such as receptor removal, kinetic proofreading, and cascades of switches. In total, we show how the architecture of molecular switches govern their response properties. We also discuss the biological implications of our findings.

Mnemons and the memorization of past signaling events

Current advances are raising our awareness of the diverse roles that protein condensation plays in the biology of cells. Particularly, findings in organisms as diverse as yeast and Drosophila suggest that cells may utilize protein condensation to establish long-lasting changes in cellular activities and thereby encode a memory of past signaling events. Proteins that oligomerize to confer such cellular memory have been termed 'mnemons'. In the forming of super-assemblies, mnemons change their function and modulate the influence that the affected protein originally had on cellular processes. Because mnemon assemblies are self-templating, they allow cells to retain the memory of past decisions over larger timescales. Here, we review the mechanisms behind the formation of cellular memory with an emphasis on mnemon-mediated memorization of past signaling events.

A predictive model of gene expression reveals the role of network motifs in the mating response of yeast

Cells use signaling pathways to receive and process information about their environment. These nonlinear systems rely on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models describing signaling pathways often lack predictive power because they are not trained on data that encompass the diverse time scales on which these regulatory mechanisms operate. We addressed this limitation by measuring transcriptional changes induced by the mating response in Saccharomyces cerevisiae exposed to different dynamic patterns of pheromone. We found that pheromone-induced transcription persisted after pheromone removal and showed long-term adaptation upon sustained pheromone exposure. We developed a model of the regulatory network that captured both characteristics of the mating response. We fit this model to experimental data with an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four architectural elements of the network in regulating gene expression. These network motifs are incoherent feedforward, positive feedback, negative feedback, and repressor binding. Experimental and computational perturbations to these network motifs established a specific role for each in coordinating the mating response to persistent and dynamic stimulation.

Design of a MAPK signalling cascade balances energetic cost versus accuracy of information transmission

Cellular processes are inherently noisy, and the selection for accurate responses in presence of noise has likely shaped signalling networks. Here, we investigate the trade-off between accuracy of information transmission and its energetic cost for a mitogen-activated protein kinase (MAPK) signalling cascade. Our analysis of the pheromone response pathway of budding yeast suggests that dose-dependent induction of the negative transcriptional feedbacks in this network maximizes the information per unit energetic cost, rather than the information transmission capacity itself. We further demonstrate that futile cycling of MAPK phosphorylation and dephosphorylation has a measurable effect on growth fitness, with energy dissipation within the signalling cascade thus likely being subject to evolutionary selection. Considering optimization of accuracy versus the energetic cost of information processing, a concept well established in physics and engineering, may thus offer a general framework to understand the regulatory design of cellular signalling systems.

Cellular signalling networks provide information to the cell, but the trade-off between accuracy of information transfer and energetic cost of doing so has not been assessed. Here, the authors investigate a MAPK signalling cascade in budding yeast and find that information is maximised per unit energetic cost.

