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October 29, 2009 February 18, 2011 ⌙ Movie Reviews
Movie review : This Is It
Buy the soundtrack for This Is It
Purchase at iTunes
In case you didn't know This Is It has a documentary format. Seems pretty obvious but I just wanted to make sure you were clear on that. This documentary is a sequence of shots from the rehearsals for MJ's planned tour. Most of the action focuses on MJ, as you would expect with a bit here and there from the dancers and musicians who were performing with him.
I'm not a huge MJ fan but I enjoyed watching This Is It. I was happy to see that MJ didn't act like a prima donna. He took direction from his advisers and offered feedback to his support cast without being overly sensitive about it.
What I really enjoyed was watching the process that everyone, even MJ, went through to produce the show. It isn't like MJ walks out and nails a dance move or musical note on the very first try. Even the greatest get it wrong more often than not on the first try.
The downside to this documentary is that it seems rushed. My guess is that the powers that be wanted to get a version of the documentary to the theaters as quickly as possible. And then in a few months we'll see the DVD that has much more footage than a movie ever could. So if you're not dying to see this then hold off and wait for the DVD which should be much more complete.
What did you think about This Is It? Share your thoughts about the movie in the comments below.
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redpajama_set_name: RedPajamaCommonCrawl


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Which usually means that you have to be a hacker yourself. Really there should be two articles: And the second could probably be condensed into two words: The problem is not so much the day to day management. Really good hackers are practically self-managing. A similar problem explains why American cars are so ugly. I call it the design paradox. You might think that you could make your products beautiful just by hiring a great designer to design them. American cars are ugly because American car companies are run by people with bad taste.
Many people in this country think of taste as something elusive, or even frivolous. And if you have really good taste, you can, as Steve Jobs does, make satisfying you the kind of problem that good people like to work on. To hackers these kinds of projects are the death of a thousand cuts. Writing a compiler is interesting because it teaches you what a compiler is. Working on nasty little problems makes you stupid.
Good hackers avoid it for the same reason models avoid cheeseburgers. Of course some problems inherently have this character. And because of supply and demand, they pay especially well. So a company that found a way to get great hackers to work on tedious problems would be very successful. How would you do it?
One place this happens is in startups. At our startup we had Robert Morris working as a system administrator. They produce new ideas; maybe the rest of the company will be able to use them. You may not have to go to this extreme. Bottom-up programming suggests another way to partition the company: If your company makes software to do x, have one group that builds tools for writing software of that type, and another that uses these tools to write the applications.
Duplo, I believe, is the technical term. Clumping Along with interesting problems, what good hackers like is other good hackers.
Great hackers tend to clump together-- sometimes spectacularly so, as at Xerox Parc. Having great hackers is not, by itself, enough to make a company successful. Sun had a good run for a while, but their business model is a down elevator. I think, though, that all other things being equal, a company that can attract great hackers will have a huge advantage.
There are people who would disagree with this. They really seemed to believe this, and I think I know why. I think what a lot of VCs are looking for, at least unconsciously, is the next Microsoft. But VCs are mistaken to look for the next Microsoft, because no startup can be the next Microsoft unless some other company is prepared to bend over at just the right moment and be the next IBM.
Microsoft is a bad data point. If you throw them out, you find that good products do tend to win in the market. What VCs should be looking for is the next Apple, or the next Google. I think Bill Gates knows this. What worries him about Google is not the power of their brand, but the fact that they have better hackers.
How do you know when you meet one? That turns out to be very hard. You may have read on Slashdot how he made his own Segway. The remarkable thing about this project was that he wrote all the software in one day in Python, incidentally. But when I first met him, I thought he was a complete idiot.
Robert says he misjudged Trevor at first too. Apparently when Robert first met him, Trevor had just begun a new scheme that involved writing down everything about every aspect of his life on a stack of index cards, which he carried with him everywhere.
The problem is compounded by the fact that hackers, despite their reputation for social obliviousness, sometimes put a good deal of effort into seeming smart. It was kind of intimidating at first. Everyone there spoke so fast. But after a while I learned the trick of speaking fast. It seems like the only way to judge a hacker is to work with him on something.
And this is the reason that high-tech areas only happen around universities. The active ingredient here is not so much the professors as the students. Startups grow up around universities because universities bring together promising young people and make them work on the same projects. However, that does not make it go away. In fact, it continues to be one of most critical subjects in the education sector. As a student, wishing that mathematics could magically disappear may not happen soon.
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Experimental studies of gene expression have identified some of the individual molecular components and elementary reactions that comprise and control cellular behavior. Given our current understanding of gene expression, and the goals of biotechnology research, both scientists and engineers would benefit from detailed simulators that can explicitly compute genome-wide expression levels as a function of individual molecular events, including the activities and interactions of molecules on DNA at single base pair resolution. However, for practical reasons including computational tractability, available simulators have not been able to represent genome-scale models of gene expression at this level of detail.
Here we develop a simulator, TABASCO http://openwetware.org/wiki/TABASCO, which enables the precise representation of individual molecules and events in gene expression for genome-scale systems. We use a single molecule computational engine to track individual molecules interacting with and along nucleic acid polymers at single base resolution. Tabasco uses logical rules to automatically update and delimit the set of species and reactions that comprise a system during simulation, thereby avoiding the need for a priori specification of all possible combinations of molecules and reaction events. We confirm that single molecule, base-pair resolved simulation using TABASCO (Tabasco) can accurately compute gene expression dynamics and, moving beyond previous simulators, provide for the direct representation of intermolecular events such as polymerase collisions and promoter occlusion. We demonstrate the computational capacity of Tabasco by simulating the entirety of gene expression during bacteriophage T7 infection; for reference, the 39,937 base pair T7 genome encodes 56 genes that are transcribed by two types of RNA polymerases active across 22 promoters.
Tabasco enables genome-scale simulation of transcription and translation at individual molecule and single base-pair resolution. By directly representing the position and activity of individual molecules on DNA, Tabasco can directly test the effects of detailed molecular processes on system-wide gene expression. Tabasco would also be useful for studying the complex regulatory mechanisms controlling eukaryotic gene expression. The computational engine underlying Tabasco could also be adapted to represent other types of processive systems in which individual reaction events are organized across a single spatial dimension (e.g., polysaccharide synthesis).
Mechanistic models of the individual biochemical events that comprise gene expression are quite detailed and are continuously improving. For example, experiments studying the kinetics of promoter initiation and single molecule studies of processive protein movements along DNA have revealed intricate regulatory processes that control gene expression [1–4]. As these details accrue, understanding how individual processes work together to determine system-level behavior becomes increasingly difficult to intuit . The ability to simulate events on and along DNA at base-pair resolution would benefit those interested in studying or attempting to control the consequences of molecular processes on system behavior. However, while currently available simulation techniques are sufficient to study the expression from a single operon at base-pair resolution, they become computational expensive if the system is of much greater scale or complexity . Also, existing simulators cannot formally account for intermolecular events along DNA, such as collisions between polymerases.
There are several mathematical approaches for computing the dynamics of systems of biochemical reactions. For example, the chemical species that define a system can be modeled as continuous variables that change over time. In this "continuous" approach, reactions between chemical species are modeled as a set of coupled ordinary differential equations (ODEs) [7, 8]. The set of ODEs are often numerically integrated using established algorithms to compute the dynamics of the system. As a second example, a system's chemical species can be treated as discrete variables that change over time. In this "discrete" approach, reactions between species are treated as individual events that update the system and can be combined into the chemical master equation . The chemical master equation is usually computationally integrated to determine the time evolution of the system using stochastic simulation algorithms (SSAs). SSAs have some theoretical advantages over continuous formulations . For instance, in some systems, an individual reaction event can cause a large difference in the likelihood that other reactions will occur, and so the precise order and timing of individual reaction events can influence overall system behavior. This situation may occur when the numbers of any particular reactant in a system are low, as in the case of a single copy of DNA bound by a protein. In these cases, SSAs provide exact calculations of the system dynamics, while the continuous approximations break down.
