Gene Network

Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described. In the discovery setting 'the rule of 5' predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the molecular weight (MWT) is greater than 500 and the calculated Log P (CLogP) is greater than 5 (or MlogP . 4.15). Computational methodology for the rule-based Moriguchi Log P (MLogP) calculation is described. Turbidimetric solubility measurement is described and applied to known drugs. High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric solubility than leads in the pre-HTS era. In the development setting, solubility calculations focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with experimental thermodynamic solubility measurements.

Population genetics, the mathematical theory of modern evolutionary biology, defines evolution as the alteration of the frequency of distinct gene variants (alleles) differing in fitness over the time. The major problem with this view is that in gene and protein sequences we can find little evidence concerning the molecular basis of phenotypic variance, especially those that would confer adaptive benefit to the bearers. Some novel data, however, suggest that a large amount of genetic variation exists in the regulatory region of genes within populations. In addition, comparison of homologous DNA sequences of various species shows that evolution appears to depend more strongly on gene expression than on the genes themselves. Furthermore, it has been demonstrated in several systems that genes form functional networks, whose products exhibit interrelated expression profiles. Finally, it has been found that regulatory circuits of development behave as evolutionary units. These data demonstrate that our view of evolution calls for a new synthesis. In this article I propose a novel concept, termed the selfish gene network hypothesis, which is based on an overall consideration of the above findings. The major statements of this hypothesis are as follows. (1) Instead of individual genes, gene networks (GNs) are responsible for the determination of traits and behaviors. (2) The primary source of microevolution is the intraspecific polymorphism in GNs and not the allelic variation in either the coding or the regulatory sequences of individual genes. (3) GN polymorphism is generated by the variation in the regulatory regions of the component genes and not by the variance in their coding sequences. (4) Evolution proceeds through continuous restructuring of the composition of GNs rather than fixing of specific alleles or GN variants.

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Essentially we show recent data to shed new light on the thorny controversy of how teeth arose in evolution. Essentially we show (a) how teeth can form equally from any epithelium, be it endoderm, ectoderm or a combination of the two and (b) that the gene expression programs of oral versus pharyngeal teeth are remarkably similar. Classic theories suggest that (i) skin denticles evolved first and odontode-inductive surface ectoderm merged inside the oral cavity to form teeth (the 'outside-in' hypothesis) or that (ii) patterned odontodes evolved first from endoderm deep inside the pharyngeal cavity (the 'inside-out' hypothesis). We propose a new perspective that views odontodes as structures sharing a deep molecular homology, united by sets of co-expressed genes defining a competent thickened epithelium and a collaborative neural crest-derived ectomesenchyme. Simply put, odontodes develop 'inside and out', wherever and whenever these co-expressed gene sets signal to one another. Our perspective complements the classic theories and highlights an agenda for specific experimental manipulations in model and non-model organisms.

An outstanding problem in the clinical management of breast cancer is overtreatment. It is estimated that approxi mately 55 to 75% of breast cancer patients who receive adjuvant chemotherapy would do equally well without it [1], but identifying this low-risk population with a high enough predictive value (≥90%) is not possible using standard prognostic factors such as lymph node status or tumour size. Several recently developed gene expression classifi ers have shown promise of achieving the required predictive values.

Background: There has been a lot of interest in recent years focusing on the modeling and simulation of Gene Regulatory Networks (GRNs). However, the evolutionary mechanisms that give rise to GRNs in the first place are still largely unknown. In an earlier work, we developed a framework to analyze the effect of objective functions, input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs.

Main achievements and results of the network are presented for the eight thematic sessions and a stakeholder session. The conference has shown that adaptive responses of trees to biotic or abiotic selection pressures can now be investigated at the gene level for traits of adaptive significance. Candidate genes have been catalogued for phenological and drought-related traits in important tree families (Salicaceae, Fagaceaea and Pinaceae), and their variation in natural populations is being explored. Genomics can now be integrated in ecological research to investigate evolutionary response to climate changes in a wide range of species. New avenues of research were also highlighted as the exploration of gene networks involved in adaptive responses and the combination of experimental and modelling approaches to disentangle components of evolutionary changes triggered by climate change. The main focus of the conference was the adaptation of trees to environmental changes. The conference was organized in eight thematic sessions ranging from genomic approaches aiming at identifying genes of adaptive significance to practical issues regarding mitigation options for combating climate change. A dialogue between scientists and end users took place in the form of an ad hoc stakeholder session. A panel of end users from various forest and policymaking institutions expressed their expectations, and the discussions with the scientists addressed the potential applications of research findings to the management of genetic

Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization, and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating, or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis -synthetic biology's system fabrication process -supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.

