SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems.

Computational models of biological systems can exploit a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod's main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community, as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings and a YouTube channel with talks and lectures of interest for the modelling community. For more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has inspired and supported the SysMod community. Its members get progressively more involved and actively contribute to the annual COSI meeting and several related community workshops and meetings, focusing on specific topics, including particular techniques for computational modelling or standardisation efforts.

A multi-approach and multi-scale platform to model CD4+ T cells responding to infections.

Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node's ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology.

ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast.

The understanding of the multi-scale nature of molecular networks represents a major challenge. For example, regulation of a timely cell cycle must be coordinated with growth, during which changes in metabolism occur, and integrate information from the extracellular environment, e.g. signal transduction. Forkhead transcription factors are evolutionarily conserved among eukaryotes, and coordinate a timely cell cycle progression in budding yeast. Specifically, Fkh1 and Fkh2 are expressed during a lengthy window of the cell cycle, thus are potentially able to function as hubs in the multi-scale cellular environment that interlocks various biochemical networks. Here we report on a novel ChIP-exo dataset for Fkh1 and Fkh2 in both logarithmic and stationary phases, which is analyzed by novel and existing software tools. Our analysis confirms known Forkhead targets from available ChIP-chip studies and highlights novel ones involved in the cell cycle, metabolism and signal transduction. Target genes are analyzed with respect to their function, temporal expression during the cell cycle, correlation with Fkh1 and Fkh2 as well as signaling and metabolic pathways they occur in. Furthermore, differences in targets between Fkh1 and Fkh2 are presented. Our work highlights Forkhead transcription factors as hubs that integrate multi-scale networks to achieve proper timing of cell division in budding yeast.

Conserved forkhead dimerization motif controls DNA replication timing and spatial organization of chromosomes in S. cerevisiae.

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.

GEMMER: GEnome-wide tool for Multi-scale Modeling data Extraction and Representation for Saccharomyces cerevisiae.

Motivation: Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available; however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner. Results: We present GEMMER (GEnome-wide tool for Multi-scale Modeling data Extraction and Representation), a web-based data-integration tool that facilitates high quality visualization of physical, regulatory and genetic interactions between proteins/genes in Saccharomyces cerevisiae. GEMMER creates network visualizations that integrate information on function, temporal expression, localization and abundance from various existing databases. GEMMER supports modeling efforts by effortlessly gathering this information and providing convenient export options for images and their underlying data. Availability and implementation: GEMMER is freely available at http://gemmer.barberislab.com. Source code, written in Python, JavaScript library D3js, PHP and JSON, is freely available at https://github.com/barberislab/GEMMER. Supplementary information: Supplementary data are available at Bioinformatics online.

Learning to read and write in evolution: from static pseudoenzymes and pseudosignalers to dynamic gear shifters.

We present a systems biology view on pseudoenzymes that acknowledges that genes are not selfish: the genome is. With network function as the selectable unit, there has been an evolutionary bonus for recombination of functions of and within proteins. Many proteins house a functionality by which they 'read' the cell's state, and one by which they 'write' and thereby change that state. Should the writer domain lose its cognate function, a 'pseudoenzyme' or 'pseudosignaler' arises. GlnK involved in Escherichia coli ammonia assimilation may well be a pseudosignaler, associating 'reading' the nitrogen state of the cell to 'writing' the ammonium uptake activity. We identify functional pseudosignalers in the cyclin-dependent kinase complexes regulating cell-cycle progression. For the mitogen-activated protein kinase pathway, we illustrate how a 'dead' pseudosignaler could produce potentially selectable functionalities. Four billion years ago, bioenergetics may have shuffled 'electron-writers', producing various networks that all served the same function of anaerobic ATP synthesis and carbon assimilation from hydrogen and carbon dioxide, but at different ATP/acetate ratios. This would have enabled organisms to deal with variable challenges of energy need and substrate supply. The same principle might enable 'gear-shifting' in real time, by dynamically generating different pseudo-redox enzymes, reshuffling their coenzymes, and rerouting network fluxes. Non-stationary pH gradients in thermal vents together with similar such shuffling mechanisms may have produced a first selectable proton-motivated pyrophosphate synthase and subsequent ATP synthase. A combination of functionalities into enzymes, signalers, and the pseudo-versions thereof may offer fitness in terms of plasticity, both in real time and in evolution.