However, challenges exist in using either approach to study high-resolution models of gene expression. Both discrete and continuous approaches to solving the time evolution of biochemical reactions share the so-called "combinatorial explosion" problem, in which the number of possible states that need to be enumerated in a system becomes exceedingly large . For example, the spatial and temporal control of gene endo16 expression during S. purpuratu development is controlled by a 2,300 base-pair (bp) sequence . This control sequence contains 33 sites that bind 15 distinct proteins . Developing a model that fully enumerates all the possible states of the endo16 gene regulatory region requires stating over 1013 distinct protein:DNA species. Not only is this computational expensive, it seems unnecessary; the number of possible species far exceeds the copy numbers of the relevant DNA and protein molecules in the cell.
Two approaches have addressed the combinatorial explosion problem with respect to protein complex formation and modification during signaling [11, 14, 15]. The first approach, single-molecule simulation, taken by the StochSim simulator, tracks individual molecules and their state (e.g., what other molecules they are bound to) so that only the complexes formed at any given time are enumerated (and not all possible complexes) . StochSim allows individual molecules to transition between states (e.g., chemical modification of a protein or complex formation), which in turn modulates the reaction rates of processes that the molecules participate in. These transition and state changes are specified at the start of simulation. Single-molecule simulators are computationally advantageous when the number of tracked molecules is much smaller than the number of possible states that the system can potentially achieve. A second approach, taken by the Molecularizer simulator, is to dynamically generate reactions as they occur based on automatic adjustments to reaction rates based on diffusion considerations . Moleculizer can construct the set of reactants and reactions that are actually executed during simulation and can use this more manageable set of reactions as input into more traditional simulators. Dynamic reaction generation simulators such as Moleculizer work well when many of the possible states of the system are never realized, and thus the simulators need only ever represent a fraction of the total possible system.
StochSim and Moleculizer were designed to model the binding and unbinding of proteins within complexes. However, these simulators are not well suited for modeling systems in which components within a complex are processive (i.e., components transitioning among many binding states) or when the number of binding states is large. For example, simulating a single RNA polymerase transcribing a 3,000 bp gene at single-base resolution using a single-molecule simulator would require a model for a DNA molecule comprised of over 3,000 different states with enumerated rules for the transitions from one base to the next. Using a dynamic reaction generation simulator, such as Moleculizer, would not lead to significant speed enhancements because most of the states of the system are reached during a simulation, albeit rarely.
Recent efforts to stochastically simulate gene expression have focused on reducing the number of states and complexity of the system being studied by approximating the kinetic delays that occur during transcriptional and translational elongation processes. For example, the stochastic simulator Dizzy allows for modeling transcription elongation as a series of equivalent steps that can be sampled in total from the gamma distribution, as Gibson and Bruck suggest [16, 17]. However, these simplifications cannot account for the effects that intermolecular interactions on the DNA may have on system dynamics. In addition, Roussel and Zhu approximated the delays in transcription that can take into account polymerase interactions using a reduced site-oriented Markov model that is accurate in cases where there are limited polymerase interactions [18, 19]. However, without a priori knowledge for how polymerase interactions affect the expression of any particular gene, especially in the case of multi-promoter, multi-gene, multi-polymerase systems, we must use a more detailed model of gene expression.
Thus, in order to efficiently and more realistically simulate base-pair resolved models of gene expression, we developed a single-molecule stochastic simulator, Tabasco, optimized to handle molecular events specific to gene expression such as the initiation, elongation and termination of transcription and translation as well as interactions among protein-DNA complexes. Tabasco tracks individual molecules and reactions on the DNA at single base resolution (e.g., RNA polymerase transcribing DNA) in order to avoid the specification of many unoccupied system states. Tabasco also uses dynamic reaction generation based on encoded rules for gene expression in order to avoid specification of all possible states and transitions within the system (e.g., all RNA polymerase molecules transcribe DNA to produce RNA). This framework provides the accurate descriptions of gene expression dynamics while allowing analysis of phenomena such as how intermolecular events between DNA-protein complexes affect system-wide gene expression.
Our motivations for creating Tabasco started from our interest in simulating the dynamics of bacteriophage T7 gene expression during phage infection. For example, past models of T7 infection used ODEs to simulate T7 gene expression dynamics. The use of ODEs limited our ability to accurately simulate the kinetics of T7 gene expression and study how intermolecular events may impact the genome-wide allocation of expression resources [20, 21]. For example, the E. coli RNA polymerase initiates expression of early T7 genes, including the gene encoding T7 RNA polymerase. Thus, newly synthesized T7 RNA polymerase first initiates transcription behind already transcribing E. coli RNA polymerase molecules. E. coli RNA polymerase has a ~45 nucleotide per second elongation rate; T7 RNA polymerase has a ~250 nucleotide per second elongation rate. What happens when a T7 RNA polymerase molecule overtakes an E. coli RNA polymerase is not well understood. However, the transition from E. coli to T7 RNA polymerase mediated genome entry will impact the timing of expression across the genome; cell-cell variation in the entry transition will limit the precision by which T7 can control infection. Thus, simulating possible polymerase-polymerase interaction models is an interesting research question. Using Tabasco, we can explore the dynamics of gene expression during T7 development at single-base resolution. In the case of T7, the increased modeling resolution afforded by Tabasco allows for direct evaluation of assumptions concerning polymerase interactions, transcriptional coupling to genome entry, and stochastic fluctuations on phage development. Although motivated by our interest in T7, Tabasco can also be used to represent other genetic systems and, as such, is generally useful to those interested in understanding how detailed molecular processes affect genome-wide gene expression.
Tabasco is a stochastic simulator that tracks individual molecules of DNA and associated proteins at single base-pair resolution. Tabasco makes use of a Gibson-accelerated Gillespie SSA to compute the reaction event timing and the resultant time-evolution of the genetic system . Tabasco uses predefined rules of transcription and translation such as initiation, elongation, termination, and protein interactions of polymerases, ribosomes, and other DNA/RNA-associated proteins. Based on these rules, Tabasco automatically updates the states of molecules and reaction events (Methods). For example, if a transcribing RNA polymerase temporarily occupies the DNA-binding site for a second protein, the simulator makes the site unavailable for binding until the polymerase is no longer occluding access to the binding site.
Tabasco transitions between two levels of resolution while simulating gene expression: "single-molecule" and "species level" (Figure 1). Reactants and events that occur on the DNA are tracked at single-molecule resolution–each copy of DNA and proteins associated with them are tracked individually by the simulator. In this regime, events such as binding/un-binding and polymerase movements are dynamically generated based on the current state of the DNA molecules and proteins on the DNA. On the other hand, the species-level resolution is akin to traditional SSAs in which reactants are tracked as groups of equivalent species. For example, Tabasco tracks all cellular protein-protein interactions at the species level.
Structure of TABASCO simulator. A simplified flow diagram of gene expression shows how Tabasco transitions from tracking individual molecules (Single Molecule Resolution) to grouping them into species (Species Level Resolution). The arrows represent reactions that can occur (process labeled in blue).
The transition between the single-molecule level and the species level occurs during the tracking of RNA abundances (Figure 1). Only those RNA molecules that are still attached to transcribing RNA polymerases are tracked as single molecules. As each coding domain on an RNA molecule is completed, it becomes part of the species-level simulation. At the species-level, each of the coding domains is treated separately as a species, and ribosomes that initiate translation on a coding domain are assumed not to interfere with one another. At both the single-molecule and species-level, translation is treated as a series of single amino acid polymerization steps, with the number of steps depending on the length of the coding domain. This gives a more accurate distribution of times for protein production than treating the whole elongation process as a single step. Based on the work of Gibson and Bruck, we compute translation steps in aggregate using the gamma distribution (Figure 2) .
Differences in single-step and multi-step elongation processes. Simulating the elongation of RNA polymerase along 50 based of DNA as a one-step elongation process versus a chain of individual elongation events gives the same average elongation time, but produces marked differences in the distribution of those elongation times. The process shown here is either a single 1.0 second elongation step (red) or fifty 0.02 second elongation steps (blue). Each histogram is the distribution of resulting times from 20,000 independent simulations.