We present a model for a synthetic gene oscillator and consider the coupling of the oscillator to a periodic process that is intrinsic to the cell. We investigate the synchronization properties of the coupled system, and show how the oscillator can be constructed to yield a significant amplification of cellular oscillations. We reduce the driven oscillator equations to a normal form, and analytically determine the amplification as a function of the strength of the cellular oscillations. The ability to couple naturally occurring genetic oscillations to a synthetically designed network could lead to possible strategies for entraining and/or amplifying oscillations in cellular protein levels.

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively coregulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for coregulation is detected through the use of quantitative trait locus mapping.

Background: Plants engineered for abiotic stress tolerance may soon be commercialized. The engineering of these plants typically involves the manipulation of complex multigene networks and may therefore have a greater potential to introduce pleiotropic effects than the simple monogenic traits that currently dominate the plant biotechnology market. While research on unintended effects in transgenic plant systems has been instrumental in demonstrating the substantial equivalence of many transgenic plant systems, it is essential that such analyses be extended to transgenic plants engineered for stress tolerance. Drought-tolerant Arabidopsis thaliana were engineered through overexpression of the transcription factor ABF3 in order to investigate unintended pleiotropic effects. In order to eliminate position effects, the Cre/lox recombination system was used to create control plant lines that contain identical T-DNA insertion sites but with the ABF3 transgene excised. This additionally allowed us to determine if Cre recombinase can cause unintended effects that impact the transcriptome.

With the ever-increasing flow of highthroughput gene expression, protein interaction and genome sequence data, researchers gradually approach a system-level understanding of cells and even multi-cellular organisms. Systems biology is an emerging field that enables us to achieve in-depth understanding at the system level. For this, we need to establish methodologies and techniques that enable us to understand biological systems as systems, which means to understand: (1) the structure of the system, such as gene/ metabolic/signal transduction networks and physical structures, (2) the dynamics of such systems, (3) methods to control systems, and (4) methods to design and modify systems to generate desired properties. However, the meaning of ''system-level understanding'' is still ambiguous. This paper reviews the current status of the field and outlines future research directions and issues that need to be addressed.

Cancer is driven by mutation. Worldwide, tobacco smoking is the major lifestyle exposure that causes cancer, exerting carcinogenicity through >60 chemicals that bind and mutate DNA. Using massively parallel sequencing technology, we sequenced a small cell lung cancer cell line, NCI-H209, to explore the mutational burden associated with tobacco smoking. 22,910 somatic substitutions were identified, including 132 in coding exons. Multiple mutation signatures testify to the cocktail of carcinogens in tobacco smoke and their proclivities for particular bases and surrounding sequence context. Effects of transcription-coupled repair and a second, more general Europe PMC Funders Author Manuscripts expression-linked repair pathway were evident. We identified a tandem duplication that duplicates exons 3-8 of CHD7 in-frame, and another two lines carrying PVT1-CHD7 fusion genes, suggesting that CHD7 may be recurrently rearranged in this disease. These findings illustrate the potential for next-generation sequencing to provide unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with cancer.

Synthetic peptides with the arginine-glycine-aspartate (RGD) motif have been used widely as inhibitors of integrin-ligand interactions to study cell growth, adhesion, migration and differentiation. We designed cyclic-RGD peptide (Tpa-RGDWPC-, cRGD) which could cause cell death in MCF-7 cell line. In order to understand the mechanism involved in cRGD-induced apoptosis, we used microarray, real-time quantitative PCR (Q-PCR) and bioinformatics to study the effects of cRGD on breast cancer cell line MCF-7. By time-series gene expression measurements and our developed software BSIP and GeneNetwork, we reconstructed an apoptosis-related gene network. In the network, caspase-9, located at the upstream, activates downstream effector caspases such as caspase-7, leading to the induction of caspase-4. Combined our previous proteomics data with newly performed docking simulation, it indicated that the cell death induced by cRGD may be triggered through blocking integrin signaling to the extracellular matrix and activation of caspase pathway. This study provides a molecular explanation of cRGD treatment in breast cancer cells and set forth a constructive far-reaching impact on breast cancer therapy.

