Induction and the Turing-Child field in development

The central problem in biological development is the understanding of epigenesis. The dominant theory of development in the last 80 years that also purports to explain epigenesis is induction theory. It suggests that development is driven by sequential inductions where each "induction" (in one sense of the word induction) is effected by the action of an inducing part of the embryo on a responding part of the embryo. The theory stems from Spemann and Mangold (W.Roux' Arch.f.Entw.d.Organis.u.mikrosk.Anat.100 (1924) 599) who transplanted a tissue from the dorsal blastopore lip of Triturus into the ventral ectoderm of another gastrula and thus initiated and "induced" (in another sense of the word induction) gastrulation and embryogenesis in the ventral side of the host that became a double embryo (siamese twins). We explain this induction, i.e. the formation of the double embryo, according to the Child theory and the Turing-Gierer-Meinhardt theory when it is also assumed that cAMP and ATP are the Turing activator and inhibitor, respectively. Spemann and Mangold (W.Roux' Arch.f.Entw.d.Organis.u.mikrosk.Anat.100 (1924) 599) also suggested that the ingressing mesoderm induces the overlying ectoderm to form the neural plate and neural tube. This 'neural induction', the 'primary embryonic induction', became the cornerstone of induction theory, i.e. of the sequential induction concept referred to above. But we argue that the metabolic gradients that precede and accompany neurulation, as obtained by Child, also for Triturus, arise through a Turing self-organization if it is assumed that cAMP and ATP are the Turing morphogens, and these gradients are the cause and primary event of neurulation. Thus there is no need to invoke the 'neural induction'. It is argued that fundamental events such as gastrulation and also organ formation are caused by the Turing-Child field and not by sequential induction. Similar principles, such as bud formation caused by a radial metabolic pattern that transforms to a longitudinal pattern, govern the formation, for example, of the mouth and the gut. The formation and localization of bottle cells is explained according to the Child-Turing field and modern biochemistry. The chemical metabolic pre-pattern precedes, and causes, morphogenesis and differentiation as envisaged by Turing. The Spemann and Mangold (W.Roux' Arch.f.Entw.d.Organis.u.mikrosk.Anat.100 (1924) 599) transplantation experiment when performed on a sea urchin duplicates not only the phenotype but also the metabolic (reduction) pattern. These experimental results, by Horstadius, predicted by Child, follow from the Turing-Gierer-Meinhardt theory if it is assumed that cAMP and ATP are the Turing morphogens. If the transplantation is performed not onto the whole sea urchin but onto only a part of it, that manifests only a part of the metabolic pattern, then from the part a phenotypic whole underlain by a normal and a whole metabolic pattern can be rescued. These experimental results of Horstadius follow from Turing theory if cAMP and ATP are the Turing morphogens. Understanding how to transform a part into a whole can be valuable in regenerative medicine. Unspecific induction of a secondary amphibian embryo is similar to the induction of posterior structures at the anterior pole of an insect, and the "double abdomen" (and Kalthoff's experimental results) of the midge Smittia resulting from UV irradiation of the anterior pole, can be explained by Meinhardt theory of unspecific induction if ATP is the Turing morphogen. When not working on regeneration, Child investigated intact living organisms and his observation method was not disruptive to normal development, whereas workers in induction theory work with pieces and in general disrupt normal development. We conclude that the Turing-Child field causes all development and explains epigenesis. Sequential induction does not explain epigenesis and does not exist in normal development. But induction in the sense of a transplantation leading to double embryo or rescuing a whole phenotype from a part is of interest.     

Simulation of nonlinear reaction-diffusion equations

Discrete particle simulation techniques developed for problems in plasma physics have been adapted to investigate one-dimensional dissipative structures. The results of the model are found to be consistent with bifurcation analysis of nonlinear reaction-diffusion equations. Two distinct type of behavior are observed corresponding to a localized steady-state solution and a time-dependent solution.     

Morphogenesis in an asymmetric medium

Some key experiments of artificial production ofsitus inversus viscerum are briefly reviewed and a two-step mechanism for the explanation of the systematic asymmetric visceral arrangement in vertebrates is proposed. A two-variable reaction-diffusion system displaying a symmetry-breaking bifurcation is considered, and it is demonstrated that a slight asymmetry of the boundary conditions can give rise to a marked asymmetry in the resulting dissipative structure in both one-and three-dimensional systems. A criterion is formulated allowing classification of reaction-diffusion systems operating in a three-dimensional space with regard to their ability to incorporate slight asymmetries at the boundaries in the form of a chiral dissipative structure.     

