Creating Order from Chaos: Epigenome Dynamics in Plants with Complex Genomes

Flowering plants have strikingly distinct genomes, although they contain a similar suite of expressed genes. The diversity of genome structures and organization is largely due to variation in transposable elements (TEs) and whole-genome duplication (WGD) events. We review evidence that chromatin modifications and epigenetic regulation are intimately associated with TEs and likely play a role in mediating the effects of WGDs. We hypothesize that the current structure of a genome is the result of various TE bursts and WGDs and it is likely that the silencing mechanisms and the chromatin structure of a genome have been shaped by these events. This suggests that the specific mechanisms targeting chromatin modifications and epigenomic patterns may vary among different species. Many crop species have likely evolved chromatin-based mechanisms to tolerate silenced TEs near actively expressed genes. These interactions of heterochromatin and euchromatin are likely to have important roles in modulating gene expression and variability within species.     

The Modeling of Global Epidemics: Stochastic Dynamics and Predictability

The global spread of emergent diseases is inevitably entangled with the structure of the population flows among different geographical regions. The airline transportation network in particular shrinks the geographical space by reducing travel time between the world's most populated areas and defines the main channels along which emergent diseases will spread. In this paper, we investigate the role of the large-scale properties of the airline transportation network in determining the global propagation pattern of emerging diseases. We put forward a stochastic computational framework for the modeling of the global spreading of infectious diseases that takes advantage of the complete International Air Transport Association 2002 database complemented with census population data. The model is analyzed by using for the first time an information theory approach that allows the quantitative characterization of the heterogeneity level and the predictability of the spreading pattern in presence of stochastic fluctuations. In particular we are able to assess the reliability of numerical forecast with respect to the intrinsic stochastic nature of the disease transmission and travel flows. The epidemic pattern predictability is quantitatively determined and traced back to the occurrence of epidemic pathways defining a backbone of dominant connections for the disease spreading. The presented results provide a general computational framework for the analysis of containment policies and risk forecast of global epidemic outbreaks. On leave from CEA-Centre d'Etudes de Bruyères-Le-Chatel, France.     

Measuring Predictability of Autonomous Network Transitions into Bursting Dynamics

Understanding spontaneous transitions between dynamical modes in a network is of significant importance. These transitions may separate pathological and normal functions of the brain. In this paper, we develop a set of measures that, based on spatio-temporal features of network activity, predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. These metrics quantify spike-timing distributions within a narrow time window as a function of the relative location of the active neurons. We applied these metrics to investigate the properties of these transitions in excitatory-only and excitatory-and-inhibitory networks and elucidate how network topology, noise level, and cellular heterogeneity affect both the reliability and the timeliness of the predictions. The developed measures can be calculated in real time and therefore potentially applied in clinical situations.     

Rotifer Population Dynamics in Two Coupled Habitats: Invasion of Chaos

We study the role of interactions between habitats in rotifer dynamics. We use a simple discrete-time model to simulate the interactions between neighboring habitats with different intrinsic dynamics. Being uncoupled, one habitat shows periodical oscillations of the rotifer biomass while the other one demonstrates chaotic oscillations. As a result of the exchange of rotifer biomass, chaos replaces regular oscillations. As a result, the rotifer dynamics becomes chaotic in both habitats. We show that the invasion of chaos is followed by the synchronization of the chaotic regimes of both habitats, and this synchronization increases as coupling between the habitats is increased. We also demonstrate that the biological invasion of the rotifer species, which show chaotic dynamics, to a neighboring habitat with intrinsically regular plankton dynamics leads to the invasion of chaos and the synchronization of chaotic oscillations of the plankton biomass in both the habitats.     