The structure of Tabasco confers at least four advantages. First, treating gene expression at base-pair resolution allows for more accurate representation of the kinetics of gene expression. For example, traditional SSAs often lump multi-step reactions as single steps causing inaccurate estimates on pre-steady state kinetics. Second, tracking the state of individual proteins on DNA and allowing internal logic to automatically generate reactions eliminates the need to enumerate all the possible states of polymerases and proteins associated with the DNA. For example, transcribing polymerases and processes such as genome entry into a cell can cause certain protein binding sites to be inaccessible (Figure 3a, b). This feature also allows us to consider and integrate many factors that may influence the rate of RNA polymerization for any particular gene, such as the binding of multiple transcription factors or the contribution of RNA polymerases that initiated transcription at a promoter connected to an upstream gene (Figure 3d). Third, protein-protein interactions that may occur on DNA, such as collisions between different polymerases, can be accounted for and simulated based on simple and explicit rules (Figure 3c). Fourth, Tabasco can be used to graphically depict the location and dynamics of individual RNA polymerase molecules transcribing DNA, providing a useful visual tool for considering genome-scale gene expression dynamics.
Advantages of single molecule resolution. Tracking individual molecules of DNA at a base-pair resolution allows for creation of implicit rules defined by the state of protein-DNA interactions and of the DNA itself. For example, when a traversing polymerase occupies a genetic element such as a protein-DNA binding site (a), the binding site is inactivated and prevented from binding a protein until the polymerase completely clears the site. Activation of genetic elements by entry of DNA into the cell (b) and inter-polymerase interactions (c) can be directly simulated as well. Finally, in complex genetic environments (d) where multiple promoters and terminators regulate the expression of genes, a priori transcription levels for each protein need not be calculated, for they are generated implicitly.
As an initial test, we simulated the expression of a 1,000 amino acid protein using both Tabasco and a standard species-level SSA (Figure 4). Both simulators used identical gene expression models except for one difference. In the standard SSA, processive transcription and translation elongation reactions along the DNA are treated as a lumped, single-step reaction sampled from an exponential distribution; in Tabasco these reactions are treated as a series of individual base-pair elongation steps. In addition, due to internal structural differences between the two simulators, the two underlying models had slightly different rate constants for RNA polymerase clearance from the promoter region (Methods). Thus, the clearance rate was adjusted in the species-level SSA to produce equal steady state levels of protein to the Tabasco simulation (Table 1). At steady state, both simulators produce statistically equivalent results (Figure 4a, b). However, an expected difference arises in the pre-steady state dynamics of the system (Figure 4c). The species-level SSA simulation first produces protein by ~30 seconds (Figure 4d). This is unrealistic; E. coli RNA polymerase transcribes at an average elongation rate of 40 bp per second, production of a 3,000 nucleotide messenger RNA should take ~75 seconds. Thus, even if a ribosome directly followed the first transcribing RNA polymerase, protein production should not take less time than RNA production. Tabasco produces protein only after a more accurate ~100 seconds (Figure 4d).
Comparison of TABASCO to species-level simulations for a simple gene. We simulated the expression of a 1,000 amino acid protein using Tabasco (blue), and a standard species-level simulator (red) in which the processes of elongation (transcription and translation) were lumped into single steps. While the steady-state averages and standard deviations (a, b) are statistically equivalent, the pre-steady state kinetics (c) are quite different. Production of the first protein product (d) is much faster in the standard species-level simulator than in Tabasco. In fact, the species-level simulator generates unrealistic times to first production of a a protein; elongation of the polymerase alone should take ~75 seconds for a gene of this size (see text). The data shown are averages of 500 individual simulations.
We compared two models of expression of a single-gene varying only if mRNA and protein elongation reactions are treated as series of individual steps or a single lumped reaction (Figure 3; Figure 6). The kclear rates do not exactly match because of small differences in the model structures (see text). The Tabasco constant for kclear was adjusted to ensure the steady-state levels of mRNA and protein were equivalent in both simulations.
The discrepancy in the time for synthesis of a first protein product occurs for two reasons . First, since the RNA polymerase and ribosome elongation steps in the species-level simulation are treated as single exponential elementary reactions, 63% of the reactions occur prior to the average reaction time (Figure 2). The second and more substantive reason is that SSAs assume that reactions are Markovian (i.e., reaction event timing only depends on the current state of the system, and not system history). In an SSA that treats elongation as a single step, a ribosome that has initiated translation has some non-zero chance of completing protein synthesis; since the SSA treats the reaction as Markovian, the greater the number of ribosomes that have initiated translation, the greater the chance that any single protein synthesis event will be completed. Thus, if many ribosomes initiate translation before any one ribosome has enough time to complete translation of a coding sequence, then then protein synthesis will be computed at completing in an unrealistically short time. This is physically unrealistic; lumped reactions representing processive reactions should not be modeled as Markovian. In other words, a ribosome that begins translation should not affect the speed at which downstream ribosomes will complete protein synthesis. The fact that Tabasco treats each elongation reaction as a series of elongation steps alleviates both of these problems. As a result, Tabasco provides a more accurate estimate of pre-steady state gene expression kinetics than simulators and models that lump transcription and translation processes into a single reaction. Accurate calculation of the pre-steady state kinetics is important for systems in which a steady state is never reached, such as during phage infection, cell cycle, or animal development, and is the reason that many groups have incorporated approximate delays into their simulation frameworks [18, 19, 22–27].
The first natural biological system that we studied using Tabasco is bacteriophage T7. During bacteriophage T7 infection, the E. coli RNA polymerase initiates expression of early T7 genes, including T7 RNA polymerase . T7 RNA polymerase first initiates transcription behind already transcribing E. coli RNA polymerase molecules. At 37°C, E. coli RNA polymerase transcribes at ~45 bp per second; T7 RNA polymerase transcribes at ~250 bp per second . Thus, T7 RNA polymerases will overtake E. coli RNA polymerases. How transcribing RNA polymerase molecules interact mechanistically is just beginning to be understood, but experimental studies show that these interactions are important [30–33].
In order to test how different models of polymerase-polymerase interactions impact gene expression, we first used Tabasco to simulate gene expression from a reduced genetic system that captures key features from the layout of the T7 genome. Briefly, this two-gene system has an E. coli promoter expressing two hypothetical genes encoded on a polycistronic mRNA (encoding Proteins 1 and 2), and a T7 promoter expressing a monocistronic mRNA that only encodes Protein 2 (Figure 5). We used Tabasco to test three distinct models for how co-transcribing RNA polymerases may interact: (1) the downstream polymerase terminates transcription allowing the upstream polymerase to continue (downstream falloff model), (2) the upstream polymerase terminates transcription while the downstream polymerase continues (upstream falloff model), or (3) the upstream polymerase follows at the speed of the downstream polymerase (traffic jam model). Each polymerase collision model leads to different levels of steady state protein production (Figure 5). For example, in the upstream falloff model, most of the T7 polymerases will prematurely terminate because they overtake slower E. coli RNA polymerases (Figure 5d). As a result, gene expression levels are similar to the situation where there is no T7 polymerase at all (Figure 5b).
Simulating models for inter-polymerase interactions. Average steady-state protein levels from simulations of the DNA molecule with promoters for different polymerases (T7 and E. coli RNA polymerases) transcribing two genes (inset). The two polymerases are expected to interact since the T7 RNA polymerase is ~5 times faster than the E. coli RNA polymerase,. The graph shows different calculated steady-state levels of the two reporter proteins given different models for the interactions between the two polymerases. The (a) 'T7 only' and (b) 'E. coli only' are controls that show expression without E. coli and T7 RNA polymerase, respectively. The 'downstream falloff' model (c) specifies that, upon a collision between two transcribing polymerases, the downstream polymerase will prematurely terminate transcription and release from the DNA. The 'upstream falloff' model (d) describes the opposite situation when the upstream polymerase falls off. Finally, the 'traffic jam' model (e) specifies that the upstream polymerase follows the downstream polymerase at the speed of the downstream polymerase.
