The eukaryotic cell cycle is the repeated sequence of events that enable the division of a cell into two daughter cells. It is divided into four phases: G₁, S, G₂, and M. Passage through the cell cycle is strictly regulated by a molecular interaction network, which involves the periodic synthesis and destruction of cyclins that bind and activate cyclin-dependent kinases that are present in nonlimiting amounts. Cyclin-dependent kinase inhibitors contribute to cell cycle control. Budding yeast is an established model organism for cell cycle studies, and several mathematical models have been proposed for its cell cycle. An area of major relevance in cell cycle control is the G₁ to S transition. In any given growth condition, it is characterized by the requirement of a specific, critical cell size, P_S, to enter S phase. The molecular basis of this control is still under discussion. The authors report a mathematical model of the G₁ to S network that newly takes into account nucleo/cytoplasmic localization, the role of the cyclin-dependent kinase Sic1 in facilitating nuclear import of its cognate Cdk1-Clb5, Whi5 control, and carbon source regulation of Sic1 and Sic1-containing complexes. The model was implemented by a set of ordinary differential equations that describe the temporal change of the concentration of the involved proteins and protein complexes. The model was tested by simulation in several genetic and nutritional setups and was found to be neatly consistent with experimental data. To estimate P_S, the authors developed a hybrid model including a probabilistic component for firing of DNA replication origins. Sensitivity analysis of P_S provides a novel relevant conclusion: P_S is an emergent property of the G₁ to S network that strongly depends on growth rate. A major property of living cells is their ability to maintain mass homeostasis throughout cell divisions. It has been proposed that in order to achieve such homeostasis, some critical event(s) in the cell cycle will take place only when the cell has grown beyond a critical cell size. In the budding yeast Saccharomyces cerevisiae, a widely used model for the study of the eukaryotic cell cycle, a large body of evidence indicates that cells have to reach a critical size before they start to replicate their DNA and to form bud, which will give rise to the daughter cell. This critical cell size is modulated by growth rate, hence by nutritional conditions and the multiplicity of genetic material (i.e., ploidy). The authors present a mathematical model of the regulatory molecular network acting at the G₁ to S transition. The major novel features of this model compared with previous models of this process are (1) the accounting for cell growth (i.e., the increase in cell volume); (2) the explicit consideration of the fact that cells have a nucleus and a cytoplasm, and that key cell cycle regulatory molecules must move between these different compartments and can only react or regulate each other if they are in the same compartment; and (3) the requirement of sequential overcoming of two molecular thresholds given by a cyclin-dependent kinase/cyclin and a cyclin-dependent kinase inhibitor. The model was tested by simulating the processes during G₁ to S transition for different growth conditions or for different mutants and by comparing the results with experimental data. A parameter sensitivity analysis (i.e., testing the model predictions when parameters are varied), newly indicates that the critical cell size is an emergent property of the G₁ to S network. The model leads to a unified interpretation of seemingly disparate experimental observations and makes predictions to be experimentally verified.

How the network around ROS protects against oxidative stress and Parkinson’s disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for “life-extending interventions”: mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.

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.

In Saccharomyces cerevisiae, Sic1, an inhibitor of Cdk (cyclin-dependent kinase), blocks the activity of S-Cdk1 (Cdk1/Clb5,6) kinase that is required for DNA replication. Deletion of Sic1 causes premature DNA replication from fewer origins, extension of the S phase and inefficient separation of sister chromatids during anaphase. Despite the well-documented relevance of Sic1 inhibition of S-Cdk1 for cell cycle control and genome instability, the molecular mechanism by which Sic1 inhibits S-Cdk1 activity remains obscure. In this paper, we show that Sic1 is functionally and structurally related to the mammalian Cki (Cdk inhibitor) p27Kip1 of the Kip/Cip family. A molecular model of the inhibitory domain of Sic1 bound to the Cdk2–cyclin A complex suggested that the yeast inhibitor might productively interface with the mammalian Cdk2–cyclin A complex. Consistent with this, Sic1 is able to bind to, and strongly inhibit the kinase activity of, the Cdk2–cyclin A complex. In addition, comparison of the different inhibitory patterns obtained using histone H1 or GST (glutathione S-transferase)–pRb (retinoblastoma protein) fusion protein as substrate (the latter of which recognizes both the docking site and the catalytic site of Cdk2–cyclin A) offers interesting suggestions for the inhibitory mechanism of Sic1. Finally, overexpression of the KIP1 gene in vivo in Saccharomyces cerevisiae, like overexpression of the related SIC1 gene, rescues the cell cycle-related phenotype of a sic1Δ strain. Taken together, these findings strongly indicate that budding yeast Sic1 and mammalian p27Kip1 are functional homologues with a structurally conserved inhibitory domain.

