text:
Oily Skin
A powerful clarifying treatment that helps reduce congestion and minimize the appearance of pores. Combines exfoliating Glycolic Acid and purifying Salicylic Acid with calming and soothing Aloe Vera.
HERO INGREDIENT: Salicylic Acid is a Beta Hydroxy Acid (BHA) that has keratolytic, anti-acne and fungicidal properties.
SALICYLIC ACID: Salicylic Acid peels are particularly useful for patients with oily skin and open comedones.
GLYCOLIC ACID: This acid reacts with the upper layers of the epidermis, weakening the bonds that hold dead cells together. This allows for the exfoliation of outer skin cells, revealing underlying, smoother-looking skin. It helps improve skin texture and diminishes the appearance of wrinkles and hyperpigmentation.
ALOE VERA: This helps cool and soothe the skin.
HYALURONIC ACID: This helps the skin retain moisture, plumping the skin and reducing flakiness.
1.0oz (Formerly Glycolic Purifying Peel 1.7oz)
Sunburn Alert: Use a sunscreen, wear protective clothing and limit sun exposure while using this product for one week afterwards.
Alcohol Denat., Aloe Barbadensis Leaf Juice, Glycolic Acid, Aqua/Water/Eau, Glycerin, Salicylic Acid, Hydroxypropyl Methylcellulose, Symphytum Officinale (Comfrey) Leaf Extract, Sodium Hyaluronate (L)
After cleansing, apply a thin layer to entire face morning and night. Follow with moisturizer and/or sunscreen.
Aloe Cleansing Gel
A foaming, sulfate-free gel that clarifies, cleanses and protects with refreshing Lavender and over 50% Aloe Vera. Perfect for all skin types.
Aloe Vera helps prevent immune suppression caused by UVB rays by repairing damage to Langerhans cells. Coconut Oil surfactants derived from these natural ingredients are known for their excellent, yet gentle cleansing properties.
Aloe Barbadensis (Aloe Vera) Leaf Juice*, Decyl Glucoside, Sodium Cocoyl Glutamate, Glycerin, Lavandula Angustifolia (Lavender) Flower Water, Xylitylglucoside, Anhydroxylitol, Xylitol, Lactobacillus Ferment, Sclerotium Gum, Coco-Glucoside, Glyceryl Oleate, Avena Sativa (Oat) Extract, Lavandula Angustifolia (Lavender) Flower Oil*, Rosa Damascena (Rose) Flower Oil*, Lavandula Burnati (Lavandin) Flower/Leaf Oil, Cananga Odorata (Ylang Ylang) Flower Oil, Potassium Sorbate, Water/Aqua/Eau, Citric Acid
*Ingredient from organic farming
99.85% of the total ingredients are from natural origin
67.56% of the total ingredients are from organic farming
Apply a dime size amount to moist skin and gently massage face. Rinse.
A light, soothing gel mask that calms and helps reduce redness with anti-inflammatoryAloe Vera while moisture-binding Prickly Pear Cactus and Hyaluronic Acid hydrate and plump the skin.
HERO INGREDIENT: Aloe Vera Leaf Juice prevents redness and inflammation caused by UVB rays by repairing damage to the skin's natural immune system, thereby preventing premature aging of the skin. Aloe Vera also assists in repairing collagen, often damaged by the release of destructive enzymes caused by chronic inflammation.
HYALURONIC ACID: Derived from yeast extract, this helps plump the skin by helping cells retain water.
PRICKLY PEAR CACTUS STEM EXTRACT: Rich in essential amino acids, vitamins, antioxidants and water-binding polysaccharides, this botanical hydrates, soothes and protects from environmental stress by blocking the release of stress markers from sensory nerve cells in the skin. Prickly Pear has excellent hydrating properties due to its high content of water-binding compounds.
GOTU KOLA EXTRACT: This has strong wound-healing abilities, as it increases the level of antioxidants in newly formed tissue.
Aloe Barbadensis (Aloe Vera) Leaf Juice*, Glycerin, Hydrolyzed Linseed Extract*, Sclerotium Gum, Leuconostoc Ferment Filtrate, Oryza Sativa (Rice) Seed Extract*, Centella Asiatica (Gotu Kola) Extract*, Crithmum Maritimum Extract, Opuntia Ficus-Indica Stem Extract*, Sodium Hyaluronate, Ribes Nigrum (Black Currant) Fruit Extract, Pyrus Communis (Pear) Extract*, Olea Europaea (Olive) Leaf Extract, Dipotassium Glycyrrhizinate, Diglycerin, Pinus Pinaster
*Ingredient from organic farming
99.99% of the total ingredients are from natural origin
88.23% of the total ingredients are from organic farming
Natural and Organic Cosmetic certified by ECOCERT Greenlife according to ECOCERT Standard available at http://cosmetics.ecocert.com
Apply a thin layer to clean skin. Leave on for 10 minutes, morning or evening. Rinse. Repeat daily as needed. For an enhanced cooling effect, store in refrigerator.
Argan & Peptide Wrinkle Repair Eye Cream
A concentrated age-defying formula that helps calm puffiness and visibly reduce the appearance of fine lines, wrinkles and dark circles 360° around the eyes. Rejuvenating Argan Plant Stem Cells, Matrixyl® and Plant Peptides leave the eye area looking youthful and refreshed.
HERO INGREDIENT: Argan Plant Stem Cells revitalize natural anti-aging processes at the source of where wrinkles begin.
Water/Aqua/Eau, Squalane, Glycerin, Cocoglycerides, Glyceryl Triacetyl Ricinoleate, Bellis Perennis (Daisy) Flower Extract, C10-18 Triglycerides, Candelilla/Jojoba/Rice Bran Polyglyceryl-3 Esters, Glyceryl Stearate, Cetearyl Alcohol, Sodium Stearoyl Lactylate, Butyrospermum Parkii (Shea) Butter, Palmitoyl Tripeptide-38, Argania Spinosa (Argan) Sprout Cell Extract, Leontopodium Alpinum (Edelweiss) Meristem Cell Culture, Superoxide Dismutase, Ascorbyl Dipalmitate, Hydrolyzed Rice Protein, Glycine Soja (Soybean) Protein, Hippophae Rhamnoides (Sea Buckthorn) Oil, Tamarindus Indica (Tamarind) Seed Polysaccharide, Porphyridium Cruentum (Red Algae) Extract, Phospholipids, Linoleic Acid, Linolenic Acid, Isomalt, Glucose, Pullulan, Hydroxypropyl Methylcellulose, Hydroxypropyl Cyclodextrin, Lactoperoxidase, Glucose Oxidase, Citric Acid, Sclerotium Gum, Xanthan Gum, Sodium Phytate
Use fingertips to lightly dab a small amount around the eye area morning and night.
Beech Tree & Ginseng Daily Moisturizer
A nourishing moisturizer that helps visibly reduce the appearance of fine lines and wrinkles with antioxidant-rich Beech Tree and Goji Berry Extracts and a firming Ginseng Complex.
Potent Beech Tree Extract helps significantly increase the oxygen consumption rate in cells, thereby enhancing moisture retention. Extract from this tree increases cellular oxygen, boosts protein synthesis, improves skin moisture and diminishes fine lines.
Ginseng contains compounds that form a film on the skin to tighten and smooth.
Goji Berries from this famous Tibetan plant grown in the Himalayan Mountains are vitamin and nutrient rich, containing beta-sitosterol, beta carotene, niacin, pyridoxine and Vitamin C.
Aqua/Water/Eau, Helianthus Annuus (Sunflower) Seed Oil*, Cetearyl Alcohol, Cetearyl Glucoside, Arachidyl Alcohol, Behenyl Alcohol, Arachidyl Glucoside, Glycerin, Fagus Sylvatica (Beechtree) Bud Extract*, Populus Tremuloides (Aspen) Bark Extract, Trehalose, Butyrospermum Parkii (Shea) Butter*, Caprylic/Capric Triglyceride, Hydrogenated Vegetable Oil, Crithmum Maritimum Extract, Rosa Damascena (Rose) Flower Water*, Sclerotium Gum, Lycium Chinensis (Gogi) Berry*, Panax Ginseng (Ginseng) Root Extract*, Rosa Damascena (Rose) Flower Oil*, Cananga Odorata (Ylang Ylang) Flower Oil, Citrus Sinensis (Orange) Peel Oil*, Rosmarinus Officinalis (Rosemary) Leaf Extract*, P-Anisic Acid
*Ingredient from organic farming
99.94% of the total ingredients are from natural origin
17.31% of the total ingredients are from organic farming
Gently smooth a pea size amount over face and neck using upward and outward movements. Apply morning and night over serum or after treatment for best results.
A daily brightening treatment that helps restore a naturally healthy glow. Contains a potent complex of tone-correcting Licorice Root, detoxifying Wine Extract and brightening Daisy Flower.
HERO INGREDIENT: Licorice Root is extensively researched for its medicinal properties and has potent skin brightening, antioxidant and anti-inflammatory benefits.
Aqua/Water/Eau, Hamamelis Virginiana (Witch Hazel) Water, Lactic Acid, Bellis Perennis (Daisy) Flower Extract, Glycyrrhiza Glabra (Licorice) Root Extract, Phylllanthus Emblica Fruit Extract, Alcohol Denat., Xanthan Gum, Glycerin, Sodium Levulinate, Sodium Anisate, Ascorbic Acid, Wine Extract, Cinchona Succirubra Bark Extract, Peumus Boldus Leaf Extract
After cleansing, apply a thin layer to entire face or affected area morning and night. Follow with moisturizer and/or sunscreen.
