Behavioral Modernity and the Cultural Transmission of Structured Information: The Semantic Axelrod Model

Cultural transmission models are coming to the fore in explaining increases in the Paleolithic toolkit richness and diversity. During the later Paleolithic, technologies increase not only in terms of diversity but also in their complexity and interdependence. As Mesoudi and O'Brien (2008) have shown, selection broadly favors social learning of information that is hierarchical and structured, and multiple studies have demonstrated that teaching within a social learning environment can increase fitness. We believe that teaching also provides the scaffolding for transmission of more complex cultural traits. Here, we introduce an extension of the Axelrod (1997} model of cultural differentiation in which traits have prerequisite relationships, and where social learning is dependent upon the ordering of those prerequisites. We examine the resulting structure of cultural repertoires as learning environments range from largely unstructured imitation, to structured teaching of necessary prerequisites, and we find that in combination with individual learning and innovation, high probabilities of teaching prerequisites leads to richer cultural repertoires. Our results point to ways in which we can build more comprehensive explanations of the archaeological record of the Paleolithic as well as other cases of technological change.Comment: 24 pages, 7 figures. Submitted to "Learning Strategies and Cultural Evolution during the Paleolithic", edited by Kenichi Aoki and Alex Mesoudi, and presented at the 79th Annual Meeting of the Society for American Archaeology, Austin TX. Revised 5/14/1

Mapping Dynamic Histone Acetylation Patterns to Gene Expression in Nanog-depleted Murine Embryonic Stem Cells

Embryonic stem cells (ESC) have the potential to self-renew indefinitely and to differentiate into any of the three germ layers. The molecular mechanisms for self-renewal, maintenance of pluripotency and lineage specification are poorly understood, but recent results point to a key role for epigenetic mechanisms. In this study, we focus on quantifying the impact of histone 3 acetylation (H3K9,14ac) on gene expression in murine embryonic stem cells. We analyze genome-wide histone acetylation patterns and gene expression profiles measured over the first five days of cell differentiation triggered by silencing Nanog, a key transcription factor in ESC regulation. We explore the temporal and spatial dynamics of histone acetylation data and its correlation with gene expression using supervised and unsupervised statistical models. On a genome-wide scale, changes in acetylation are significantly correlated to changes in mRNA expression and, surprisingly, this coherence increases over time. We quantify the predictive power of histone acetylation for gene expression changes in a balanced cross-validation procedure. In an in-depth study we focus on genes central to the regulatory network of Mouse ESC, including those identified in a recent genome-wide RNAi screen and in the PluriNet, a computationally derived stem cell signature. We find that compared to the rest of the genome, ESC-specific genes show significantly more acetylation signal and a much stronger decrease in acetylation over time, which is often not reflected in an concordant expression change. These results shed light on the complexity of the relationship between histone acetylation and gene expression and are a step forward to dissect the multilayer regulatory mechanisms that determine stem cell fate.Comment: accepted at PLoS Computational Biolog

A General Model of Dynamics on Networks with Graph Automorphism Lumping

In this paper we introduce a general Markov chain model of dynamical processes on networks. In this model, nodes in the network can adopt a finite number of states and transitions can occur that involve multiple nodes changing state at once. The rules that govern transitions only depend on measures related to the state and structure of the network and not on the particular nodes involved. We prove that symmetries of the network can be used to lump equivalent states in state-space. We illustrate how several examples of well-known dynamical processes on networks correspond to particular cases of our general model. This work connects a wide range of models specified in terms of node-based dynamical rules to their exact continuous-time Markov chain formulation

Mathematical models for estimating effective diffusion parameters of spherical drug delivery devices