In order to test the effectiveness of Tabasco on genome-scale systems, we simulated gene expression during bacteriophage T7 infection. The 39,937 base pair T7 genome, as represented in our test simulation, is comprised of 52 coding domains, 22 host and phage promoters, two transcriptional terminators, and three distinct transcriptional feedback loops; four T7 coding domains were not represented as they are the result of translational frameshifts or alternative start sites for other genes [28, 34]. Because there are relatively few transcriptional terminators and many promoters, the rates of transcription for individual T7 genes depends on the combined levels of transcription initiation from all upstream promoters, as well as the presence of different types of RNA polymerases. In the case of T7, there is a further complication in that not all genes are available for transcription at once, since the entry of the T7 genome is a relatively slow process that is itself mediated by transcribing RNA polymerases.
Using realistic parameter sets, we simulated a single cell being simultaneously infected by three phage particles (Methods). The protein-DNA interactions on the DNA were tracked independently for each infecting phage, but use the same pools of soluble proteins in the cell; thus, these simulations track a combined 119,811 base pairs of DNA. We incorporated known mechanisms of cellular entry of T7 DNA into our simulations. We also developed a method to visualize the results of our simulations that allows for the graphical display of dynamic Tabasco output (Methods). Simulation of a full-scale genomic model of T7 gene expression on a single AMD Athlon MP 2100+ 1.8 GHz processor takes between 1–2 hours for a model that represents 30 minutes of real-time infection. Visualization of Tabasco output displays both T7 and E. coli RNA polymerases transcribing the DNA, the extent of genome entry, and the resulting mRNA and protein levels from the 52 encoded genes (Additional file 1). Over the course of the simulation, the phage DNA enters and is transcribed by the E. coli RNA polymerase. As the T7 RNA polymerase is produced, host transcription is attenuated while the T7 RNA polymerase takes over transcription and entry of the remaining T7 genes. For the first time, we are able to simulate gene expression at single base resolution for all gene expression in a particular organism during its development. In turn this allows us to test specific hypotheses of polymerase interactions, stochastic gene expression, and coupling of entry and transcription. An experimental analysis of the T7 gene expression program, using Tabasco, will be presented in a forthcoming paper [Keller H, Endy D, Kosuri S, in preparation].
We designed and developed Tabasco to revisit approximations encoded within previously available gene expression simulation algorithms, and to develop a method for efficiently computing single-molecule, base-pair resolved models of gene expression. Previous tools required either modeling smaller systems at increased resolution, supercomputers, or simplifications and assumptions of the biophysical models that result in reduced simulation accuracy. For example, Tabasco's implementation of a Gibson-accelerated Gillespie SSA allowed us to bypass simplifications that are often made for promoter binding by RNA polymerase and transcription initiation [18–20, 35]. We have shown here that the increased resolution of Tabasco provides more accurate pre-steady state kinetics of gene expression, gives us the ability to test models of polymerase and protein interactions on the DNA, and allows us to fully simulate the dynamics of gene expression during development of bacteriophage T7 at base-pair resolution.
Like Moleculizer, Tabasco dynamically generates reactions based on rules. Specifically, Tabasco uses the mechanics of gene expression as rules in order to avoid specification of all the states and transitions prior to simulation . Like Stochsim, Tabasco is a single-molecule simulator, which allows Tabasco to track the state of individual molecules of DNA, rather than tracking individually all states of a system that could be reached . As a result, Tabasco differs from previous gene expression simulators in that it is able to automatically represent the effects of molecular collisions, such as polymerase-polymerase interactions and the occlusions of DNA elements by transiting molecules.
Since Tabasco tracks individual molecules of DNA, the processing power required for Tabasco simulation scales with the copy number and length of the template DNA. While requiring significant processing power, Tabasco is computationally tractable in regimes for which stochastic simulations are often needed to produce accurate results (for example, when the number of DNA molecules in the system is low). Those interested in using simulators similar to Tabasco to study detailed interactions at the RNA level would face challenges due to the higher numbers of RNA compared to DNA in the cell. Such challenges may be overcome in the future by incorporating the work of others on increasing the efficiency of SSAs without significantly sacrificing accuracy [36–39]. In particular, if the probabilistic simplifications of Russell and Zhu can be extended to simulations in larger, more complex systems such as T7, we would expect to see increases in simulation efficiency .
While Tabasco is an exact SSA, it only exactly simulates the kinetics of the already simplified models that we give it. For example, we model transcription elongation as a first-order reaction, even though we know that this process is more complicated . In addition, the general use of physics models based on well-mixed elementary chemical reactions may sometimes be an inappropriate approximation of the inside of a cell. For example, it is known that the binding of proteins interacting with DNA often involves one-dimensional diffusion along a DNA template [41, 42]. The effects of these simplifications on simulation accuracy are not well studied, and are common across currently available simulators of gene expression dynamics. Finally, other processes that affect transcription such as mRNA secondary structure, sequence-specific kinetics, regulated pausing and backtracking, and many other known biophysical phenomena are not considered here. Such processes can and should be incorporated into gene expression simulation frameworks as the particular biological system being studied or engineered warrants.
Executables, source code, documentation, and usage notes for Tabasco are freely available, and should facilitate future extensions on the current design (Availability and Requirements). We have already found Tabasco to be useful in constraining models of T7 gene expression by comparing simulator output to new experimental measurements of absolute copies of mRNA abundance during infection; these results will be presented in a forthcoming paper [Keller S, Endy D, Kosuri S, in preparation]. In addition, Tabasco should be a good base to further study interactions on DNA that lead to transcription and translation. For example, Tabasco provides a platform to explicitly simulate hypotheses how many transcription factors can interact to direct eukaryotic gene expression, such as in control of endo16 expression . Finally, the general approach of using simulator-encoded logic and tracking of one-dimensional reaction systems should be useful for studying other biological phenomena – for example, oligosaccharide modifications of proteins.
Tabasco implements a modified version of the Gibson Next Reaction Method (NRM) . Gibson's NRM is an exact SSA that extends Gillespie's original First Reaction method by (1) updating only the minimum number of reactions through the use of a dependency graph and using absolute tentative reaction times, and (2) using an efficient data structure, the indexed priority queue, to store and sort reactions. At the start of the NRM, all reactions are defined and their tentative time of next execution ("tentative reaction time") is calculated and stored within an indexed priority queue. In addition, a dependency graph, which allows an executed reaction to call an update on only those reactions that are affected, is generated. The use of absolute times when calculating tentative reaction times allows reactions that are not affected by the execution of the last reaction to remain valid for the next iteration. The indexed priority queue sorts these reactions efficiently to allow quick searches for the minimum tentative time as well as quick lookups for any particular reaction. The reaction with the next tentative reaction time is executed, any tentative reaction times that are affected by the execution of the current reaction are updated, and the indexed priority queue structure is reordered to reflect the new times. The NRM does not change the tentative reaction times of reactions that are not affected by the currently executing reaction. The process is repeated to compute the time evolution of the entire system. Gibson and Bruck proved that the NRM is equivalent to the exact SSA algorithms developed by Gillespie.
The NRM uses a dependency graph to determine which reactions are affected by any particular reaction's execution. In the NRM, the dependency graph is constructed only once at the start of simulation and remains unchanged afterwards. However, since Tabasco creates reactions and complexes at the single-molecule level dynamically during simulation, a static dependency graph and indexed priority queue will not work. In order to solve this problem, Tabasco contains two specialized classes per DNA molecule within the overall indexed priority queue that are used to track a set of dynamically generated reactions. Each class contains a dynamic priority queue that stores the dynamically generated transcriptional and translational reactions and their tentative reaction times, as well the dependencies of any particular reaction (these specialized priority queues and the overall indexed priority queue are easy to confuse, and thus we will refer to the prior as the dynamic priority queue). The minimum tentative reaction time for all the dynamic reactions is set as the tentative reaction time of that dynamic priority queue with respect to the overall priority queue. Since, as in the NRM, Tabasco uses absolute times for determining the next reaction, the particular choice of the data structures containing the reactions does not affect the simulation results. The reason to separate the dynamic queues from the main indexed priority queue is to allow the size of the dynamic queues to change over time. As long as all dependencies are accurately updated upon reaction execution in the dynamic priority queues, the results are equivalent to the NRM. The structure of these dynamic priority queues will now be discussed in more detail.