Sunburn Alert: Use a sunscreen, wear protective clothing and limit sun exposure while using this product for one week afterwards.
A refreshing toner that blends antiseptic Lavender with calming and hydrating Honey to balance the skin and mind.
Lavender Angustifolia Flower Water is a soothing, mildly antiseptic, water-based solution created when essential oils are steam distilled. This ingredient possesses a clean, crisp herbal scent and is commonly used for its soothing, calming and balancing properties.
Lavandin Flower Oil is renowned for its gentle, soothing, relaxing effects due to its high ester content.
Honey is a natural humectant with antioxidant and mildly antiseptic qualities.
Aloe Barbadensis (Aloe Vera) Leaf Juice*, Lavandula Angustifolia (Lavender) Flower Water*, Leuconostoc/Radish Root Ferment Filtrate, Candida Bombicola/Glucose/Methyl Rapeseed Ferment, Water, Lavandula Angustifolia (Lavender) Flower Oil*, Lavandula Hybridia (Lavandin) Flower Oil*, Honey/Mel/Miel*, Caprylyl Capryl Glucoside
*Ingredient from organic farming
99.99% of the total ingredients are from natural origin
97.31% of the total ingredients are from organic farming
Shake before use. After cleansing, mist directly onto skin to tone and balance. Repeat to refresh skin and mind throughout the day.
Lavender Protective Moisturizer SPF 17
A nourishing moisturizer with micronized Zinc Oxide to help guard the skin from UVA/UVB damage. Turmeric Root helps prevent sun spots while fragrant Lavender calms irritation and soothes the senses.
Micronized Zinc Oxide contains tiny reflective particles that protect the skin from the sun's damaging rays without leaving a "chalky" appearance. This acts as a physical barrier to reflect UVA/UVB rays.
Tetrahydrocurcuminoids (THC) identical to those found in Turmeric Root, these polyphenolic compounds quench free radicals and prevent inflammation caused by lipid peroxidation.
Lavender Vera calms irritation due to sun exposure.
Vitamin E & Beta Clucin help battle free radical damage.
SPF 17 blocks over 95% of the sun's damaging rays.
Active Ingredient: Zinc Oxide 7.02%
Inactive Ingredients: Aqua (Water), Caprylic/Capric Triglyceride, Cetearyl Olivate, Glycerin, Cyclopentasiloxane, Sorbitan Olivate, Squalane, Triticum Vulgare (Wheat) Germ Oil, Olea Europaea (Olive) Fruit Oil, Simmondsia Chinensis (Jojoba) Seed Oil, Xanthan Gum, Magnesium Aluminum Silicate, 1,2-Hexanediol, Glyceryl Isostearate, Caprylyl Clycol, Alcohol Denat., Populus Tremuloides Bark Extract, Leuconostoc/Radish Root Ferment Filtrate, Citric Acid, Polyhydroxystearic Acid, Tocopheryl Acetate (D-alpha), Lavandula Angustifolia (Lavender) Oil, Beta-Glucan (D), Gluconolactone, Phytic Acid, Potassium Sorbate, Lonicera Caprifolium (Honeysuckle) Flower Extract, Lonicera Japonica (Honeysuckle) Flower Extract, Cinnamomum Camphora (Camphor) Bark Oil, Hamamelis Virginiana (Witch Hazel) Water, Tropolone, Glycine Soja (Soybean) Oil, Tocotrienols, Elaeis Guineensis (Palm) Oil, Alcohol, Tocopherol (D-alpha), Tetrahydrodiferuloylmethane, Tetrahydrodemethoxydiferuloylmethane, Tetrahydrobisdemethoxydiferuloylmethane, Astaxanthin
Apply a liberal amount to skin 15 minutes prior to sun exposure. Suitable for daily use. Non-waterproof. Reapply at least every two hours.
A purifying mask and spot treatment that combines pore-refining Moss Extract with oil-absorbingKaolin and Bentonite to leave skin looking healthy and clear.
HERO INGREDIENT: Kaolin and Bentonite Clay have natural absorbent properties that help dry excess oil.Aqua/Water/Eau, Glycerin, Sulfur, Bentonite, Caprylic/Capric Triglyceride, Hamamelis Virginiana (Witch Hazel) Water, Coco-Caprylate/Caprate, Kaolin, Alcohol Denat., Cetearyl Glucoside, Chondrus Crispus (Carrageenan) Extract, Lavandula Angustifolia (Lavender) Oil, Lonicera Caprifolium (Honeysuckle) Flower Extract, Lonicera Japonica (Honeysuckle) Flower Extract, Glyceryl Stearate, Stearic Acid, Squalane, Titanium Dioxide, Cocamidopropyl Hydroxysultaine, Citric Acid, Xanthan Gum, Calendula Officinalis Flower Extract, Cetearyl Alcohol, Chamomilla Recutita (Matricaria) Flower Extract, Fructooligosaccharides (D-beta), Cistus Ladaniferus (Labdanum) Resin, Alcohol, Phytic Acid, Potassium Sorbate
Apply as a spot treatment to affected areas. For a clarifying mask, mix a dime size amount with two pumps of a Naturopathica mist and apply to entire face. Leave on for ten minutes. Rinse.
Formulated for dull or congested skin, this clarifying mask exfoliates with Pumpkin Enzymes to promote a clear and radiant complexion.
HERO INGREDIENT: Pumpkin is a powerful antioxidant due to its high Beta Carotene content. It is also a natural exfoliator.
Cucurbita Pepo (Pumpkin), Cucurbita Pepo (Pumpkin) Wine, Lactobacillus/Pumpkin Ferment Extract, Lactic Acid (L), Glycerin, Aqua/Water/Eau, Lonicera Caprifolium (Honeysuckle) Flower Extract, Lonicera Japonica (Honeysuckle) Flower Extract, Cinnamomum Zeylanicum (Cinnamon) Bark Extract, Origanum Vulgare (Oregano) Leaf Extract, Rosmarinus Officinalis (Rosemary) Leaf Extract, Thymus Vulgaris (Thyme) Flower/Leaf Extract, Lavandula Angustifolia (Lavender) Flower Extract, Hydrastis Canadensis (Golden Seal) Root Extract, Eugenia Caryophyllus (Clove) Leaf Oil, Ginger Oil, Potassium Sorbate
Apply a thin layer and massage into clean skin. Leave on for 3-5 minutes. Rinse well. Repeat 1-2 times per week.
Sunburn Alert: Use a sunscreen, wear protective clothing and limit sun exposure while using this product for one week afterwards.
Rosemary Oil Reducing Moisturizer
A light, mattifying moisturizer that hydrates skin, absorbs oil and refines pores with antiseptic Rosemary and Alpine Willow.
Rosemary Oil helps tighten skin with antioxidant, astringent and antimicrobial properties.
Traditionally, Rosemary has been used for a variety of health benefits, including improving memory, curing gout, improving digestion and easing nervous headaches.
Alpine Willow is cultivated in the Swiss Alps, this herb helps reduce sebum production and refine pores.
Aloe Vera is an effective moisturizer and healing agent containing minerals, amino acids, proteins and polysaccharides.
Hyaluronic Acid aides the skin to retain moisture, plumping the skin and reducing flakiness.
Water/Aqua/Eau, Aloe Barbadensis (Aloe Vera) Leaf Juice*, Arachidyl Alcohol, Behenyl Alcohol, Arachidyl Glucoside, Sodium Hyaluronate, Squalane, Glycerin*, Alcohol Denatured*, Sodium Levulinate, Sodium Anisate, Epilobium Fleischeri (Alpaflor) Extract*, Xanthan Gum, Sodium Stearoyl Glutamate, Rosmarinus Officinalis (Rosemary) Leaf Oil, Potassium Sorbate, Citric Acid
*Ingredient from organic farming
99.87% of the total ingredients are from natural origin
12.01% of the total ingredients are from organic farming
Gently smooth a pea size amount over face and neck using upward and outward movements. Apply morning and night over serum or after treatment for best results.
Naturopathica's Vitamin C Serum is a celebrity favorite - a powerful antioxidant blend of Vitamins A, C and E, combined with Kudzu to help reduce the visible signs of aging. Lightweight and ideal for all skin types, this anti aging serum is a sure-fire way to brighten and clarify your complexion.
1.7 oz
SUPEROXIDE DISMUTASE (SOD): a powerful plant derived enzymatic antioxidant known for its ability to neutralize the highly reactive superoxide radical and slow down the signs of aging
VITAMINS A, C & E: this blend of potent antioxidants helps to neutralize free radicals and protect skin from environmental assault
LIPOSOME DELIVERY SYSTEM: allows for deep penetration into the skin to get visible results
KUDZU EXTRACT: a plant renowned for its beauty and versatility, it has been used in traditional Asian medicine for centuries. Symbiosomes (part of the root system) are rich in proteins and Isoflavones. This extract guards against the breakdown of collagen and elastin and helps to maintain skin youthfulness
Aqua/Water/Eau, Algae Extract, Glycerin, Helianthus Annuus (Sunflower) Seed Oil, Aloe Barbadensis (Aloe Vera) Leaf Juice, Squalane, Leuconostoc/ Radish Root Ferment Filtrate, Rosa Damascena (Rose) Flower Water, Ascorbic Acid Polypeptide, Oat Beta Glucan, Glycoproteins, Triticum Vulgare (Wheat) Germ Oil, Butyrospermum Parkii (Shea) Butter, Phospholipids, Tocopheryl Acetate, Retinyl Palmitate, Ascorbyl Palmitate, Superoxide Dismutase, Borago Officinalis (Borage) Seed Oil, Sclerotium Gum, Pueraria Lobata (Kudzu) Symbiosome Extract, Urtica Dioica (Nettle) Leaf Extract, Rosa Centifolia (Rose) Bud Extract, Lecithin, Rosa Damascena (Rose) Flower Oil, Citrus Sinensis (Blood Orange) Peel Oil, Tilia Cordata (Linden) Flower Extract, Rosmarinus Officinalis (Rosemary) Leaf Extract, Xanthan Gum, P-Anisic Acid
Gently smooth a pea size amount over face and neck using upward and outward movements. Apply morning and night over serum or after treatment for best results.