Efficient phase coding in hippocampal place cells

Neural codes have been postulated to build efficient representations of the external world. The hippocampus, an encoding system, employs neuronal firing rates and spike phases to encode external space. Although the biophysical origin of such codes is at a single neuronal level, the role of neural components in efficient coding is not understood. The complexity of this problem lies in the dimensionality of the parametric space encompassing neural components, and is amplified by the enormous biological heterogeneity observed in each parameter. A central question that spans encoding systems therefore is how neurons arrive at efficient codes in the face of widespread biological heterogeneities. To answer this, we developed a conductance-based spiking model for phase precession, a phase code of external space exhibited by hippocampal place cells. Our model accounted for several experimental observations on place cell firing and electrophysiology: the emergence of phase precession from exact spike timings of conductance-based models with neuron-specific ion channels and receptors; biological heterogeneities in neural components and excitability; the emergence of subthreshold voltage ramp, increased firing rate, enhanced theta power within the place field; a signature reduction in extracellular theta frequency compared to its intracellular counterpart; and experience-dependent asymmetry in firing-rate profile. We formulated phase-coding efficiency, using Shannon's information theory, as an information maximization problem with spike phase as the response and external space within a single place field as the stimulus. We employed an unbiased stochastic search spanning an 11-dimensional neural space, involving thousands of iterations that accounted for the biophysical richness and neuron-to-neuron heterogeneities. We found a small subset of models that exhibited efficient spatial information transfer through the phase code, and investigated the distinguishing features of this subpopulation at the parametric and functional scales. At the parametric scale, which spans the molecular components that defined the neuron, several nonunique parametric combinations with weak pairwise correlations yielded models with similar high phase-coding efficiency. Importantly, placing additional constraints on these models in terms of matching other aspects of hippocampal neural responses did not hamper parametric degeneracy. We provide quantitative evidence demonstrating this parametric degeneracy to be a consequence of a many-to-one relationship between the different parameters and phase-coding efficiency. At the functional scale, involving the cellular-scale neural properties, our analyses revealed an important higher-order constraint that was exclusive to models exhibiting efficient phase coding. Specifically, we found a counterbalancing negative correlation between neuronal gain and the strength of external synaptic inputs as a critical functional constraint for the emergence of efficient phase coding. These observations implicate intrinsic neural properties as important contributors in effectuating such counterbalance, which can be achieved by recruiting nonunique parametric combinations. Finally, we show that a change in afferent statistics, manifesting as input asymmetry onto these neuronal models, induced an adaptive shift in the phase code that preserved its efficiency. Together, our analyses unveil parametric degeneracy as a mechanism to harness widespread neuron-to-neuron heterogeneity towards accomplishing stable and efficient encoding, provided specific higher-order functional constraints on the relationship of neural gain to external inputs are satisfied.

Negative feedback-loop mechanisms regulating HOG- and pheromone-MAPK signaling in yeast

The pheromone response and the high osmolarity glycerol (HOG) pathways are considered the prototypical MAPK signaling systems. They are the best-understood pathways in eukaryotic cells, yet they continue to provide insights in how cells relate with the environment. These systems are subjected to tight regulatory circuits to prevent hyperactivation in length and intensity. Failure to do this may be a matter of life or death specially for unicellular organisms such as Saccharomyces cerevisiae. The signaling pathways are fine-tuned by positive and negative feedback loops exerted by pivotal control elements that allow precise responses to specific stimuli, despite the fact that some elements of the systems are common to different signaling pathways. Here we describe the experimentally proven negative feedback loops that modulate the pheromone response and the HOG pathways. As described in this review, MAP kinases are central mechanistic components of these feedback loops. They have the capacity to modulate basal signaling activity, a fast extranuclear response, and a longer-lasting transcriptional process.

Application of information theory in systems biology

Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work.

G-protein-coupled Receptors in Fungi

G-protein-coupled receptors (GPCRs) are the largest family of transmembrane receptors in fungi. These receptors have an important role in the transduction of extracellular signals into intracellular sites in response to diverse stimuli. They enable fungi to coordinate cell function and metabolism, thereby promoting their survival and propagation, and sense certain fundamentally conserved elements, such as nutrients, pheromones, and stress, for adaptation to their niches, environmental stresses, and host environment, causing disease and pathogen virulence. This chapter highlights the role of GPCRs in fungi in coordinating cell function and metabolism. Fungal cells sense the molecular interactions between extracellular signals. Their respective sensory systems are described here in detail.

Insights about collective decision-making at the genetic level

By living in a collective, individuals can share and aggregate information to base their decisions on the many rather than on the one, thereby increasing accuracy. But a collective can also be defined at the molecular level. In the following, we reason that genes, by working collectively, share fundamental features with social organisms, which ends, without invoking cognition, in wiser responses. For that, we compile into a single picture the terms redundancy, stochastic resonance, intrinsic and extrinsic noise, and cross-regulation.