Polarity and form regulation in development and reconstitution

In the literature, it is often assumed, for example with respect to Hydra, that several Turing systems coexist and it is also assumed that maintaining the polar profile, even when the system increases in size, is important for the polarity of the final phenotype. It is shown here that in reality there is only one Turing system, Child's system. To obtain a complete polar individual or organ, whether in reconstitution or development, it is essential that the complete succession of metabolic patterns occurs. Child's concepts of physiological dominance, subordination and isolation are interpreted in the light of Turing theory and in particular the Turing wavelength. It is emphasised, particularly by pointing to Child's metabolic patterns in coelenterates, both in development and in reconstitution, that it is the elongation that drives the succession polar metabolic pattern-->bipolar metabolic pattern, and this corresponds to the prediction of Turing theory supporting the thesis that Child's metabolic pattern is a Turing pattern. It is shown that if we assume that ATP is the Turing inhibitor then the many results of Child about the reduction of the scale of organisation with the decrease in the intensity of the energy metabolism correspond to the reduction of the Turing wavelength. The interplay between the Turing wavelength and the length of the form explains the conditions of reconstitution under which partial forms, wholes and form regulation are obtained. It is suggested that higher metabolism is responsible for both larger size and larger Turing wavelength thus securing form regulation. The results could be of importance in modern 'regenerative biology'. Heteromorphosis, i.e. animals with two heads (or two tails), one at each end, is explained by a bipolar Turing-Child metabolic pattern replacing a polar metabolic pattern. This can be brought about by chemical or by genetic means and indeed the prediction for Drosophila that the transition, wild type-->Bicaudal D, occurs because a polar Turing pattern is replaced by bipolar Turing pattern is confirmed, again if we accept that Child's metabolic pattern is the underlying Turing pattern. Child's experiments on Drosophila, including the requirement of critical length for metabolic polarity, are explained by Turing theory. Phenocopies and phenotypes are explained by the Turing-Child theory. It is shown that both Child's results about metabolic patterns and modern results for Hydra about gap junctions, 'endogeneous inhibitor' and gene expression, are correlated and explained by (cAMP, ATP) Turing theory. It is argued that the double-gradient hypothesis is incorrect in its original formulation and that it is Child's conception of succeeding metabolic patterns that is the correct one and that it corresponds to the prediction of the Turing theory.     

Diffusion versus network models as descriptions for the spread of prion diseases in the brain

In this paper we will discuss different modeling approaches for the spread of prion diseases in the brain. Firstly, we will compare reaction-diffusion models with models of epidemic diseases on networks. The solutions of the resulting reaction-diffusion equations exhibit traveling wave behavior on a one-dimensional domain, and the wave speed can be estimated. The models can be tested for diffusion-driven (Turing) instability, which could present a possible mechanism for the formation of plaques. We also show that the reaction-diffusion systems are capable of reproducing experimental data on prion spread in the mouse visual system. Secondly, we study classical epidemic models on networks, and use these models to study the influence of the network topology on the disease progression.     

Turing instabilities in general systems

We present necessary and sufficient conditions on the stability matrix of a general n(≥2)-dimensional reaction-diffusion system which guarantee that its uniform steady state can undergo a Turing bifurcation. The necessary (kinetic) condition, requiring that the system be composed of an unstable (or activator) and a stable (or inhibitor) subsystem, and the sufficient condition of sufficiently rapid inhibitor diffusion relative to the activator subsystem are established in three theorems which form the core of our results. Given the possibility that the unstable (activator) subsystem involves several species (dimensions), we present a classification of the analytically deduced Turing bifurcations into p (1 ≤p≤ (n− 1)) different classes. For n = 3 dimensions we illustrate numerically that two types of steady Turing pattern arise in one spatial dimension in a generic reaction-diffusion system. The results confirm the validity of an earlier conjecture [12] and they also characterise the class of so-called strongly stable matrices for which only necessary conditions have been known before [23, 24]. One of the main consequences of the present work is that biological morphogens, which have so far been expected to be single chemical species [1–9], may instead be composed of two or more interacting species forming an unstable subsystem. Received: 21 September 1999 / Revised version: 21 June 2000 / Published online: 24 November 2000     