In mammals, somatic growth is rapid in early postnatal life but decelerates with age and eventually halts, thus determining the adult body size of the species. This growth deceleration, which reflects declining proliferation, occurs simultaneously in multiple organs yet appears not to be coordinated by a systemic mechanism. We therefore hypothesized that growth deceleration results from a growth-limiting genetic program that is common to multiple tissues. Here, we identified a set of 11 imprinted genes that show down-regulation of mRNA expression with age in multiple organs. For these genes, Igf2, H19, Plagl1, Mest, Peg3, Dlk1, Gtl2, Grb10, Ndn, Cdkn1c, and SLC38a4, the declines show a temporal pattern similar to the decline in growth rate. All 11 genes have been implicated in the control of cell proliferation or somatic growth. Thus, our findings suggest that the declining expression of these genes contributes to coordinate growth deceleration in multiple tissues. We next hypothesized that the coordinate decline in expression of these imprinted genes is caused by altered methylation and consequent silencing of the expressed allele. Contrary to this hypothesis, the methylation status of the promoter regions of Mest, Peg3 and Plagl1 did not change with age.

Background: Information generated via microarrays might uncover interactions between the mammary gland and Streptococcus uberis (S. uberis) that could help identify control measures for the prevention and spread of S. uberis mastitis, as well as improve overall animal health and welfare, and decrease economic losses to dairy farmers. The main objective of this study was to determine the most affected gene networks and pathways in mammary tissue in response to an intramammary infection (IMI) with S. uberis and relate these with other physiological measurements associated with immune and/or metabolic responses to mastitis challenge with S. uberis O140J. Results: Streptococcus uberis IMI resulted in 2,102 (1,939 annotated) differentially expressed genes (DEG). Within this set of DEG, we uncovered 20 significantly enriched canonical pathways (with 20 to 61 genes each), the majority of which were signaling pathways. Among the most inhibited were LXR/RXR Signaling and PPARα/RXRα Signaling. Pathways activated by IMI were IL-10 Signaling and IL-6 Signaling which likely reflected counter mechanisms of mammary tissue to respond to infection. Of the 2,102 DEG, 1,082 were up-regulated during IMI and were primarily involved with the immune response, e.g., IL6, TNF, IL8, IL10, SELL, LYZ, and SAA3. Genes down-regulated (1,020) included those associated with milk fat synthesis, e.g., LPIN1, LPL, CD36, and BTN1A1. Network analysis of DEG indicated that TNF had positive relationships with genes involved with immune system function (e.g., CD14, IL8, IL1B, and TLR2) and negative relationships with genes involved with lipid metabolism (e.g., GPAM, SCD, FABP4, CD36, and LPL) and antioxidant activity (SOD1).

Genomic alterations lead to cancer complexity and form a major hurdle for a comprehensive understanding of the molecular mechanisms underlying oncogenesis. In this review, we describe the recent advances in studying cancer-associated genes from a systems biological point of view. The integration of known cancer genes onto protein and signaling networks reveals the characteristics of cancer genes within networks. This approach shows that cancer genes often function as network hub proteins which are involved in many cellular processes and form focal nodes in the information exchange between many signaling pathways. Literature mining allows constructing gene-gene networks, in which new cancer genes can be identified. The gene expression profiles of cancer cells are used for reconstructing gene regulatory networks. By doing so, the genes, which are involved in the regulation of cancer progression, can be picked up from these networks after which their functions can be further confirmed in the laboratory.

