Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

Yeast Cellular Stress: Impacts on Bioethanol Production

Bioethanol is the largest biotechnology product and the most dominant biofuel globally. Saccharomyces cerevisiae is the most favored microorganism employed for its industrial production. However, obtaining maximum yields from an ethanol fermentation remains a technical challenge, since cellular stresses detrimentally impact on the efficiency of yeast cell growth and metabolism. Ethanol fermentation stresses potentially include osmotic, chaotropic, oxidative, and heat stress, as well as shifts in pH. Well-developed stress responses and tolerance mechanisms make S. cerevisiae industrious, with bioprocessing techniques also being deployed at industrial scale for the optimization of fermentation parameters and the effective management of inhibition issues. Overlap exists between yeast responses to different forms of stress. This review outlines yeast fermentation stresses and known mechanisms conferring stress tolerance, with their further elucidation and improvement possessing the potential to improve fermentation efficiency.

Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants

Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the 630 genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.

Yeast systems biology in understanding principles of physiology underlying complex human diseases

Complex human diseases commonly arise from deregulation of cell growth, metabolism, and/or gene expression. Yeast is a eukaryal model organism that is widely used to study these processes. Yeast systems biology benefits from the ability to exert fine experimental control over the cell growth rate and nutrient composition, which allows orthogonal experimental design and generation of multi-omics data at high resolution. This has led to several insights on the principles of cellular physiology, including many cellular processes associated with complex human diseases. Here we review these biological insights together with experimental and modeling approaches developed in yeast to study systems biology. The role of yeast systems biology to further advance systems and personalized therapies for complex diseases is discussed.

Promoter-dependent nuclear RNA degradation ensures cell cycle-specific gene expression

Cell cycle progression depends on phase-specific gene expression. Here we show that the nuclear RNA degradation machinery plays a lead role in promoting cell cycle-dependent gene expression by triggering promoter-dependent co-transcriptional RNA degradation. Single molecule quantification of RNA abundance in different phases of the cell cycle indicates that relative curtailment of gene expression in certain phases is attained even when transcription is not completely inhibited. When nuclear ribonucleases are deleted, transcription of the Saccharomyces cerevisiae G1-specific axial budding gene AXL2 is detected throughout the cell cycle and its phase-specific expression is lost. Promoter replacement abolished cell cycle-dependent RNA degradation and rendered the RNA insensitive to the deletion of nuclear ribonucleases. Together the data reveal a model of gene regulation whereby RNA abundance is controlled by promoter-dependent induction of RNA degradation.

A Novel Data-Driven Boolean Model for Genetic Regulatory Networks

A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug.

A Mechanistic Computational Model Reveals That Plasticity of CD4+ T Cell Differentiation Is a Function of Cytokine Composition and Dosage

CD4⁺ T cells provide cell-mediated immunity in response to various antigens. During an immune response, naïve CD4⁺ T cells differentiate into specialized effector T helper (Th1, Th2, and Th17) cells and induced regulatory (iTreg) cells based on a cytokine milieu. In recent studies, complex phenotypes resembling more than one classical T cell lineage have been experimentally observed. Herein, we sought to characterize the capacity of T cell differentiation in response to the complex extracellular environment. We constructed a comprehensive mechanistic (logical) computational model of the signal transduction that regulates T cell differentiation. The model's dynamics were characterized and analyzed under 511 different environmental conditions. Under these conditions, the model predicted the classical as well as the novel complex (mixed) T cell phenotypes that can co-express transcription factors (TFs) related to multiple differentiated T cell lineages. Analyses of the model suggest that the lineage decision is regulated by both compositions and dosage of signals that constitute the extracellular environment. In this regard, we first characterized the specific patterns of extracellular environments that result in novel T cell phenotypes. Next, we predicted the inputs that can regulate the transition between the canonical and complex T cell phenotypes in a dose-dependent manner. Finally, we predicted the optimal levels of inputs that can simultaneously maximize the activity of multiple lineage-specifying TFs and that can drive a phenotype toward one of the co-expressed TFs. In conclusion, our study provides new insights into the plasticity of CD4⁺ T cell differentiation, and also acts as a tool to design testable hypotheses for the generation of complex T cell phenotypes by various input combinations and dosages.