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

The eukaryotic cell cycle is robustly designed, with interacting molecules organized within a definite topology that ensures temporal precision of its phase transitions. Its underlying dynamics are regulated by molecular switches, for which remarkable insights have been provided by genetic and molecular biology efforts. In a number of cases, this information has been made predictive, through computational models. These models have allowed for the identification of novel molecular mechanisms, later validated experimentally. Logical modeling represents one of the youngest approaches to address cell cycle regulation. We summarize the advances that this type of modeling has achieved to reproduce and predict cell cycle dynamics. Furthermore, we present the challenge that this type of modeling is now ready to tackle: its integration with intracellular networks, and its formalisms, to understand crosstalks underlying systems level properties, ultimate aim of multi-scale models. Specifically, we discuss and illustrate how such an integration may be realized, by integrating a minimal logical model of the cell cycle with a metabolic network.

The Peculiar Glycolytic Pathway in Hyperthermophylic Archaea: Understanding Its Whims by Experimentation In Silico.

Mathematical models are key to systems biology where they typically describe the topology and dynamics of biological networks, listing biochemical entities and their relationships with one another. Some (hyper)thermophilic Archaea contain an enzyme, called non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase (GAPN), which catalyzes the direct oxidation of glyceraldehyde-3-phosphate to 3-phosphoglycerate omitting adenosine 5'-triphosphate (ATP) formation by substrate-level-phosphorylation via phosphoglycerate kinase. In this study we formulate three hypotheses that could explain functionally why GAPN exists in these Archaea, and then construct and use mathematical models to test these three hypotheses. We used kinetic parameters of enzymes of Sulfolobus solfataricus (S. solfataricus) which is a thermo-acidophilic archaeon that grows optimally between 60 and 90 °C and between pH 2 and 4. For comparison, we used a model of Saccharomyces cerevisiae (S. cerevisiae), an organism that can live at moderate temperatures. We find that both the first hypothesis, i.e., that the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) plus phosphoglycerate kinase (PGK) route (the alternative to GAPN) is thermodynamically too much uphill and the third hypothesis, i.e., that GAPDH plus PGK are required to carry the flux in the gluconeogenic direction, are correct. The second hypothesis, i.e., that the GAPDH plus PGK route delivers less than the 1 ATP per pyruvate that is delivered by the GAPN route, is only correct when GAPDH reaction has a high rate and 1,3-bis-phosphoglycerate (BPG) spontaneously degrades to 3PG at a high rate.

Systems Pharmacology: An opinion on how to turn the impossible into grand challenges.

A pharmacology that hits single disease-causing molecules with a single drug passively distributing to the target tissue, was almost ready. Such a pharmacology is not (going to be) effective however: a great many diseases are systems biology diseases; complex networks of some hundred thousand types of molecule, determine the functions that constitute human health, through nonlinear interactions. Malfunctions are caused by a variety of molecular failures at the same time; rarely the same variety in different individuals; in complex constellations of OR and AND logics. Few molecules cause disease single-handedly and few drugs will cure the disease all by themselves when dosed for a limited amount of time. We here discuss the implications that this discovery of the network nature of disease should have for pharmacology. We suggest ways in which pharmacokinetics, pharmacodynamics, but also systems biology and genomics may have to change so as better to deal with systems-biology diseases.

Metabolic imbalance driving immune cell phenotype switching in autoimmune disorders: Tipping the balance of T- and B-cell interactions.

The interplay between the immune system and the metabolic state of a cell is intricate. In all phases of an immune response, the corresponding metabolic changes shall occur to support its modulation, in addition to the signalling through the cytokine environment and immune receptor stimulation. While autoimmune disorders may develop because of a metabolic imbalance that modulates switching between T-cell phenotypes, the effects that the interaction between T and B cells have on one another's cellular metabolism are yet to be understood in disease context. Here, we propose a perspective which highlights the potential of targeting metabolism to modulate T- and B-cell subtypes populations as well as T-B and B-T cell interactions to successfully treat autoimmune disorders. Specifically, we envision how metabolic changes can tip the balance of immune cells interactions, through definite mechanisms in both health and disease, to explain phenotype switches of B and T cells. Within this scenario, we highlight targeting metabolism that link inflammation, immunometabolism, epigenetics and ageing, is critical to understand inflammatory disorders. The combination of treatments targeting immune cells that cause (T/B) cell phenotype imbalances, and the metabolic pathways involved, may increase the effectiveness of treatment of autoimmune disorders, and/or ameliorate their symptoms to improve patients' quality of life.