Cells offer natural examples of highly efficient networks of nanomachines. Accordingly, both intracellular and intercellular communication mechanisms in nature are looked to as a source of inspiration and instruction for engineered nanocommunication. Harnessing biological functionality in this manner requires an interdisciplinary approach that integrates systems biology, synthetic biology, and nanofabrication. Here, we present a model system that exemplifies the synergism between these realms of research. We propose a synthetic gene network for operation in a nanofabricated cell mimic array that propagates a biomolecular signal over long distances using the phenomenon of stochastic resonance. Our system consists of a bacterial quorum sensing signal molecule, a bistable genetic switch triggered by this signal, and an array of nanofabricated cell mimic wells that contain the genetic system. An optimal level of noise in the system helps to propagate a time-varying AHL signal over long distances through the array of mimics. This noise level is determined both by the system volume and by the parameters of the genetic network. Our proposed genetically driven stochastic resonance system serves as a testbed for exploring the potential harnessing of gene expression noise to aid in the transmission of a time-varying molecular signal. (P. Siuti), darr@ornl.gov (R. D. Dar), rettererst@ornl.gov (S. T. Retterer), doktyczmj@ornl.gov (M. J. Doktycz), simpsonml1@ornl.gov (M. L. Simpson) . Center for Nanophase Materials Sciences (CNMS). Roy's interests include using stochastic gene expression to probe gene circuit structure-function relationships and has worked with both synthetic and natural gene circuits in E.coli, yeast, and mammalian cells.

Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. We propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of ...

Much of the heart, including the atria, right ventricle and outflow tract (OFT) is derived from a progenitor cell population termed the second heart field (SHF) that contributes progressively to the embryonic heart during cardiac looping. Several studies have revealed anterior-posterior patterning of the SHF, since the anterior region (anterior heart field) contributes to right ventricular and OFT myocardium whereas the posterior region gives rise to the atria. We have previously shown that Retinoic Acid (RA) signal participates to this patterning. We now show that Hoxb1, Hoxa1, and Hoxa3, as downstream RA targets, are expressed in distinct sub-domains within the SHF. Our genetic lineage tracing analysis revealed that Hoxb1, Hoxa1 and Hoxa3expressing cardiac progenitor cells contribute to both atria and the inferior wall of the OFT, which subsequently gives rise to myocardium at the base of pulmonary trunk. By contrast to Hoxb1 Cre , the contribution of Hoxa1-enhIII-Cre and Hoxa3 Cre -labeled cells is restricted to the distal regions of the OFT suggesting that proximo-distal patterning of the OFT is related to SHF sub-domains characterized by combinatorial Hox genes expression. Manipulation of RA signaling pathways showed that RA is required for the correct deployment of Hox-expressing SHF cells. This report provides new insights into the regulatory gene network in SHF cells contributing to the atria and sub-pulmonary myocardium.

Inner ear neurons develop from the otic placode and connect hair cells with central neurons in auditory brain stem nuclei. Otic neurogenesis is a developmental process which can be separated into different cellular states that are characterized by a distinct combination of molecular markers. Neurogenesis is highly regulated by a network of extrinsic and intrinsic factors, whose participation in auditory neurogenesis is discussed. Trophic factors include the fibroblast growth factor, neurotrophins and insulin-like peptide families. The expression domains of transcription factor families and their roles in the regulation of intracellular signaling pathways associated with neurogenesis are also discussed. Understanding and defining the key factors and gene networks in the development and function of the inner ear represents an important step towards defeating deafness.

Plant roots have a large range of functions, including acquisition of water and nutrients, as well as structural support. Dissecting the genetic and molecular mechanisms controlling rice root development is critical for the development of new rice ideotypes that are better adapted to adverse conditions and for the production of sustainably achieved rice yield potential. Most knowledge regarding the gene networks involved in root development has been accumulated in the model dicotyledon plant species Arabidopsis thaliana. Rice, the model monocotyledon species, presents several singularities compared to A. thaliana, including a root architecture characterized by a fibrous root system comprising five types of embryonic and postembryonic roots. The anatomy and morphology of the rice root system, which is typical for a cereal, differs from that of A. thaliana, for instance, by the presence of a lysigenous cortex and additional cell layers compared to the dicotyledon model. Moreover, the structure and functions of the root apical meristem (RAM) of rice are distinct from those of A. thaliana. Recently, several rice root mutants have been identified via forward or reverse genetics, and these will aid in forming hypothesis to characterize either the divergence or conservation of genetic pathways relative to A. thaliana. Furthermore, these mutants will help to identify key genes in rice roots that may be missing in A. thaliana. This review summarizes both classical and recent data concerning the molecular genetics of rice root development, including root anatomy and morphology, RAM structure, RAM patterning, and root mutants.