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With the upcoming adaptation of 300 blowing the minds of all those who see it, I think it's safe to say that director Zack Snyder is here to stay. After remaking Dawn of the Dead and impressing more people than it pissed off (no small feat, that), there was some concern he might be done with our genre.
Such is not the case as The Hollywood Reporter announced this morning that Snyder is now attached to follow up 300 with Cobalt 60, another comic book though one admittedly more cult-y than Frank Miller's classic. The story is set in a post-apocalyptic world inhabited by mutants and other fantastic creatures, in which the titular hero sets out on a quest to avenge the death of his parents at the hands of the evil Strontium 90.
The comic was originally created by Vaughn Bode but scrapped after one story. Years later his son, Mark, would continue the story and lead the book to cult history, as it were.
Snyder will direct, but a writer is being sought for it as we speak. Stick around for more!
Miss the days of post-apocalypse movies in our forums!

subset: commoncrawl


text:
Mathematical modelling of eukaryotic DNA replication
- First Online:
DOI: 10.1007/s10577-009-9092-4
- Cite this article as:
- Hyrien, O. & Goldar, A. Chromosome Res (2010) 18: 147. doi:10.1007/s10577-009-9092-4
- 809 Downloads
Eukaryotic DNA replication is a complex process. Replication starts at thousand origins that are activated at different times in S phase and terminates when converging replication forks meet. Potential origins are much more abundant than actually fire within a given S phase. The choice of replication origins and their time of activation is never exactly the same in any two cells. Individual origins show different efficiencies and different firing time probability distributions, conferring stochasticity to the DNA replication process. High-throughput microarray and sequencing techniques are providing increasingly huge datasets on the population-averaged spatiotemporal patterns of DNA replication in several organisms. On the other hand, single-molecule replication mapping techniques such as DNA combing provide unique information about cell-to-cell variability in DNA replication patterns. Mathematical modelling is required to fully comprehend the complexity of the chromosome replication process and to correctly interpret these data. Mathematical analysis and computer simulations have been recently used to model and interpret genome-wide replication data in the yeast Saccharomyces cerevisiae and Schizosaccharomyces pombe, in Xenopus egg extracts and in mammalian cells. These works reveal how stochasticity in origin usage confers robustness and reliability to the DNA replication process.
KeywordsReplication origins Replication fork progression Genome stability 1D nucleation and growth processes Stochastic models
Ataxia-telangiectasia mutated
ATM and Rad3 related
- AT-rich
Adenine- and thymine-rich
- Cdc6
Cell division cycle (factor) 6
- Cdc45
Cell division cycle (factor) 45
- Cdk2
Cell division kinase 2
- Cdt1
cdc10-Dependent transcript 1
- Clb5
Cyclin B5
- Clb6
Cyclin B6
- Dbf4
Dumbell-forming (factor) 4
- Drf1
dbf4-Related factor 1
Kolmogorov–Johnson–Mehl–Avrami (model for nucleation-and-growth)
Minichromosome maintenance (proteins) 2 to 7
Origin recognition complex
The duplication of eukaryotic chromosomes starts at multiple origins that are spread at irregular intervals and fire at different times in S phase (Huberman and Riggs 1968; Machida et al. 2005; Aladjem 2007). A large degree of flexibility in origin usage is observed between different tissues, developmental stages or even between individual cells of the same type (McKnight and Miller 1977; Hyrien et al. 1995; Norio et al. 2005; Czajkowsky et al. 2008). This suggests that potential origins are more abundant than are used in a single S phase, and that origin choice is partly stochastic.
A molecular basis for origin redundancy has been suggested by biochemical studies of the DNA replication "licensing" system (Blow and Dutta 2005). Chromatin is licensed for replication in late mitosis and G1 by the loading of multiple complexes of the MCM2-7 proteins in a process that depends on the origin recognition complex (ORC), Cdc6 and Cdt1 proteins. Cyclin- and Drf1/Dbf4-dependent protein kinases then phosphorylate MCM2-7 and other replication factors in S phase, resulting in the assembly of replication forks at origins. MCM2-7 provides helicase activity in front of the forks and are displaced from replicated DNA. Once S phase has started, a variety of mechanisms prevent MCM2-7 from rebinding chromatin until past mitosis, so that DNA is licenced and replicated only once per cell cycle. Importantly, 10–20 times more MCM2-7 complexes are loaded onto chromatin than ORC and actual initiation events (Burkhart et al. 1995; Lei et al. 1996; Rowles et al. 1996). In some organisms, MCM2-7 complexes appear to spread at varying distances from their ORC-bound loading sites, and it has been suggested that each of them is competent for initiation, although only a small fraction of them are actually used during a given S phase (Lucas et al. 2000; Edwards et al. 2002; Harvey and Newport 2003). Thus, MCM2-7 chromatin loading can be artificially reduced to a significant extent without compromising genome duplication kinetics (Mahbubani et al. 1997; Ying and Gautier 2005; Woodward et al. 2006). Nevertheless, excess MCM2-7 complexes become critical for genome stability in the presence of replication inhibitors, as they provide back-up origins than can rescue stalled forks (Woodward et al. 2006; Ge et al. 2007). Further reduction of MCM2-7 loading can compromise genome stability during normal S phase (Ibarra et al. 2008).
Genome duplication must be completed prior to cell division. This necessitates an appropriate distribution of initiation events and safeguard mechanisms that protect against replication fork failure (Hyrien et al. 2003; Willis and Rhind 2009). The number of activated origins per genome per cell cycle ranges from a few hundreds in yeasts to hundreds of thousands in frog early embryos. Individual origins have such a broad range of efficiencies (see Nomenclature for definitions) and firing times that the history of initiation events is never the same in any two cells.
Furthermore, replication fork velocity, as estimated by the slopes of replication timing profiles in yeast (Raghuraman et al. 2001) and by DNA combing in mammalian cells (Conti et al. 2007), appears to vary over a 10-fold range when travelling along different parts of the genome. However, the vast majority are within ±2-fold of the mean. The extremes of the range are quite rare and may be arte factual. Velocities may be overestimated using DNA combing if closely spaced replicons fire and merge during a single labelling pulse, and in population-averaged replication timing profiles, slopes no longer reflect fork velocities when the considered region can be replicated in opposite directions from alternative origins.
High-throughput techniques are providing increasingly huge datasets on the spatiotemporal patterns of DNA replication in several organisms, both in the form of population-averaged profiles and single-molecule data, which provide unique information about cell-to-cell variability. Mathematical modelling is required to fully comprehend the complexity of the chromosome replication process and to correctly interpret these data. Mathematical modelling has been used to address several important questions. Is stochastic origin usage compatible with a reliable replication end time? What is the time-dependent rate of origin usage and how is it controlled? Can stochastic origin usage offer protection against replication fork failure? How should we take stochasticity into account to interpret whole-genome replication timing profiles?
The random completion problem: modelling DNA replication in Xenopus
Life without specific origins
Early embryos of the frog Xenopus laevis provide an extreme case of stochasticity in DNA replication (Hyrien et al. 2003). Early embryonic S phase is very brief (20 min) although the fork progression rate is homogeneously slow (v = 0.5 kb/min at 20°C) across the genome. Rapid replication is ensured by a close spacing of initiation events. Quantitative 2D gel analysis of replication intermediates in Xenopus eggs, egg extracts and early embryos have shown that replication initiates at random with respect to DNA sequence and at mean ∼10-kb intervals (Hyrien and Méchali 1992, 1993; Mahbubani et al. 1992; Lucas et al. 2000). However, a random distribution of origins with a mean 10-kb spacing would result in e−20/10 = 13.5% of interorigin distances >20 kb, the maximum length of DNA that can be replicated by a pair of forks within 20 min. Nevertheless, early embryonic blastomeres reliably complete replication within 20 min.
Two potential solutions have been proposed to explain this paradox, known as the random completion problem (Hyrien et al. 2003). The "regular spacing" model assumes that origins are located at regular (not random) intervals, albeit with no regard to specific DNA sequences, and are activated with a close to 100% efficiency (Hyrien and Méchali 1993; Blow et al. 2001). The "origin redundancy" model instead assumes that potential origins are in excess and that the probability of origin firing increases as replication progresses so that additional initiations ensure the completion of unreplicated gaps before the end of S phase (Lucas et al. 2000). Electron microscopy, DNA combing and other DNA fibre techniques have been used to study the distribution of replication "eyes" (or bubbles; these terms come from the appearance of replicated segments on electron micrographs) on single DNA molecules replicating in egg extracts (Herrick et al. 2000; Lucas et al. 2000; Blow et al. 2001; Marheineke and Hyrien 2001). These studies revealed that (1) initiation occurs throughout S phase; (2) eye-to-eye distances are not regularly distributed; (3) the ease with which potential origins fire, deduced from the abundance of small eyes at various stages of replication, increases as replication progresses. These data favoured the origin redundancy/increasing firing probability model. The way origin firing probability changes during S phase defines the replication timing programme of the early Xenopus embryo. Once the initiations have been set, the rest of the process is deterministic.
