Mechanical constraint from growing jaw facilitates mammalian dental diversity

Much of the basic information about individual organ development comes from studies using model species. Whereas conservation of gene regulatory networks across higher taxa supports generalizations made from a limited number of species, generality of mechanistic inferences remains to be tested in tissue culture systems. Here, using mammalian tooth explants cultured in isolation, we investigate self-regulation of patterning by comparing developing molars of the mouse, the model species of mammalian research, and the bank vole. A distinct patterning difference between the vole and the mouse molars is the alternate cusp offset present in the vole. Analyses of both species using 3D reconstructions of developing molars and jaws, computational modeling of cusp patterning, and tooth explants cultured with small braces show that correct cusp offset requires constraints on the lateral expansion of the developing tooth. Vole molars cultured without the braces lose their cusp offset, and mouse molars cultured with the braces develop a cusp offset. Our results suggest that cusp offset, which changes frequently in mammalian evolution, is more dependent on the 3D support of the developing jaw than other aspects of tooth shape. This jaw–tooth integration of a specific aspect of the tooth phenotype indicates that organs may outsource specific aspects of their morphology to be regulated by adjacent body parts or organs. Comparative studies of morphologically different species are needed to infer the principles of organogenesis.

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In Silico Design of Self-Optimizing Integrated Metabolic and Gene Regulatory Networks

10.1101/007658 ◽

2014 ◽

Author(s):

Timo Maarleveld ◽

Bennet K NG ◽

Herbert Sauro ◽

Kyung Kim

Keyword(s):

Gene Regulatory Networks ◽

Environmental Conditions ◽

In Silico ◽

Regulatory Networks ◽

Metabolic Networks ◽

Self Regulation ◽

Protein Signaling ◽

Target Network ◽

Gene Regulatory ◽

Protein Signaling Networks

Biological organisms acclimatize to varying environmental conditions via active self-regulation of internal gene regulatory networks, metabolic networks, and protein signaling networks. While much work has been done to elucidate the topologies of individual networks in isolation, understanding of inter-network regulatory mechanisms remains limited. This shortcoming is of particular relevance to synthetic biology. Synthetic biological circuits tend to lose their engineered functionality over generational time, primarily due to the deleterious stress that they exert on their host organisms. To reduce this stress (and thus minimize loss of functionality) synthetic circuits must be sensitive to the health of the host organism. Development of integrated regulatory systems is therefore essential to robust synthetic biological systems. The aim of this study was to develop integrated gene-regulatory and metabolic networks which self-optimize in response to varying environmental conditions. We performed \emph{in silico} evolution to develop such networks using a two-step approach: (1) We optimized metabolic networks given a constrained amount of available enzyme. Here, we found that a proportional relationship between flux control coefficients and enzyme mass holds in all linear sub-networks of branched networks, except those sub-networks which contain allosteric regulators. Network optimization was performed by iteratively redistributing enzyme until flux through the network was maximized. Optimization was performed for a range of boundary metabolite conditions to develop a profile of optimal enzyme distributions as a function of environmental conditions. (2) We generated and evolved randomized gene regulatory networks to modulate the enzymes of a target metabolic pathway. The objective of the gene regulatory networks was to produce the optimal distribution of metabolic network enzymes given specific boundary metabolite conditions of the target network. Competitive evolutionary algorithms were applied to optimize the specific structures and kinetic parameters of the gene regulatory networks. With this method, we demonstrate the possibility of algorithmic development of integrated adaptive gene and metabolic regulatory networks which dynamically self-optimize in response to changing environmental conditions.

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Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits

10.1101/188672 ◽

2017 ◽

Cited By ~ 2

Author(s):

Sergei Tarasov

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Markov Models ◽

Phylogenetic Inference ◽

Phenotypic Traits ◽

Character State ◽

Body Parts ◽

Gene Regulatory ◽

Hidden States ◽

New Framework

AbstractModeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes – hierarchical and hidden – which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of structured Markov models with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of gene regulatory networks and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing tail color problem (that aims at coding tail when it is absent) and presents a previously unknown issue called the “two-scientist paradox”. The latter issue refers to the confounding nature of the coding of a trait and the hidden processes driving the trait’s evolution; failing to account for the hidden process may result in a bias, which can be avoided by using hidden state models. All this provides a clear guideline for coding traits into characters. This paper gives practical examples of using the new framework for phylogenetic inference and comparative analysis.

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