Mathematical model of a cell size checkpoint

Growth and the cell cycle in green algae dividing by multiple fission

Most cells divide into two daughter cells; however, some green algae can have different division patterns in which a single mother cell can sometimes give rise to up to thousands of daughter cells. Although such cell cycle patterns can be very complex, they are governed by the same general concepts as the most common binary fission. Moreover, cell cycle progression appears to be connected with size, since cells need to ensure that their size after division will not drop below the limit required for survival. Although the exact mechanism that lets cells measure cell size remains largely unknown, there have been several prominent hypotheses that try to explain it.

eGFRD in all dimensions

Biochemical reactions often occur at low copy numbers but at once in crowded and diverse environments. Space and stochasticity therefore play an essential role in biochemical networks. Spatial-stochastic simulations have become a prominent tool for understanding how stochasticity at the microscopic level influences the macroscopic behavior of such systems. While particle-based models guarantee the level of detail necessary to accurately describe the microscopic dynamics at very low copy numbers, the algorithms used to simulate them typically imply trade-offs between computational efficiency and biochemical accuracy. eGFRD (enhanced Green's Function Reaction Dynamics) is an exact algorithm that evades such trade-offs by partitioning the N-particle system into M ≤ N analytically tractable one- and two-particle systems; the analytical solutions (Green's functions) then are used to implement an event-driven particle-based scheme that allows particles to make large jumps in time and space while retaining access to their state variables at arbitrary simulation times. Here we present "eGFRD2," a new eGFRD version that implements the principle of eGFRD in all dimensions, thus enabling efficient particle-based simulation of biochemical reaction-diffusion processes in the 3D cytoplasm, on 2D planes representing membranes, and on 1D elongated cylinders representative of, e.g., cytoskeletal tracks or DNA; in 1D, it also incorporates convective motion used to model active transport. We find that, for low particle densities, eGFRD2 is up to 6 orders of magnitude faster than conventional Brownian dynamics. We exemplify the capabilities of eGFRD2 by simulating an idealized model of Pom1 gradient formation, which involves 3D diffusion, active transport on microtubules, and autophosphorylation on the membrane, confirming recent experimental and theoretical results on this system to hold under genuinely stochastic conditions.

A simple molecular mechanism explains multiple patterns of cell-size regulation

Increasingly accurate and massive data have recently shed light on the fundamental question of how cells maintain a stable size trajectory as they progress through the cell cycle. Microbes seem to use strategies ranging from a pure sizer, where the end of a given phase is triggered when the cell reaches a critical size, to pure adder, where the cell adds a constant size during a phase. Yet the biological origins of the observed spectrum of behavior remain elusive. We analyze a molecular size-control mechanism, based on experimental data from the yeast S. cerevisiae, that gives rise to behaviors smoothly interpolating between adder and sizer. The size-control is obtained from the accumulation of an activator protein that titrates an inhibitor protein. Strikingly, the size-control is composed of two different regimes: for small initial cell size, the size-control is a sizer, whereas for larger initial cell size, it is an imperfect adder, in agreement with recent experiments. Our model thus indicates that the adder and critical size behaviors may just be different dynamical regimes of a single simple biophysical mechanism.

Tunable ultrasensitivity: functional decoupling and biological insights

Sensitivity has become a basic concept in biology, but much less is known about its tuning, probably because allosteric cooperativity, the best known mechanism of sensitivity, is determined by rigid conformations of interacting molecules and is thus difficult to tune. Reversible covalent modification (RCM), owing to its systems-level ingenuity, can generate concentration based, tunable sensitivity. Using a mathematical model of regulated RCM, we find sensitivity tuning can be decomposed into two orthogonal modes, which provide great insights into vital biological processes such as tissue development and cell cycle progression. We find that decoupling of the two modes of sensitivity tuning is critical to fidelity of cell fate decision; the decoupling is thus important in development. The decomposition also allows us to solve the ‘wasteful degradation conundrum’ in budding yeast cell cycle checkpoint, which further leads to discovery of a subtle but essential difference between positive feedback and double negative feedback. The latter guarantees revocability of stress-induced cell cycle arrest; while the former does not. By studying concentration conditions in the system, we extend applicability of ultrasensitivity and explain the ubiquity of reversible covalent modification.