Mathematical modeling of drug delivery is of increasing academic and industrial importance in manyaspects. In this paper, we propose an optimization approach for the estimation of the parameters characterizing the diffusion process of a drug from a spherical porous polymer device to an external finite volume. The approach is based on a nonlinear least-squares method and a novel mathematical model which takes into consideration both boundary layer effect and initial burst phenomenon. Ananalytical solution to the model is derived and a formula for the ratio of the mass released in a given time interval and the total mass released in infinite time is also obtained. The approach has been tested using experimental data of the diffusion of prednisolone 21-hemisuccinate sodium saltfrom spherical devices made of porous poly(2-hydroxyethyl methacrylate) hydrogels. The effectiveness and accuracy of the method are well demonstrated by the numerical results. The model was used to determine the diffusion parameters including the effective diffusion coefficient of the drug from a series of devices that vary in both the porous structure and the drug loading levels. The computed diffusion parameters are discussed in relation to the physical properties of the devices

Predicting Pancreas Cell Fate Decisions and Reprogramming with a Hierarchical Multi-Attractor Model

Cell fate reprogramming, such as the generation of insulin-producing β cells from other pancreas cells, can be achieved by external modulation of key transcription factors. However, the known gene regulatory interactions that form a complex network with multiple feedback loops make it increasingly difficult to design the cell reprogramming scheme because the linear regulatory pathways as schemes of causal influences upon cell lineages are inadequate for predicting the effect of transcriptional perturbation. However, sufficient information on regulatory networks is usually not available for detailed formal models. Here we demonstrate that by using the qualitatively described regulatory interactions as the basis for a coarse-grained dynamical ODE (ordinary differential equation) based model, it is possible to recapitulate the observed attractors of the exocrine and β, δ, α endocrine cells and to predict which gene perturbation can result in desired lineage reprogramming. Our model indicates that the constraints imposed by the incompletely elucidated regulatory network architecture suffice to build a predictive model for making informed decisions in choosing the set of transcription factors that need to be modulated for fate reprogramming

Arena3D: visualizing time-driven phenotypic differences in biological systems

<p>Abstract</p> <p>Background</p> <p>Elucidating the genotype-phenotype connection is one of the big challenges of modern molecular biology. To fully understand this connection, it is necessary to consider the underlying networks and the time factor. In this context of data deluge and heterogeneous information, visualization plays an essential role in interpreting complex and dynamic topologies. Thus, software that is able to bring the network, phenotypic and temporal information together is needed. Arena3D has been previously introduced as a tool that facilitates link discovery between processes. It uses a layered display to separate different levels of information while emphasizing the connections between them. We present novel developments of the tool for the visualization and analysis of dynamic genotype-phenotype landscapes.</p> <p>Results</p> <p>Version 2.0 introduces novel features that allow handling time course data in a phenotypic context. Gene expression levels or other measures can be loaded and visualized at different time points and phenotypic comparison is facilitated through clustering and correlation display or highlighting of impacting changes through time. Similarity scoring allows the identification of global patterns in dynamic heterogeneous data. In this paper we demonstrate the utility of the tool on two distinct biological problems of different scales. First, we analyze a medium scale dataset that looks at perturbation effects of the pluripotency regulator Nanog in murine embryonic stem cells. Dynamic cluster analysis suggests alternative indirect links between Nanog and other proteins in the core stem cell network. Moreover, recurrent correlations from the epigenetic to the translational level are identified. Second, we investigate a large scale dataset consisting of genome-wide knockdown screens for human genes essential in the mitotic process. Here, a potential new role for the gene <it>lsm14a </it>in cytokinesis is suggested. We also show how phenotypic patterning allows for extensive comparison and identification of high impact knockdown targets.</p> <p>Conclusions</p> <p>We present a new visualization approach for perturbation screens with multiple phenotypic outcomes. The novel functionality implemented in Arena3D enables effective understanding and comparison of temporal patterns within morphological layers, to help with the system-wide analysis of dynamic processes. Arena3D is available free of charge for academics as a downloadable standalone application from: <url>http://arena3d.org/</url>.</p