Diet quality and anxiety: a critical overview with focus on the gut microbiome.

Anxiety disorders disproportionally affect females and are frequently comorbid with eating disorders. With the emerging field of nutritional psychiatry, focus has been put on the impact of diet quality in anxiety pathophysiology and gut microbiome underlying mechanisms. While the relationship between diet and anxiety is bidirectional, improving dietary habits could better facilitate the actions of pharmacological and psychological therapies, or prevent their use. A better understanding of how gut bacteria mediate and moderate such relationship could further contribute to develop personalized programs and inform probiotics and prebiotics manufacturing. To date, studies that look simultaneously at diet, the gut microbiome, and anxiety are missing as only pairwise relationships among them have been investigated. Therefore, this study aims at summarizing and integrating the existing knowledge on the dietary effects on anxiety with focus on gut microbiome. Findings on the effects of diet on anxiety are critically summarized and reinterpreted in relation to findings on (i) the effects of diet on the gut microbiome composition, and (ii) the associations between the abundance of certain gut bacteria and anxiety. This novel interpretation suggests a theoretical model where the relationship between diet and anxiety is mediated and/or modulated by the gut microbiome through multiple mechanisms. In parallel, this study critically evaluates methodologies employed in the nutritional field to investigate the effects of diet on anxiety highlighting a lack of systematic operationalization and assessment strategies. Therefore, it ultimately proposes a novel evidence-based approach that can enhance studies validity, reliability, systematicity, and translation to clinical and community settings.

Unraveling interactions of cell cycle-regulating proteins Sic1 and B-type cyclins in living yeast cells: a FLIM-FRET approach.

Sic1, cyclin-dependent kinase inhibitor of budding yeast, is synthesized in anaphase and largely degraded at the S-phase onset to regulate timing of DNA synthesis. Sic1 interacts with phase-specific B-type cyclin (Clb)-kinase (Cdk1) complexes, central regulators in cell cycle control. Its appearance is timed to mediate reduction in kinase activities at appropriate stages. Clbs are unstable proteins with extremely short half-lives. Interactions of Sic1 with Clbs have been detected both in vitro and in vivo by high-throughput genome-wide screenings. Furthermore, we have recently shown that Sic1 regulates waves of Clbs, acting as a timer in their appearance, thus controlling Cdk1-Clbs activation. The molecular mechanism is not yet fully understood but is hypothesized to occur via stoichiometric binding of Sic1 to Cdk1-Clb complexes. Using Förster resonance energy transfer (FRET) via fluorescence lifetime imaging microscopy (FLIM), we showed association of Sic1 to Clb cyclins in living yeast cells. This finding is consistent with the notion that inhibition of kinase activity can occur over the whole cell cycle progression despite variable Sic1 levels. Specifically, Sic1/Clb3 interaction was observed for the first time, and Sic1/Clb2 and Sic1/Clb5 pairs were confirmed, but no Sic1/Clb4 interaction was found, which suggests that, despite high functional homology between Clbs, only some of them can target Sic1 function in vivo.

Exploring cell cycle-mediated regulations of glycolysis in budding yeast.

Coordination of cell cycle with metabolism exists in all cell types that grow by division. It serves to build a new cell, (i) fueling building blocks for the synthesis of proteins, nucleic acids, and membranes, and (ii) producing energy through glycolysis. Cyclin-dependent kinases (Cdks) play an essential role in this coordination, thereby in the regulation of cell division. Cdks are functional homologs across eukaryotes and are the engines that drive cell cycle events and the clocks that time them. Their function is counteracted by stoichiometric inhibitors; specifically, inhibitors of cyclin-cyclin dependent kinase (cyclin/Cdk) complexes allow for their activity at specific times. Here, we provide a new perspective about the yet unknown cell cycle mechanisms impacting on metabolism. We first investigated the effect of the mitotic cyclin/Cdk1 complex Cyclin B/Cdk1-functional homolog in mammalian cells of the budding yeast Clb2/Cdk1-on yeast metabolic enzymes of, or related to, the glycolysis pathway. Six glycolytic enzymes (Glk1, Hxk2, Pgi1, Fba1, Tdh1, and Pgk1) were subjected to in vitro Cdk-mediated phosphorylation assays. Glucose-6-phosphate dehydrogenase (Zwf1), the first enzyme in the pentose phosphate pathway that is important for NADPH production, and 6-phospho-fructo-2-kinase (Pfk27), which catalyzes fructose-2,6-bisphosphate synthesis, a key regulator of glycolysis, were also included in the study. We found that, among these metabolic enzymes, Fba1 and Pgk1 may be phosphorylated by Cdk1, in addition to the known Cdk1-mediated phosphorylation of Gph1. We then investigated the possible effect of Sic1, stoichiometric inhibitor of mitotic cyclin/Cdk1 complexes in budding yeast, on the activities of three most relevant glycolytic enzymes: Hxk2, Glk1, and Tdh1. We found that Sic1 may have a negative effect on Hxk2. Altogether, we reveal possible new routes, to be further explored, through which cell cycle may regulate cellular metabolism. Because of the functional homology of cyclin/Cdk complexes and their stoichiometric inhibitors across evolution, our findings may be relevant for the regulation of cell division in eukaryotes.

