Robust development as a consequence of generated positional information

Evolution of hierarchy and irreversibility in theoretical cell differentiation model

The process of cell differentiation in multicellular organisms is characterized by hierarchy and irreversibility in many cases. However, the conditions and selection pressures that give rise to these characteristics remain poorly understood. By using a mathematical model, here we show that the network of differentiation potency (differentiation diagram) becomes necessarily hierarchical and irreversible by increasing the number of terminally differentiated states under certain conditions. The mechanisms generating these characteristics are clarified using geometry in the cell state space. The results demonstrate that the hierarchical organization and irreversibility can manifest independently of direct selection pressures associated with these characteristics, instead they appear to evolve as byproducts of selective forces favoring a diversity of differentiated cell types. The study also provides a new perspective on the structure of gene regulatory networks that produce hierarchical and irreversible differentiation diagrams. These results indicate some constraints on cell differentiation, which are expected to provide a starting point for theoretical discussion of the implicit limits and directions of evolution in multicellular organisms.

An Evo-Devo Perspective on Multicellular Development of Myxobacteria

The transition to multicellularity, recognized as one the major transitions in evolution, has occurred independently several times. While multicellular development has been extensively studied in zygotic organisms including plant and animal groups, just a few aggregative multicellular organisms have been employed as model organisms for the study of multicellularity. Studying different evolutionary origins and modes of multicellularity enables comparative analyses that can help identifying lineage-specific aspects of multicellular evolution and generic factors and mechanisms involved in the transition to multicellularity. Among aggregative multicellular organisms, myxobacteria are a valuable system to explore the particularities that aggregation confers to the evolution of multicellularity and mechanisms shared with clonal organisms. Moreover, myxobacteria species develop fruiting bodies displaying a range of morphological diversity. In this review, we aim to synthesize diverse lines of evidence regarding myxobacteria development and discuss them in the context of Evo-Devo concepts and approaches. First, we briefly describe the developmental processes in myxobacteria, present an updated comparative analysis of the genes involved in their developmental processes and discuss these and other lines of evidence in terms of co-option and developmental system drift, two concepts key to Evo-Devo studies. Next, as has been suggested from Evo-Devo approaches, we discuss how broad comparative studies and integration of diverse genetic, physicochemical, and environmental factors into experimental and theoretical models can further our understanding of myxobacterial development, phenotypic variation, and evolution.

Partial Redundancy and Morphological Homeostasis: Reliable Development through Overlapping Mechanisms

How might organisms grow into their desired physical forms in spite of environmental and genetic variation? How do they maintain this form in spite of physical insults? This article presents a case study in simulated morphogenesis, using a physics-based model for embryonic epithelial tissue. The challenges of the underlying physics force the introduction of closed-loop controllers for both spatial patterning and geometric structure. Reliable development is achieved not through elaborate control procedures or exact solutions, but through crude layering of independent, overlapping mechanisms. As a consequence, development and regeneration together become one process, morphological homeostasis, which, owing to its internal feedbacks and partially redundant architecture, is remarkably robust to both knockout damage and environmental variation. The incomplete nature of such redundancy furnishes an evolutionary rationale for its preservation, in spite of individual knockout experiments that may suggest it has little purpose.

Morphogenetic fields in embryogenesis, regeneration, and cancer: non-local control of complex patterning

Establishment of shape during embryonic development, and the maintenance of shape against injury or tumorigenesis, requires constant coordination of cell behaviors toward the patterning needs of the host organism. Molecular cell biology and genetics have made great strides in understanding the mechanisms that regulate cell function. However, generalized rational control of shape is still largely beyond our current capabilities. Significant instructive signals function at long range to provide positional information and other cues to regulate organism-wide systems properties like anatomical polarity and size control. Is complex morphogenesis best understood as the emergent property of local cell interactions, or as the outcome of a computational process that is guided by a physically encoded map or template of the final goal state? Here I review recent data and molecular mechanisms relevant to morphogenetic fields: large-scale systems of physical properties that have been proposed to store patterning information during embryogenesis, regenerative repair, and cancer suppression that ultimately controls anatomy. Placing special emphasis on the role of endogenous bioelectric signals as an important component of the morphogenetic field, I speculate on novel approaches for the computational modeling and control of these fields with applications to synthetic biology, regenerative medicine, and evolutionary developmental biology.

Predicting pancreas cell fate decisions and reprogramming with a hierarchical multi-attractor model

Cell fate reprogramming, such as the generation of insulin-producing β cells from other pancreas cells, can be achieved by external modulation of key transcription factors. However, the known gene regulatory interactions that form a complex network with multiple feedback loops make it increasingly difficult to design the cell reprogramming scheme because the linear regulatory pathways as schemes of causal influences upon cell lineages are inadequate for predicting the effect of transcriptional perturbation. However, sufficient information on regulatory networks is usually not available for detailed formal models. Here we demonstrate that by using the qualitatively described regulatory interactions as the basis for a coarse-grained dynamical ODE (ordinary differential equation) based model, it is possible to recapitulate the observed attractors of the exocrine and β, δ, α endocrine cells and to predict which gene perturbation can result in desired lineage reprogramming. Our model indicates that the constraints imposed by the incompletely elucidated regulatory network architecture suffice to build a predictive model for making informed decisions in choosing the set of transcription factors that need to be modulated for fate reprogramming.

