French flag gradients and Turing reaction-diffusion versus differentiation waves as models of morphogenesis

Information integration during bioelectric regulation of morphogenesis of the embryonic frog brain

Spatiotemporal patterns of cellular resting potential regulate several aspects of development. One key aspect of the bioelectric code is that transcriptional and morphogenetic states are determined not by local, single-cell, voltage levels but by specific distributions of voltage across cell sheets. We constructed and analyzed a minimal dynamical model of collective gene expression in cells based on inputs of multicellular voltage patterns. Causal integration analysis revealed a higher-order mechanism by which information about the voltage pattern was spatiotemporally integrated into gene activity, as well as a division of labor among and between the bioelectric and genetic components. We tested and confirmed predictions of this model in a system in which bioelectric control of morphogenesis regulates gene expression and organogenesis: the embryonic brain of the frog Xenopus laevis. This study demonstrates that machine learning and computational integration approaches can advance our understanding of the information-processing underlying morphogenetic decision-making, with a potential for other applications in developmental biology and regenerative medicine.

Macroevolution, differentiation trees, and the growth of coding systems

An open process of evolution of multicellular organisms is based on the rearrangement and growth of the program of differentiation that underlies biological morphogenesis. The maintenance of the final (adult) stable non-equilibrium state (stasis) of a developmental system determines the direction of the evolutionary process. This state is achieved via the sequence of differentiation events representable as differentiation trees. A special type of morphogenetic code, acting as a metacode governing gene expression, may include electromechanical signals appearing as differentiation waves. The excessive energy due to the incorporation of mitochondria in eukaryotic cells resulted not only in more active metabolism but also in establishing the differentiation code for interconnecting cells and forming tissues, which fueled the evolutionary process. The "invention" of "continuing differentiation" distinguishes multicellular eukaryotes from other organisms. The Janus-faced control, involving both top-down control by differentiation waves and bottom-up control via the mechanical consequences of cell differentiations, underlies the process of morphogenesis and results in the achievement of functional stable final states. Duplications of branches of the differentiation tree may be the basis for continuing differentiation and macroevolution, analogous to gene duplication permitting divergence of genes. Metamorphoses, if they are proven to be fusions of disparate species, may be classified according to the topology of fusions of two differentiation trees. In the process of unfolding of morphogenetic structures, microevolution can be defined as changes of the differentiation tree that preserve topology of the tree, while macroevolution represents any change that alters the topology of the differentiation tree.

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

Complex living agents consist of cells, which are themselves competent sub-agents navigating physiological and metabolic spaces. Behaviour science, evolutionary developmental biology and the field of machine intelligence all seek to understand the scaling of biological cognition: what enables individual cells to integrate their activities to result in the emergence of a novel, higher-level intelligence with large-scale goals and competencies that belong to it and not to its parts? Here, we report the results of simulations based on the TAME framework, which proposes that evolution pivoted the collective intelligence of cells during morphogenesis of the body into traditional behavioural intelligence by scaling up homeostatic competencies of cells in metabolic space. In this article, we created a minimal in silico system (two-dimensional neural cellular automata) and tested the hypothesis that evolutionary dynamics are sufficient for low-level setpoints of metabolic homeostasis in individual cells to scale up to tissue-level emergent behaviour. Our system showed the evolution of the much more complex setpoints of cell collectives (tissues) that solve a problem in morphospace: the organization of a body-wide positional information axis (the classic French flag problem in developmental biology). We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve the target morphology as well as the ability to recover from perturbation (robustness) and long-term stability (even though neither of these was directly selected for). Moreover, we observed an unexpected behaviour of sudden remodelling long after the system stabilizes. We tested this prediction in a biological system-regenerating planaria-and observed a very similar phenomenon. We propose that this system is a first step towards a quantitative understanding of how evolution scales minimal goal-directed behaviour (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.

EyeVolve, a modular PYTHON based model for simulating developmental eye type diversification

Vision is among the oldest and arguably most important sensory modalities for animals to interact with their external environment. Although many different eye types exist within the animal kingdom, mounting evidence indicates that the genetic networks required for visual system formation and function are relatively well conserved between species. This raises the question as to how common developmental programs are modified in functionally different eye types. Here, we approached this issue through EyeVolve, an open-source PYTHON-based model that recapitulates eye development based on developmental principles originally identified in Drosophila melanogaster. Proof-of-principle experiments showed that this program’s animated timeline successfully simulates early eye tissue expansion, neurogenesis, and pigment cell formation, sequentially transitioning from a disorganized pool of progenitor cells to a highly organized lattice of photoreceptor clusters wrapped with support cells. Further, tweaking just five parameters (precursor pool size, founder cell distance and placement from edge, photoreceptor subtype number, and cell death decisions) predicted a multitude of visual system layouts, reminiscent of the varied eye types found in larval and adult arthropods. This suggests that there are universal underlying mechanisms that can explain much of the existing arthropod eye diversity. Thus, EyeVolve sheds light on common principles of eye development and provides a new computational system for generating specific testable predictions about how development gives rise to diverse visual systems from a commonly specified neuroepithelial ground plan.