One of the most fundamental and critical functions of embryological development is the control and regulation of differential genes and gene networks. The study of the gene networks involved in development is a mechanism for understanding the developmental process at its most basic level. An evolutionary change in a morphological feature or features must depend on a reorganization or co-option of one or more developmental gene regulatory network just as retention of an ancestral morphological trait must rely on retention of a common gene regulatory network. Studying two closely related classes in the same phylum with the same essential morphological feature yet with unique developmental characteristics provides insight into the evolution of these evolutionarily resolute gene regulatory networks. We have developed a new model system using brittle stars to further these studies. In this investigation I have identified key genes of the gene regulatory network (GRN) found in embryonic e...

In the last decade, recurrent neural networks (RNNs) have attracted more efforts in inferring genetic regulatory networks (GRNs), using time series gene expression data from microarray experiments. This is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. However, RNNs are well known for training difficulty. Traditional gradient descent-based methods are easily stuck in local minima and the computation of the derivatives is also not always possible. Here, the performance of three evolutionary-swarm computation technology-based methods, known as differential evolution (DE), particle swarm optimization (PSO), and the hybrid of DE and PSO (DEPSO), in training RNNs is investigated. Furthermore, the gene networks are reconstructed via the identification of the gene interactions, which are explained through corresponding connection weight matrices. The experimental results on two data sets studied in this paper demonstrate that the DEPSO algorithm performs better in RNN training. Also, the RNN-based model can provide meaningful insight in capturing the nonlinear dynamics of genetic networks and revealing genetic regulatory interactions.

Systems biology of plants offers myriad opportunities and many challenges in modeling. A number of technical challenges stem from paucity of computational methods for discovery of the most fundamental properties of complex dynamical systems. In systems engineering, eigen-mode analysis have proved to be a powerful approach. Following this philosophy, we introduce a new theory that has the benefits of eigen-mode analysis, while it allows investigation of complex dynamics prior to estimation of optimal scales and resolutions. Information Surfaces organizes the many intricate relationships among "eigen-modes" of gene networks at multiple scales and via an adaptable multi-resolution analytic approach that permits discovery of the appropriate scale and resolution for discovery of functions of genes in the model plant Arabidopsis. Applications are many, and some pertain developments of crops that sustainable agriculture requires.

Background: Periodic patterning of iterative structures is a fundamental process during embryonic organization and development. Studies have shown how gene networks are employed to pattern butterfly eyespots, fly bristles and vertebrate epithelial appendages such as teeth, feathers, hair and mammary glands. Despite knowledge of how these features are organized, little is known about how diversity in periodic patterning is generated in nature. We address this problem through the molecular analysis of oral jaw dental diversity in Lake Malawi cichlids, where closely related species exhibit from 1 to 20 rows of teeth, with total teeth counts ranging from around 10 to 700.

Comprehensive identification of DNA cis-regulatory elements is crucial for a predictive understanding of transcriptional network dynamics. Strong evidence suggests that these DNA sequence motifs are highly conserved between related species, reflecting strong selection on the network of regulatory interactions that underlie common cellular behavior. Here, we exploit a systems-level aspect of this conservation-the network-level topology of these interactions-to map transcription factor (TF) binding sites on a genomic scale. Using network-level conservation as a constraint, our algorithm finds 71% of known TF binding sites in the yeast Saccharomyces cerevisiae, using only 12% of the sequence of a phylogenetic neighbor. Most of the novel predicted motifs show strong features of known TF binding sites, such as functional category and/or expression profile coherence of their corresponding genes. Network-level conservation should provide a powerful constraint for the systematic mapping of TF binding sites in the larger genomes of higher eukaryotes.

Long-term mammary expression patterns of lipogenic gene networks due to dietary lipid remain largely unknown. Mammary tissue was biopsied for transcript profiling of 29 genes at 0, 7, and 21 days of feeding cows saturated lipid (EB100) or a blend of fish/soybean oil (FSO) to depress milk fat. Milk fat yield decreased gradually with FSO and coincided with lower molar yield of fatty acids synthesized de novo, stearic acid, and oleic acid. The PPARγ targets LPIN1 and SREBF1 along with ACSS2, ACACA, FASN, and LPL increased by day 7 of feeding EB100, but differences between diets disappeared by day 21. Expression of SCAP increased markedly over time with FSO and differed from EB100 by approximately sevenfold on day 21. Expression of THRSP decreased by day 7 with both diets and returned to basal levels by day 21. SCD expression increased linearly through 7 days and remained elevated with both diets, a likely mechanism to ensure the proper level of endogenous oleic acid via desaturation of dietary stearate (EB100) or via more SCD protein to account for the reduction in stearate supply from the rumen (FSO). Despite this response, endogenous oleate was insufficient to restore normal milk fat synthesis. Only 2 of 29 genes differed in expression between diets on day 21, suggesting that transcriptional control mechanisms regulating fat synthesis were established as early as 7 days post-feeding. Gene expression reflected vastly different physiological responses by mammary tissue to adjust its metabolism to the influx of saturated fatty acids, trans10-18:1, and/or to the lack of stearic acid.

