735 research outputs found
I'd Like to Write the World an Ad: A Compositional Analysis of Popular Jingles
For nearly a century, advertisers have used music to communicate messages on behalf of companies. However, despite their prevalence as a marketing tool, very little research has been conducted on what makes jingles an effective means of reaching consumers. In 2010, Forbes magazine published a list of the “10 Greatest Jingles of All Time,” as voted on by a panel of C.M.Os. However, when they were asked what made these jingles “enduring”, the response was simply “sticking power.”This goal of this research is to elucidate what creates the “sticking power” found in a successful jingle. Through critical analysis of the lyrical, musical, and visual aspects of the ten jingle-based advertisements on the Forbes list, the goal of this thesis is two-fold; to clarify what elements these ten advertisements have in common, and to lay the groundwork for future research into the question, “What makes a good jingle?”Bachelor of Art
Boolean network model predicts cell cycle sequence of fission yeast
A Boolean network model of the cell-cycle regulatory network of fission yeast
(Schizosaccharomyces Pombe) is constructed solely on the basis of the known
biochemical interaction topology. Simulating the model in the computer,
faithfully reproduces the known sequence of regulatory activity patterns along
the cell cycle of the living cell. Contrary to existing differential equation
models, no parameters enter the model except the structure of the regulatory
circuitry. The dynamical properties of the model indicate that the biological
dynamical sequence is robustly implemented in the regulatory network, with the
biological stationary state G1 corresponding to the dominant attractor in state
space, and with the biological regulatory sequence being a strongly attractive
trajectory. Comparing the fission yeast cell-cycle model to a similar model of
the corresponding network in S. cerevisiae, a remarkable difference in
circuitry, as well as dynamics is observed. While the latter operates in a
strongly damped mode, driven by external excitation, the S. pombe network
represents an auto-excited system with external damping.Comment: 10 pages, 3 figure
Del silencio al empoderamiento comunicacional: Una propuesta de gestiĂłn digital para el Foro Nacional de Mujeres de Partidos PolĂticos de Panamá
El Fonamupp Ă©s una organitzaciĂł del Tercer Sector conformada per militants de diferents partits amb l'objectiu de defensar els drets polĂtics de les dones. La investigaciĂł proposa, a partir de l'autonomia en l'Ăşs de la tecnologia, una gestiĂł organitzada del seu ecosistema digital per augmentar la visibilitat de les dones polĂtiques. Es pretĂ©n amplificar el seu ja existent, però poc actiu, perfil a les xarxes socials per tal de connectar i generar aliances amb altres veus interessades en la transformaciĂł de les formes hegemòniques de participaciĂł polĂtica.El Fonamupp es una organizaciĂłn del Tercer Sector, conformado por militantes de distintos partidos cuyo objetivo es defender los derechos polĂticos de las mujeres. La investigaciĂłn propone, a partir de la autonomĂa en el uso de la tecnologĂa, una gestiĂłn organizada de su ecosistema digital para aumentar la visibilidad de las mujeres polĂticas. Se pretende amplificar su ya existente, pero poco activa, perfil en las redes sociales a fin de conectar y generar alianzas con otras voces interesadas en la transformaciĂłn de las formas hegemĂłnicas de participaciĂłn polĂtica.Fonamupp is an organization of the Third Sector, made up of members of different parties whose objective is to defend the political rights of women. The research proposes, based on the autonomy in the use of technology, an organized management of its digital ecosystem to increase the visibility of women politicians. The aim is to amplify their already existing, but not very active, profile in social media in order to connect and generate alliances with other voices interested in the transformation of hegemonic forms of political participation
Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity
We present a novel formulation for biochemical reaction networks in the
context of signal transduction. The model consists of input-output transfer
functions, which are derived from differential equations, using stable
equilibria. We select a set of 'source' species, which receive input signals.