Application of the KJMA model to DNA replication kinetics
Bechhoefer and colleagues have adapted theories of phase transition kinetics to extract an analytical expression for the time-dependent changes in origin activation probability from the DNA fibre data (Herrick et al. 2002; Jun and Bechhoefer 2005; Jun et al. 2005; Zhang and Bechhoefer 2006). Some phase transition phenomena, e.g. growth of a crystal from a liquid, result from three simultaneous processes: nucleation of solid domains, growth of existing domains, and coalescence (merge of expanding domains), which are formally analogous to replication initiation, elongation and termination. In the 1930s, Kolmogorov (1937), Johnson and Mehl (1939), and Avrami (1939, 1940, 1941) independently derived a stochastic model (the KJMA model) that could describe the fraction of volume f(t) of a liquid that has crystallised by time t, which experimentally is a sigmoidal curve. In the model's simplest form, nucleation occurs within the liquid at a constant rate in space and time and growth occurs at a constant speed. In the 1980s, Sekimoto (1984a, b, 1991) showed that if growth occurs in only one dimension (as in DNA replication) the analysis can be pushed further to describe the statistics and time evolution of solid domain sizes and spacing (replication eyes, gaps and eye-to-eye distances). Bechhoefer and colleagues extended this analysis to the case of an arbitrary initiation rate I(t), defined as the spatially averaged number of initiations per unit time per unit length of unreplicated DNA. They developed a numerical inversion procedure to infer I(t) from v, the observed velocity of replication forks and f(t), the replicated fraction at time t.
I(t) is assumed to be spatially homogeneous, which is justified if potential origins are abundant and independent, but it needs not be constant in time. Note that I(t) is a different concept from origin efficiency. Origin efficiency is the overall frequency at which an origin fires within a population of cells, whereas I(t) measures the ease with which unused origins fire at different times in S phase.
The extracted I(t) was found to markedly increase halfway through S phase then decrease sharply (Herrick et al. 2002; Zhang and Bechhoefer 2006). Initially, the decreasing part of I(t) was not taken in consideration because the numerical inversion procedure tends to amplify noise and the possibility of systematic errors at the end of S phase could not be discarded. When an analytical expression of I(t) that fits the increasing part of the data was incorporated into Monte-Carlo simulations of a random replication process, the simulations could account for the mean eye lengths, gap lengths and eye-to-eye distances observed at different times in S phase. It was concluded that this kinetic model, starting from the two fundamental parameters I(t) and v, adequately describes the progress of DNA replication, independently of the underlying biochemical mechanisms.
Relating replication initiation rate to replication end time distributions
Firing origins more rapidly as S phase progresses offers a plausible mechanism to accelerate the completion of unreplicated gaps. But what is the robustness of this solution? Using concepts from extreme-value statistics, the distribution of replication end times for an arbitrary I(t) was mathematically calculated (Bechhoefer and Marshall 2007; Yang and Bechhoefer 2008). Adjusting the amplitude of I(t) so as to fit the mode of the distribution to the data, it was found that initiating all origins at the beginning of S phase would lead to the broadest possible dispersion of end times around the mode. In contrast, a constant rate of initiation narrowed this distribution, a linearly increasing I(t) narrowed it more, and a quadratically increasing I(t), which best fit the experimental I(t), narrowed it even more, ensuring a more reproducible length of S phase.
Recently, the I(t) profile was determined without relying on a numerical inversion procedure but by counting individual initiation events on combed DNA fibres at different f(t) (Goldar et al. 2008). This analysis showed that the decreasing part of I(t), which was not initially taken into account, is not a statistical artefact but a real feature of chromosome replication. The inclusion of the decreasing part of I(t) did not dramatically alter the distribution of replication end times, suggesting that increasing origin usage around mid-S phase rather than at its end can also ensure a reliable replication end-time despite random initiation (Yang and Bechhoefer 2008). Interestingly, this shape of I(t) was found to be conserved among distant eukaryotes, suggesting that it serves more general purposes than resolving the random completion problem (Goldar et al. 2009; see below).
Explaining the observed rate of origin usage
Assuming that potential origins are in excess and independent, the temporal programme of DNA replication is defined solely by I(t) and v. All other observables (fork density, replicated fraction, distributions of eye, gap and eye-to-eye sizes) follow from these two fundamental parameters. Two models to explain the observed shape of I(t) in terms of protein-DNA interactions have been proposed.
Goldar et al. (2008) have performed simulations wherein the encounter of a limiting, recyclable replication fork factor with potential origins results in productive initiation with probability P. When forks meet, the factor is released and made available again for initiation. The total number of factor molecules NT and the probability P were either held constant or allowed to change with time. When NT and P were held constant, the calculated I(t) profile could not match the data, even after extensive parameter testing. However, an excellent match was obtained when two conditions were combined: NT linearly increased with time, and P increased with fork density in a self-limiting manner. By itself, the first condition allowed a match to the increasing but not the decreasing part of the data. The second condition alone resulted in I(t) profiles that increased then decreased but could not match the data.
The requirement for an increasing NT can be related to the observation that several replication factors are actively concentrated in the nucleus during replication in egg extracts (Walter et al. 1998). An interpretation of the self-limiting dependency of initiation on fork density is that forks may facilitate nearby initiation until their density exceeds some threshold. Forks may stimulate initiation because Cdc45, a stable fork component, can recruit Cdk2, a kinase involved in origin activation, at replication foci (Alexandrow and Hamlin 2005). The limitation of fork-stimulated initiation at high fork density may be related to the observation that ATM/ATR checkpoints negatively regulate initiation through a feedback mechanism originating from exposure of single-stranded DNA at replication forks (Marheineke and Hyrien 2004; Shechter et al. 2004).
Gauthier and Bechhoefer (2009) have proposed a different model to explain the observed shape of I(t), based on anomalous diffusion theories. In their model, the initiation rate is set by the time needed for initiation factors to find (by diffusion) and then activate (reaction) potential origins. The search time ts for one initiator molecule to find one potential origin was calculated according to previous theories in which a searcher particle diffuses in the 3D space until it nonspecifically binds to the DNA, then perform a 1D search along the DNA, then dissociates and starts another search cycle until it finds its target (Berg et al. 1981). The model assumes no spatial correlations between consecutive nonspecific bindings. The search time ts was expressed as a function of: the duration of one search cycle; the average 1D-search distance; the density of potential origins; the fork velocity; the genome length; the replicated fraction; and the diffusion exponent α, which defines how the mean-square displacement of a particle scales with time \( \left( {\left\langle {{x^2}} \right\rangle \sim {t^\alpha }} \right) \). Assuming a constant activation time tr, the number of initiations per time per length of unreplicated DNA for Ns searchers was then calculated. Two conditions were required to fit this expression to combing data. First, the number of searchers, Ns(t), had to increase with time, as proposed by Goldar et al. (2008). Second, the decreasing part of the data could be explained if α < 1, i.e. if the interaction between DNA and searchers is sub-diffusive. It was concluded that initiation is reaction-limited during most of S phase but becomes suppressed at the end of S phase because the diffusion-limited search time dominates.
In conclusion, both models require that the amount of a limiting replication factor increases during S phase, e.g. by nuclear import, to explain the increasing part of I(t). The diffusion model can naturally account for both the increasing and the decreasing part of I(t) without explicitly tying initiation rate to fork density. However, the fork-density dependent model can more easily incorporate biological observations such as the downregulation of initiation by ATM/ATR when fork density increases above some threshold.
Is DNA replication completely random in space and time in Xenopus egg extracts?
Some experimental data do not entirely agree with a purely random model for Xenopus DNA replication. First, initiation shows a ∼2-fold preference for AT-rich DNA sequences (Labit et al. 2008; Stanojcic et al. 2008). Second, in Xenopus as in other eukaryotes, active replication forks at any given time in S phase are not randomly distributed in the nucleus but are concentrated in a few hundreds of intranuclear sites that are known as replication foci and are believed to represent large (∼1 Mb) chromosomal segments that replicate at specific times (Nakamura et al. 1986; Berezney et al. 2000). Labit et al. (2008) labelled DNA replicated at the onset of two successive S phases in two different colours. When doubly labelled nuclei were visualised, a strong coincidence of both labels was observed at the level of replication foci, showing that chromosomal segments comprising hundreds of replicons are replicated in a non-random, reproducible temporal sequence. When the DNA from similarly labelled nuclei was purified and combed; however, the two labels did not coincide on the combed fibres; therefore, within each domain origins are activated in a random temporal order. Although the determinants of the large-scale, deterministic temporal order of replication are unknown, its existence has implications for the interpretation of I(t) profiles. Calculations of I(t) profiles assumed that DNA fibres of comparable f(t) have similar replication starting times, which is not entirely exact. The time origin in the I(t) profiles more exactly reflects each fibre's starting time than the start of S phase.
