A Dynamic Gene Regulatory Network Model That Recovers the Cyclic Behavior of Arabidopsis thaliana Cell Cycle

Cell cycle control is fundamental in eukaryotic development. Several modeling efforts have been used to integrate the complex network of interacting molecular components involved in cell cycle dynamics. In this paper, we aimed at recovering the regulatory logic upstream of previously known components of cell cycle control, with the aim of understanding the mechanisms underlying the emergence of the cyclic behavior of such components. We focus on Arabidopsis thaliana, but given that many components of cell cycle regulation are conserved among eukaryotes, when experimental data for this system was not available, we considered experimental results from yeast and animal systems. We are proposing a Boolean gene regulatory network (GRN) that converges into only one robust limit cycle attractor that closely resembles the cyclic behavior of the key cell-cycle molecular components and other regulators considered here. We validate the model by comparing our in silico configurations with data from loss- and gain-of-function mutants, where the endocyclic behavior also was recovered. Additionally, we approximate a continuous model and recovered the temporal periodic expression profiles of the cell-cycle molecular components involved, thus suggesting that the single limit cycle attractor recovered with the Boolean model is not an artifact of its discrete and synchronous nature, but rather an emergent consequence of the inherent characteristics of the regulatory logic proposed here. This dynamical model, hence provides a novel theoretical framework to address cell cycle regulation in plants, and it can also be used to propose novel predictions regarding cell cycle regulation in other eukaryotes.     

Modeling the temporal dynamics of master regulators and CtrA proteolysis in Caulobacter crescentus cell cycle

The cell cycle of Caulobacter crescentus involves the polar morphogenesis and an asymmetric cell division driven by precise interactions and regulations of proteins, which makes Caulobacter an ideal model organism for investigating bacterial cell development and differentiation. The abundance of molecular data accumulated on Caulobacter motivates system biologists to analyze the complex regulatory network of cell cycle via quantitative modeling. In this paper, We propose a comprehensive model to accurately characterize the underlying mechanisms of cell cycle regulation based on the study of: a) chromosome replication and methylation; b) interactive pathways of five master regulatory proteins including DnaA, GcrA, CcrM, CtrA, and SciP, as well as novel consideration of their corresponding mRNAs; c) cell cycle-dependent proteolysis of CtrA through hierarchical protease complexes. The temporal dynamics of our simulation results are able to closely replicate an extensive set of experimental observations and capture the main phenotype of seven mutant strains of Caulobacter crescentus. Collectively, the proposed model can be used to predict phenotypes of other mutant cases, especially for nonviable strains which are hard to cultivate and observe. Moreover, the module of cyclic proteolysis is an efficient tool to study the metabolism of proteins with similar mechanisms.     

Logical analysis of the budding yeast cell cycle

The budding yeast Saccharomyces cerevisiae is a model organism that is commonly used to investigate control of the eukaryotic cell cycle. Moreover, because of the extensive experimental data on wild type and mutant phenotypes, it is also particularly suitable for mathematical modelling and analysis. Here, I present a new Boolean model of the budding yeast cell cycle. This model is consistent with a wide range of wild type and mutant phenotypes and shows remarkable robustness against perturbations, both to reaction times and the states of component genes/proteins. Because of its simple logical nature, the model is suitable for sub-network analysis, which can be used to identify a four node core regulatory circuit underlying cell cycle regulation. Sub-network analysis can also be used to identify key sub-dynamics that are essential for viable cell cycle control, as well as identifying the sub-dynamics that are most variable between different mutants.     