Two-way communication between cell cycle and metabolism in budding yeast: what do we know?

Coordination of cell cycle and metabolism exists in all cells. The building of a new cell is a process that requires metabolic commitment to the provision of both Gibbs energy and building blocks for proteins, nucleic acids, and membranes. On the other hand, the cell cycle machinery will assess and regulate its metabolic environment before it makes decisions on when to enter the next cell cycle phase. Furthermore, more and more evidence demonstrate that the metabolism can be regulated by cell cycle progression, as different biosynthesis pathways are preferentially active in different cell cycle phases. Here, we review the available literature providing a critical overview on how cell cycle and metabolism may be coupled with one other, bidirectionally, in the budding yeast Saccharomyces cerevisiae.

Molecular systems biology of Sic1 in yeast cell cycle regulation through multiscale modeling.

Cell cycle control is highly regulated to guarantee the precise timing of events essential for cell growth, i.e., DNA replication onset and cell division. Failure of this control plays a role in cancer and molecules called cyclin-dependent kinase (Cdk) inhibitors (Ckis) exploit a critical function in cell cycle timing. Here we present a multiscale modeling where experimental and computational studies have been employed to investigate structure, function and temporal dynamics of the Cki Sic1 that regulates cell cycle progression in Saccharomyces cerevisiae. Structural analyses reveal molecular details of the interaction between Sic1 and Cdk/cyclin complexes, and biochemical investigation reveals Sic1 function in analogy to its human counterpart p27(Kip1), whose deregulation leads to failure in timing of kinase activation and, therefore, to cancer. Following these findings, a bottom-up systems biology approach has been developed to characterize modular networks addressing Sic1 regulatory function. Through complementary experimentation and modeling, we suggest a mechanism that underlies Sic1 function in controlling temporal waves of cyclins to ensure correct timing of the phase-specific Cdk activities.

T cell phenotype switching in autoimmune disorders: Clinical significance of targeting metabolism.

Increasing efforts points to the understanding of how to maximize the capabilities of the adaptive immune system to fight against the development of immune and inflammatory disorders. Here we focus on the role of T cells as immune cells which subtype imbalance may lead to disease onset. Specifically, we propose that autoimmune disorders may develop as a consequence of a metabolic imbalance that modulates switching between T cell phenotypes. We highlight a Systems Biology strategy that integrates computational metabolic modelling with experimental data to investigate the metabolic requirements of T cell phenotypes, and to predict metabolic genes that may be targeted in autoimmune inflammatory diseases. Thus, we propose a new perspective of targeting T cell metabolism to modulate the immune response and prevent T cell phenotype imbalance, which may help to repurpose already existing drugs targeting metabolism for therapeutic treatment.

Unveiling Forkhead-mediated regulation of yeast cell cycle and metabolic networks.

Transcription factors are regulators of the cell's genomic landscape. By switching single genes or entire molecular pathways on or off, transcription factors modulate the precise timing of their activation. The Forkhead (Fkh) transcription factors are evolutionarily conserved to regulate organismal physiology and cell division. In addition to molecular biology and biochemical efforts, genome-wide studies have been conducted to characterize the genomic landscape potentially regulated by Forkheads in eukaryotes. Here, we discuss and interpret findings reported in six genome-wide Chromatin ImmunoPrecipitation (ChIP) studies, with a particular focus on ChIP-chip and ChIP-exo. We highlight their power and challenges to address Forkhead-mediated regulation of the cellular landscape in budding yeast. Expression changes of the targets identified in the binding assays are investigated by taking expression data for Forkhead deletion and overexpression into account. Forkheads are revealed as regulators of the metabolic network through which cell cycle dynamics may be temporally coordinated further, in addition to their well-known role as regulators of the gene cluster responsible for cell division.

