Evolving modular genetic regulatory networks with a recursive,top-down approach

Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a ‘top-down’ approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.     

Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution

Phenotypic variation is the raw material of adaptive Darwinian evolution. The phenotypic variation found in organismal development is biased towards certain phenotypes, but the molecular mechanisms behind such biases are still poorly understood. Gene regulatory networks have been proposed as one cause of constrained phenotypic variation. However, most pertinent evidence is theoretical rather than experimental. Here, we study evolutionary biases in two synthetic gene regulatory circuits expressed in Escherichia coli that produce a gene expression stripe—a pivotal pattern in embryonic development. The two parental circuits produce the same phenotype, but create it through different regulatory mechanisms. We show that mutations cause distinct novel phenotypes in the two networks and use a combination of experimental measurements, mathematical modelling and DNA sequencing to understand why mutations bring forth only some but not other novel gene expression phenotypes. Our results reveal that the regulatory mechanisms of networks restrict the possible phenotypic variation upon mutation. Consequently, seemingly equivalent networks can indeed be distinct in how they constrain the outcome of further evolution.     

Voiceprint identification and its application to sociological studies of wild Japanese monkeys (Macaca fuscata yakui)

Japanese monkeys often exchange the particular vocal sound, “coo,” especially when they feed or move as a group. It was considered that the “coo” sound had no positive social meaning, perhaps because the “coo” sound network and its function were hidden behind other behavioral observations. For identification of the vocalizer only from hearing the “coo” sound, three phonetic values, i.e., the “fundamental,” “duration,” and “formants,” plus other characteristics were used as indices of voiceprints. The results indicated that these were effective for identifying the vocalizer in two-thirds of the adults in the study troop which was composed of 12 adults and 16 immature members. The “coo” sound exchange network among the troop members (adults) was drawn on the basis of the voiceprint identification. The network showed three characteristics as follows: (1) matriarchs of the kin-groups frequently exchanged “coo” sounds with each other; (2) the other females exchanged “coo” sounds mostly within their own kin-groups; and (3) males seldom participated in the “coo” sound exchange. This suggests that “coo” sound exchange plays a central role for the matriarch of kin-groups in binding each kin-group and, ultimately, in binding all members together into an organized troop.     

Highly covarying residues have a functional role in antibody constant domains

The ability to generate and design antibodies recognizing specific targets has revolutionized the pharmaceutical industry and medical imaging. Engineering antibody therapeutics in some cases requires modifying their constant domains to enable new and altered interactions. Engineering novel specificities into antibody constant domains has proved challenging due to the complexity of inter‐domain interactions. Covarying networks of residues that tend to cluster on the protein surface and near binding sites have been identified in some proteins. However, the underlying role these networks play in the protein resulting in their conservation remains unclear in most cases. Resolving their role is crucial, because residues in these networks are not viable design targets if their role is to maintain the fold of the protein. Conversely, these networks of residues are ideal candidates for manipulating specificity if they are primarily involved in binding, such as the myriad interdomain interactions maintained within antibodies. Here, we identify networks of evolutionarily‐related residues in C‐class antibody domains by evaluating covariation, a measure of propensity with which residue pairs vary dependently during evolution. We computationally test whether mutation of residues in these networks affects stability of the folded antibody domain, determining their viability as design candidates. We find that members of covarying networks cluster at domain‐domain interfaces, and that mutations to these residues are diverse and frequent during evolution, precluding their importance to domain stability. These results indicate that networks of covarying residues exist in antibody domains for functional reasons unrelated to thermodynamic stability, making them ideal targets for antibody design. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Kolmogorov complexity of epithelial pattern formation: The role of regulatory network configuration