Cell Wall Stress Stimulates the Activity of the Protein Kinase StkP of Streptococcus pneumoniae,Leading to Multiple Phosphorylation

Streptococcus pneumoniae is an opportunistic human pathogen that encodes a single eukaryotic-type Ser/Thr protein kinase StkP and its functional counterpart, the protein phosphatase PhpP. These signaling enzymes play critical roles in coordinating cell division and growth in pneumococci. In this study, we determined the proteome and phosphoproteome profiles of relevant mutants. Comparison of those with the wild-type provided a representative dataset of novel phosphoacceptor sites and StkP-dependent substrates. StkP phosphorylates key proteins involved in cell division and cell wall biosynthesis in both the unencapsulated laboratory strain Rx1 and the encapsulated virulent strain D39. Furthermore, we show that StkP plays an important role in triggering an adaptive response induced by a cell wall-directed antibiotic. Phosphorylation of the sensor histidine kinase WalK and downregulation of proteins of the WalRK core regulon suggest crosstalk between StkP and the WalRK two-component system. Analysis of proteomic profiles led to the identification of gene clusters regulated by catabolite control mechanisms, indicating a tight coupling of carbon metabolism and cell wall homeostasis. The imbalance of steady-state protein phosphorylation in the mutants as well as after antibiotic treatment is accompanied by an accumulation of the global Spx regulator, indicating a Spx-mediated envelope stress response. In summary, StkP relays the perceived signal of cell wall status to key cell division and regulatory proteins, controlling the cell cycle and cell wall homeostasis.     

suc1 is an essential gene involved in both the cell cycle and growth in fission yeast

The gene suc1 encodes a product which suppresses certain temperature sensitive mutants of the cell cycle control gene cdc2 of Schizosaccharomyces pombe. Mutants in the suc1 gene or over-expression of its product leads to delays in mitotic and meiotic nuclear division. Deletion of the suc1 gene is lethal and generates some cells blocked in the cell cycle and others impaired in cellular growth. It is likely that the suc1 gene product binds and forms unstable complexes with the cdc2 protein kinase and with other proteins necessary for the cell cycle and cellular growth. suc1 may have a regulatory role in these processes.     

Mathematical modeling of fission yeast Schizosaccharomyces pombe cell cycle: exploring the role of multiple phosphatases

Cell cycle is the central process that regulates growth and division in all eukaryotes. Based on the environmental condition sensed, the cell lies in a resting phase G0 or proceeds through the cyclic cell division process (G1??S??G2??M). These series of events and phase transitions are governed mainly by the highly conserved Cyclin dependent kinases (Cdks) and its positive and negative regulators. The cell cycle regulation of fission yeast Schizosaccharomyces pombe is modeled in this study. The study exploits a detailed molecular interaction map compiled based on the published model and experimental data. There are accumulating evidences about the prominent regulatory role of specific phosphatases in cell cycle regulations. The current study emphasizes the possible role of multiple phosphatases that governs the cell cycle regulation in fission yeast S. pombe. The ability of the model to reproduce the reported regulatory profile for the wild-type and various mutants was verified though simulations.     

The architecture and conservation pattern of whole-cell control circuitry

The control circuitry that directs and paces Caulobacter cell cycle progression involves the entire cell operating as an integrated system. This control circuitry monitors the environment and the internal state of the cell, including the cell topology, as it orchestrates orderly activation of cell cycle subsystems and Caulobacter's asymmetric cell division. The proteins of the Caulobacter cell cycle control system and its internal organization are co-conserved across many alphaproteobacteria species, but there are great differences in the regulatory apparatus' functionality and peripheral connectivity to other cellular subsystems from species to species. This pattern is similar to that observed for the “kernels” of the regulatory networks that regulate development of metazoan body plans. The Caulobacter cell cycle control system has been exquisitely optimized as a total system for robust operation in the face of internal stochastic noise and environmental uncertainty. When sufficient details accumulate, as for Caulobacter cell cycle regulation, the system design has been found to be eminently rational and indeed consistent with good design practices for human-designed asynchronous control systems.     