A special class tracks the transcriptional processes for each DNA molecule. This class contains a dynamic priority queue that keeps track of all reactions related to that DNA molecule, such as transcriptional elongation processes. All reactions that are stored in this dynamic priority queue have tentative reaction times (as calculated by the NRM), and the minimum tentative reaction time is used to set when this class should be called to execute within the indexed priority queue. In order to determine what the effects of a particular binding or elongation reaction are, the class contains two arrays where each element represents one DNA base. Each element of the first array contains pointers to transcriptional elements encoded at that location such as promoters and terminators. The second array contains pointers to all DNA-protein complexes that reside on the DNA such as elongating RNA polymerase. Only one transcriptional control element or DNA complex can occupy any particular position at any time.
The framework we use here to simulate protein-DNA binding is shown in Figure 6A. Interactions from the species level to this specialized reaction occur when proteins bind the DNA. For example, RNA polymerase or other proteins can bind free promoters to form a protein-DNA complex, which causes the DNA complex array to be updated, the number of available promoters and RNA polymerase to decrement, and finally places two reactions into the dynamic priority queue. The two reactions compete to either have the complex form an initiation complex or fall off the DNA. If an initiation complex is formed, a stochastic decision is made as to whether the complex will recycle back to an initiation complex with some characteristic time, or moves on to be become an elongation complex. This stochastic decision is instantaneous in reaction time and will be discussed below. The polymerase can undergo abortive initiation step, recycling to the initiation complex, or form an elongation complex. A promoter is not made available for rebinding until the footprint of an initiating polymerase has cleared the entire promoter region.
Model structures for Tabasco and species-level simulations of expression of a single gene. We simulated expression of a single gene using a standard species-level SSA, and using the Tabasco gene expression simulator (Figure 3). The Tabasco-based simulation uses a transcription initiation model (a) with the rest of gene expression using the general schematic shown in Figure 1. We constructed the model for the species-level simulator (b) to mimic the Tabasco-based model, except that elongation reactions were treated as single-lumped reactions.
Once an elongation complex is formed, each elongation reaction causes the complex to move one base pair, and the elongation reaction is updated with a new time for the next elongation step. When an elongation reaction executes, the algorithm also checks whether another complex's footprint prevents the current polymerase from moving forward by checking the array containing all complex positions on the DNA. If there is another polymerase blocking the current polymerase's path, the polymerases will behave according to the polymerase interaction model chosen at the start of simulation. For example, the upstream polymerase can terminate, or signal the downstream polymerase to terminate the next time the polymerase is set to elongate, or simply hold its position until the next opportunity to elongate. The elongation reaction also does a check for transcriptional elements on the DNA such as promoters and transcriptional terminators. Upon arriving at a transcriptional terminator, a polymerase will either continue transcribing or terminate depending on a stochastic decision that will also be discussed below. If a transcribing polymerase occludes an open promoter, then the promoter will be decremented and unavailable for binding.
The "stochastic decisions" described above for transcriptional termination and abortive initiation on the promoter are used as methods to simplify parameterization in the models, so as to be consistent with how experimental data for such events is typically reported. In the case of termination, a uniform random number is chosen, and if that number is less than the termination efficiency, the polymerase will terminate transcription and fall off the DNA. Analogously, in the case of abortive initiation, if the uniform random number is less than the abortive initiation percentage, then the next reaction will be an abortive initiation step rather than formation of an elongation complex. These decisions take no simulator time, but cause changes in the downstream actions the complexes may take. This simplification is not done for computational efficiency, but more for model simplicity. Tabasco could be modified, with little computational load, to replace the stochastic decisions into competing rates of different reactions. However, these rates are not well understood, and thus we chose to incorporate the measurable models of termination efficiencies and abortive initiation frequencies into the simulator.
The transcribing RNA polymerase complexes also produce mRNA, which are tracked by a separate class. Since there are many more copies of RNA than DNA during simulation, and because we were uncertain as to the importance of protein-protein interactions on the mRNA, we chose to treat the majority of translation at the species level. However, if, for example, an RNA polymerase prematurely terminates before reaching the translation stop site, the mRNA and the ribosomes translating it will not produce function proteins. Thus, so long as the coding sequence is still being transcribed, we must treat all translation events at the single-molecule level as well. However, as soon as the entire coding sequence of an open reading frame is transcribed, Tabasco transitions to tracking species of mRNA molecules.
We used two classes to model the formation of RBSs and their respective start sites, the Nascent RBSs and Mature RBSs, in order to differentiate when mRNA should be tracked at the single-molecule level or the species-level, respectively. At the start of simulation, one more array of genome length is created that contains the positions of translation start and stop sites on the DNA. As RNA polymerase elongates (as described above), if the polymerase transcribes past a translational start site, a Nascent RBS is made. This Nascent RBS is available for binding by free ribosomes, and a reaction is automatically created and placed into the dynamic priority queue for translation processes. Once the polymerase arrives at the corresponding translation termination site, the Nascent RBS is converted to a Mature RBS, which is tracked at the species-level in the overall Indexed Priority Queue.
Translation, both at the single-molecule level and species level, occurs in three steps. First, ribosomes can bind either Nascent or Mature RBSs; there is an initial reaction to form the (Nascent or Mature) Initiation Complex. Second, these initiation complexes are converted to (Nascent or Mature) Elongation Complexes and an RBS at a rate that depends on the speed of the ribosome and the length the ribosome must travel to clear the ribosome binding site. This reaction is treated as a single step, however the distribution of times is chosen from a gamma distribution, in order to better represent a series of individual elongation steps. Third, the Elongation Complex then forms a finished protein and a free ribosome at a rate proportional to the remaining length of the open reading frame. This reaction also is computed via a gamma distribution.
At the single molecule level, two additional mechanisms allow for the state of the transcribing RNA polymerase to affect translations. First, if the transcribing RNA polymerase terminates before reaching a corresponding translation stop site, all bound ribosomes are immediately released, the Nascent RBS is removed, and no protein product is formed. Also, for any particular Nascent RNA, the maximal number of Nascent Elongation Complexes that can be formed is capped at the length of the currently transcribed portion of the open reading frame divided by the footprint of the ribosome.
Finally, to note, all transitions between single-molecule tracking and species-level tracking occur when a reaction, such as RNA polymerase termination or Initiation Complex conversion into an Elongation Complex. Thus, the transition from single-molecule tracking back to the species level tracking should also not affect the validity of using the NRM.
We developed multiple models to represent DNA entry into a cell or compartment (such a step can be useful in starting a simulation of infection or transformation). First, DNA can enter the cell via a zero-order constant reaction rate. Second, RNA polymerases have themselves been implicated as molecular motors that can drive DNA entry [29, 43]. Thus, in Tabasco, RNA polymerases that reach the end of a DNA molecule that has not yet fully entered the cell can cause DNA internalization at the rate of transcription elongation. Both of these mechanisms were used during our simulation of T7 gene expression.
Tabasco is written in Java® 1.4. The input file to the simulator is an XML file that describes and parameterizes the relevant genetic elements, initial conditions, and any other reactions that occur. The visualization is created by producing images that are then merged using Quicktime® to create a movie. The source code, executables, along with documentation and instructions for use are available within Additional Files 2, 3 and 4 and via the TABASCO website (Availability & Requirements).
All constants used are provided for completeness. Please see the Supplementary Materials for input files and exact constants used.