Disappearance of Temporal Collinearity in Vertebrates and Its Eventual Reappearance

Simple Summary

In 1999 T. Kondo and D. Duboule performed excisions of posterior upstream DNA domains in mouse embryos and they observed that for an extended excision (including Evx gene) the Hox genes of the cluster were simultaneously expressed with the first Hoxd1 gene ‘as if’ Temporal Collinearity (TC) had disappeared. According to a Biophysical Model (BM) during Hox gene expression, Hox clusters behave similar toexpanding elastic springs. For the extended upstream DNA excision, BM predicts the TC disappearance and an experiment is proposed to test this BM prediction. In the chick limb bud C. Tickle et al. observed that the excision of the apical ectodermal ridge (AER) caused the inhibition of HoxA13 expression. However, the implantation of FGF soaked beads at the tip of the limb could surprisingly rescue HoxA13 expression after 24 h so that TC is restored.Brachyury transcription factor (TF) is essential in identifying the targets of this transcription and a chromatin immunoprecipitation microarray chip (ChIP-chip) was produced which can be inserted in the mouse embryonic cells. It is here proposed to insert this chip in the mutant cells where TC has disappeared and compare it to the limb bud case.Is TC restored? It is an important issue worth exploring.

Abstract

It was observed that a cluster of ordered genes (Hox1, Hox2, Hox3…) in the genome are activated in the ontogenetic units (1, 2, 3 …) of an embryo along the Anterior/Posterior axis following the same order of the Hox genes. This Spatial Collinearity (SC) is very strange since it correlates events of very different spatial dimensions. It was later observed in vertebrates, that, in the above ordering, first is Hox1expressed in ontogenetic unit 1, followed later by Hox2 in unit 2 and even later Hox3 in unit 3. This temporal collinearity (TC) is an enigma and even to-day is explored in depth. In 1999 T. Kondo and D. Duboule, after posterior upstream extended DNA excisions, concluded that the Hox cluster behaves ‘as if’ TC disappears. Here the consideration of TC really disappearing is taken face value and its repercussions are analyzed. Furthermore, an experiment is proposed to test TC disappearance. An outcome of this experiment could be the reappearance (partial or total) of TC.

Physical Laws Shape Up HOX Gene Collinearity

Hox gene collinearity (HGC) is a multi-scalar property of many animal phyla particularly important in embryogenesis. It relates entities and events occurring in Hox clusters inside the chromosome DNA and in embryonic tissues. These two entities differ in linear size by more than four orders of magnitude. HGC is observed as spatial collinearity (SC), where the Hox genes are located in the order (Hox1, Hox2, Hox3 …) along the 3′ to 5′ direction of DNA in the genome and a corresponding sequence of ontogenetic units (E1, E2, E3, …) located along the Anterior—Posterior axis of the embryo. Expression of Hox1 occurs in E1, Hox2 in E2, Hox3 in E3, etc. Besides SC, a temporal collinearity (TC) has been also observed in many vertebrates. According to TC, first Hox1 is expressed in E1; later, Hox2 is expressed in E2, followed by Hox3 in E3, etc. Lately, doubt has been raised about whether TC really exists. A biophysical model (BM) was formulated and tested during the last 20 years. According to BM, physical forces are created which pull the Hox genes one after the other, driving them to a transcription factory domain where they are transcribed. The existing experimental data support this BM description. Symmetry is a physical–mathematical property of matter that was explored in depth by Noether who formulated a ground-breaking theory (NT) that applies to all sizes of matter. NT may be applied to biology in order to explain the origin of HGC in animals developing not only along the A/P axis, but also to animals with circular symmetry.

A short tutorial on the Janus-faced logic of differentiation waves and differentiation trees and their evolution

Differentiation waves offer a different perspective on causality in embryogenesis from that of molecular developmental biology. Janus-faced cybernetic logic, with global and local top down/bottom up dynamics, eschews reductionism, is distinct from emergence, and outlines the process theoretically. Most aspects of differentiation waves require further investigation.