Molecular control of the Wee1 regulatory pathway by the SAD kinase Cdr2

Cell growth and division are tightly coordinated to maintain cell size constant during successive cell cycles. In Schizosaccharomyces pombe, the SAD kinase Cdr2 regulates the cell size at division and the positioning of the division plane. Cdr2 forms nodes on the medial cortex containing factors that constitute an inhibitory pathway for Wee1. This pathway is regulated by polar gradients of the DYRK kinase Pom1, and involves a direct inhibitor of Wee1, the SAD kinase Cdr1. Cdr2 also interacts with the anillin Mid1, which defines the division plane, and with additional components of the medial cortical nodes, including Blt1, which participate in the mitotic-promoting and cytokinetic functions of nodes. Here, we show that the interaction of Cdr2 with Wee1 and Mid1 requires the UBA domain of Cdr2, which is necessary for its kinase activity. In contrast, Cdr1 associates with the C-terminus of Cdr2, which is composed of basic and KA-1 lipid-binding domains. Mid1 also interacts with the C-terminus of Cdr2 and might bridge the N- and C-terminal domains, whereas Blt1 associates with the central spacer region. We propose that the association of Cdr2 effectors with different domains might constrain Cdr1 and Wee1 spatially to promote Wee1 inhibition upon Cdr2 kinase activation.

Aggregation-fragmentation model of robust concentration gradient formation

Concentration gradients of signaling molecules are essential for patterning during development and they have been observed in both unicellular and multicellular systems. In subcellular systems, clustering of the signaling molecule has been observed. We develop a theoretical model of cluster-mediated concentration gradient formation based on the Becker-Döring equations of aggregation-fragmentation processes. We show that such a mechanism produces robust concentration gradients on realistic time and spatial scales so long as the process of clustering does not significantly stabilize the signaling molecule. Finally, we demonstrate that such a model is applicable to the pom1p subcellular gradient in fission yeast.

Cortical regulation of cell size by a sizer cdr2p

Cells can, in principle, control their size by growing to a specified size before commencing cell division. How any cell actually senses its own size remains poorly understood. The fission yeast Schizosaccharomyces pombe are rod-shaped cells that grow to ∼14 µm in length before entering mitosis. In this study, we provide evidence that these cells sense their surface area as part of this size control mechanism. We show that cells enter mitosis at a certain surface area, as opposed to a certain volume or length. A peripheral membrane protein kinase cdr2p has properties of a dose-dependent 'sizer' that controls mitotic entry. As cells grow, the local cdr2p concentration in nodes at the medial cortex accumulates as a measure of cell surface area. Our findings, which challenge a previously proposed pom1p gradient model, lead to a new model in which cells sense their size by using cdr2p to probe the surface area over the whole cell and relay this information to the medial cortex. DOI: http://dx.doi.org/10.7554/eLife.02040.001.

Distinct levels in Pom1 gradients limit Cdr2 activity and localization to time and position division

Where and when cells divide are fundamental questions. In rod-shaped fission yeast cells, the DYRK-family kinase Pom1 is organized in concentration gradients from cell poles and controls cell division timing and positioning. Pom1 gradients restrict to mid-cell the SAD-like kinase Cdr2, which recruits Mid1/Anillin for medial division. Pom1 also delays mitotic commitment through Cdr2, which inhibits Wee1. Here, we describe quantitatively the distributions of cortical Pom1 and Cdr2. These reveal low profile overlap contrasting with previous whole-cell measurements and Cdr2 levels increase with cell elongation, raising the possibility that Pom1 regulates mitotic commitment by controlling Cdr2 medial levels. However, we show that distinct thresholds of Pom1 activity define the timing and positioning of division. Three conditions-a separation-of-function Pom1 allele, partial downregulation of Pom1 activity, and haploinsufficiency in diploid cells-yield cells that divide early, similar to pom1 deletion, but medially, like wild-type cells. In these cells, Cdr2 is localized correctly at mid-cell. Further, Cdr2 overexpression promotes precocious mitosis only in absence of Pom1. Thus, Pom1 inhibits Cdr2 for mitotic commitment independently of regulating its localization or cortical levels. Indeed, we show Pom1 restricts Cdr2 activity through phosphorylation of a C-terminal self-inhibitory tail. In summary, our results demonstrate that distinct levels in Pom1 gradients delineate a medial Cdr2 domain, for cell division placement, and control its activity, for mitotic commitment.

Robust cell size checkpoint from spatiotemporal positive feedback loop in fission yeast

Cells must maintain appropriate cell size during proliferation. Size control may be regulated by a size checkpoint that couples cell size to cell division. Biological experimental data suggests that the cell size is coupled to the cell cycle in two ways: the rates of protein synthesis and the cell polarity protein kinase Pom1 provide spatial information that is used to regulate mitosis inhibitor Wee1. Here a mathematical model involving these spatiotemporal regulations was developed and used to explore the mechanisms underlying the size checkpoint in fission yeast. Bifurcation analysis shows that when the spatiotemporal regulation is coupled to the positive feedback loops (active Cdc2 promotes its activator, Cdc25, and suppress its inhibitor, Wee1), the mitosis-promoting factor (MPF) exhibits a bistable steady-state relationship with the cell size. The switch-like response from the positive feedback loops naturally generates the cell size checkpoint. Further analysis indicated that the spatial regulation provided by Pom1 enhances the robustness of the size checkpoint in fission yeast. This was consistent with experimental data.