The distribution of expected times for a reaction to occur in a stochastic simulator depends on the underlying model (Figure 2). Elementary chemical reactions will follow an exponential distribution in arrival times. However, this is not true of non-elementary reactions. Treating an imaginary elongation process as one step versus 50 individual steps has significant consequences. To obtain the distribution time for the two cases, we used uniformly-distributed pseudorandom numbers and transformed them into exponential- or gamma-distributed random numbers. The exponential distribution is used for the single step representation. Exponentially distributed numbers are calculated by simply taking the negative natural log of a uniformly-distributed pseudorandom number. A series of exponentially distributed arrival times, as in the case of the multi-step elongation process, is given exactly by the gamma distribution. Gamma distributed numbers are calculated from uniformly-distributed pseudorandom numbers by an implementation of the rejection method . Pseudorandom numbers are generated from Java's implementation (java.util.Random) of a linear congruential pseudorandom number generator with a 48-bit seed .
We simulated two models of a promoter driving expression of a coding domain. The first model, termed single-molecule simulation, used the described Tabasco simulator to account for each reaction step during transcription and translation. The model uses the schemes shown in Figures 1 and 6a. The second model, termed the species-level simulation, treats transcriptional and translational elongation as a single step as shown in Figure 6b. The main parameters used in both models are shown in Table 1. Constants were adjusted slightly to account for small differences in model structure to give equal steady state values of mRNA and protein levels. Finally, the input files for the simulations can be found in the Supplementary Materials; the input files can be used to either run the simulation using Tabasco, or check all parameters used for the simulation.
The simulations for the polymerase interactions and bacteriophage T7 development used parameters that can be found in the input files in the Supplementary Materials. Table S1 details the meaning of each of the parameters in the input files. The parameters used were based on empirical measurements where possible. However, in general, the exact values of the constants are ancillary to this section, which is to show that Tabasco is able to simulate processes such as polymerase interactions and entire genetic systems such as T7. The parameter derivations are detailed are detailed elsewhere .
Any restrictions to use by non-academics: n/a.
We thank Mark Allen Weiss and Jonathan Goler for use of source code for the priority queue implementation and XML parser, respectively. We thank Kirsten Benjamin for providing pre-publication results of the number of Ste5 molecules in a yeast cell. We thank Heather Keller, Ty Thomson, and Rebecca Ward for critical reading of earlier versions of this manuscript. This research was supported by grants to DE from the US NSF, DARPA, and NIH. SK received additional support via an Andrew and Edna Viterbi Fellowship in Computational Biology and the MIT/NIGMS Biotechnology Training Program Fellowship. Additional support was provided by MIT.
SK and DE developed the idea of Tabasco. SK did all work on Tabasco design, code development, modeling and simulation, and drafted the manuscript. SK and JRK developed the Tabasco visualization software. SK and DE edited the manuscript. All authors read and approved the final manuscript.


Road trips are a tradition for many families, and with gas costing around $2 a gallon (down 40 cents from a year ago), gas prices and other travel costs are at historical lows. According to AAA, 55 percent of Americans are more likely to take a road trip this year due to lower gas prices. Now that a 400-mile drive costs less than $40, this is a perfect summer for a road trip. But where should you go? To answer that question, it depends on your travel style.
Family vacation time can be a rewarding break from the regular challenges of work. Take advantage of family time by spending a week travelling through the canyons in Arizona and Southern Utah. Then, stop just outside Las Vegas for a tour of the impressive Hoover Dam, heading east for hiking and eagle-watching on the south rim of the Grand Canyon, which is guaranteed to inspire awe even with jaded teenagers. Drive 130 miles north and spend a night in Page—don't miss the Antelope Slot Canyon—on your way to Bryce Canyon, which is famous for trippy sandstone hoodoos and phenomenal stargazing. Finish the vacation 1.5 hours away in magisterial Zion National Park, where ranger-led tours explain how the rushing Virgin River still carves canyons.
Two hours north of New York City, the Berkshires satisfy the need for fresh culture and provide unique, urban tastes. Drive up the scenic Taconic Parkway to Great Barrington, Mass., whose many restaurants reward a day of hiking in Mount Washington State Forest. Next, pick up local cheese and bread at Rubiner's for a picnic on the lawn at Tanglewood, which is the summer home of the Boston Symphony Orchestra. After a night in bustling Lenox, take meandering country roads to Williamstown, nestled below the Taconic Mountains and home to two gemlike museums: the Clark Art Institute and the Williams College Museum of Art. Close out the long weekend with a play at the excellent Williamstown Theatre Festival.
The atmospheric towns in the Georgia and South Carolina Lowcountry are frugal spots in a 100-mile coastal stretch that allow your inner fun side with good value. Start in Charleston, where you can wander the streets on a self-guided architecture tour and dip into a luxurious interior for a mere $12 at the Nathaniel Russell House. The next day, drive to laid-back Folly Beach to surf and sample fresh oysters, then spend a night in Beaufort, S.C., a dignified cluster of antebellum homes set among tidal marshes. From there, it's just 50 miles to Savannah, Ga., whose top attractions include its 22 colonial squares (free) and Bonaventure Cemetery (also free), a Southern Gothic marvel dripping in Spanish moss. With ample portions of brisket from the likes of Wiley's Championship BBQ and beer for sale in to-go cups, mealtime here is as cheap as it is fun.
A road trip of the Pacific Northwest should start in Seattle, where you can explore the region's big ideas at the Bezos Center of Innovation and stop by the Microsoft Visitor Center. Then shuttle down to the growing tech hub of Tacoma, Wash., to see sparks fly at a workshop in the Museum of Glass, which showcases local visionary Dale Chihuly. It's 150 miles south to Portland, Ore., trendsetter in everything from renewable energy to craft coffee; at Wiz Bang Bar, even ice cream gets taken to inventive heights (black truffle shell on that vanilla cone?). Finally, get out on the Columbia River—GoPro in hand—to try kiteboarding in Hood River. Strong winds make the town a mecca for the sport and also power the sustainable Full Sail Brewing Company, one of many experimental microbreweries in the area.
Lower travel costs create great opportunities to explore fun new adventures, so take advantage this summer to explore the unlimited amount of locations that provide enjoyment based on your inner self.
Photo Credit: iStock | Rebecca Dalzell is a journalist and historian. Her writing has appeared in The Washington Post, New York magazine, Travel + Leisure, TimeOut and other publications.


It s easy to imagine how you d raise a boy all the golf outings, lawnmower lessons, and Little League championships you d attend but playing dad to a little princess may take some education In Oh Boy, You re Having a Girl, Brian, a father of three girls, shares his tactics for surviving this new and glittery world From baby dolls and bedtime rituals to potty trainingIt s easy to imagine how you d raise a boy all the golf outings, lawnmower lessons, and Little League championships you d attend but playing dad to a little princess may take some education In Oh Boy, You re Having a Girl, Brian, a father of three girls, shares his tactics for surviving this new and glittery world From baby dolls and bedtime rituals to potty training and dance recitals, he leads you through all the trials and tribulations you ll face as you re raising your daughter He ll also show you how to navigate your way through tough situations, like making sure that she doesn t start dating until she s fifty.Complete with commandments for restroom trips and properly participating in a tea party, Oh Boy, You re Having a Girl will brace you for all those hours playing house and psych you up for the awesomeness of raising a daughter who has you lovingly wrapped around her little finger.
Another poor sports metaphor and I think I'll puke. Oh, and stop assuming your entire audience is a group of clueless meat-heads that has nothing but single friends with whom you used to participate in various forms of debauchery with.
didn't know if I was laughing, crying or just nodding my head the whole way through this. Much more than accurate!
Funny! Engaging! Written by a man, but wow, can I as a mom relate to all that he is saying about raising three girls!
Amazingly funny! I loved every single page. The humor and witty conversations had me going back to read passages out loud to the wife. I can not think of another book that made me laugh out loud so much. As a Dad of a 2 yo daughter and another daughter on the way I highly recommend!