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

Negative Feedback Phosphorylation of Gγ Subunit Ste18 and the Ste5 Scaffold Synergistically Regulates MAPK Activation in Yeast

Heterotrimeric G proteins (Gαβγ) are essential transducers in G protein signaling systems in all eukaryotes. In yeast, G protein signaling differentially activates mitogen-activated protein kinases (MAPKs)—Fus3 and Kss1—a phenomenon controlled by plasma membrane (PM) association of the scaffold protein Ste5. Here, we show that phosphorylation of the yeast Gγ subunit (Ste18), together with Fus3 docking on Ste5, controls the rate and stability of Ste5/PM association. Disruption of either element alone by point mutation has mild but reciprocal effects on MAPK activation. Disabling both elements results in ultra-fast and stable bulk Ste5/PM localization and Fus3 activation that is 6 times faster and 4 times more amplified compared to wild-type cells. These results further resolve the mechanism by which MAPK negative feedback phosphorylation controls pathway activation and provides compelling evidence that Gγ subunits can serve as intrinsic regulators of G protein signaling.

PyBioNetFit and the Biological Property Specification Language

In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit, which in addition supports checking models against known system properties and solving design problems. PyBioNetFit introduces Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving various example problems, including the challenging problem of parameterizing a 153-parameter model of cell cycle control in yeast based on both quantitative and qualitative data. We demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of targeted drug interventions in autophagy signaling.

Mechanical stress impairs pheromone signaling via Pkc1-mediated regulation of the MAPK scaffold Ste5

This study shows that Pkc1 inhibits yeast pheromone signaling upon intrinsic and extrinsic mechanical stress. Pkc1 phosphorylates the RING-H2 domains of the scaffolds Ste5 and Far1, thereby preventing their interaction with Gβγ at the plasma membrane. This crosstalk mechanism regulates polarized growth and cell–cell fusion during mating.

Cells continuously adapt cellular processes by integrating external and internal signals. In yeast, multiple stress signals regulate pheromone signaling to prevent mating under unfavorable conditions. However, the underlying crosstalk mechanisms remain poorly understood. Here, we show that mechanical stress activates Pkc1, which prevents lysis of pheromone-treated cells by inhibiting polarized growth. In vitro Pkc1 phosphorylates conserved residues within the RING-H2 domains of the scaffold proteins Far1 and Ste5, which are also phosphorylated in vivo. Interestingly, Pkc1 triggers dispersal of Ste5 from mating projections upon mechanically induced stress and during cell–cell fusion, leading to inhibition of the MAPK Fus3. Indeed, RING phosphorylation interferes with Ste5 membrane association by preventing binding to the receptor-linked Gβγ protein. Cells expressing nonphosphorylatable Ste5 undergo increased lysis upon mechanical stress and exhibit defects in cell–cell fusion during mating, which is exacerbated by simultaneous expression of nonphosphorylatable Far1. These results uncover a mechanical stress–triggered crosstalk mechanism modulating pheromone signaling, polarized growth, and cell–cell fusion during mating.

Quantitative analysis of the yeast pheromone pathway

The pheromone response pathway of the yeast Saccharomyces cerevisiae is a well-established model for the study of G proteins and mitogen-activated protein kinase (MAPK) cascades. Our longstanding ability to combine sophisticated genetic approaches with established functional assays has provided a thorough understanding of signalling mechanisms and regulation. In this report, we compare new and established methods used to quantify pheromone-dependent MAPK phosphorylation, transcriptional induction, mating morphogenesis, and gradient tracking. These include both single-cell and population-based assays of activity. We describe several technical advances, provide example data for benchmark mutants, highlight important differences between newer and established methodologies, and compare the advantages and disadvantages of each as applied to the yeast model. Quantitative measurements of pathway activity have been used to develop mathematical models and reveal new regulatory mechanisms in yeast. It is our expectation that experimental and computational approaches developed in yeast may eventually be adapted to human systems biology and pharmacology.