A third inconsistency with a purely random replication model is the existence of a small but significant size correlation between adjacent replication eyes, suggesting that neighbouring initiations tend to occur synchronously (Blow et al. 2001; Jun et al. 2004; Marheineke and Hyrien 2004). Furthermore, the distributions of eye-to-eye distances are slightly peakier than predicted by the KJMA model, suggesting spatial correlations (Jun et al. 2004). A model in which loop formation between a potential origin and a fork stimulates initiation was proposed to explain these spatiotemporal correlations (Jun et al. 2004). In a semi-flexible polymer, loops that are too small cost too much bending energy, while loops that are too large cost too much entropy because they reduce the number of conformations that the chain can explore. Balancing these effects gives an optimal loop size that depends on the fibre's stiffness, usually expressed as its persistence length. Jun et al. (2004) estimated this parameter by fitting a theoretical loop size probability distribution to the eye-to-eye distance distribution, then incorporated the calculated persistence length in simulations in which each origin had a different probability of initiation depending on how far it was from a left and right approaching forks (note that this model implicitly ties the initiation rate to the local fork density and enforces synchrony of neighbouring origins). The results predicted an optimal loop size of ∼11 kb and an eye-to-eye distance distribution that better agreed with the data than the standard 1D KJMA model, suggesting that mechanical properties of the chromatin fibre may contribute to regularising the distribution of initiation events.
Yang and Bechhoefer (2008) investigated whether the experimentally observed degree of spatial ordering was sufficient to make the replication end-time distribution deviate from the random case. The genome was modelled as a lattice of potential origins whose spacing and efficiency were continuously increased, allowing a continuous interpolation from complete randomness to perfect periodicity while maintaining the total initiation probability constant. The resulting end-time distribution was unaltered as long as the lattice spacing remained <6.5 kb, the average inter-origin spacing in the random case. Spacing origins further apart shifted the end-time distribution to longer times but did not alter its width. As many observed eye-to-eye distances are <6.5 kb, any underlying regularity in origin placement must be too weak to be pertinent to the random completion problem.
In summary, a combination of biochemical experiments, mathematical modelling and computer simulations has shown how stochastic location and activation of origins ensures the fast and reliable replication needed for rapid development of early Xenopus embryos. These works provide a solid foundation to formulate more complex models appropriate to cells where origin location is less random and fork velocity is more variable (Yang et al. 2009). The fact that a large genome can be faithfully duplicated while initiating origins randomly raises the question why initiation is random neither in space nor in time in many other organisms or in later developmental stages. Non-randomness probably results from the need to coordinate replication and transcription. This constraint is absent in early Xenopus embryos whose genome is transcriptionally silent until the mid-blastula stage.
Modelling DNA replication in Schizosaccharomyces pombe
Origin islands in a sea of genes
Replication origins in S. pombe were first identified using autonomous plasmid replication assays (Maundrell et al. 1988) and 2D gel electrophoresis of replication-fork-containing restriction fragments (Zhu et al. 1992; Dubey et al. 1994). Origins are 500–1,000 bp long, are relatively inefficient (30% on average) and display no consensus sequence except for an extreme AT richness, which has been used bioinformatically to identify 384 potential origins (Segurado et al. 2003). Later microarray studies confirmed these predictions and mapped nearly all potential origins in S. pombe (Feng et al. 2006; Heichinger et al. 2006; Eshaghi et al. 2007; Hayashi et al. 2007). All origins map to intergenic regions, and their sequences properties are similar to those of inter-genes in general. The relative ability of an inter-gene to serve as an origin does not depend on its precise sequence but only on its length and AT content, suggesting that origins active in any given S phase are recruited stochastically among these intergenic regions (Dai et al. 2005). DNA combing experiments have shown that (1) eye-to-eye distances are well fit by an exponential distribution; (2) there is no significant correlation between firing of neighbouring origins, inconsistent with coordinated regulation of origin clusters and (3) there is no correlation between origin firings in sequential S phases (Patel et al. 2006). All these results are consistent with stochastic firing of origins, although in contrast to Xenopus embryos, origins do not occur at random sequences and display a broad range of efficiencies.
Defining origin efficiency
Origin efficiencies have been estimated by several methods. 2D gel electrophoresis of replication intermediates reveals the fraction of replication intermediates of a specific restriction fragment that contain either a replication bubble (initiation) or a single fork (passive replication). These studies first suggested that S. pombe origins fire in a variable and often small fraction of the cell cycles (Dubey et al. 1994). Combing DNA molecules labelled with BrdU in the presence of hydroxyurea (HU), an agent which slows down replication forks and confines DNA synthesis to the vicinity of origins, showed that origins fired early on from 8% or less to 59% of fibres (mean 33%) (Patel et al. 2006). Heichinger et al. (2006) used microarrays to identify 904 potential origins and defined their efficiencies as the relative increase in DNA content in cells synchronously released from a late G2 block into S phase in the presence of HU. Only a few origins were >50% efficient and most were <10% efficient, consistent with the combing study. Plotting efficiency as a function of replication time (determined in the absence of HU) revealed that early origins tend to be more efficient. A caveat in these experiments is that cells were exposed to HU for a time that only allowed limited DNA synthesis, so that late-firing but efficient origins may have gone unnoticed. In an independent microarray study, Eshaghi et al. (2007) have defined efficiency as the rate of copy number increase in cells released from an HU block and reported that efficiently replicating origins tend to fire late, an opposite conclusion to Heichinger et al. However, the method of Eshaghi et al. does not discriminate between initiation and passive replication by incoming forks from neighbouring origins. More experiments are required to determine the fraction of cells in which each potential origin fires in unperturbed cells, and to determine the time-dependent rate of initiation of individual origins.
Stochastic hybrid model of DNA replication in S. pombe
Lygeros et al. (2008) have proposed a model for DNA replication in S. pombe where initiations events are characterised by uncertainty in time and space. Each origin was characterised by an intrinsic "propensity" to fire, which denotes the instantaneous probability of firing of an as yet unfired origin (see Nomenclature). This concept is similar to I(t) except that in S. pombe, unlike in Xenopus, the probability of firing is not spatially homogeneous. Initially, the firing propensity of each origin, λi, was assumed to be constant in time, so that in the absence of passive replication, the probability that an origin has fired by time t is \( P(t) = 1 - {e^{\lambda it}} \). This equation implies that strong origins fire early and weak origins fire late. To estimate the parameter λi for each origin, P(t) was equated with the origin efficiencies determined by Heichinger et al. (2006), setting t = 20 min as the normal duration of S phase. Fork velocity was set constant at 3 kb/min. The model was used to simulate the DNA combing experiment of Patel et al. (2006). A close agreement was observed between the simulated and observed inter-bubble distances. However, the predicted replication end time was broadly distributed with a much longer mean (67 min) than observed (20 min), although the number of fired origins was close to observed. Increasing the firing propensity of all origins decreased the predicted duration of S phase but also increased the number of fired origins to unrealistic values. Increasing fork velocity (to 6 kb/min) or introducing additional inefficient origins decreased S phase length to 45 min, a still too long time. The model was then modified to let the firing propensity of unfired origins increase during S phase, keeping the total firing propensity constant through S phase. This constraint is equivalent to postulating that a limiting initiation factor, whose concentration remains constant during S phase, is released following origin firing and binds again to unfired origins. In this scenario the average end time was reduced to 33 min. Similar results (average end time = 37 min) were obtained when the limiting factor was released when forks meet or when an origin is passively replicated.
The authors concluded that increasing the firing propensity of individual origins during S phase offers a possible solution to the random completion problem in fission yeast, as in Xenopus. They also considered that DNA replication could in fact take longer than usually accepted and extend into what we now define as G2-phase. This possibility can be rejected in Xenopus because S phase is immediately followed by mitosis. However, G2 makes the largest part of the fission yeast cell cycle and a prolongation of DNA synthesis across different regions in different cells may have gone unnoticed so far.
The model assumes that the limiting factor released from fired origins binds efficiently to unfired origins and predicts that the mean firing propensity increases most markedly during the last 5 min of S phase (Figure S2B in Lygeros et al. (2008)). However, simulations have shown that recycling is not efficient when the number of unfired origins decreases as S phase progresses, even if the affinity of potential origins for the factor is very high (Figure 2A in Goldar et al. (2008)). Furthermore, the spatially averaged profile of I(t), which is equivalent to the mean firing propensity, was recently extracted (Goldar et al. 2009) from the published S. pombe replication timing profiles (Heichinger et al. 2006; Eshaghi et al. 2007). It was found that I(t) increases during the first half of S phase then decreases, in contrast to the predictions of the model. As it seems to us, it would be interesting to match the time-dependent change in mean firing propensity with the experimental I(t) profile and examine if the replication end time distribution becomes more realistic.
Modelling DNA replication in Saccharomyces cerevisiae
Replication programme in S. cerevisiae
In contrast to other eukaryotes, replication origins in S. cerevisiae are strictly determined by DNA sequence. An 11-bp consensus sequence and additional sequences variable in size and location relative to this consensus are required for replication initiation (Newlon and Theis 1993). The efficiencies of most of the origins of chromosome VI have been found by 2D gel and fork direction analysis techniques to be extremely variable, ranging from <5% to >90% (Friedman et al. 1997; Yamashita et al. 1997). The first genome-wide replication timing profile of S. cerevisiae was reported by Raghuraman et al. (2001), who identified 332 origins as regions that replicate before their neighbours. Origins show a continuum of replication times. Although an origin's replication time is often assumed to reflect its activation time, this is only true for the most efficient origins. For less efficient origins, replication time is an average of initiation and passive replication times. Fork rates were estimated from the slopes of these peaks to 0.5–11 kb/min, with a mean of 2.9 kb/min and a median of 2.3 kb/min (as explained in the "Introduction", the extremes of this range may result from misinterpretation of the profile slopes). Since then, experimental and computational studies (Wyrick et al. 2001; Yabuki et al. 2002; Feng et al. 2006; Nieduszynski et al. 2006; Xu et al. 2006) have identified and mapped a total of 732 origins (Nieduszynski et al. 2007), and there are replication timing data for 454 of them in the study of Raghuraman et al. (2001).