A must read for any dad expecting a daughter. Full of funny and true scenarios that I could imagine myself having. I found myself bookmarking several pages to refer back to when my daughter gets here.
Absolutely hilarious. This is a great read for dads, moms, aunts, uncles and anyone looking for a good laugh. This book is both light-hearted and heart-felt. I highly recommend it.
A very funny must-read for any dad or mom with a daughter. Several times while reading this book, I found myself laughing out loud. Well done! I'm hoping there is a follow-up book.
Humorous and sweet. Really enjoyable.
This is the funniest book I've ever read!
All i can do is laugh! I could probably write a similar book, with subjects more closely related to my interests. However, watching television shows with my wife turned into long affairs of hitting the pause button while I recited lines that had me reduced to tears of laughter. I will definitely revisit the dad tips!
An extremely funny book that had me laughing out loud lots! As the mother of two boys, this book helped me understand what ring a father of girls would be like. Highly recommend!
As an, expecting father, I foud this to be a very funny and somewhat helpful book. Some of the advice will be helpful early on, like how to react to all of the pink my wife and I have been receiving. Some I'll have to use later on, like how to encourage her to play soccer and not do ballet. It's more of a funny guide, but one that I would recommend to anyone in my shoes.
More 3.5 than 4 stars. Surprisingly fun readActually made me laugh out loud several times.
I got this book as a Christmas present based on the fact that I've become the father to a daughter and am about to have my second daughter in 2017. I couldn't stop laughing while reading this book. If you have daughters, you should be able to totally relate to Klems. The book skews toward daughters when they are younger -- but, then again, so does your brain when you're plotting how to keep them little forever and save them from the world.
Not very practical, but funny and affirming If you're looking for actual advice on parenting, look somewhere else. That being said, it's lots of fun, it reads like standup comedy, and it's reassuring and comforting for new dads that might be scared. One too many sports metaphors for me, but most guys dig that stuff.
As a parent to be please don't waste your time. This book probably better served as a blog on a website format but does not translate over well into a book. There is nothing informative about raising a girl. I am sure the guy is a great Dad but I question the publishing of this book. I'm glad I just got it at the library.
Heavy on the jokes. Good but not great.The Dora and Disney chapters look like good prep for the fateful day. The last 2 chapters were hilarious also but there was a lot of hit and miss with his jokes.
A little noring for me. Maybe being from another culture, country and gender has something to do for not fully enjoying the book.
not nearly as funny as he thinks he is. just keeps hitting on every male stereotype you've heard hundreds of times before, while still seeming somewhat uncomfortable that he had girls.


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Littleton, CO (W Ken Caryl Ave / S Depew St)
W Ken Caryl Ave / S Depew St
About Littleton, CO (W Ken Caryl Ave / S Depew St)
W Ken Caryl Ave / S Depew St median real estate price is $497,370, which is more expensive than 59.8% of the neighborhoods in Colorado and 78.1% of the neighborhoods in the U.S.
The average rental price in W Ken Caryl Ave / S Depew St is currently $2,833, based on NeighborhoodScout's exclusive analysis. The average rental cost in this neighborhood is higher than 85.9% of the neighborhoods in Colorado.
W Ken Caryl Ave / S Depew St is a suburban neighborhood (based on population density) located in Littleton, Colorado.
W Ken Caryl Ave / S Depew St real estate is primarily made up of medium sized (three or four bedroom) to large (four, five or more bedroom) single-family homes and townhomes. Most of the residential real estate is owner occupied. Many of the residences in the W Ken Caryl Ave / S Depew St neighborhood are established but not old, having been built between 1970 and 1999. A number of residences were also built between 1940 and 1969.
In W Ken Caryl Ave / S Depew St, the current vacancy rate is 3.2%, which is a lower rate of vacancies than 85.1% of all neighborhoods in the U.S. This means that the housing supply in W Ken Caryl Ave / S Depew St is very tight compared to the demand for property here.
When you see a neighborhood for the first time, the most important thing is often the way it looks, like its homes and its setting. Some places look the same, but they only reveal their true character after living in them for a while because they contain a unique mix of occupational or cultural groups. This neighborhood is very unique in some important ways, according to NeighborhoodScout's exclusive exploration and analysis.
Real estate in the W Ken Caryl Ave / S Depew St neighborhood is almost exclusively owner-occupied. NeighborhoodScout's analysis reveals that this neighborhood has a higher rate of owner-occupied housing than is found in 97.6% of U.S. neighborhoods. If you are seeking to rent, this neighborhood may not have many options, but high rates of ownership often indicate stability in a neighborhood.
If you're looking for a great spot to raise a family, then look no further than the W Ken Caryl Ave / S Depew St neighborhood. NeighborhoodScout's analysis found that the combination of good quality public schools, above-average safety from crime, and a high rate of home ownership in predominantly single-family homes, help make this neighborhood among the top 12.7% of family-friendly neighborhoods across the state of Colorado. In addition, there are a high proportion of other families with school-aged children living here, making it easy for parents and their children to socialize and develop a sense of community support. In addition, families here highly value education, as is reflected by the strength of the local schools, in part due to the educational attainment of the parents here, who vote in support of the public schools.
Did you know that the W Ken Caryl Ave / S Depew St neighborhood has more Yugoslav and Hungarian ancestry people living in it than nearly any neighborhood in America? It's true! In fact, 1.0% of this neighborhood's residents have Yugoslav ancestry and 1.9% have Hungarian ancestry.
W Ken Caryl Ave / S Depew St is also pretty special linguistically. Significantly, 6.7% of its residents five years old and above primarily speak Italian at home. While this may seem like a small percentage, it is higher than 95.4% of the neighborhoods in America.
The neighbors in the W Ken Caryl Ave / S Depew St neighborhood in Littleton are wealthy, making it among the 15% highest income neighborhoods in America. NeighborhoodScout's exclusive analysis reveals that this neighborhood has a higher income than 85.3% of the neighborhoods in America. In addition, 2.3% of the children seventeen and under living in this neighborhood are living below the federal poverty line, which is a lower rate of childhood poverty than is found in 81.6% of America's neighborhoods.
A neighborhood is far different if it is dominated by enlisted military personnel rather than people who earn their living by farming. It is also different if most of the neighbors are clerical support or managers. What is wonderful is the sheer diversity of neighborhoods, allowing you to find the type that fits your lifestyle and aspirations.
In the W Ken Caryl Ave / S Depew St neighborhood, 46.5% of the working population is employed in executive, management, and professional occupations. The second most important occupational group in this neighborhood is sales and service jobs, from major sales accounts, to working in fast food restaurants, with 23.0% of the residents employed. Other residents here are employed in manufacturing and laborer occupations (16.6%), and 13.9% in clerical, assistant, and tech support occupations.
The languages spoken by people in this neighborhood are diverse. These are tabulated as the languages people preferentially speak when they are at home with their families. The most common language spoken in the W Ken Caryl Ave / S Depew St neighborhood is English, spoken by 94.4% of households. Other important languages spoken here include Italian and Polish.
Culture is the shared learned behavior of peoples. Undeniably, different ethnicities and ancestries have different cultural traditions, and as a result, neighborhoods with concentrations of residents of one or another ethnicities or ancestries will express those cultures. It is what makes the North End in Boston so fun to visit for the Italian restaurants, bakeries, culture, and charm, and similarly, why people enjoy visiting Chinatown in San Francisco.
In the W Ken Caryl Ave / S Depew St neighborhood in Littleton, CO, residents most commonly identify their ethnicity or ancestry as German (23.7%). There are also a number of people of Irish ancestry (15.1%), and residents who report English roots (13.0%), and some of the residents are also of Mexican ancestry (7.1%), along with some Italian ancestry residents (5.8%), among others.
Even if your neighborhood is walkable, you may still have to drive to your place of work. Some neighborhoods are located where many can get to work in just a few minutes, while others are located such that most residents have a long and arduous commute. The greatest number of commuters in W Ken Caryl Ave / S Depew St neighborhood spend between 15 and 30 minutes commuting one-way to work (32.4% of working residents), which is shorter than the time spent commuting to work for most Americans.