Background: Comparative analysis of genome wide temporal gene expression data has a broad potential area of application, including evolutionary biology, developmental biology, and medicine. However, at large evolutionary distances, the construction of global alignments and the consequent comparison of the time-series data are difficult. The main reason is the accumulation of variability in expression profiles of orthologous genes, in the course of evolution. We applied Pearson distance matrices, in combination with other noise-suppression techniques and data filtering to improve alignments. This novel framework enhanced the capacity to capture the similarities between the temporal gene expression datasets separated by large evolutionary distances. We aligned and compared the temporal gene expression data in budding (Saccharomyces cerevisiae) and fission (Schizosaccharomyces pombe) yeast, which are separated by more then ~400 myr of evolution. We found that the global alignment (time warping) properly matched the duration of cell cycle phases in these distant organisms, which was measured in prior studies. At the same time, when applied to individual ortholog pairs, this alignment procedure revealed groups of genes with distinct alignments, different from the global alignment. Our alignment-based predictions of differences in the cell cycle phases between the two yeast species were in a good agreement with the existing data, thus supporting the computational strategy adopted in this study. We propose that the existence of the alternative alignments, specific to distinct groups of genes, suggests presence of different synchronization modes between the two organisms and possible functional decoupling of particular physiological gene networks in the course of evolution.

Our approach to tissue modeling incorporates biologically derived primitives into a computational engine (CellSim) coupled with a genetic search algorithm. By expanding an evolved synthetic genome CellSim is capable of developing a virtual tissue with higher order properties. Using primitives based on cell signaling, gene networks, cell division, growth, and death, we have encoded a 64-cell cube-shaped tissue with emergent capacity to repair itself when up to 60% of its cells are destroyed. Other tissue shapes such as sheets of cells also repair themselves. Capacity for self-repair is an emergent property derived from, but not specified by, the rule sets used to generate these virtual tissues.

GeneRank is a new engine technology for the analysis of microarray experiments. It combines gene expression information with a network structure derived from gene notations or expression profile correlations. Using matrix decomposition techniques, we first give a matrix analysis of the GeneRank model. We reformulate the GeneRank vector as a linear combination of three parts in the general case when the matrix in question is non-diagonalizable. We then propose two Krylov subspace methods for computing GeneRank. Numerical experiments show that, when the GeneRank problem is very large, the new algorithms are appropriate choices.

Background: Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.

The breadth of genomic diversity found among organisms in nature allows populations to adapt to diverse environments 1,2 . However, genomic diversity is difficult to generate in the laboratory and new phenotypes do not easily arise on practical timescales 3 . Although in vitro and directed evolution methods 4-9 have created genetic variants with usefully altered phenotypes, these methods are limited to laborious and serial manipulation of single genes and are not used for parallel and continuous directed evolution of gene networks or genomes. Here, we describe multiplex automated genome engineering (MAGE) for large-scale programming and evolution of cells. MAGE simultaneously targets many locations on the chromosome for modification in a single cell or across a population of cells, thus producing combinatorial genomic diversity. Because the process is cyclical and scalable, we constructed prototype devices that automate the MAGE technology to facilitate rapid and continuous generation of a diverse set of genetic changes (mismatches, insertions, deletions). We applied MAGE to optimize the 1-deoxy-Dxylulose-5-phosphate (DXP) biosynthesis pathway in Escherichia coli to overproduce the industrially important isoprenoid lycopene. Twenty-four genetic components in the DXP pathway were modified simultaneously using a complex pool of synthetic DNA, creating over 4.3 billion combinatorial genomic variants per day. We isolated variants with more than fivefold increase in lycopene production within 3 days, a significant improvement over existing metabolic engineering techniques. Our multiplex approach embraces engineering in the context of evolution by expediting the design and evolution of organisms with new and improved properties.