Molecular Docking of Known Carcinogen 4- (Methyl-nitrosamino)-1-(3-pyridyl)-1-butanone (NNK) with Cyclin Dependent Kinases towards Its Potential Role in Cell Cycle Perturbation

Cell cycle is maintained almost all the times and is controlled by various regulatory proteins and their complexes (Cdk+Cyclin) in different phases of interphase (G₁, S and G₂) and mitosis of cell cycle. A number of mechanisms have been proposed for the initiation and progression of carcinogenesis by abruption in cell cycle process. One of the important features of cancer/carcinogenesis is functional loss of these cell cycle regulatory proteins particularly in CDKs and cyclins. We hypothesize that there is a direct involvement of these cell cycle regulatory proteins not only at the genetic level but also proteins level, during the initiation of carcinogenesis. Therefore, it becomes significant to determine inconsistency in the functioning of regulatory proteins due to interaction with carcinogen 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Hence, we investigated the interaction efficiency of NNK, against cell cycle regulatory proteins. We found a different value of ΔG (free energy of binding) among the studied proteins ranging between -3.29 to -7.25 kcal/mol was observed. To validate the results, we considered Human Oxy-Hemoglobin at 1.25 Å Resolution, [PDB_ID:1HHO] as a +ve control, (binding energy -6.06 kcal/mol). Finally, the CDK8 (PDB_ID:3RGF) and CDK2 (PDB_ID:3DDP) regulatory proteins showing significantly strong molecular interaction with NNK -7.25 kcal/mol, -6.19 kcal/mol respectively were analyzed in details. In this study we predicted that CDK8 protein fails to form functional complex with its complementary partner cyclin C in presence of NNK. Consequently, inconsistency of functioning in regulatory proteins might lead to the abruption in cell cycle progression; contribute to the loss of cell cycle control and subsequently increasing the possibility of carcinogenesis.     

Cell size at S phase initiation: an emergent property of the G1/S network

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.     

Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models

Background

The physical periphery of a biological cell is mainly described by signaling pathways which are triggered by transmembrane proteins and receptors that are sentinels to control the whole gene regulatory network of a cell. However, our current knowledge about the gene regulatory mechanisms that are governed by extracellular signals is severely limited.

Results

The purpose of this paper is three fold. First, we infer a gene regulatory network from a large-scale B-cell lymphoma expression data set using the C3NET algorithm. Second, we provide a functional and structural analysis of the largest connected component of this network, revealing that this network component corresponds to the peripheral region of a cell. Third, we analyze the hierarchical organization of network components of the whole inferred B-cell gene regulatory network by introducing a new approach which exploits the variability within the data as well as the inferential characteristics of C3NET. As a result, we find a functional bisection of the network corresponding to different cellular components.

Conclusions

Overall, our study allows to highlight the peripheral gene regulatory network of B-cells and shows that it is centered around hub transmembrane proteins located at the physical periphery of the cell. In addition, we identify a variety of novel pathological transmembrane proteins such as ion channel complexes and signaling receptors in B-cell lymphoma.     

Regulators of cell death in disease resistance

Cell death and disease resistance are intimately connected in plants. Plant disease resistance genes (R genes) are key components in pathogen perception and have a potential to activate cell death pathways. Analysis of R proteins suggests common molecular mechanisms for pathogen recognition and signal emission whereas the subsequent signalling unexpectedly involves a network of pathways of parallel, branching and converging action. Disease resistance signalling mutants have revealed novel types of regulatory proteins whose biochemical functions are still unknown. Accumulation of small molecules such as salicylic acid, reactive oxygen intermediates, and nitric oxide amplifies resistance responses and directs cells to initiate cell death programs. Genetic analyses of lesion mimic mutants provide a glimpse of how cell death thresholds are set via an interplay of positive and negative regulatory components.     

A quantitative study of the division cycle of Caulobacter crescentus stalked cells

Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genome replication and partitioning to daughter cells. From published experimental evidence, we propose a molecular mechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on the synthesis and degradation of three “master regulator” proteins (CtrA, GcrA, and DnaA), is converted into a quantitative model, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts for important details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. It reproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, and predicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating the cell cycle of C. crescentus are conserved among many genera of α-proteobacteria, the proposed mechanism may be applicable to other species of importance in agriculture and medicine.     