Stability analysis of non-autonomous reaction-diffusion systems: the effects of growing domains

By using asymptotic theory, we generalise the Turing diffusively-driven instability conditions for reaction-diffusion systems with slow, isotropic domain growth. There are two fundamental biological differences between the Turing conditions on fixed and growing domains, namely: (i) we need not enforce cross nor pure kinetic conditions and (ii) the restriction to activator-inhibitor kinetics to induce pattern formation on a growing biological system is no longer a requirement. Our theoretical findings are confirmed and reinforced by numerical simulations for the special cases of isotropic linear, exponential and logistic growth profiles. In particular we illustrate an example of a reaction-diffusion system which cannot exhibit a diffusively-driven instability on a fixed domain but is unstable in the presence of slow growth.     

The Role of Type 4 Phosphodiesterases in Generating Microdomains of cAMP: Large Scale Stochastic Simulations

Cyclic AMP (cAMP) and its main effector Protein Kinase A (PKA) are critical for several aspects of neuronal function including synaptic plasticity. Specificity of synaptic plasticity requires that cAMP activates PKA in a highly localized manner despite the speed with which cAMP diffuses. Two mechanisms have been proposed to produce localized elevations in cAMP, known as microdomains: impeded diffusion, and high phosphodiesterase (PDE) activity. This paper investigates the mechanism of localized cAMP signaling using a computational model of the biochemical network in the HEK293 cell, which is a subset of pathways involved in PKA-dependent synaptic plasticity. This biochemical network includes cAMP production, PKA activation, and cAMP degradation by PDE activity. The model is implemented in NeuroRD: novel, computationally efficient, stochastic reaction-diffusion software, and is constrained by intracellular cAMP dynamics that were determined experimentally by real-time imaging using an Epac-based FRET sensor (H30). The model reproduces the high concentration cAMP microdomain in the submembrane region, distinct from the lower concentration of cAMP in the cytosol. Simulations further demonstrate that generation of the cAMP microdomain requires a pool of PDE4D anchored in the cytosol and also requires PKA-mediated phosphorylation of PDE4D which increases its activity. The microdomain does not require impeded diffusion of cAMP, confirming that barriers are not required for microdomains. The simulations reported here further demonstrate the utility of the new stochastic reaction-diffusion algorithm for exploring signaling pathways in spatially complex structures such as neurons.     

In search of Turing in vivo: understanding Nodal and Lefty behavior

The Turing reaction-diffusion model proposes that short-range activators and long-range inhibitors can generate complex patterns. In a Science study, Müller et al. (2012) assess behavioral determinants of Nodal and Lefty, TGFβ-related molecules that constitute an activator/inhibitor system, and provide evidence that the factors indeed form a reaction-diffusion system.     

Mixed-mode pattern in Doublefoot mutant mouse limb--Turing reaction-diffusion model on a growing domain during limb development

It has been suggested that the Turing reaction-diffusion model on a growing domain is applicable during limb development, but experimental evidence for this hypothesis has been lacking. In the present study, we found that in Doublefoot mutant mice, which have supernumerary digits due to overexpansion of the limb bud, thin digits exist in the proximal part of the hand or foot, which sometimes become normal abruptly at the distal part. We found that exactly the same behaviour can be reproduced by numerical simulation of the simplest possible Turing reaction-diffusion model on a growing domain. We analytically showed that this pattern is related to the saturation of activator kinetics in the model. Furthermore, we showed that a number of experimentally observed phenomena in this system can be explained within the context of a Turing reaction-diffusion model. Finally, we make some experimentally testable predictions.     

A feedback quenched oscillator produces turing patterning with one diffuser

Efforts to engineer synthetic gene networks that spontaneously produce patterning in multicellular ensembles have focused on Turing's original model and the "activator-inhibitor" models of Meinhardt and Gierer. Systems based on this model are notoriously difficult to engineer. We present the first demonstration that Turing pattern formation can arise in a new family of oscillator-driven gene network topologies, specifically when a second feedback loop is introduced which quenches oscillations and incorporates a diffusible molecule. We provide an analysis of the system that predicts the range of kinetic parameters over which patterning should emerge and demonstrate the system's viability using stochastic simulations of a field of cells using realistic parameters. The primary goal of this paper is to provide a circuit architecture which can be implemented with relative ease by practitioners and which could serve as a model system for pattern generation in synthetic multicellular systems. Given the wide range of oscillatory circuits in natural systems, our system supports the tantalizing possibility that Turing pattern formation in natural multicellular systems can arise from oscillator-driven mechanisms.     