Morphogenesis by coupled regulatory networks: reliable control of positional information and proportion regulation

Based on a non-equilibrium mechanism for spatial pattern formation we study how position information can be controlled by locally coupled discrete dynamical networks, similar to gene regulation networks of cells in a developing multicellular organism. As an example we study the developmental problems of domain formation and proportion regulation in the presence of noise, as well as in the presence of cell flow. We find that networks that solve this task exhibit a hierarchical structure of information processing and are of similar complexity as developmental circuits of living cells. Proportion regulation is scalable with system size and leads to sharp, precisely localized boundaries of gene expression domains, even for large numbers of cells. A detailed analysis of noise-induced dynamics, using a mean-field approximation, shows that noise in gene expression states stabilizes (rather than disrupts) the spatial pattern in the presence of cell movements, both for stationary as well as growing systems. Finally, we discuss how this mechanism could be realized in the highly dynamic environment of growing tissues in multicellular organisms.

Chaotic expression dynamics implies pluripotency: when theory and experiment meet

Background

During normal development, cells undergo a unidirectional course of differentiation that progressively decreases the number of cell types they can potentially become. Pluripotent stem cells can differentiate into several types of cells, but terminally differentiated cells cannot differentiate any further. A fundamental problem in stem cell biology is the characterization of the difference in cellular states, e.g., gene expression profiles, between pluripotent stem cells and terminally differentiated cells.

Presentation of the hypothesis

To address the problem, we developed a dynamical systems model of cells with intracellular protein expression dynamics and interactions with each other. According to extensive simulations, cells with irregular (chaotic) oscillations in gene expression dynamics have the potential to differentiate into other cell types. During development, such complex oscillations are lost successively, leading to a loss of pluripotency. These simulation results, together with recent single-cell-level measurements in stem cells, led us to the following hypothesis regarding pluripotency: Chaotic oscillation in the expression of some genes leads to cell pluripotency and affords cellular state heterogeneity, which is supported by itinerancy over quasi-stable states. Differentiation stabilizes these states, leading to a loss of pluripotency.

Testing the hypothesis

To test the hypothesis, it is crucial to measure the time course of gene expression levels at the single-cell level by fluorescence microscopy and fluorescence-activated cell sorting (FACS) analysis. By analyzing the time series of single-cell-level expression data, one can distinguish whether the variation in protein expression level over time is due only to stochasticity in expression dynamics or originates from the chaotic dynamics inherent to cells, as our hypothesis predicts. By further analyzing the expression in differentiated cell types, one can examine whether the loss of pluripotency is accompanied by a loss of oscillation.

Implications of the hypothesis

Recovery of pluripotency from determined cells is a long-standing aspiration, from both scientific and clinical perspectives. Our hypothesis suggests a feasible route to recover the potential to differentiate, i.e., by increasing the variety of expressed genes to restore chaotic expression dynamics, as is consistent with the recent generation of induced pluripotent stem (iPS) cells.

Reviewers

This article was reviewed by David Krakauer, Jeroen van Zon (nominated by Rob de Boer), and Williams S. Hlavacek.

A system for modelling cell-cell interactions during plant morphogenesis

BACKGROUND AND AIMS

During the development of multicellular organisms, cells are capable of interacting with each other through a range of biological and physical mechanisms. A description of these networks of cell-cell interactions is essential for an understanding of how cellular activity is co-ordinated in regionalized functional entities such as tissues or organs. The difficulty of experimenting on living tissues has been a major limitation to describing such systems, and computer modelling appears particularly helpful to characterize the behaviour of multicellular systems. The experimental difficulties inherent to the multitude of parallel interactions that underlie cellular morphogenesis have led to the need for computer models.

METHODS

A new generic model of plant cellular morphogenesis is described that expresses interactions amongst cellular entities explicitly: the plant is described as a multi-scale structure, and interactions between distinct entities is established through a topological neighbourhood. Tissues are represented as 2D biphasic systems where the cell wall responds to turgor pressure through a viscous yielding of the cell wall.

KEY RESULTS

This principle was used in the development of the CellModeller software, a generic tool dedicated to the analysis and modelling of plant morphogenesis. The system was applied to three contrasting study cases illustrating genetic, hormonal and mechanical factors involved in plant morphogenesis.

CONCLUSIONS

Plant morphogenesis is fundamentally a cellular process and the CellModeller software, through its underlying generic model, provides an advanced research tool to analyse coupled physical and biological morphogenetic mechanisms.

Signaling between cells attached to a surface

We present a kinetic model allowing one to classify likely scenarios of protein-mediated communication between attached cells of two distinct types. In our treatment, messenger proteins, synthesized in type-1 cells, are considered to penetrate the external membrane of these cells, diffuse in the extracellular medium, associate with the receptors in the external membrane of cells of both types, and induce intracellular signal transduction cascades, influencing the development of cells. Protein degradation inside and outside cells is taken into account as well.

Conceptual models to understand tissue stem cell organization

PURPOSE OF REVIEW

Theoretic and, in particular, mathematic models can help biologists to select and design experiments, to highlight general principles, to discriminate similar and to link different phenomena, and to predict novel features. Specifically, they contribute to an understanding of latent mechanisms and crucial parameters of biologic processes. The following review gives an overview of recent developments in the field of hematopoietic tissue stem cell modeling.

RECENT FINDINGS

A number of experimental findings on heterogeneity, flexibility, and plasticity of hematopoietic and other tissue stem cells are challenging the classic stem cell concept of a predefined intrinsic stem cell program. Self-organizing systems provide a more elegant and comprehensive alternative to explain experimental data.

SUMMARY

Within the last few decades, modeling approaches in stem cell biology have evolved and now encompass a broad spectrum of phenomena, ranging from the cellular level to the tissue level. The application of theoretic models is currently suggesting that we abandon the classic assumption of a strict developmental hierarchy and understand stem cell organization as a dynamic, functional process. Such a perspective has implications for a prospective characterization of tissue stem cells (eg, regarding gene expression profiles and genetic regulation patterns).