Signals are transmitted to all other species in the system (the 'target'
species) with a specific delay and transmission strength. The delay is computed
as the maximal reaction time until a stable equilibrium for the target species
is reached, in the context of all other reactions in the system. The
transmission strength is the concentration change of the target species. The
computed input-output transfer functions can be stored in a matrix, fitted with
parameters, and recalled to build discrete dynamical models. By separating
reaction time and concentration we can greatly simplify the model,
circumventing typical problems of complex dynamical systems. The transfer
function transformation can be applied to mass-action kinetic models of signal
transduction. The paper shows that this approach yields significant insight,
while remaining an executable dynamical model for signal transduction. In
particular we can deconstruct the complex system into local transfer functions
between individual species. As an example, we examine modularity and signal
integration using a published model of striatal neural plasticity. The modules
that emerge correspond to a known biological distinction between
calcium-dependent and cAMP-dependent pathways. We also found that overall
interconnectedness depends on the magnitude of input, with high connectivity at
low input and less connectivity at moderate to high input. This general result,
which directly follows from the properties of individual transfer functions,
contradicts notions of ubiquitous complexity by showing input-dependent signal
transmission inactivation.Comment: 13 pages, 5 tables, 15 figure
Stable Heterogeneity for the Production of Diffusible Factors in Cell Populations
The production of diffusible molecules that promote survival and growth is common in bacterial and eukaryotic cell populations, and can be considered a form of cooperation between cells. While evolutionary game theory shows that producers and non-producers can coexist in well-mixed populations, there is no consensus on the possibility of a stable polymorphism in spatially structured populations where the effect of the diffusible molecule extends beyond one-step neighbours. I study the dynamics of biological public goods using an evolutionary game on a lattice, taking into account two assumptions that have not been considered simultaneously in existing models: that the benefit of the diffusible molecule is a non-linear function of its concentration, and that the molecule diffuses according to a decreasing gradient. Stable coexistence of producers and non-producers is observed when the benefit of the molecule is a sigmoid function of its concentration, while strictly diminishing returns lead to coexistence only for very specific parameters and linear benefits never lead to coexistence. The shape of the diffusion gradient is largely irrelevant and can be approximated by a step function. Since the effect of a biological molecule is generally a sigmoid function of its concentration (as described by the Hill equation), linear benefits or strictly diminishing returns are not an appropriate approximations for the study of biological public goods. A stable polymorphism of producers and non-producers is in line with the predictions of evolutionary game theory and likely to be common in cell populations
Inverse bifurcation analysis: application to simple gene systems
BACKGROUND: Bifurcation analysis has proven to be a powerful method for understanding the qualitative behavior of gene regulatory networks. In addition to the more traditional forward problem of determining the mapping from parameter space to the space of model behavior, the inverse problem of determining model parameters to result in certain desired properties of the bifurcation diagram provides an attractive methodology for addressing important biological problems. These include understanding how the robustness of qualitative behavior arises from system design as well as providing a way to engineer biological networks with qualitative properties. RESULTS: We demonstrate that certain inverse bifurcation problems of biological interest may be cast as optimization problems involving minimal distances of reference parameter sets to bifurcation manifolds. This formulation allows for an iterative solution procedure based on performing a sequence of eigen-system computations and one-parameter continuations of solutions, the latter being a standard capability in existing numerical bifurcation software. As applications of the proposed method, we show that the problem of maximizing regions of a given qualitative behavior as well as the reverse engineering of bistable gene switches can be modelled and efficiently solved
Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain
Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional
organization of the network as a whole. However, these structural
motifs can only tell one part of the functional story because in this
analysis each node and edge is treated on an equal footing. In real
networks, two motifs that are topologically identical but whose nodes
perform very different functions will play very different roles in the
network.
Methodology/Principal Findings: Here, we combine structural information
derived from the topology of the neuronal network of the nematode C.
elegans with information about the biological function of these nodes,
thus coloring nodes by function. We discover that particular
colorations of motifs are significantly more abundant in the worm brain
than expected by chance, and have particular computational functions
that emphasize the feed-forward structure of information processing in
the network, while evading feedback loops. Interneurons are strongly
over-represented among the common motifs, supporting the notion that
these motifs process and transduce the information from the sensor
neurons towards the muscles. Some of the most common motifs identified
in the search for significant colored motifs play a crucial role in the
system of neurons controlling the worm's locomotion.
Conclusions/Significance: The analysis of complex networks in terms of
colored motifs combines two independent data sets to generate insight
about these networks that cannot be obtained with either data set
alone. The method is general and should allow a decomposition of any
complex networks into its functional (rather than topological) motifs
as long as both wiring and functional information is available
Network integration meets network dynamics
Molecular interaction networks provide a window on the workings of the cell. However, combining various types of networks into one coherent large-scale dynamic model remains a formidable challenge. A recent paper in BMC Systems Biology describes a promising step in this direction
Process Algebra with Layers: Multi-scale Integration Modelling applied to Cancer Therapy
We present a novel Process Algebra designed for multi-scale integration modelling: Process Algebra with Layers (PAL). The unique feature of PAL is the modularisation of scale into integrated layers: Object and Population. An Object can represent a molecule, organelle, cell, tissue, organ or any organism. Populations hold specific types of Object, for example, life stages, cell phases and infectious states. The syntax and semantics of this novel language are presented. A PAL model of the multi-scale system of cell growth and damage from cancer treatment is given. This model allows the analysis of different scales of the system. The Object and Population levels give insight into the length of a cell cycle and cell population growth respectively. The PAL model results are compared to wet laboratory survival fractions of cells given different doses of radiation treatment [1]. This comparison shows how PAL can be used to aid in investigations of cancer treatment in systems biology
Reliability of Transcriptional Cycles and the Yeast Cell-Cycle Oscillator
A recently published transcriptional oscillator associated with the yeast cell cycle provides clues and raises questions about the mechanisms underlying autonomous cyclic processes in cells. Unlike other biological and synthetic oscillatory networks in the literature, this one does not seem to rely on a constitutive signal or positive auto-regulation, but rather to operate through stable transmission of a pulse on a slow positive feedback loop that determines its period. We construct a continuous-time Boolean model of this network, which permits the modeling of noise through small fluctuations in the timing of events, and show that it can sustain stable oscillations. Analysis of simpler network models shows how a few building blocks can be arranged to provide stability against fluctuations. Our findings suggest that the transcriptional oscillator in yeast belongs to a new class of biological oscillators
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