Forkhead Box (Fox) proteins share the Forkhead domain, a winged-helix DNA binding module, which is conserved among eukaryotes from yeast to humans. These sequence-specific DNA binding proteins have been primarily characterized as transcription factors regulating diverse cellular processes from cell cycle control to developmental fate, deregulation of which contributes to developmental defects, cancer, and aging. We recently identified Saccharomyces cerevisiae Forkhead 1 (Fkh1) and Forkhead 2 (Fkh2) as required for the clustering of a subset of replication origins in G₁ phase and for the early initiation of these origins in the ensuing S phase, suggesting a mechanistic role linking the spatial organization of the origins and their activity. Here, we show that Fkh1 and Fkh2 share a unique structural feature of human FoxP proteins that enables FoxP2 and FoxP3 to form domain-swapped dimers capable of bridging two DNA molecules in vitro. Accordingly, Fkh1 self-associates in vitro and in vivo in a manner dependent on the conserved domain-swapping region, strongly suggestive of homodimer formation. Fkh1- and Fkh2-domain-swap-minus (dsm) mutations are functional as transcription factors yet are defective in replication origin timing control. Fkh1-dsm binds replication origins in vivo but fails to cluster them, supporting the conclusion that Fkh1 and Fkh2 dimers perform a structural role in the spatial organization of chromosomal elements with functional importance. Significance The spatial organization of chromatin within the nucleus regulates genomic functions including DNA repair, transcription, and replication. For example, replication origins cluster prior to initiating replication, likely to aggregate the many necessary factors, but the mechanism is poorly understood. We recently discovered yeast “Forkhead Box” (Fox) DNA binding proteins, Forkhead 1 (Fkh1) and Forkhead 2 (Fkh2), as required for this origin clustering and regulation of initiation timing. This study reveals that Fkh1 and Fkh2 share a structural motif that allows dimerization to bring distal DNA binding sites into close proximity. Mutation that disrupts dimerization ablates origin clustering and deregulates origin activation, suggesting causality between origin clustering and initiation control. We propose that Fkh1 and Fkh2 and related Fox proteins in metazoans establish chromatin architecture.

Sirtuins are a family of highly conserved NAD+-dependent proteins and this dependency links Sirtuins directly to metabolism. Sirtuins’ activity has been shown to extend the lifespan of several organisms and mainly through the post-translational modification of their many target proteins, with deacetylation being the most common modification. The seven mammalian Sirtuins, SIRT1 through SIRT7, have been implicated in regulating physiological responses to metabolism and stress by acting as nutrient sensors, linking environmental and nutrient signals to mammalian metabolic homeostasis. Furthermore, mammalian Sirtuins have been implicated in playing major roles in mammalian pathophysiological conditions such as inflammation, obesity and cancer. Mammalian Sirtuins are expressed heterogeneously among different organs and tissues, and the same holds true for their substrates. Thus, the function of mammalian Sirtuins together with their substrates is expected to vary among tissues. Any therapy depending on Sirtuins could therefore have different local as well as systemic effects. Here, an introduction to processes relevant for the actions of Sirtuins, such as metabolism and cell cycle, will be followed by reasoning on the system-level function of Sirtuins and their substrates in different mammalian tissues. Their involvement in the healthy metabolism and metabolic disorders will be reviewed and critically discussed.