Transition from polar to duplicate patterns

The existence of symmetric nonuniform solutions in nonlinear reaction-diffusion systems is examined. In the first part of the paper, we establish systematically the bifurcation diagram of small amplitude solutions in the vicinity of the two first bifurcation points. It is shown that:

1. The system can adopt a stable symmetric solution (basic wave number 2) if the value of the bifurcation parameter is changed or if the initial polar structure (basic wave number 1) is sufficiently perturbed.
2. This behavior is independent of the particular reaction-diffusion model proposed and of the number of intermediate components (?2) involved.

In the second part of the paper, analogies are established between the possibilities offered by the bifurcation diagrams, involving only the two first primary branches, and the observation that in the early development of different organisms, appropriate experimental manipulations may switch the normal (polar) developmental pattern to a duplicate structure.     

Propagation of Turing patterns in a plankton model

The paper is devoted to a reaction-diffusion system of equations describing phytoplankton and zooplankton distributions. Linear stability analysis of the model is carried out. Turing and Hopf stability boundaries are found. Emergence of two-dimensional spatial structures is illustrated by numerical simulations. Travelling waves between various stationary solutions are investigated. Transitions between homogeneous in space stationary solutions and Turing structures are studied.     

Modeling the Role of Diffusion Coefficients on Turing Instability in a Reaction-diffusion Prey-predator System

The paper is concerned with the effect of variable dispersal rates on Turing instability of a non-Lotka-Volterra reaction-diffusion system. In ecological applications, the dispersal rates of different species tends to oscillate in time. This oscillation is modeled by temporal variation in the diffusion coefficient with large as well as small periodicity. The case of large periodicity is analyzed using the theory of Floquet multipliers and that of the small periodicity by using Hill's equation. The effect of such variation on the resulting Turing space is studied. A comparative analysis of the Turing spaces with constant diffusivity and variable diffusivities is performed. Numerical simulations are carried out to support analytical findings.     

Chemical morphogenesis: Turing patterns in an experimental chemical system

Patterns resulting from the sole interplay between reaction and diffusion are probably involved in certain stages of morphogenesis in biological systems, as initially proposed by Alan Turing. Self-organization phenomena of this type can only develop in nonlinear systems (i.e. involving positive and negative feedback loops) maintained far from equilibrium. We present Turing patterns experimentally observed in a chemical system. An oscillating chemical reaction, the CIMA reaction, is operated in an open spatial reactor designed in order to obtain a pure reaction-diffusion system. The two types of Turing patterns observed, hexagonal arrays of spots and parallel stripes, are characterized by an intrinsic wavelength. We identify the origin of the necessary difference of diffusivity between activator and inhibitor. We also describe a pattern growth mechanism by spot splitting that recalls cell division.     

Spontaneous polarization in eukaryotic gradient sensing: a mathematical model based on mutual inhibition of frontness and backness pathways

A key problem of eukaryotic cell motility is the signaling mechanism of chemoattractant gradient sensing. Recent experiments have revealed the molecular correlate of gradient sensing: Frontness molecules, such as PI3P and Rac, localize at the front end of the cell, and backness molecules, such as Rho and myosin II, accumulate at the back of the cell. Importantly, this frontness-backness polarization occurs spontaneously even if the cells are exposed to uniform chemoattractant profiles. The spontaneous polarization suggests that the gradient sensing machinery undergoes a Turing bifurcation. This has led to several classical activator-inhibitor and activator-substrate models which identify the frontness molecules with the activator. Conspicuously absent from these models is any accounting of the backness molecules. This stands in sharp contrast to experiments which show that the backness pathways inhibit the frontness pathways. Here, we formulate a model based on the mutually inhibitory interaction between the frontness and backness pathways. The model builds upon the mutual inhibition model proposed by Bourne and coworkers [Xu et al., 2003. Divergent signals and cytoskeletal assemblies regulate self-organizing polarity in neutrophils. Cell 114, 201-214.]. We show that mutual inhibition alone, without the help of any positive feedback (autocatalysis), can trigger spontaneous polarization of the frontness and backness pathways. The spatial distribution of the frontness and backness molecules in response to inhibition and activation of the frontness and backness pathways are consistent with those observed in experiments. Furthermore, depending on the parameter values, the model yields spatial distributions corresponding to chemoattraction (frontness pathways in-phase with the external gradient) and chemorepulsion (frontness pathways out-of-phase with the external gradient). Analysis of the model suggests a mechanism for the chemorepulsion-to-chemoattraction transition observed in neurons.     