Czajkowsky et al. (2008) have used DNA combing to visualise the replication patterns of single chromosome VI molecules. Contrary to what would be expected for a strictly deterministic temporal program, no two molecules exhibited the same pattern, and replication of different regions of the same chromosome occurred independently of each other. Nevertheless, averaging the patterns of all the fibres examined recapitulated the ensemble-averaged patterns obtained from population studies. One extreme interpretation of the single molecule data is that the observed kinetics of replication simply results from stochastic processes acting on origins with different probabilities of activation. Indeed, 2D gel studies suggested that there is a strong—though not absolute—correlation between origin efficiency and firing order (Yamashita et al. 1997).
To investigate whether origin firing time is probabilistic or deterministic, McCune et al. (2008) have compared genome-wide kinetics of replication in the wild-type and in a clb5∆ mutant. During normal S phase, origins are activated by Clb5- or Clb6- dependent kinases. Clb6 is rapidly degraded as cells enter S phase whereas Clb5 persists until mitosis (Jackson et al. 2006). In the absence of Clb6, S phase proceeds with no apparent defect whereas in the absence of Clb5 S phase requires 50% more time for completion (Schwob and Nasmyth 1993; Donaldson et al. 1998). McCune et al. (2008) show that regions that replicate early in the wild-type replicate with the same kinetics in the clb5∆ mutant whereas regions that replicate late in the wild-type replicate even later in the mutant. The late origins do not seem a preferred substrate for Clb5-dependent kinase since expression of a long-lived version of Clb6 completely rescues replication kinetics. McCune et al. suggest that these results argue against a purely stochastic model of origin firing because the efficiency of only some origins is affected. However, it remains possible that the most efficient origins have all fired before Clb6 becomes degraded so that only the less efficient late origins are detectably affected. Resolving this issue requires quantitative modelling (see also the review by Rhind et al. in this issue of Chromosome Research).
A deterministic model of S. cerevisiae genome replication
The data described above suggest that origins are incompletely efficient and have a wide range of firing time distributions. Neglecting this complexity, Spiesser et al. (2009) have provided a deterministic model for DNA replication in S. cerevisiae. They used the length of the chromosomes, the position of 454 origins, their mean initiation time and a fork migration rate assumed to be constant at 3 kb/min to recalculate the replication timing profiles of all 16 chromosomes, assuming that the efficiency of the selected 454 origins was 100%. In spite of these approximations, the recalculated profiles matched the experimental profiles surprisingly well for chromosomes II, V, VII, XI, XIII, XV, XVI. However, significant differences were observed for the other chromosomes, with many sequences replicating later than predicted. Whereas the slope of the recalculated profiles is constant due to the constant fork rate implemented, the experimental curve is smooth with a varying slope, which may reflect a changing fork progression rate or the activation of inefficient origins that were not included in the simulation.
Spiesser et al. (2009) modelled replication in the clb5∆ mutant by stopping origin activation at a time point that corresponds to the mean value of origin firing times (27 min, about the midpoint of a normal 60-min S phase). The delayed regions in the calculated profiles exactly matched the in vivo data for chromosomes I to VIII and IX, less exactly for chromosomes IX, X and XIV and very poorly for chromosomes XII, XIII, XV and XVI. A better match may be obtained when origins are each assigned a distribution of firing times and a range of efficiencies, and when Clb6 is modelled to decay over a 5–10 min period as observed in vivo (Jackson et al. 2006) rather than abruptly at 27 min. This will be required to conclusively determine whether other factors than origin efficiencies contribute to their order of activation.
Stochastic models of S. cerevisiae chromosome replication
De Moura et al. (C. Nieduszynski, pers. comm.) have recently developed a stochastic model for S. cerevisiae chromosome VI replication in which each origin is characterized by its position, its "activation competence" (as estimated from plasmid maintenance assays; (Shirahige et al. 1993; Chang et al. 2008)), and a Gaussian distribution of activation times where the mean is estimated from timed 2D gels (Yamashita et al. 1997) and the standard deviation (σ) is simulated. The fork velocity is held constant (1.4 kb/min, according to Friedman et al. (1997)), and origins are assumed to fire independently of each other. Stochastic simulation of chromosome replication gives the replication time profile of a single cell, and the simulation is repeated a large number of times from which population averages are compared with experimental data, assuming perfect cell synchrony in the population. Note that according to the definitions listed in Nomenclature, plasmid maintenance assays are strictly measurements of potential efficiency, not competence. In practice, however, the difference can perhaps be neglected. Origin competence is a distinct notion from origin efficiency and origin firing probability. Origin efficiency is equal to origin competence times the probability that the origin is not passively replicated, which depends on the positions, competences and activation times of neighbour origins. Note also that by assuming a Gaussian firing time distribution, the model implies that firing probability changes during S phase, to peak at the mean firing time.
Initial simulations assuming a constant σ for all origins reproduced the replication timing curve better than the deterministic model of Spiesser et al. (2009). Varying σ from 0 to 15 min did not dramatically alter the replication timing curves but affected the observed efficiency of each origin to a varying extent. However, no single value of σ resulted in efficiencies agreeing closely with the data. When σ was assumed to be equal to half the activation time for each origin, a better agreement was obtained.
De Moura et al. have stressed that a peak height in a replication timing profile is influenced by both origin activation time and competence as well as by neighbour origins, so that it is not possible to directly interpret a peak height as an origin activation time. To more rigorously interpret replication timing profiles, the locations of origins were assumed and a genetic algorithm was used to estimate competences, activation times, fork velocity and σ. The estimated origin activation times best agreed with the experimental data of Friedman et al. (1997) when the authors used experimental determination of the proportion of chromosome VI that are replicated at various times in S phase (Alvino et al. 2007), allowing for different values of σ for each origin. The estimated σ values tended to increase with activation time. The estimated competences were not concordant with those measured by plasmid loss experiments (Shirahige et al. 1993; Chang et al. 2008) or in HU-treated checkpoint deficient cells (Feng et al. 2006). However, estimated origin efficiencies were in close agreement with those determined by 2D-gel analyses (Friedman et al. 1997; Yamashita et al. 1997).
Yang et al. (J. Bechhoefer, pers. comm.) have recently developed an analytical model that extends the previous formalism elaborated for Xenopus to incorporate variable origin position, variable fork velocities and probabilistic initiation. They used this general formalism describing DNA replication to compute ensemble averages of replication timing profiles and performed fits to the S. cerevisiae microarray data of McCune et al. (2008), which includes eight time points from 10 to 45 min into S phase at 5-min resolution. They were able to extract the distribution of firing times for each origin and found that using a constant fork velocity (2 kb/min) gives an equally good fit to the data as using a space- and time-dependent velocity. They also were able to identify origins that were not previously recognised because they do not have apparent peaks on the timing profile but are crucial to the fit. Importantly, the model recapitulates the observed firing-times and initiation rates and the fit reveals that later-firing origins have slower replication kinetics and greater variation in firing times, consistent with lower potential efficiencies. This work conclusively establishes that the reproducible temporal order of replication observed on population averages can emerge as a consequence of stochastic initiations, as proposed by Czajkowsky et al. (2008) (see also the review by Rhind et al. in this issue of Chromosome Research). In the future, this analytical model may be used to extract origin firing-time distributions and fork velocities genome-wide from sufficiently time- and space-resolved datasets for any organism.
Is there an optimal time-dependent rate of origin usage?
Recently, published replication timing profiles of entire genomes or chromosomes have been used to extract the spatially averaged I(t) functions of S. cerevisiae, S. pombe, Drosophila melanogaster and Homo sapiens (Goldar et al. 2009). In these profiles, the mean replication time of each locus is plotted against chromosomal position such that efficient origins appear as peaks. The profiles were cut into slices of time and I(t) was calculated for each time point as the ratio between the number of fired origins and the amount of unreplicated DNA. All the I(t) profiles showed a strikingly similar shape to the I(t) extracted from X. laevis DNA combing data (Goldar et al. 2008), increasing during the first half of S phase then decreasing before its end. In this work, the mean replication time of only the most efficient origins, rather than the full distribution of origin firing times, was analysed. The result is nevertheless intriguing, given the reported differences in replication initiation control between these organisms. One common feature of all eukaryotes, however, is origin redundancy and its corollary, incomplete efficiency, which confers randomness in origin choice and firing time even to sequence-determined origins. The I(t) function represents the genome-averaged dynamic processes that controls the dynamics of replication fork assembly from soluble factors that are available in limited amounts and its universal shape suggests that it has been selected for optimal use of these factors. Models elaborated to explain the shape of I(t) in Xenopus may thus more generally apply.
Evolution probably acts to minimise the rate of cell death due to replication completion failure. This rate has been estimated to <3.10−3 in Xenopus embryos and can be taken as a measure of the reliability of S phase (Yang and Bechhoefer 2008). Although the amplitude of any I(t) could be adjusted to meet a predefined reliability constraint, this would change the maximal number of forks that need to be simultaneously active at one time. Bechhoefer and colleagues have explored the relationship between I(t) and the maximal number of required forks (Bechhoefer and Marshall 2007; Yang and Bechhoefer 2008). They found that the I(t) that minimises the maximum number of required forks to meet a given reliability constraint is the one that maintains a constant number of forks throughout S phase or, in other words, recycles available fork proteins so efficiently that they work all the time. Confining initiation to the start of S phase, on the other hand, would require an 18-fold higher number of forks. The experimentally observed I(t) requires a maximum number of forks that is only 3-fold higher than the theoretical optimum.