Dynamics of Pre-replicative Complex Assembly

The pre-replicative complex (pre-RC) is formed at all potential origins of replication through the action of the origin recognition complex (ORC), Cdc6, Cdt1, and the Mcm2-7 complex. The end result of pre-RC formation is the loading of the Mcm2-7 replicative helicase onto origin DNA. We examined pre-RC formation in vitro and found that it proceeds through separable binding events. Origin-bound ORC recruits Cdc6, and this ternary complex then promotes helicase loading in the presence of a pre-formed Mcm2-7-Cdt1 complex. Using a stepwise pre-RC assembly assay, we investigated the fate of pre-RC components during later stages of the reaction. We determined that helicase loading is accompanied by dissociation of ORC, Cdc6, and Cdt1 from origin DNA. This dissociation requires ATP hydrolysis at a late stage of pre-RC assembly. Our results indicate that pre-RC formation is a dynamic process.     

Nonlinear Dynamics Analysis of a Self-Organizing Recurrent Neural Network: Chaos Waning

Self-organization is thought to play an important role in structuring nervous systems. It frequently arises as a consequence of plasticity mechanisms in neural networks: connectivity determines network dynamics which in turn feed back on network structure through various forms of plasticity. Recently, self-organizing recurrent neural network models (SORNs) have been shown to learn non-trivial structure in their inputs and to reproduce the experimentally observed statistics and fluctuations of synaptic connection strengths in cortex and hippocampus. However, the dynamics in these networks and how they change with network evolution are still poorly understood. Here we investigate the degree of chaos in SORNs by studying how the networks'' self-organization changes their response to small perturbations. We study the effect of perturbations to the excitatory-to-excitatory weight matrix on connection strengths and on unit activities. We find that the network dynamics, characterized by an estimate of the maximum Lyapunov exponent, becomes less chaotic during its self-organization, developing into a regime where only few perturbations become amplified. We also find that due to the mixing of discrete and (quasi-)continuous variables in SORNs, small perturbations to the synaptic weights may become amplified only after a substantial delay, a phenomenon we propose to call deferred chaos.     

Dynamics of Major Histocompatibility Complex Class I Association with the Human Peptide-loading Complex

Although the human peptide-loading complex (PLC) is required for optimal major histocompatibility complex class I (MHC I) antigen presentation, its composition is still incompletely understood. The ratio of the transporter associated with antigen processing (TAP) and MHC I to tapasin, which is responsible for MHC I recruitment and peptide binding optimization, is particularly critical for modeling of the PLC. Here, we characterized the stoichiometry of the human PLC using both biophysical and biochemical approaches. By means of single-molecule pulldown (SiMPull), we determined a TAP/tapasin ratio of 1:2, consistent with previous studies of insect-cell microsomes, rat-human chimeric cells, and HeLa cells expressing truncated TAP subunits. We also report that the tapasin/MHC I ratio varies, with the PLC population comprising both 2:1 and 2:2 complexes, based on mutational and co-precipitation studies. The MHC I-saturated PLC may be particularly prevalent among peptide-selective alleles, such as HLA-C4. Additionally, MHC I association with the PLC increases when its peptide supply is reduced by inhibiting the proteasome or by blocking TAP-mediated peptide transport using viral inhibitors. Taken together, our results indicate that the composition of the human PLC varies under normal conditions and dynamically adapts to alterations in peptide supply that may arise during viral infection. These findings improve our understanding of the quality control of MHC I peptide loading and may aid the structural and functional modeling of the human PLC.     

Estimation of Instantaneous Complex Dynamics through Lyapunov Exponents: A Study on Heartbeat Dynamics

Measures of nonlinearity and complexity, and in particular the study of Lyapunov exponents, have been increasingly used to characterize dynamical properties of a wide range of biological nonlinear systems, including cardiovascular control. In this work, we present a novel methodology able to effectively estimate the Lyapunov spectrum of a series of stochastic events in an instantaneous fashion. The paradigm relies on a novel point-process high-order nonlinear model of the event series dynamics. The long-term information is taken into account by expanding the linear, quadratic, and cubic Wiener-Volterra kernels with the orthonormal Laguerre basis functions. Applications to synthetic data such as the Hénon map and Rössler attractor, as well as two experimental heartbeat interval datasets (i.e., healthy subjects undergoing postural changes and patients with severe cardiac heart failure), focus on estimation and tracking of the Instantaneous Dominant Lyapunov Exponent (IDLE). The novel cardiovascular assessment demonstrates that our method is able to effectively and instantaneously track the nonlinear autonomic control dynamics, allowing for complexity variability estimations.     