Image classification of human carcinoma cells using complex wavelet-based covariance descriptors

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.

An investigation of spatial signal transduction in cellular networks

Background

Spatial signal transduction plays a vital role in many intracellular processes such as eukaryotic chemotaxis, polarity generation and cell division. Furthermore it is being increasingly realized that the spatial dimension to signalling may play an important role in other apparently purely temporal signal transduction processes. It is increasingly being recognized that a conceptual basis for studying spatial signal transduction in signalling networks is necessary.

Results

In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non-diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling.

Conclusions

Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual processes.

How to build a robust intracellular concentration gradient

Concentration gradients of morphogens are critical regulators of patterning in developmental biology. Increasingly, intracellular concentration gradients have also been found to orchestrate spatial organization, but inside single cells, where they regulate processes such as cell division, polarity and mitotic spindle dynamics. Here, we discuss recent progress in understanding how such intracellular gradients can be built robustly. We focus particularly on the Pom1p gradient in fission yeast, elucidating how various buffering mechanisms operate to ensure precise gradient formation. In this case, a systems-level understanding of the entire mechanism of precise gradient construction is now within reach, with important implications for gradients in both intracellular and developmental contexts.

Noise reduction in the intracellular pom1p gradient by a dynamic clustering mechanism

Chemical gradients can generate pattern formation in biological systems. In the fission yeast Schizosaccharomyces pombe, a cortical gradient of pom1p (a DYRK-type protein kinase) functions to position sites of cytokinesis and cell polarity and to control cell length. Here, using quantitative imaging, fluorescence correlation spectroscopy, and mathematical modeling, we study how its gradient distribution is formed. Pom1p gradients exhibit large cell-to-cell variability, as well as dynamic fluctuations in each individual gradient. Our data lead to a two-state model for gradient formation in which pom1p molecules associate with the plasma membrane at cell tips and then diffuse on the membrane while aggregating into and fragmenting from clusters, before disassociating from the membrane. In contrast to a classical one-component gradient, this two-state gradient buffers against cell-to-cell variations in protein concentration. This buffering mechanism, together with time averaging to reduce intrinsic noise, allows the pom1p gradient to specify positional information in a robust manner.

Spatial modeling of cell signaling networks

The shape of a cell, the sizes of subcellular compartments, and the spatial distribution of molecules within the cytoplasm can all control how molecules interact to produce a cellular behavior. This chapter describes how these spatial features can be included in mechanistic mathematical models of cell signaling. The Virtual Cell computational modeling and simulation software is used to illustrate the considerations required to build a spatial model. An explanation of how to appropriately choose between physical formulations that implicitly or explicitly account for cell geometry and between deterministic versus stochastic formulations for molecular dynamics is provided, along with a discussion of their respective strengths and weaknesses. As a first step toward constructing a spatial model, the geometry needs to be specified and associated with the molecules, reactions, and membrane flux processes of the network. Initial conditions, diffusion coefficients, velocities, and boundary conditions complete the specifications required to define the mathematics of the model. The numerical methods used to solve reaction-diffusion problems both deterministically and stochastically are then described and some guidance is provided in how to set up and run simulations. A study of cAMP signaling in neurons ends the chapter, providing an example of the insights that can be gained in interpreting experimental results through the application of spatial modeling.

Roles of the DYRK kinase Pom2 in cytokinesis, mitochondrial morphology, and sporulation in fission yeast

Pom2 is predicted to be a dual-specificity tyrosine-phosphorylation regulated kinase (DYRK) related to Pom1 in Schizosaccharomyces pombe. DYRKs share a kinase domain capable of catalyzing autophosphorylation on tyrosine and exogenous phosphorylation on serine/threonine residues. Here we show that Pom2 is functionally different from the well-characterized Pom1, although they share 55% identity in the kinase domain and the Pom2 kinase domain functionally complements that of Pom1. Pom2 localizes to mitochondria throughout the cell cycle and to the contractile ring during late stages of cytokinesis. Overexpression but not deletion of pom2 results in severe defects in cytokinesis, indicating that Pom2 might share an overlapping function with other proteins in regulating cytokinesis. Gain and loss of function analyses reveal that Pom2 is required for maintaining mitochondrial morphology independently of microtubules. Intriguingly, most meiotic pom2Δ cells form aberrant asci with meiotic and/or forespore membrane formation defects. Taken together, Pom2 is a novel DYRK kinase involved in regulating cytokinesis, mitochondrial morphology, meiosis, and sporulation in fission yeast.