MAPK modulation of yeast pheromone signaling output and the role of phosphorylation sites in the scaffold protein Ste5

Mitogen-activated protein kinases (MAPKs) mediate numerous eukaryotic signaling responses. They also can modulate their own signaling output via positive or negative feedback loops. In the yeast pheromone response pathway, the MAPK Fus3 triggers negative feedback that dampens its own activity. One target of this feedback is Ste5, a scaffold protein that promotes Fus3 activation. Binding of Fus3 to a docking motif (D motif) in Ste5 causes signal dampening, which was proposed to involve a central cluster of phosphorylation sites in Ste5. Here, we reanalyzed the role of these central sites. Contrary to prior claims, phosphorylation-mimicking mutations at these sites did not impair signaling. Also, the hyperactive signaling previously observed when these sites were mutated to nonphosphorylatable residues arose from their replacement with valine residues and was not observed with other substitutes. Instead, a cluster of N-terminal sites in Ste5, not the central sites, is required for the rapid dampening of initial responses. Further results suggest that the role of the Fus3 D motif is most simply explained by a tethering effect that promotes Ste5 phosphorylation, rather than an allosteric effect proposed to regulate Fus3 activity. These findings substantially revise our understanding of how MAPK feedback attenuates scaffold-mediated signaling in this model pathway.

CDK and MAPK Synergistically Regulate Signaling Dynamics via a Shared Multi-site Phosphorylation Region on the Scaffold Protein Ste5

We report an unanticipated system of joint regulation by cyclin-dependent kinase (CDK) and mitogen-activated protein kinase (MAPK), involving collaborative multi-site phosphorylation of a single substrate. In budding yeast, the protein Ste5 controls signaling through a G1 arrest pathway. Upon cell-cycle entry, CDK inhibits Ste5 via multiple phosphorylation sites, disrupting its membrane association. Using quantitative time-lapse microscopy, we examined Ste5 membrane recruitment dynamics at different cell-cycle stages. Surprisingly, in S phase, where Ste5 recruitment should be blocked, we observed an initial recruitment followed by a steep drop-off. This delayed inhibition revealed a requirement for both CDK activity and negative feedback from the pathway MAPK Fus3. Mutagenesis, mass spectrometry, and electrophoretic analyses suggest that the CDK and MAPK modify shared sites, which are most extensively phosphorylated when both kinases are active and able to bind their docking sites on Ste5. Such collaborative phosphorylation can broaden regulatory inputs and diversify output dynamics of signaling pathways.

Properties of cell signaling pathways and gene expression systems operating far from steady-state

Ligand-receptor systems, covalent modification cycles, and transcriptional networks are basic units of signaling systems and their steady-state properties are well understood. However, the behavior of such systems before steady-state is poorly characterized. Here, we analyzed the properties of input-output curves for each of these systems as they approach steady-state. In ligand-receptor systems, the EC₅₀ (concentration of the ligand that occupies 50% of the receptors) is higher before the system reaches steady-state. Based on this behavior, we have previously defined PRESS (for pre-equilibrium sensing and signaling), a general “systems level” mechanism cells may use to overcome input saturation. Originally, we showed that, given a step stimulation, PRESS operates when the kinetics of ligand-receptor binding are slower than the downstream signaling steps. Now, we show that, provided the input increases slowly, it is not essential for the ligand binding reaction itself to be slow. In addition, we demonstrate that covalent modification cycles and gene expression systems may also operate in PRESS mode. Thus, nearly all biochemical processes may operate in PRESS mode, suggesting that this mechanism may be ubiquitous in cell signaling systems.

