Molecular self-organisation in a developmental model for the evolution of large-scale artificial neural networks

We argue that molecular self-organisation during embryonic development allows evolution to perform highly nonlinear combinatorial optimisation. A structured approach to architectural optimisation of large-scale Artificial Neural Networks using this principle is presented. We also present simulation results demonstrating the evolution of an edge detecting retina using the proposed methodology

Cis-regulatory logic in the endo16 gene: switching from a specification to a differentiation mode of control

The endo16 gene of Strongylocentrotus purpuratus encodes a secreted protein of the embryonic and larval midgut. The overall functional organization of the spatial and temporal control system of this gene are relatively well known from a series of earlier cis-regulatory studies. Our recent computational model for the logic operations of the proximal region of the endo16 control system (Module A) specifies the function of interactions at each transcription factor target site of Module A. Here, we extend sequence level functional analysis to the adjacent cis-regulatory region, Module B. The computational logic model is broadened to include B/A interactions as well as other Module B functions. Module B drives expression later in development and its major activator is responsible for a sharp, gut-specific increase in transcription after gastrulation. As shown earlier, Module B output undergoes a synergistic amplification that requires interactions within Module A. The interactions within Module B that are required to generate and transmit its output to Module A are identified. Logic considerations predicted an internal cis-regulatory switch by which spatial control of endo16 expression is shifted from Module A (early) to Module B (later). This prediction was confirmed experimentally and a distinct set of interactions in Module B that mediate the switch function was demonstrated. The endo16 computational model now provides a detailed explanation of the information processing functions executed by the cis-regulatory system of this gene throughout embryogenesis. Early in development the gene participates in the specification events that define the endomesoderm; later it functions as a gut-specific differentiation gene. The cis-regulatory switch mediates this functional change

The gene regulatory network basis of the “community effect,” and analysis of a sea urchin embryo example

The “Community Effect” denotes intra-territorial signaling amongst cells which constitute a particular tissue or embryonic progenitor field. The cells of the territory express the same transcriptional regulatory state, and the intra-territorial signaling is essential to maintenance of this specific regulatory state. The structure of the underlying gene regulatory network (GRN) subcircuitry explains the genomically wired mechanism by which community effect signaling is linked to the continuing transcriptional generation of the territorial regulatory state. A clear example is afforded by the oral ectoderm GRN of the sea urchin embryo where cis-regulatory evidence, experimental embryology, and network analysis combine to provide a complete picture. We review this example and consider less well known but similar cases in other developing systems where the same subcircuit GRN topology is present. To resolve mechanistic issues that arise in considering how community effect signaling could operate to produce its observed effects, we construct and analyze the behavior of a quantitative model of community effect signaling in the sea urchin embryo oral ectoderm. Community effect network topology could constitute part of the genomic regulatory code that defines transcriptional function in multicellular tissues composed of cells in contact, and hence may have arisen as a metazoan developmental strategy

Stochastic approach to molecular interactions and computational theory of metabolic and genetic regulations

Binding and unbinding of ligands to specific sites of a macromolecule are one of the most elementary molecular interactions inside the cell that embody the computational processes of biological regulations. The interaction between transcription factors and the operators of genes and that between ligands and binding sites of allosteric enzymes are typical examples of such molecular interactions. In order to obtain the general mathematical framework of biological regulations, we formulate these interactions as finite Markov processes and establish a computational theory of regulatory activities of macromolecules based mainly on graphical analysis of their state transition diagrams. The contribution is summarized as follows: (1) Stochastic interpretation of Michaelis-Menten equation is given. (2) Notion of \textit{probability flow} is introduced in relation to detailed balance. (3) A stochastic analogy of \textit{Wegscheider condition} is given in relation to loops in the state transition diagram. (4) A simple graphical method of computing the regulatory activity in terms of ligands' concentrations is obtained for Wegscheider cases.Comment: 20 pages, 13 figure

Visualization, documentation, analysis, and communication of large-scale gene regulatory networks

Genetic regulatory networks (GRNs) are complex, large-scale, and spatially and temporally distributed. These characteristics impose challenging demands on software tools for building GRN models, and so there is a need for custom tools. In this paper, we report on our ongoing development of BioTapestry, an open source, freely available computational tool designed specifically for building GRN models. We also outline our future development plans, and give some examples of current applications of BioTapestry

Effect of Fe-rich intermetallics on tensile behavior of Al-Cu 206 cast alloys at solid and near-solid states