Stigma/Style Cell-Cycle Inhibitor 1, a Regulator of Cell Proliferation, Interacts With a Specific 14-3-3 Protein and Is Degraded During Cell Division.

The final shape and size of plant organs are determined by a network of genes that modulate cell proliferation and expansion. Among those, SCI1 (Stigma/style Cell-cycle Inhibitor 1) functions by inhibiting cell proliferation during pistil development. Alterations in SCI1 expression levels can lead to remarkable stigma/style size changes. Recently, we demonstrated that SCI1 starts to be expressed at the specification of the Nicotiana tabacum floral meristem and is expressed at all floral meristematic cells. To elucidate how SCI1 regulates cell proliferation, we screened a stigma/style cDNA library through the yeast two-hybrid (Y2H) system, using SCI1 as bait. Among the interaction partners, we identified the 14-3-3D protein of the Non-Epsilon group. The interaction between SCI1 and 14-3-3D was confirmed by pulldown and co-immunoprecipitation experiments. 14-3-3D forms homo- and heterodimers in the cytoplasm of plant cells and interacts with SCI1 in the nucleus, as demonstrated by Bimolecular Fluorescence Complementation (BiFC). Analyses of SCI1-GFP fluorescence through the cell-cycle progression revealed its presence in the nucleoli during interphase and prophase. At metaphase, SCI1-GFP fluorescence faded and was no longer detected at anaphase, reappearing at telophase. Upon treatment with the 26S proteasome inhibitor MG132, SCI1-GFP was stabilized during cell division. Site-directed mutagenesis of seven serines into alanines in the predicted 14-3-3 binding sites on the SCI1 sequence prevented its degradation during mitosis. Our results demonstrate that SCI1 degradation at the beginning of metaphase is dependent on the phosphorylation of serine residues and on the action of the 26S proteasome. We concluded that SCI1 stability/degradation is cell-cycle regulated, consistent with its role in fine-tuning cell proliferation.

Quantitative model of eukaryotic Cdk control through the Forkhead CONTROLLER.

In budding yeast, synchronization of waves of mitotic cyclins that activate the Cdk1 kinase occur through Forkhead transcription factors. These molecules act as controllers of their sequential order and may account for the separation in time of incompatible processes. Here, a Forkhead-mediated design principle underlying the quantitative model of Cdk control is proposed for budding yeast. This design rationalizes timing of cell division, through progressive and coordinated cyclin/Cdk-mediated phosphorylation of Forkhead, and autonomous cyclin/Cdk oscillations. A "clock unit" incorporating this design that regulates timing of cell division is proposed for both yeast and mammals, and has a DRIVER operating the incompatible processes that is instructed by multiple CLOCKS. TIMERS determine whether the clocks are active, whereas CONTROLLERS determine how quickly the clocks shall function depending on external MODULATORS. This "clock unit" may coordinate temporal waves of cyclin/Cdk concentration/activity in the eukaryotic cell cycle making the driver operate the incompatible processes, at separate times.

Cyclin/Forkhead-mediated coordination of cyclin waves: an autonomous oscillator rationalizing the quantitative model of Cdk control for budding yeast.

Networks of interacting molecules organize topology, amount, and timing of biological functions. Systems biology concepts required to pin down 'network motifs' or 'design principles' for time-dependent processes have been developed for the cell division cycle, through integration of predictive computer modeling with quantitative experimentation. A dynamic coordination of sequential waves of cyclin-dependent kinases (cyclin/Cdk) with the transcription factors network offers insights to investigate how incompatible processes are kept separate in time during the eukaryotic cell cycle. Here this coordination is discussed for the Forkhead transcription factors in light of missing gaps in the current knowledge of cell cycle control in budding yeast. An emergent design principle is proposed where cyclin waves are synchronized by a cyclin/Cdk-mediated feed-forward regulation through the Forkhead as a transcriptional timer. This design is rationalized by the bidirectional interaction between mitotic cyclins and the Forkhead transcriptional timer, resulting in an autonomous oscillator that may be instrumental for a well-timed progression throughout the cell cycle. The regulation centered around the cyclin/Cdk-Forkhead axis can be pivotal to timely coordinate cell cycle dynamics, thereby to actuate the quantitative model of Cdk control.