Active dendrites regulate the spatiotemporal spread of signaling microdomains

Microdomains that emerge from spatially constricted spread of biochemical signaling components play a central role in several neuronal computations. Although dendrites, endowed with several voltage-gated ion channels, form a prominent structural substrate for microdomain physiology, it is not known if these channels regulate the spatiotemporal spread of signaling microdomains. Here, we employed a multiscale, morphologically realistic, conductance-based model of the hippocampal pyramidal neuron that accounted for experimental details of electrical and calcium-dependent biochemical signaling. We activated synaptic N-Methyl-d-Aspartate receptors through theta-burst stimulation (TBS) or pairing (TBP) and assessed microdomain propagation along a signaling pathway that included calmodulin, calcium/calmodulin-dependent protein kinase II (CaMKII) and protein phosphatase 1. We found that the spatiotemporal spread of the TBS-evoked microdomain in phosphorylated CaMKII (pCaMKII) was amplified in comparison to that of the corresponding calcium microdomain. Next, we assessed the role of two dendritically expressed inactivating channels, one restorative (A-type potassium) and another regenerative (T-type calcium), by systematically varying their conductances. Whereas A-type potassium channels suppressed the spread of pCaMKII microdomains by altering the voltage response to TBS, T-type calcium channels enhanced this spread by modulating TBS-induced calcium influx without changing the voltage. Finally, we explored cross-dependencies of these channels with other model components, and demonstrated the heavy mutual interdependence of several biophysical and biochemical properties in regulating microdomains and their spread. Our conclusions unveil a pivotal role for dendritic voltage-gated ion channels in actively amplifying or suppressing biochemical signals and their spatiotemporal spread, with critical implications for clustered synaptic plasticity, robust information transfer and efficient neural coding.

The spatiotemporal spread of biochemical signals in neurons and other cells regulate signaling specificity, tuning of signal propagation, along with specificity and clustering of adaptive plasticity. Theoretical and experimental studies have demonstrated a critical role for cellular morphology and the topology of signaling networks in regulating this spread. In this study, we add a significantly complex dimension to this narrative by demonstrating that voltage-gated ion channels on the plasma membrane could actively amplify or suppress the strength and spread of downstream signaling components. Given the expression of different ion channels with wide-ranging heterogeneity in gating kinetics, localization and density, our results point to an increase in complexity of and degeneracy in signaling spread, and unveil a powerful mechanism for regulating biochemical-signaling pathways across different cell types.

Experimental and engineering approaches to intracellular communication

Communication between and within cells is essential for multicellular life. While intracellular signal transduction pathways are often specified in molecular terms, the information content they transmit remains poorly defined. Here, we review research efforts to merge biological experimentation with concepts of communication that emerge from the engineering disciplines of signal processing and control theory. We discuss the challenges of performing experiments that quantitate information transfer at the molecular level, and we highlight recent studies that have advanced toward a clearer definition of the information content carried by signaling molecules. Across these studies, we emphasize a theme of increasingly well-matched experimental and theoretical approaches to decode the data streams directing cellular behavior.

Transferring information without distortion

Despite employing diverse molecular mechanisms, many different cell signaling systems avoid losing information by transmitting it in a linear manner.

Intrinsic limits of information transmission in biochemical signalling motifs

All living things have evolved to sense changes in their environment in order to respond in adaptive ways. At the cellular level, these sensing systems generally involve receptor molecules at the cell surface, which detect changes outside the cell and relay those changes to the appropriate response elements downstream. With the advent of experimental technologies that can track signalling at the single-cell level, it has become clear that many signalling systems exhibit significant levels of 'noise,' manifesting as differential responses of otherwise identical cells to the same environment. This noise has a large impact on the capacity of cell signalling networks to transmit information from the environment. Application of information theory to experimental data has found that all systems studied to date encode less than 2.5 bits of information, with the majority transmitting significantly less than 1 bit. Given the growing interest in applying information theory to biological data, it is crucial to understand whether the low values observed to date represent some sort of intrinsic limit on information flow given the inherently stochastic nature of biochemical signalling events. In this work, we used a series of computational models to explore how much information a variety of common 'signalling motifs' can encode. We found that the majority of these motifs, which serve as the basic building blocks of cell signalling networks, can encode far more information (4-6 bits) than has ever been observed experimentally. In addition to providing a consistent framework for estimating information-theoretic quantities from experimental data, our findings suggest that the low levels of information flow observed so far in living system are not necessarily due to intrinsic limitations. Further experimental work will be needed to understand whether certain cell signalling systems actually can approach the intrinsic limits described here, and to understand the sources and purpose of the variation that reduces information flow in living cells.