核孔复合体的动态组装

核孔是介导所有大分子入核出核的唯一通道。在整个生命活动中,核孔复合体的组成蛋白总是处于动态变化中。核孔复合体的动态组装改变了核质转运状态,并最终改变了细胞的功能。     

The Impact of Fine-Scale Disturbances on the Predictability of Vegetation Dynamics and Carbon Flux

Predictions from forest ecosystem models are limited in part by large uncertainties in the current state of the land surface, as previous disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect. Likewise, future disturbances also present a challenge to prediction as their dynamics are episodic and complex and occur across a range of spatial and temporal scales. While large extreme events such as tropical cyclones, fires, or pest outbreaks can produce dramatic consequences, small fine-scale disturbance events are typically much more common and may be as or even more important. This study focuses on the impacts of these smaller disturbance events on the predictability of vegetation dynamics and carbon flux. Using data on vegetation structure collected for the same domain at two different times, i.e. “repeat lidar data”, we test high-resolution model predictions of vegetation dynamics and carbon flux across a range of spatial scales at an important tropical forest site at La Selva Biological Station, Costa Rica. We found that predicted height change from a height-structured ecosystem model compared well to lidar measured height change at the domain scale (~150 ha), but that the model-data mismatch increased exponentially as the spatial scale of evaluation decreased below 20 ha. We demonstrate that such scale-dependent errors can be attributed to errors predicting the pattern of fine-scale forest disturbances. The results of this study illustrate the strong impact fine-scale forest disturbances have on forest dynamics, ultimately limiting the spatial resolution of accurate model predictions.     

Detection of Appearing and Disappearing Objects in Complex Acoustic Scenes

The ability to detect sudden changes in the environment is critical for survival. Hearing is hypothesized to play a major role in this process by serving as an “early warning device,” rapidly directing attention to new events. Here, we investigate listeners'' sensitivity to changes in complex acoustic scenes—what makes certain events “pop-out” and grab attention while others remain unnoticed? We use artificial “scenes” populated by multiple pure-tone components, each with a unique frequency and amplitude modulation rate. Importantly, these scenes lack semantic attributes, which may have confounded previous studies, thus allowing us to probe low-level processes involved in auditory change perception. Our results reveal a striking difference between “appear” and “disappear” events. Listeners are remarkably tuned to object appearance: change detection and identification performance are at ceiling; response times are short, with little effect of scene-size, suggesting a pop-out process. In contrast, listeners have difficulty detecting disappearing objects, even in small scenes: performance rapidly deteriorates with growing scene-size; response times are slow, and even when change is detected, the changed component is rarely successfully identified. We also measured change detection performance when a noise or silent gap was inserted at the time of change or when the scene was interrupted by a distractor that occurred at the time of change but did not mask any scene elements. Gaps adversely affected the processing of item appearance but not disappearance. However, distractors reduced both appearance and disappearance detection. Together, our results suggest a role for neural adaptation and sensitivity to transients in the process of auditory change detection, similar to what has been demonstrated for visual change detection. Importantly, listeners consistently performed better for item addition (relative to deletion) across all scene interruptions used, suggesting a robust perceptual representation of item appearance.     

Experimental Manipulation of Forest Structure: Near-Term Effects on Gap and Stand Scale C Dynamics