Quantitative Systems Biology to decipher design principles of a dynamic cell cycle network: the "Maximum Allowable mammalian Trade-Off-Weight" (MAmTOW)

Network complexity is required to lend cellular processes flexibility to respond timely to a variety of dynamic signals, while simultaneously warranting robustness to protect cellular integrity against perturbations. The cell cycle serves as a paradigm for such processes; it maintains its frequency and temporal structure (although these may differ among cell types) under the former, but accelerates under the latter. Cell cycle molecules act together in time and in different cellular compartments to execute cell type-specific programs. Strikingly, the timing at which molecular switches occur is controlled by abundance and stoichiometry of multiple proteins within complexes. However, traditional methods that investigate one effector at a time are insufficient to understand how modulation of protein complex dynamics at cell cycle transitions shapes responsiveness, yet preserving robustness. To overcome this shortcoming, we propose a multidisciplinary approach to gain a systems-level understanding of quantitative cell cycle dynamics in mammalian cells from a new perspective. By suggesting advanced experimental technologies and dedicated modeling approaches, we present innovative strategies (i) to measure absolute protein concentration in vivo, and (ii) to determine how protein dosage, e.g., altered protein abundance, and spatial (de)regulation may affect timing and robustness of phase transitions. We describe a method that we name “Maximum Allowable mammalian Trade–Off–Weight” (MAmTOW), which may be realized to determine the upper limit of gene copy numbers in mammalian cells. These aspects, not covered by current systems biology approaches, are essential requirements to generate precise computational models and identify (sub)network-centered nodes underlying a plethora of pathological conditions.

A Clb/Cdk1-mediated regulation of Fkh2 synchronizes CLB expression in the budding yeast cell cycle

Precise timing of cell division is achieved by coupling waves of cyclin-dependent kinase (Cdk) activity with a transcriptional oscillator throughout cell cycle progression. Although details of transcription of cyclin genes are known, it is unclear which is the transcriptional cascade that modulates their expression in a timely fashion. Here, we demonstrate that a Clb/Cdk1-mediated regulation of the Fkh2 transcription factor synchronizes the temporal mitotic CLB expression in budding yeast. A simplified kinetic model of the cyclin/Cdk network predicts a linear cascade where a Clb/Cdk1-mediated regulation of an activator molecule drives CLB3 and CLB2 expression. Experimental validation highlights Fkh2 as modulator of CLB3 transcript levels, besides its role in regulating CLB2 expression. A Boolean model based on the minimal number of interactions needed to capture the information flow of the Clb/Cdk1 network supports the role of an activator molecule in the sequential activation, and oscillatory behavior, of mitotic Clb cyclins. This work illustrates how transcription and phosphorylation networks can be coupled by a Clb/Cdk1-mediated regulation that synchronizes them.

A dynamic coupling of cyclin-dependent kinase with transcription factors in yeast offers insights into the timely cell cycle progression. An international team lead by Matteo Barberis from University of Amsterdam in The Netherlands studied the molecular mechanisms responsible for the coordination of DNA replication with cell division. The researchers have demonstrated how the sequential order of waves of mitotic cyclins activating cyclin-dependent kinase, or Cdk, is achieved by synchronizing Cdk with transcriptional activities. They have generated a mathematical model that predicts a cyclin/Cdk-mediated regulation of an activator molecule to stimulate mitotic cyclin expression. This prediction was successfully validated experimentally, identifying Forkhead transcription factors, or Fkh, as pivotal molecules. Cyclin waves are temporally synchronized by Fkh, and a mitotic Clb/Cdk1-mediated regulation of Fkh modulates cyclin expression. The findings reveal a novel principle of design, with kinase and transcription activities interlocked to guarantee a timely cell cycle.