Real-Time Genetic Compensation Defines the Dynamic Demands of Feedback Control

Biological signaling networks use feedback control to dynamically adjust their operation in real time. Traditional static genetic methods such as gene knockouts or rescue experiments can often identify the existence of feedback interactions but are unable to determine what feedback dynamics are required. Here, we implement a new strategy, closed-loop optogenetic compensation (CLOC), to address this problem. Using a custom-built hardware and software infrastructure, CLOC monitors, in real time, the output of a pathway deleted for a feedback regulator. A minimal model uses these measurements to calculate and deliver-on the fly-an optogenetically enabled transcriptional input designed to compensate for the effects of the feedback deletion. Application of CLOC to the yeast pheromone response pathway revealed surprisingly distinct dynamic requirements for three well-studied feedback regulators. CLOC, a marriage of control theory and traditional genetics, presents a broadly applicable methodology for defining the dynamic function of biological feedback regulators.

Heat-stress triggers MAPK crosstalk to turn on the hyperosmotic response pathway

Cells make decisions based on a combination of external and internal signals. In yeast, the high osmolarity response (HOG) is a mitogen-activated protein kinase (MAPK) pathway that responds to a variety of stimuli, and it is central to the general stress response. Here we studied the effect of heat-stress (HS) on HOG. Using live-cell reporters and genetics, we show that HS promotes Hog1 phosphorylation and Hog1-dependent gene expression, exclusively via the Sln1 phosphorelay branch, and that the strength of the activation is larger in yeast adapted to high external osmolarity. HS stimulation of HOG is indirect. First, we show that HS causes glycerol loss, necessary for HOG activation. Preventing glycerol efflux by deleting the glyceroporin FPS1 or its regulators RGC1 and ASK10/RGC2, or by increasing external glycerol, greatly reduced HOG activation. Second, we found that HOG stimulation by HS depended on the operation of a second MAPK pathway, the cell-wall integrity (CWI), a well-known mediator of HS, since inactivating Pkc1 or deleting the MAPK SLT2 greatly reduced HOG activation. Our data suggest that the main role of the CWI in this process is to stimulate glycerol loss. We found that in yeast expressing the constitutively open channel mutant (Fps1-Δ11), HOG activity was independent of Slt2. In summary, we suggest that HS causes a reduction in turgor due to the loss of glycerol and the accompanying water, and that this is what actually stimulates HOG. Thus, taken together, our findings highlight a central role for Fps1, and the metabolism of glycerol, in the communication between the yeast MAPK pathways, essential for survival and reproduction in changing environments.

Signaling pathways as linear transmitters

One challenge in biology is to make sense of the complexity of biological networks. A good system to approach this is signaling pathways, whose well-characterized molecular details allow us to relate the internal processes of each pathway to their input-output behavior. In this study, we analyzed mathematical models of three metazoan signaling pathways: the canonical Wnt, MAPK/ERK, and Tgfβ pathways. We find an unexpected convergence: the three pathways behave in some physiological contexts as linear signal transmitters. Testing the results experimentally, we present direct measurements of linear input-output behavior in the Wnt and ERK pathways. Analytics from each model further reveal that linearity arises through different means in each pathway, which we tested experimentally in the Wnt and ERK pathways. Linearity is a desired property in engineering where it facilitates fidelity and superposition in signal transmission. Our findings illustrate how cells tune different complex networks to converge on the same behavior.

Modeling Procedures

Being concerned by the understanding of the mechanism underlying chronic degenerative diseases , we presented in the previous chapter the medical systems biology conceptual framework that we present for that purpose in this volume. More specifically, we argued there the clear advantages offered by a state-space perspective when applied to the systems-level description of the biomolecular machinery that regulates complex degenerative diseases. We also discussed the importance of the dynamical interplay between the risk factors and the network of interdependencies that characterizes the biochemical, cellular, and tissue-level biomolecular reactions that underlie the physiological processes in health and disease. As we pointed out in the previous chapter, the understanding of this interplay (articulated around cellular phenotypic plasticity properties, regulated by specific kinds of gene regulatory networks) is necessary if prevention is chosen as the human-health improvement strategy (potentially involving the modulation of the patient's lifestyle). In this chapter we provide the medical systems biology mathematical and computational modeling tools required for this task.