Bud-Localization of CLB2 mRNA Can Constitute a Growth Rate Dependent Daughter Sizer

Maintenance of cellular size is a fundamental systems level process that requires balancing of cell growth with proliferation. This is achieved via the cell division cycle, which is driven by the sequential accumulation and destruction of cyclins. The regulatory network around these cyclins, particularly in G₁, has been interpreted as a size control network in budding yeast, and cell size as being decisive for the START transition. However, it is not clear why disruptions in the G₁ network may lead to altered size rather than loss of size control, or why the S-G₂-M duration also depends on nutrients. With a mathematical population model comprised of individually growing cells, we show that cyclin translation would suffice to explain the observed growth rate dependence of cell volume at START. Moreover, we assess the impact of the observed bud-localisation of the G₂ cyclin CLB2 mRNA, and find that localised cyclin translation could provide an efficient mechanism for measuring the biosynthetic capacity in specific compartments: The mother in G₁, and the growing bud in G₂. Hence, iteration of the same principle can ensure that the mother cell is strong enough to grow a bud, and that the bud is strong enough for independent life. Cell sizes emerge in the model, which predicts that a single CDK-cyclin pair per growth phase suffices for size control in budding yeast, despite the necessity of the cell cycle network around the cyclins to integrate other cues. Size control seems to be exerted twice, where the G₂/M control affects bud size through bud-localized translation of CLB2 mRNA, explaining the dependence of the S-G₂-M duration on nutrients. Taken together, our findings suggest that cell size is an emergent rather than a regulatory property of the network linking growth and proliferation.     

A Cell Cycle and Nutritional Checkpoint Controlling Bacterial Surface Adhesion

In natural environments, bacteria often adhere to surfaces where they form complex multicellular communities. Surface adherence is determined by the biochemical composition of the cell envelope. We describe a novel regulatory mechanism by which the bacterium, Caulobacter crescentus, integrates cell cycle and nutritional signals to control development of an adhesive envelope structure known as the holdfast. Specifically, we have discovered a 68-residue protein inhibitor of holdfast development (HfiA) that directly targets a conserved glycolipid glycosyltransferase required for holdfast production (HfsJ). Multiple cell cycle regulators associate with the hfiA and hfsJ promoters and control their expression, temporally constraining holdfast development to the late stages of G1. HfiA further functions as part of a ‘nutritional override’ system that decouples holdfast development from the cell cycle in response to nutritional cues. This control mechanism can limit surface adhesion in nutritionally sub-optimal environments without affecting cell cycle progression. We conclude that post-translational regulation of cell envelope enzymes by small proteins like HfiA may provide a general means to modulate the surface properties of bacterial cells.     

Elementary network reconstruction: A framework for the analysis of regulatory networks in biological systems

Complexity of regulatory networks arises from the high degree of interaction between network components such as DNA, RNA, proteins, and metabolites. We have developed a modeling tool, elementary network reconstruction (ENR), to characterize these networks. ENR is a knowledge-driven, steady state, deterministic, quantitative modeling approach based on linear perturbation theory. In ENR we demonstrate a novel means of expressing control mechanisms by way of dimensionless steady state gains relating input and output variables, which are purely in terms of species abundances (extensive variables). As a result of systematic enumeration of network species in n×n matrix, the two properties of linear perturbation are manifested in graphical representations: transitive property is evident in a special L-shape structure, and additive property is evident in multiple L-shape structures arriving at the same matrix cell. Upon imposing mechanistic (lowest-level) gains, network self-assembly through transitive and additive properties results in elucidation of inherent topology and explicit cataloging of higher level gains, which in turn can be used to predict perturbation results. Application of ENR to the regulatory network behind carbon catabolite repression in Escherichia coli is presented. Through incorporation of known molecular mechanisms governing transient and permanent repressions, the ENR model correctly predicts several key features of this regulatory network, including a 50% downshift in intracellular cAMP level upon exposure to glucose. Since functional genomics studies are mainly concerned with redistribution of species abundances in perturbed systems, ENR could be exploited in the system-level analysis of biological systems.