Iron is one of the most common impurity elements in Al-Cu 206 cast alloys as it often causes the precipitation of Fe-rich intermetallic phases during solidification due to its extremely low solid solubility in aluminum. The characteristics of the Fe-rich intermetallics, such as type, morphology, size, and distribution, have significant influences on the tensile behaviors of the Al alloys. In the present work, two Al-Cu 206 cast alloys containing different types of Fe-rich intermetallics (dominated by either platelet β-Fe or Chinese script α-Fe) were cast and their tensile tests were performed at both solid (room temperature) and near-solid (2.8 vol. % liquid) states. It is found that the tensile properties in both solid and near-solid states are improved when the Fe-rich intermetallics change from platelet to Chinese script morphologies. During the solid state tensile deformation, the failure occurs mainly along the platelet β-Fe intermetallics/Al matrix interface or within the Chinese script α-Fe particles. In the near-solid state, the alloy containing mainly Chinese script α-Fe is found to have more free flow paths for liquid feeding, leading to improved tensile properties. By contrast, the platelet β-Fe can cause the blockage of the liquid flow paths, leading to the degraded tensile properties and worsened susceptibility to hot tearing

How to Build Transcriptional Network Models of Mammalian Pattern Formation

Genetic regulatory networks of sequence specific transcription factors underlie pattern formation in multicellular organisms. Deciphering and representing the mammalian networks is a central problem in development, neurobiology, and regenerative medicine. Transcriptional networks specify intermingled embryonic cell populations during pattern formation in the vertebrate neural tube. Each embryonic population gives rise to a distinct type of adult neuron. The homeodomain transcription factor Lbx1 is expressed in five such populations and loss of Lbx1 leads to distinct respecifications in each of the five populations. allele, respectively. Microarrays were used to show that expression levels of 8% of all transcription factor genes were altered in the respecified pool. These transcription factor genes constitute 20–30% of the active nodes of the transcriptional network that governs neural tube patterning. Half of the 141 regulated nodes were located in the top 150 clusters of ultraconserved non-coding regions. Generally, Lbx1 repressed genes that have expression patterns outside of the Lbx1-expressing domain and activated genes that have expression patterns inside the Lbx1-expressing domain.nalysis, and think that it will be generally useful in discovering and assigning network interactions to specific populations. We discuss how ANCEA, coupled with population partitioning analysis, can greatly facilitate the systematic dissection of transcriptional networks that underlie mammalian patterning

Dynamic modeling of gene expression in prokaryotes: application to glucose-lactose diauxie in Escherichia coli

Coexpression of genes or, more generally, similarity in the expression profiles poses an unsurmountable obstacle to inferring the gene regulatory network (GRN) based solely on data from DNA microarray time series. Clustering of genes with similar expression profiles allows for a course-grained view of the GRN and a probabilistic determination of the connectivity among the clusters. We present a model for the temporal evolution of a gene cluster network which takes into account interactions of gene products with genes and, through a non-constant degradation rate, with other gene products. The number of model parameters is reduced by using polynomial functions to interpolate temporal data points. In this manner, the task of parameter estimation is reduced to a system of linear algebraic equations, thus making the computation time shorter by orders of magnitude. To eliminate irrelevant networks, we test each GRN for stability with respect to parameter variations, and impose restrictions on its behavior near the steady state. We apply our model and methods to DNA microarray time series' data collected on Escherichia coli during glucose-lactose diauxie and infer the most probable cluster network for different phases of the experiment.Comment: 20 pages, 4 figures; Systems and Synthetic Biology 5 (2011

Design of a combinatorial DNA microarray for protein-DNA interaction studies

BACKGROUND: Discovery of precise specificity of transcription factors is an important step on the way to understanding the complex mechanisms of gene regulation in eukaryotes. Recently, double-stranded protein-binding microarrays were developed as a potentially scalable approach to tackle transcription factor binding site identification. RESULTS: Here we present an algorithmic approach to experimental design of a microarray that allows for testing full specificity of a transcription factor binding to all possible DNA binding sites of a given length, with optimally efficient use of the array. This design is universal, works for any factor that binds a sequence motif and is not species-specific. Furthermore, simulation results show that data produced with the designed arrays is easier to analyze and would result in more precise identification of binding sites. CONCLUSION: In this study, we present a design of a double stranded DNA microarray for protein-DNA interaction studies and show that our algorithm allows optimally efficient use of the arrays for this purpose. We believe such a design will prove useful for transcription factor binding site identification and other biological problems