Topological Strata of Weighted Complex Networks

The statistical mechanical approach to complex networks is the dominant paradigm in describing natural and societal complex systems. The study of network properties, and their implications on dynamical processes, mostly focus on locally defined quantities of nodes and edges, such as node degrees, edge weights and --more recently-- correlations between neighboring nodes. However, statistical methods quickly become cumbersome when dealing with many-body properties and do not capture the precise mesoscopic structure of complex networks. Here we introduce a novel method, based on persistent homology, to detect particular non-local structures, akin to weighted holes within the link-weight network fabric, which are invisible to existing methods. Their properties divide weighted networks in two broad classes: one is characterized by small hierarchically nested holes, while the second displays larger and longer living inhomogeneities. These classes cannot be reduced to known local or quasilocal network properties, because of the intrinsic non-locality of homological properties, and thus yield a new classification built on high order coordination patterns. Our results show that topology can provide novel insights relevant for many-body interactions in social and spatial networks. Moreover, this new method creates the first bridge between network theory and algebraic topology, which will allow to import the toolset of algebraic methods to complex systems.Comment: 26 pages, 19 figures, 1 tabl

Descriptive analysis of childbirth healthcare costs in an area with high levels of immigration in Spain

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to estimate the cost of childbirth in a teaching hospital in Barcelona, Spain, including the costs of prenatal care, delivery and postnatal care (3 months). Costs were assessed by taking into account maternal origin and delivery type.</p> <p>Methods</p> <p>We performed a cross-sectional study of all deliveries in a teaching hospital to mothers living in its catchment area between October 2006 and September 2007. A process cost analysis based on a full cost accounting system was performed. The main information sources were the primary care program for sexual and reproductive health, and hospital care and costs records. Partial and total costs were compared according to maternal origin and delivery type. A regression model was fit to explain the total cost of the childbirth process as a function of maternal age and origin, prenatal care, delivery type, maternal and neonatal severity, and multiple delivery.</p> <p>Results</p> <p>The average cost of childbirth was 4,328€, with an average of 18.28 contacts between the mother or the newborn and the healthcare facilities. The delivery itself accounted for more than 75% of the overall cost: maternal admission accounted for 57% and neonatal admission for 20%. Prenatal care represented 18% of the overall cost and 75% of overall acts. The average overall cost was 5,815€ for cesarean sections, 4,064€ for vaginal instrumented deliveries and 3,682€ for vaginal non-instrumented deliveries (p < 0.001). The regression model explained 45.5% of the cost variability. The incremental cost of a delivery through cesarean section was 955€ (an increase of 31.9%) compared with an increase of 193€ (6.4%) for an instrumented vaginal delivery. The incremental cost of admitting the newborn to hospital ranged from 420€ (14.0%) to 1,951€ (65.2%) depending on the newborn's severity. Age, origin and prenatal care were not statistically significant or economically relevant.</p> <p>Conclusions</p> <p>Neither immigration nor prenatal care were associated with a substantial difference in costs. The most important predictors of cost were delivery type and neonatal severity. Given the impact of cesarean sections on the overall cost of childbirth, attempts should be made to take into account its higher cost in the decision of performing a cesarean section.</p

Mammalian species richness on islands on the Sunda Shelf, Southeast Asia

A rich mammalian fauna is found on islands that lie on the Sunda Shelf, a continental shelf extending from Vietnam to Borneo and Java that was periodically exposed as dry land during the Pleistocene. The correlation between log of island area and number of species is high ( r 2 =0.94); the slope of the curve is moderate ( z =0.235). Distance from small islands to “source areas” (=Borneo, Sumatra, and the Malay Peninsula) does not appear to affect species richness, nor does depth of water to source area (a measure of isolation time). A species-area curve for forest reserves of varying sizes on the Malay Peninsula has a low slope ( z =0.104); comparison of the mainlaind and island curves indicates that decreasing island area is strongly correlated with increased extinction. Extinction has left reduced but ecologically balanced sets of species on all islands, except that carnivores are under-represented on all but the largest islands. Initial body size and rarity appear to play a significant role in determining the probability of extinction of individual species.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47749/1/442_2004_Article_BF00379083.pd