Timing of gene expression in a cell-fate decision system

During development, morphogens provide extracellular cues allowing cells to select a specific fate by inducing complex transcriptional programs. The mating pathway in budding yeast offers simplified settings to understand this process. Pheromone secreted by the mating partner triggers the activity of a MAPK pathway, which results in the expression of hundreds of genes. Using a dynamic expression reporter, we quantified the kinetics of gene expression in single cells upon exogenous pheromone stimulation and in the physiological context of mating. In both conditions, we observed striking differences in the timing of induction of mating‐responsive promoters. Biochemical analyses and generation of synthetic promoter variants demonstrated how the interplay between transcription factor binding and nucleosomes contributes to determine the kinetics of transcription in a simplified cell‐fate decision system.

Single-cell profiling screen identifies microtubule-dependent reduction of variability in signaling

Populations of isogenic cells often respond coherently to signals, despite differences in protein abundance and cell state. Previously, we uncovered processes in the Saccharomyces cerevisiae pheromone response system (PRS) that reduced cell-to-cell variability in signal strength and cellular response. Here, we screened 1,141 non-essential genes to identify 50 "variability genes". Most had distinct, separable effects on strength and variability of the PRS, defining these quantities as genetically distinct "axes" of system behavior. Three genes affected cytoplasmic microtubule function: BIM1, GIM2, and GIM4 We used genetic and chemical perturbations to show that, without microtubules, PRS output is reduced but variability is unaffected, while, when microtubules are present but their function is perturbed, output is sometimes lowered, but its variability is always high. The increased variability caused by microtubule perturbations required the PRS MAP kinase Fus3 and a process at or upstream of Ste5, the membrane-localized scaffold to which Fus3 must bind to be activated. Visualization of Ste5 localization dynamics demonstrated that perturbing microtubules destabilized Ste5 at the membrane signaling site. The fact that such microtubule perturbations cause aberrant fate and polarity decisions in mammals suggests that microtubule-dependent signal stabilization might also operate throughout metazoans.

Transcriptionally inducible Pleckstrin homology-like domain, family A, member 1, attenuates ErbB receptor activity by inhibiting receptor oligomerization

Feedback control is a key mechanism in signal transduction, intimately involved in regulating the outcome of the cellular response. Here, we report a novel mechanism by which PHLDA1, Pleckstrin homology-like domain, family A, member 1, negatively regulates ErbB receptor signaling by inhibition of receptor oligomerization. We have found that the ErbB3 ligand, heregulin, induces PHILDA1 expression in MCF-7 cells. Transcriptionally-induced PHLDA1 protein directly binds to ErbB3, whereas knockdown of PHLDA1 increases complex formation between ErbB3 and ErbB2. To provide insight into the mechanism for our time-course and single-cell experimental observations, we performed a systematic computational search of network topologies of the mathematical models based on receptor dimer-tetramer formation in the ErbB activation processes. Our results indicate that only a model in which PHLDA1 inhibits formation of both dimers and tetramer can explain the experimental data. Predictions made from this model were further validated by single-molecule imaging experiments. Our studies suggest a unique regulatory feature of PHLDA1 to inhibit the ErbB receptor oligomerization process and thereby control the activity of receptor signaling network.

Cell-specific responses to the cytokine TGFβ are determined by variability in protein levels

The cytokine TGFβ provides important information during embryonic development, adult tissue homeostasis, and regeneration. Alterations in the cellular response to TGFβ are involved in severe human diseases. To understand how cells encode the extracellular input and transmit its information to elicit appropriate responses, we acquired quantitative time-resolved measurements of pathway activation at the single-cell level. We established dynamic time warping to quantitatively compare signaling dynamics of thousands of individual cells and described heterogeneous single-cell responses by mathematical modeling. Our combined experimental and theoretical study revealed that the response to a given dose of TGFβ is determined cell specifically by the levels of defined signaling proteins. This heterogeneity in signaling protein expression leads to decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome. Negative feedback regulators promote heterogeneous signaling, as a SMAD7 knock-out specifically affected the signal duration in a subpopulation of cells. Taken together, we propose a quantitative framework that allows predicting and testing sources of cellular signaling heterogeneity.