Twenty-eight clinical fungal isolates were characterised by morphological (macro- and micro-features and growth response at 25, 30 and 37 °C) and molecular (nuclear rDNA-internal transcriber spacer, calmodulin, cytochrome c oxidase 1 and the largest subunit of RNA polymerase II) analyses. The clinical fungal isolates were ascribed to the following taxa: Penicillium chrysogenum, Verticillium sp., Aspergillus tubingensis, Aspergillus minutus, Beauveria bassiana and Microsporum gypseum. In addition, in vitro susceptibility testing of the isolates to conventional antifungal agents and to two chemically well-defined chemotypes of Thymus schimperi essential oil was performed. Most of the isolates were resistant to amphotericin B (except A. minutus), and itraconazole, while terbinafine was quite active on these fungi. T. schimperi essential oil showed antifungal activity against all of the tested fungal isolates with minimal inhibitory concentration values similar or lower than those of terbinafine. Transmission electron microscopy analyses revealed that fungal growth inhibition by essential oil was accompanied by marked morphological and cytological changes.

The persistence of large blocks of homologous synteny and a high frequency of breakpoint reuse are distinctive features of mammalian chromosomes that are not well understood in evolutionary terms. To gain a better understanding of the evolutionary forces that affect genome architecture, synteny relationships among 10 amniotes (human, chimp, macaque, rat, mouse, pig, cattle, dog, opossum, and chicken) were compared at <1 human-Mbp resolution. Homologous synteny blocks (HSBs; N = 2233) and chromosome evolutionary breakpoint regions (EBRs; N = 1064) were identified from pairwise comparisons of all genomes. Analysis of the size distribution of HSBs shared in all 10 species' chromosomes (msHSBs) identified three (>20 Mbp) that are larger than expected by chance. Gene network analysis of msHSBs >3 human-Mbp and EBRs <1 Mbp demonstrated that msHSBs are significantly enriched for genes involved in development of the central nervous and other organ systems, whereas EBRs are enriched for genes associated with adaptive functions. In addition, we found EBRs are significantly enriched for structural variations (segmental duplications, copy number variants, and indels), retrotransposed and zinc finger genes, and single nucleotide polymorphisms. These results demonstrate that chromosome breakage in evolution is nonrandom and that HSBs and EBRs are evolving in distinctly different ways. We suggest that natural selection acts on the genome to maintain combinations of genes and their regulatory elements that are essential to fundamental processes of amniote development and biological organization. Furthermore, EBRs may be used extensively to generate new genetic variation and novel combinations of genes and regulatory elements that contribute to adaptive phenotypes.

Strains of mice that differ in voluntary alcohol consumption (VAC) are valuable models for the identification of genes involved in the complex etiology of alcohol effects and alcoholism. These mice offer a novel approach to the identification of strain-specific ethanol responsive (SSER) genes in tissues directly involved in alcohol metabolism and preference. We assessed mRNA from the liver and brain from male mice representing C57BL/6J, BALB/c, A/J, and DBA/2J strains following ethanol treatment (chronic ethanol fed liquid diet for 14 days or acute i.p. injection at two doses; 4 g/kg or 8 g/kg), using an expression array containing 588 genes (Clontech #7741–1). The results have identified NADPH cytochrome P450 oxidoreductase, insulin-like growth factor binding protein-1, glutathione S-transferase Mu 1, and cathepsin L as ethanol responsive genes in the liver. Further, we have established that IkB-α and clusterin genes in the brain are ethanol responsive, but only at the lower dose of the ethanol challenge. Although a number of other genes showing subtle (<;2X) differences across strains and treatment combinations were reproducible in repeated blots, they were not confirmed by still evolving independent technologies of gene specific mRNA quantitation. The results demonstrate that comparative expression studies are an efficient approach to discover interacting gene networks that underlie the etiology of complex phenotypes including response to alcohols.

Background: Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes. Computational biology tools such as clustering are then used to group together genes based on their similarity in expression profiles. Genes in each group are probably functionally related. The functional relevance among the genes in each group is usually characterized by utilizing available biological knowledge in public databases such as Gene Ontology (GO), KEGG pathways, association between a transcription factor (TF) and its target genes, and/or gene networks.