Why does the observed I(t) differ from the theoretical optimum? The calculation of this optimum assumes that replication fork protein availability is constant through S phase, which is probably not the case, and that all the available factors can instantaneously assemble in forks at the start of S phase, and there is no obvious physiological mechanism for such a sharp transition. Maintaining a constant number of forks also implies that the frequency of initiation and the local density of forks increase to infinite values at the end of S phase, which is unrealistic. Replication termination may also require specific factors that can become limiting, and the time-dependent frequency of termination is set by the dynamics of origin activation. It might be advantageous for the cell to spread termination events throughout S phase rather than concentrate them at the end of S phase. If so, the optimum I(t) must be the best compromise between an efficient recycling of rate-limiting initiation, elongation and termination factors. The optimum I(t) defined in this way has not been calculated but it would be interesting to see if it better approximates the experimentally observed I(t).
Modelling the consequences of replication fork failure in metazoan cells
The amount of MCM2-7 complexes loaded onto chromatin is in excess over the origins normally used. In Xenopus extracts, inhibiting ATM/ATR kinases with caffeine stimulates initiation ∼2-fold, partly by derepressing new origin clusters and partly by enhancing initiations within clusters (Marheineke and Hyrien 2004; Shechter et al. 2004). When aphidicolin is added to slow down forks ∼3-fold, caffeine addition can revert replication kinetics to normal without stimulating fork velocity, implying a 3-fold increase in origin firings compared to normal S phase (Woodward et al. 2006). In mammalian tissue culture cells, aphidicolin or HU can stimulate origin firing ∼2-fold without the need to abolish checkpoint activity, and new initiations are preferentially directed towards active clusters (Ge et al. 2007). These experiments suggest that potential origins are at least in 3-fold excess but are normally prevented to fire by checkpoints or by passive replication. The amount of chromatin-bound MCM2-7 can be reduced several fold without impairing normal DNA replication kinetics. In these circumstances, however, fewer origins can be activated following replicative stress and the cells become hypersensitive to stress (Ge et al. 2007; Ibarra et al. 2008). Thus, activation of redundant origins depends on loading a large amount of MCM2-7 proteins and is an important mechanism to protect against replication fork failure and prevent genetic instability.
Can stochastic initiation from redundant origins suffice to explain the levels of origin activation seen in vivo when forks are slowed down? Blow and Ge (2009) modelled a mammalian replicon cluster as a circular 250 kb DNA molecule containing from five to 100 potential origins whose initiation probabilities were constant in time and picked from a random distribution such that an average of five initiations in the 250-kb cluster was maintained. Simulations showed that replication end times were little affected by the trade-off between origin redundancy and efficiency. The increased origin firing obtained when human cells were treated with HU was modelled by reducing fork velocity to 25–33% of normal. As the forks took longer to reach potential origins, some of them underwent initiation in later time steps. In order to reach a mean spacing between fired origins that match the in vivo data, the model required at least 1–3 potential origins for each fired origin, which is well within the ∼10-fold excess of MCM2-7 complexes over origins. Thus, the protective effect of redundant origins against fork stalling may not require any special pathway to regulate origin firing.
Forks can stall irreversibly when they encounter DNA lesions. To model how redundant origins can rescue the double stall of two converging forks, clusters were replicated with a fork stall rate of either 5 × 10−6 or 5 × 10−7 per base pair. Increasing the number of potential origins per 250-kb cluster decreased the percentage of double fork stall from 25% with five potential origins to 5% with 25 origins, while the total number of origin firings increased only slightly. Thus, origin redundancy not only solves the random completion problem but also protects against double-fork stalls while minimising the use of replication fork proteins. As the probability of origin activation was set constant in time, the replication completion time increased significantly with fork stalling. It would be interesting to see how these results are affected when initiation probability increases during S phase.
- Origin
A site in the genome where replication can initiate. In some organisms and cell types, such sites may be well defined by cis-acting sequence features; in other organisms or cell types many or even all, sites in the genome may act as origins.
- Origin competence
The fraction of cells in a population in which an origin is biochemically competent to fire. A biochemically competent origin may fail to fire and be passively replicated.
- Origin firing, Origin activation or Origin initiation (used interchangeably)
The irreversible conversion of a competent origin into bidirectional replication forks.
- Origin efficiency
The fraction of cells in which an origin fires during S phase.
- Potential origin efficiency
The fraction of cells in which an origin would fire during S phase if not passively replicated. Experimentally, this can be measured in yeasts by plasmid maintenance assays since passive replication is impossible when the plasmid contains a single origin. Note that potential efficiency is always lower than competence because some competent origins may fail to fire (in the absence of passive replication) during a finite length S phase.
- Firing probability or Instantaneous firing probability
The probability that an as yet unfired origin will fire during a specific time period. Mathematically, the instantaneous firing probability is described by a probability density function. Lygeros et al. (2008) have used the term origin firing propensity with the same meaning.
- Firing rate or Instantaneous firing rate
The number of origins initiated per time per length of unreplicated genome.
We thank C. Nieduszynski, J. Bechhoefer and N. Rhind for communicating unpublished work (De Moura et al. and Yang et al.) and their review in this volume and for their comments on the manuscript. The O.H. lab is supported by the Association pour la Recherche sur le Cancer, the Ligue Nationale Contre le Cancer and the Fondation pour la Recherche Médicale (équipe labellisée). A.G. is supported by the Commissariat à l'Energie Atomique.


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Josey Little
James Lockhart Little (1821-?) known as Josey or the captivating captain was a soldier and steeplechase rider whose most notable racing victory was on board Chandler to win the 1848 Grand National.
Lockhart was born on November 14th 1821 at Shabden Park, Reigate and enlisted in the 40th Regiment of Foot on August 7th 1840. In December the same year he became a cornet in the more prestigious 1st King's Dragoon Guards before his Grand National victory in 1848 ensured for him the funds necessary to purchase his rank as Captain.
In 1850 Little was almost ruined in a bank collapse and was forced to transfer to the less prestigious 81st Regiment of Foot before retiring from the service in 1851.[1]
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Amanda SteeleFraserville, Ontario, CA
I live in Ontario Canada, about an hour outside of Toronto. Really outgoing, very friendly and can get along with most anyone. I am Caucasian, with medium length brown hair. I have blue/gray/green eyes. I am also about 5'7 and am 14 years old. But my Birthday is on June 10, 1996. Although I am young, I am mature for my age and and can be counted on for special responsibility's.
English (North American), French (Canadian)
French (Québécois), North American
Special Skills
Audiobooks, Business, Cartoons, Documentaries, Educational, Internet, Jingles, Movie Trailers, Music, Podcasting, Radio, Telephone, Television, Videogames
I don't have much experience, but I have done multiple small theater productions. I also go to a school that has a special program for the arts. I was accepted for Drama and will hopefully be slowly gaining more experience as I finish my years at this school.
Currently in grade 9.
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Affiliations: Lexington, KY ,
Dr. Mannino is Associate Professor, Department of Preventive Medicine and Environmental Health, University of Kentucky College of Public Health.
Correspondence to: David M. Mannino, MD, FCCP, Department of Preventive Medicine and Environmental Health, University of Kentucky College of Public Health, 121 Washington Ave, Lexington, KY 40536; e-mail: firstname.lastname@example.com
COPD is an increasingly important cause of morbidity and mortality both in the United States and globally.1–3 Hospitalizations for COPD comprise one of the most costly components of care for this disease.4–5 In addition, COPD hospitalizations, which often represent severe exacerbations, frequently result in death.6–7 Better understanding the factors that result in COPD hospitalizations and subsequent deaths may provide us with additional strategies to target specific populations by developing prevention strategies.
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Girl from the Sea(1973)
A novel by Ruth Abbey
Carenza Pearce, eighteen, leaves the cottage where she was born to travel to Fistral Castle. She takes with her a letter of introduction from her late grandmother, but the owners of the castle turn her away angrily. Alone in the world and penniless, Carenza is talked into a marriage of convenience, but she soon discovers that much mystery surrounds the man she has married. This is a tale of passion and intrigue in nineteenth-century Cornwall.
Used availability for Ruth Abbey's Girl from the Sea
See all available used copies of this book at Abebooks UK or Abebooks US
Hardback Editions
May 1973 : UK Hardback
|Title: Girl from the Sea
Author(s): Ruth Abbey
ISBN: 0-7091-3430-4 / 978-0-7091-3430-5 (UK edition)
Publisher: Robert Hale Ltd
Availability: Amazon Amazon UK Amazon CA
Paperback Editions
February 1994 : UK Paperback
|Title: Girl from the Sea (Dales Large Print Romance)
Author(s): Ruth Abbey
ISBN: 1-85389-454-0 / 978-1-85389-454-1 (UK edition)
Publisher: Dales Large Print
Availability: Amazon Amazon UK Amazon CA
Genre Pages


text: So much history of the city has taken shape here – young MF Hussain, Sabbavala, Shiva Naipaul and Amitabh Bachchan all ate here.The first time I heard of Samovar Cafe ,it was in a Amol Palekar movie film. This small cafe well in Jehangir Art Gallery,overlooking the museum gardens has it's own charm. Simple decor with ceiling adorned with inverted Gujrati mirror worked umbrellas,kites on the wall,cane chairs, wooden tables may not excite… Read More


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I dont know your hand size but my wife loves her Glock 19 and the 23 is the same size in .40. SIG 239 might be a good choice or possibly the Walther PPS. I have not shot the Walther but my wife did and liked it.
This is a discussion on Woman Concealed Carry 40 cal within the Defensive Carry Guns forums, part of the Defensive Carry Discussions category; Hello I am new here and I am in the process of purchasing a cc gun. I really am thinking of the xd 40 cal ...