Here most residents (92.4%) drive alone in a private automobile to get to work. In a neighborhood like this, as in most of the nation, many residents find owning a car useful for getting to work.
AGE OF Littleton, CO (W Ken Caryl Ave / S Depew St) HOMES
TYPE OF Littleton, CO (W Ken Caryl Ave / S Depew St) HOMES
SIZE OF Littleton, CO (W Ken Caryl Ave / S Depew St) HOMES
Employment Industries in W Ken Caryl Ave / S Depew St
in W Ken Caryl Ave / S Depew St
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in Colorado
Littleton VIOLENT CRIMES
Littleton Property CRIMES
Rates the quality of all K-12 public schools that your children would be exposed to if you lived in this neighborhood.
Schools In This Neighborhood
Depending on where you live in this neighborhood, your children may attend these schools or other schools outside the neighborhood. Always check with your local school department to determine which schools your children may attend based on your specific address and your child's grade-level.
Quality Rating Compared to CO*
Columbine Hills Elementary School
6005 West Canyon Avenue
Ken Caryl Middle School
6509 West Ken Caryl Avenue
JEFFERSON COUNTY SCHOOL DISTRICT NO. R-1
District Quality Compared to Colorado
Better than of CO school districts.
Denver-Aurora-Lakewood, CO Metro Area regional investment potential
Housing Affordability Trends: Denver-Aurora-Lakewood, CO Metro Area
Popular real estate near W Ken Caryl Ave / S Depew St
Cherry Hills Village, CO (City Center)
Cherry Hills Village, CO (E Quincy Ave / S Downing St)
Denver, CO (S University Blvd / E Exposition Ave)
Golden, CO (Genesee)
Greenwood Village, CO (City Center)
Greenwood Village, CO (E Belleview Ave / S Quebec St)
Greenwood Village, CO (E Belleview Ave / S Yosemite St)
Littleton, CO (Fenders / Phillipsburg)
Littleton, CO (N Highlands Ranch Pky / Fairview Pky)
Morrison, CO (W Belleview Ave / Colorow Dr)
86% Match ‐ Arvada, CO (W 80th Ave / Hoyt Way)
84% Match ‐ Littleton, CO (W Ken Caryl Ave / S Wadsworth Blvd)
84% Match ‐ Arvada, CO (W 86th Pky / W 84th Pl)
84% Match ‐ Centennial, CO (E Orchard Rd / S Steele St)
83% Match ‐ Littleton, CO (Ken Caryl)


Thanks for coming back for the second part. Enjoy! For so long, I could not figure out what the big deal was with using drugs within the spiritual community. All I could get was – not to do so. I have never been a fan of being told no (hello, free will anyone)? So that never sat well with my soul.
As I asked the questions about using drugs, the answer was that it is not the DRUGS within themselves that are the problem. The drugs are HERE and part of our reality. The real issue with using drugs comes about when a person compromises their integrity FOR the drug.
Like the argument that there are addicts and there are drug users, one size does not fit ALL. However, the fact is, drugs can and will make a person compromise their integrity to the point that they have no core "self". They have so removed themselves from the equation, they are now lost in the drug and instead of free will, it is actually the will of the drug making the choices for the person= an addict.
It seems so basic, anyone should be able to see it. When you allow anything to chip away at your core self, with each and every compromise, you lose. You lose what you stand for, you lose your self respect, you lose the respect and trust of others and the list just grows and grows. Your very essence is slowly being chipped away.
When a core-less person shows up in your life, bless that person with love, shield yourself in love and light and always (as you should at this time) pay attention to every message you receive regarding all your interactions. Pay attention to your core integrity and stay your course.


The PLR team has released 8.3.0.18 which is compatible with PostgreSQL version 11. There are no external changes. The only changes are internal which were required for changes in the internal PostgreSQL procedural language API.


You' re a totally normal tennage gal. It' s just that your best pals are ninja turtles who live deep down in the sewers and your father figure is a giant rat. No biggie!We don' t think you' re weird. We think you' re April O' Neil! And we' re kinda psychic about these things. So before you plan on reporting the news on your favorite friends ,you need to slip into this TMNT I Am April O' Neil Juniors Tunic Tank. It's a cute and quick way to look like the quirky Teenage Mutant Ninja Turtles deuteragonist,and it is also the best way to make sure Leonardo ,Raphael ,Michelangelo ,and (especially) Donatello recognize you when you come to the scene of their latest heroics–as if they could forget!We know you' ve been training with Splinter ,so don' t worry ,this officially licensed tunic is totally simple; it only looks like a complicated yellow jumpsuit. You can slip it on in a moment' s notice ,so the second that you sense danger you can get right into character.
What do you do when you have to oppress the entire galaxy at 6:30 but you have a relaxed cocktail party at Tarkin'Äôs downtown apartment at 8:15 that same night? You still have to be a dominating and terrifying villain, but you don'Äôt have time to change, so you'Äôre going to want to be comfortable and dressed-down. Hmmmm. The answer is obvious–this cute and stylish dress! With this I am Vader Long Sleeve Skater Dress you can still manage to look totally in charge while commanding the Imperial fleet to crush the rebels on Hoth or while torturing Han Solo on Bespin, but you'Äôll also feel at home at the quiet loft gathering with a beverage in your hand (whatever it is Vader that is sipping on these days). Don'Äôt let anyone convince you otherwise; just because you are a Sith Lord does not mean you have to dress like a hulking juggernaut. In fact, it'Äôs probably good to take off that daunting outfit every once in awhile and, light saber in hand, let your skin feel the sun and breathe some fresh, natural air. You don'Äôt want to find yourself in thirty years, with chalk-white skin, barely able to survive for more than a few minutes without your suit doing most of the work'Ķ.do you? Of course not!This Darth Vader printed dress lets everyone you know that you are all about keeping a firm grip on the galaxy, while also being all about having a good time in a relaxed and comfortable way. Explore your gray area; you don'Äôt have to be either/or. Let your hair down and let yourself enjoy that night on the town. You'Äôve earned it after all that Death Star business.
The popularity of the cabinet toys of Toy Story isn't hard to understand. They are a great group of friends with a wide assortment of unexpected powers. Sure, there are the obvious ones, but who ever expected Slinky Dog to function as a tight wire or climbing aid!? Among all, though, Woody obviously takes a special place in people's hearts. Not only was he the favorite toy for the longest time, once he got over his hint of jealousy over Buzz's arrival, he continued as the leader and voice of compassion for the whole box. Of course, actually trying to live up to that precise image is a daunting task! The mere act of trying to convince all those varied personalities to work together to defeat the problems of the week is a task best left to the real heroes. That said…What is one to do when you want to show your affection for the cowboy and feel a bit of that bravery but don't have the lasso or any experience wrestling down bulls? Well, you pick up this adorable Toy Story I Am Woody Costume T-Shirt! The comfy cotton tee has Woody's iconic plaid shirt print as well as his cowhide vest. And, what cowboy regalia would be complete without the big buffalo head buckle shining at your waist? Assemble the rest of the toy box, and don't forget to pick up your cowboy hat and boots!
Couldn'Äôt get a full costume together this year? It'Äôs OK. Leo may be a bit of a planner, but you, Raphael, can roll with the punches and still make an impulsive Halloween look that works!Not to say that you are exactly like Raphael. We'Äôre sure you a little less…grumpy than he is. Maybe it was all the sewer smells. Or someone always decides against his pizza topping choice. Maybe his sais are always poking the top of his thighs… whatever the reason, he'Äôs always the one with an attitude. But, we like people with attitude! We'Äôre guessing you do, too. Raphael has his good traits, too. He is the most fiercely loyal, fiery, sarcastic (you know we love that!) and protective of the brothers, so wearing this I Am Raphael TMNT Costume T-Shirt sends a strong message that you are not one to be messed with. Who knew one shirt could do so much, and all without you even having to worry about that whole 'Äúmutating into a monster'Äù thing!