Stem cells are essential for animal development and adult tissue homeostasis, and the quest for an ancestral gene fingerprint of stemness is a major challenge for evolutionary developmental biology. Recent studies have indicated that a series of genes, including the transposon silencer Piwi and the translational activator Vasa, specifically involved in germline determination and maintenance in classical bilaterian models (e.g., vertebrates, fly, nematode), are more generally expressed in adult multipotent stem cells in other animals like flatworms and hydras. Since the progeny of these multipotent stem cells includes both somatic and germinal derivatives, it remains unclear whether Vasa, Piwi, and associated genes like Bruno and PL10 were ancestrally linked to stemness, or to germinal potential. We have investigated the expression of Vasa, two Piwi paralogues, Bruno and PL10 in Pleurobrachia pileus, a member of the early-diverging phylum Ctenophora, the probable sister group of cnidarians. These genes were all expressed in the male and female germlines, and with the exception of one of the Piwi paralogues, they showed similar expression patterns within somatic territories (tentacle root, comb rows, aboral sensory complex). Cytological observations and EdU DNAlabelling and long-term retention experiments revealed concentrations of stem cells closely matching these gene expression areas. These stem cell pools are spatially restricted, and each specialised in the production of particular types of somatic cells. These data unveil important aspects of cell renewal within the ctenophore body and suggest that Piwi, Vasa, Bruno, and PL10 belong to a gene network ancestrally acting in two distinct contexts: (i) the germline and (ii) stem cells, whatever the nature of their progeny.

Functional gene analysis requires the possibility of overexpression, as well as down-regulation of one, or ideally several, potentially interacting genes. Lentiviral vectors are well suited for this purpose as they ensure stable expression of cDNAs, as well as shRNAs, and can efficiently transduce a wide spectrum of cell targets when packaged within the coat proteins of other viruses. Here we introduce a multi-color panel of novel lentiviral "gene ontology" (LeGO) vectors designed according to the "building blocks" principle. Using a wide spectrum of different fluorescent markers, including drug-selectable eGFP-and dTomato-blasticidin-S resistance fusion proteins, LeGO vectors allow simultaneous analysis of multiple genes and shRNAs of interest within single, easily identifiable cells. Furthermore, each functional module is flanked by unique cloning sites, ensuring flexibility and individual optimization. The efficacy of these vectors for analyzing multiple genes in a single cell was demonstrated in several different cell types, including hematopoietic, endothelial and neural stem and progenitor cells, as well as hepatocytes. LeGO vectors thus represent a valuable tool for investigating gene networks using conditional ectopic expression and knockdown approaches simultaneously.

Background: Macrophages play essential roles in both innate and adaptive immune responses. Bacteria require endotoxin, a complex lipopolysaccharide, for outer membrane permeability and the host interprets endotoxin as a signal to initiate an innate immune response. The focus of this study is kinetic and global transcriptional analysis of the chicken macrophage response to in vitro stimulation with endotoxin from Salmonella typhimurium-798. Results: The 38535-probeset Affymetrix GeneChip Chicken Genome array was used to profile transcriptional response to endotoxin 1, 2, 4, and 8 hours post stimulation (hps). Using a maximum FDR (False Discovery Rate) of 0.05 to declare genes as differentially expressed (DE), we found 13, 33, 1761 and 61 DE genes between endotoxinstimulated versus non-stimulated cells at 1, 2, 4 and 8 hps, respectively. QPCR demonstrated that endotoxin exposure significantly affected the mRNA expression of IL1B, IL6, IL8, and TLR15, but not IL10 and IFNG in HD 11 cells. Ingenuity Pathway Analysis showed that 10% of the total DE genes were involved in inflammatory response. Three, 9.7, 96.8, and 11.8% of the total DE inflammatory response genes were significantly differentially expressed with endotoxin stimulation at 1, 2, 4 and 8 hps, respectively. The NFKBIA, IL1B, IL8 and CCL4 genes were consistently induced at all times after endotoxin treatment. NLRC5 (CARD domain containing, NOD-like receptor family, RCJMB04_18i2), an intracellular receptor, was induced in HD11 cells treated with endotoxin.