Hello I am new here and I am in the process of purchasing a cc gun. I really am thinking of the xd 40 cal subcompact. Any women carry this? I am worried it will to bulky for my 110 pound 5' 5" body? If any women carry this what is your method? This is my first carry gun but not my first hand gun! Any input is appreciated.
I dont know your hand size but my wife loves her Glock 19 and the 23 is the same size in .40. SIG 239 might be a good choice or possibly the Walther PPS. I have not shot the Walther but my wife did and liked it.
Glock 36, 30SF, 31, 32, 21 Gen4 - Carry guns
Ruger Mini-14 and Remington 870 by the bed both wearing Surefire lights.
Always carry a knife-they are handy to have
Always carry a reload-You probably won't need it but it is good insurance .
Always carry a light- To see in the dark
Sara, go check this site - lots of good info for women:
Cornered Cat - Table of Contents
Oh - and welcome!
AZCDL Life Member
NRA Patron Member
NROI Chief Range Officer
my wife carries a Glock 27, and has no problem at all in concealment, or recoil.
KY Concealed Carry Instructor
Taser X2/X26 Instructor
"It is the tradition that a Kentuckian never runs. He does not have to [he] is entitled to stand his ground, and meet any (life-threatening) attack made upon him with a deadly weapon ." Gibson v. KY, 34SW936(Ky.1931)
I recommend you look at the Springfield XD40sc, Glock 27, Walther PPS, Smith & Wesson M&P Compact, Heckler & Koch P2000sk and the Kahr PM40. These are all great choices for concealed carry in terms of quality, reliability, size, weight and ergonomics. It all comes down to what adjusts better to your hand size, carry style and personal preferences.
Here's a good video about concealed carry options for women:
"If you carry a gun, people will call you paranoid. That's ridiculous... If I have a gun, what in the hell do I have to be paranoid for?" [Clint Smith - Thunder Ranch]
I'm a great big old tall fat dude, and I carry what I consider to be the smallest .40 S&W pistol that is still accurate and easy to shoot. The Walther PPS:
It has a striker type trigger system, so it operates just like the Glock and XD pistols do when it comes to shooting them. Main difference is that it has a thinner single stack magazine, which makes the pistol far thinner. The drawback to that, though, is that you have less ammo capacity.
But it still holds 6 + 1 rounds, for a total of 7 in .40 S&W.
When trying out different guns, I not only hold and shoot them but also literally "try" them on with a holster. The smallest bit of width can be a surprisingly big factor when putting a gun on a small frame. I've carried a single stack 239 and a double stack G19 and the glock does add more wardrobe limitations.
Also consider the width of the extra magazine. I can conceal a gun just fine but that extra mag just seems really difficult sometimes. Esp with it is the double stack Glock vs the single 239. And don't forget to make room for a light!
Having said all this, how could you go wrong with the xd, as long as you commit to wardrobe parameters that allow effective cc? They are fantastic guns - accurate, user friendly, lots of holsters available, large capacity. You will just need to buy some new clothes to accomodate it - how bad can that be?
The peace of mind you will get from carrying the xd 40 will add a glow that no makeup can provide.
The thing about a 40 is the recoil in a small gun is quite differentr than in a 9mm. The single stack 40s expecially in polymer can be quite a shock for somebody like me 6'3" 285, and I carry a G23 EDC. A lady may especially not like the sharp perceived recoil to the hand. You really owe it to your self to shoot some of the contenders, before you buy.
Also checkout limatunes posts here she carries everything from big ol 1911s to a double stack G19 and she is a tiny petite lil thing. She also has some good instructional videos on Youtube, under Limalife.
Now in regards to your gun recommendation...I personally thing that balance (ratio of barrel weight to grip etc) is very important in a CCW. The XD IMHO has very poor balance. If I were to buy a small sub compact 40 I would have to look towards the G27, p239, and possibly the M&P 40c, and Kahr K40 Go to the LGS and handle the guns not just one at a time but one in each hand and point them one handed with your strong and weak hand. In the single stack arena and polymer, it would be the Walther PPS and Kahr P40 or CW40.
ETA: also forgot to mention the CZ line of handguns...their Rami line would be worth a looks and is very affordable.
Last edited by jwhite75; April 3rd, 2011 at 11:13 AM.
Friends don't let friends be MALL NINJAS.
I am just as nice as anyone lets me be and can be just as mean as anyone makes me. - Quoted from Terryger, New member to our forum.
Lot's of good advice. Based on your OP I'd emphasize to be sure and take a close look at the PPS .40
Helpful hints on pushing back and strengthening the 2A:
Walther PPS is good. I'm putting in a vote for the Kahr PM40 or CW40 tho. It's worth looking at all your options.
NRA Member
Glock 26 XD9sc
Ruger SR9c Ruger LCP
If you are willing to carry a 9mm, look at the PF9, LC9, and the Kahr 9mms - they are significantly thinner and a bunch easier to conceal on a frame your size than the much thicker double stacks double stacks like the XD and Glock. The faliaphotography video linked above is excellent. My niece watched that and immediately bought an LCP. She knows she can always hide that gun. She will spend the summer shooting her Dad's Glock 26, my LC9 and Glock 19 while considering what she might get to carry when the season calls for clothes that conceal a bigger gun. She's a jock, 4 years of HS and college basketball. She has good combat accuracy with a 9mm (G26 or G19) loaded with 115g COR-BON DPX.
The whole aim of practical politics is to keep the populace alarmed (and hence clamorous to be led to safety), by menacing it with an endless series of hobgoblins, all of them imaginary. by H. L. Mencken
You want a gun that fits your hand in a caliber you can control. You will need to handle them for yourself, but will probably eliminate all the double stacks including those with interchangeable back straps. FWIW the baby GLOCK and XD are quite snappy in .40 and very susceptible to malfunctions with slightest amount of limp-wristing.
Align the gun with your forearm and check trigger reach. For a single action you need enough trigger finger to reach the center of the pad of the first joint. For a traditional double action you need enough trigger finger to reach the outside edge of the trigger with the crease of the first joint (power crease) in order to have enough leverage to pull it.
As a suggestion, handle/shoot a Kahr K-9. It is a single stack all steel 9mm and has surprisingly soft recoil, is small and light enough to carry well but still shoots well.
Now, if after shooting a couple of boxes of ammo in a double stack .40 you are doing well with it - more power to ya!


text:
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I loved the vintage Robin look that I was able to pull together. I didn't want a "sexy" costume and so I was really excited to find the Robin skater dress and matching Robin socks. They both fit well and looked great! Thank you Spirit for having cute work appropriate options.


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Private ocean view Villa on over one half acre. Set behind gates and down a pr ivate drive, yet conveniently located to PCH, local beaches, schools and shopping. Beautifully landscaped with meandering gardens, terraces and fruit trees. Nicely upgraded inside and out, including cook's kitchen with stainless steel appliances and granite counter tops, formal dining overlooking Koi pond and waterfall, spacious ocean view living room with romantic European style fireplace, large master suite with private balcony and Queen's Necklace and Catalina Island views, plus two very comfortable ocean v iew guest rooms. A perfect setting — private, yet convenient, with beautiful ocean views and where old world charm is complimented by modern amenities and a host of upgrades and improvements.
Villa – Malibu, California, USA
2,295,000 $
Total area: 258 m² 3 bedrooms 3 separate bathrooms
- Built in 1979


text:
Another Good Cruise
Sail Date: September 01, 2014
Reviewed: 2 years ago
Traveled As: Family (older children)
Room Type: Balcony
Pros: Schedule\, wait staff\,
Cons: Not enough electric outlets in rooms. CPAP machine required a 30 foot extension cord across the room. No clocks anywhere on the ship. Quite an annoyance to have to ask everyone the time. The live singing at evening diner was not appropriate. Disruptive. Balcony Doors Slam Constantly.
Recommends Carnival Magic? Yes!
Category Ratings
Click table then swipe to view all columns.
Click table then swipe to view all columns.
|Ship Quality
|Food and Dining
|Good food and variety.
|Cabin / Stateroom
|Felt a little small (balcony suite).
|Service and Staff
|Wonderful Very friendly.
|Average. Didn't attend many.


text: As most of you film nuts out there such as myself are aware, the 7th Annual Tribeca Film Festival is currently happening in the Big Apple and some amazing talent have been walking the red carpet for the last few days. The festival itself will be running from April 23 to May 4 and we're going to be bringing you some great coverage as your favourite hotties arrives for their film screenings. Over the course of this week, we'll be bringing you constant updates on who came, what they saw and how friggin' hot they looked.To start off our coverage, we go to a woman that has melted my heart for the past few years now since her big break in "Kiss Kiss Bang Bang" and "MI:3". She's cute, sexy, talented and has a career going nowhere but up at this point and that is the beautiful Michelle Monaghan. At Tribeca this year, Michelle strolled down the red carpet for the premiere of a smaller film she has starred in a new drama called "Trucker". She looked unbelievable at the premiere as you can see below. Michelle is definitely a great way to start our coverage so make sure you check out all the amazing shots below and stay tuned as we bring you more coverage very soon.


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Comet Android dialer is the best in the "Market".With new cool and attractive outlook.It is the 1st release by Bansberry. This dialer comes with great new features besides making sip calls & phone calls only, some of which are mentioned below.
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* Making and receiving VoIP calls.
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* Supports IP-PBX.
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* Echo Cancellation.
* Create new account functionality.
* SMS functionality.
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