The tissues of multicellular organisms are made of differentiated cells arranged in organized patterns. This organization emerges during development from the coupling of dynamic intra- and intercellular regulatory networks. This work applies the methods of information theory to understand how regulatory network structure both within and between cells relates to the complexity of spatial patterns that emerge as a consequence of network operation. A computational study was performed in which undifferentiated cells were arranged in a two dimensional lattice, with gene expression in each cell regulated by identical intracellular randomly generated Boolean networks. Cell–cell contact signalling between embryonic cells is modeled as coupling among intracellular networks so that gene expression in one cell can influence the expression of genes in adjacent cells. In this system, the initially identical cells differentiate and form patterns of different cell types. The complexity of network structure, temporal dynamics and spatial organization is quantified through the Kolmogorov-based measures of normalized compression distance and set complexity. Results over sets of random networks that operate in the ordered, critical and chaotic domains demonstrate that: (1) ordered and critical networks tend to create the most information-rich patterns; (2) signalling configurations in which cell-to-cell communication is non-directional mostly produce simple patterns irrespective of the internal network domain; and (3) directional signalling configurations, similar to those that function in planar cell polarity, produce the most complex patterns, but only when the intracellular networks function in non-chaotic domains.     

Mono- and dinuclear manganese(II) complexes derived from the cis/trans isomers of 2,4,5-tris(2-pyridyl)-4,5-dihydroimidazole

Two new manganese(II) complexes, [Mn(L1)(L1H)(ClO₄)(H₂O)][ClO₄]₂·0.5CH₃CN·H₂O (1) [L1 = trans-(±)2-(2,5-di(pyridin-2-yl)-4,5-dihydro-1H-imidazol-4-yl)pyridine)] and [Mn₂(μ-L2)₂(H₂O)₃(CH₃CN)₃][ClO₄]₄·2CH₃CN (2) [L2 = cis-(±)2-(2,5-di(pyridin-2-yl)-4,5-dihydro-1H-imidazol-4-yl)pyridine)], have been prepared and examined by single-crystal X-ray diffraction analysis, showing that complex 1 is a mononuclear compound, whereas complex 2 is a dinuclear species. The cis/trans isomers L1 and L2 have similar coordination properties, but behave as bidentate and tridentate chelating ligands, respectively, giving distorted octahedral metal coordination geometries. X-ray diffraction studies revealed that the molecular and crystal structures are stabilized by a series of intra- and intermolecular interactions. In both cases extended supramolecular networks are generated, in compound 1 through O-H···O, O-H···N, N-H···O, N-H···N, C-H···O, C-H···N, C-H···π and π···π interactions, and in compound 2 through O-H···O, O-H···N, C-H···O and π···π interactions. The observed structural differences between the two metal complexes might be a consequence of these stabilizing effects.     

Host–parasitoid population density prediction using artificial neural networks: diamondback moth and its natural enemies

• 1 An integrated pest management (IPM) system incorporating the introduction and field release of Diadegma semiclausum (Hellén), a parasitoid of diamondback moth (DBM) Plutella xylostella (L.), comprising the worst insect pest of the cabbage family, has been developed in Kenya to replace the pesticides‐only approach.
• 2 Mathematical modelling using differential equations has been used in theoretical studies of host–parasitoid systems. Although, this method helps in gaining an understanding of the system's dynamics, it is generally less accurate when used for prediction. The artificial neural network (ANN) approach was therefore chosen to aid prediction.
• 3 The ANN methodology was applied to predict the population density of the DBM and D. semiclausum, its larval parasitoid. Two data sets, each from different release areas in the Kenya highlands, and both collected during a 3‐year period after the release of the parasitoid, were used in the present study. Two ANN models were developed using these data.
• 4 The ANN approach gave satisfactory results for DBM and for D. semiclausum. Sensitivity analysis suggested that pest populations may be naturally controlled by rainfall.
• 5 The ANN provides a powerful tool for predicting host–parasitoid population densities and made few assumptions on the field data. The approach allowed the use of data collected at any appropriate scale of the system, bypassing the assumptions and uncertainties that could have occurred when parameters are imported from other systems. The methodology can be explored with respect to the development of tools for monitoring and forecasting the population densities of a pest and its natural enemies. In addition, the model can be used to evaluate the relative effectiveness of the natural enemies and to investigate augmentative biological control strategies.