Analysis of the thresholds for transcriptional activation by the yeast MAP kinases Fus3 and Kss1

Signaling in the pheromone response pathway of budding yeast activates two distinct MAP kinases (MAPKs), Fus3 and Kss1. Either MAPK alone can mediate pheromone-induced transcription, but it has been unclear to what degree each one contributes to transcriptional output in wild-type cells. Here, we report that transcription reflects the ratio of active to inactive MAPK, and not simply the level of active MAPK. For Kss1 the majority of MAPK molecules must be converted to the active form, whereas for Fus3 only a small minority must be activated. These different activation thresholds reflect two opposing effects of each MAPK, in which the inactive forms inhibit transcription, whereas the active forms promote transcription. Moreover, negative feedback from Fus3 limits activation of Kss1 so that it does not meet its required threshold in wild-type cells but does so only when hyperactivated in cells lacking Fus3. The results suggest that the normal transcriptional response involves asymmetric contributions from the two MAPKs, in which pheromone signaling reduces the negative effect of Kss1 while increasing the positive effect of Fus3. These findings reveal new functional distinctions between these MAPKs, and help illuminate how inhibitory functions shape positive pathway outputs in both pheromone and filamentation pathways.

Asymmetry in Signal Oscillations Contributes to Efficiency of Periodic Systems

Oscillations are an important feature of cellular signaling that result from complex combinations of positive- and negative-feedback loops. The encoding and decoding mechanisms of oscillations based on amplitude and frequency have been extensively discussed in the literature in the context of intercellular and intracellular signaling. However, the fundamental questions of whether and how oscillatory signals offer any competitive advantages-and, if so, what-have not been fully answered. We investigated established oscillatory mechanisms and designed a study to analyze the oscillatory characteristics of signaling molecules and system output in an effort to answer these questions. Two classic oscillators, Goodwin and PER, were selected as the model systems, and corresponding no-feedback models were created for each oscillator to discover the advantage of oscillating signals. Through simulating the original oscillators and the matching no-feedback models, we show that oscillating systems have the capability to achieve better resource-to-output efficiency, and we identify oscillatory characteristics that lead to improved efficiency.

Mitogen-activated protein kinase (MAPK) dynamics determine cell fate in the yeast mating response

In the yeast Saccharomyces cerevisiae, the exposure to mating pheromone activates a prototypic mitogen-activated protein kinase (MAPK) cascade and triggers a dose-dependent differentiation response. Whereas a high pheromone dose induces growth arrest and formation of a shmoo-like morphology in yeast cells, lower pheromone doses elicit elongated cell growth. Previous population-level analysis has revealed that the MAPK Fus3 plays an important role in mediating this differentiation switch. To further investigate how Fus3 controls the fate decision process at the single-cell level, we developed a specific translocation-based reporter for monitoring Fus3 activity in individual live cells. Using this reporter, we observed strikingly different dynamic patterns of Fus3 activation in single cells differentiated into distinct fates. Cells committed to growth arrest and shmoo formation exhibited sustained Fus3 activation. In contrast, most cells undergoing elongated growth showed either a delayed gradual increase or pulsatile dynamics of Fus3 activity. Furthermore, we found that chemically perturbing Fus3 dynamics with a specific inhibitor could effectively redirect the mating differentiation, confirming the causative role of Fus3 dynamics in driving cell fate decisions. MAPKs mediate proliferation and differentiation signals in mammals and are therapeutic targets in many cancers. Our results highlight the importance of MAPK dynamics in regulating single-cell responses and open up the possibility that MAPK signaling dynamics could be a pharmacological target in therapeutic interventions.