Interspecific influence on mobility and Turing instability

In this paper we formulate a multi-patch multi-species model in which the percapita emigration rate of one species depends on the density of some other species. We then focus on Turing instability to examine if and when this cross-emigration response has crucial effects. We find that the type of interaction matters greatly. In the case of competition a cross-emigration response promotes pattern formation by exercising a destabilizing influence; in particular, it may lead to diffusive instability provided that the response is sufficiently strong, which contrasts sharply with the well-known fact that the standard competition system does not exhibit Turing instability. In the case of prey-predator or activator-inhibitor interaction it acts against pattern formation by exerting a stabilizing effect; in particular, the diffusive instability, even though it may happen in a standard system, never occurs when the response is sufficiently strong. We conclude that the cross-emigration response is an important factor that should not be ignored when pattern formation is the issue.     

Turing instability in pioneer/climax species interactions

Systems of pioneer and climax species are used to model interactions of species whose reproductive capacity is sensitive to population density in their shared ecosystem. Intraspecies interaction coefficients can be adjusted so that spatially homogeneous solutions are stable to small perturbations. In a reaction-diffusion pioneer/climax model we will determine the critical value of the diffusion rate of the climax species, below which the equilibrium solution is unstable to non-homogeneous perturbations. For diffusion rates smaller than this critical value, an equilibrium solution remains stable to spatially homogeneous perturbations but is unstable to non-homogeneous perturbations. A Turing (diffusional) bifurcation leads to the formation of spatial patterns in species' densities. Forcing, interpreted as stocking or harvesting of the species, can reverse the bifurcation and establish equilibrium solutions which are stable to small perturbations. The implicit function theorem is used to determine whether stocking or harvesting of one of the species in the model is the appropriate remedy for diffusional instability. The use of stocking or harvesting by a natural resource manager thus influences the long-term dynamics and spatial distribution of species in a pioneer/climax ecosystem.     

Unravelling the Turing bifurcation using spatially varying diffusion coefficients

. The Turing bifurcation is the basic bifurcation generating spatial pattern, and lies at the heart of almost all mathematical models for patterning in biology and chemistry. In this paper the authors determine the structure of this bifurcation for two coupled reaction diffusion equations on a two-dimensional square spatial domain when the diffusion coefficients have a small explicit variation in space across the domain. In the case of homogeneous diffusivities, the Turing bifurcation is highly degenerate. Using a two variable perturbation method, the authors show that the small explicit spatial inhomogeneity splits the bifurcation into two separate primary and two separate secondary bifurcations, with all solution branches distinct. This splitting of the bifurcation is more effective than that given by making the domain slightly rectangular, and shows clearly the structure of the Turing bifurcation and the way in which the! var ious solution branches collapse together as the spatial variation is reduced. The authors determine the stability of the solution branches, which indicates that several new phenomena are introduced by the spatial variation, including stable subcritical striped patterns, and the possibility that stable stripes lose stability supercritically to give stable spotted patterns.. Received: 10 January 1996/Revised version: 3 July 1996     

Bifurcation analysis of nonlinear reaction-diffusion equations—II. Steady state solutions and comparison with numerical simulations

The steady state spatial patterns arising in nonlinear reaction-diffusion systems beyond an instability point of the thermodynamic branch are studied on a simple model network. A detailed comparison between the analytical solutions of the kinetic equations, obtained by bifurcation theory, and the results of computer simulations is presented for different boundary conditions. The characteristics of the dissipative structures are discussed and it is shown that the observed behavior depends strongly on both the boundary and initial conditions. The theoretical expressions are limited to the neighborhood of the marginal stability point. Computer simulations allow not only the verification of their predictions but also the investigation of the behavior of the system for larger deviations from the instability point. It is shown that new features such as multiplicity of solutions and secondary bifurcations can appear in this region.