Canopy gaps and coarse woody debris are two forest structural features that are more abundant in old-growth forests than in second-growth, even-aged stands. These features directly influence the carbon balance of the ecosystem, yet few studies have quantified their interactive effects. We experimentally manipulated the forest structure of a second-growth northern hardwood forest in north-central Wisconsin (USA) and measured the shift of C between pools of the ecosystem components. Here, we question the longevity of the changes to the aboveground pools and address their implications for total ecosystem C (TEC) and net ecosystem production (NEP) at both the gap and stand scale. At the scale of the gap, the harvest and removal of trees significantly reduced NEP (?3.2 to ?3.5 Mg C ha^?1 for gaps vs 2.2 to 2.5 Mg C ha^?1 for reference conditions), but did not alter heterotrophic respiration. The addition of woody debris without harvest significantly increased heterotrophic respiration, decreasing soil C storage of the gap area (?0.5 to ?1.1 Mg C ha^?1). The combined treatment of gap creation and woody debris addition made the gap area a significant C source to the atmosphere for the 3 years of the study (?4.9 to ?5.1 Mg C ha^?1). We also estimated how these structural features would affect C dynamics at a broader scale. The conversion of 10% of the stand canopy to gap conditions caused only a brief decrease in the stand NEP with the C balance returning to reference conditions by the third year following tree harvest. The woody debris additions caused an increase in both TEC and heterotrophic respiration. When combined the addition of canopy gaps and woody debris caused plots to initially become significant C sources, relative to undisturbed locations that were consistently accumulating C, with an annual NEP ranging from 2.1 to 2.8 Mg C ha^?1 y^?1. Understanding the effects of these structural features on forest C dynamics is highly relevant as the maturing forests of the region transition to more structurally complex forests and the demand for managing ecosystems for long-term C sequestration increases.     

Molecular Dynamics Simulations of DNA-Polycation Complex Formation

Complexes formed from DNA and polycations are of interest because of their potential use in gene therapy; however, there remains a lack of understanding of the structure and formation of DNA-polycation complexes at atomic scale. In this work, molecular dynamics simulations of the DNA duplex d(CGCGAATTCGCG) in the presence of polycation chains are carried out to shed light on the specific atomic interaction that result in complex formation. The structures of complexes formed from DNA with polyethylenimine, which is considered one of the most promising DNA vector candidates, and a second polycation, poly-L-lysine, are compared. After an initial separation of ∼50 Å, the DNA and polycation come together and form a stable complex within 10 ns. The DNA does not undergo any major structural changes on complexation and remains in the B-form. In the formed complex, the charged amine groups of the polycation mainly interact with DNA phosphate groups, with polycation intrusion into the major and minor grooves dependent on the identity and charge state of the polycation. The ability of the polycation to effectively neutralize the charge of the DNA phosphate groups and the resulting influence on the DNA helix interaction are discussed.     

Climate-Based Models for Pulsed Resources Improve Predictability of Consumer Population Dynamics: Outbreaks of House Mice in Forest Ecosystems

Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts) are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT) for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer–resource dynamics to predict invasive house mouse (Mus musculus) outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year’s advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer–resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events.     

Complex genetic diseases: controversy over the Croesus code

The polarization of views on how best to exploit new information from the Human Genome Project for medicine reflects our ignorance of the genetic architecture underlying common diseases: are susceptibility alleles common or rare, neutral or deleterious, few or many? Single-nucleotide polymorphism (SNP) technology is almost in place to dissect such diseases and to create a personalized medicine, but success is critically dependent on the biology and "Nature to be commanded must be obeyed" (Francis Bacon, 1620, Novum Organum).     

Microbial Bebop: Creating Music from Complex Dynamics in Microbial Ecology

In order for society to make effective policy decisions on complex and far-reaching subjects, such as appropriate responses to global climate change, scientists must effectively communicate complex results to the non-scientifically specialized public. However, there are few ways however to transform highly complicated scientific data into formats that are engaging to the general community. Taking inspiration from patterns observed in nature and from some of the principles of jazz bebop improvisation, we have generated Microbial Bebop, a method by which microbial environmental data are transformed into music. Microbial Bebop uses meter, pitch, duration, and harmony to highlight the relationships between multiple data types in complex biological datasets. We use a comprehensive microbial ecology, time course dataset collected at the L4 marine monitoring station in the Western English Channel as an example of microbial ecological data that can be transformed into music. Four compositions were generated (www.bio.anl.gov/MicrobialBebop.htm.) from L4 Station data using Microbial Bebop. Each composition, though deriving from the same dataset, is created to highlight different relationships between environmental conditions and microbial community structure. The approach presented here can be applied to a wide variety of complex biological datasets.     

Dynamics of a Boreal Lake Ecosystem during a Long-Term Manipulation of Top Predators

We assessed the long-term (16 years) effects of introducing piscivores (northern pike) into a small, boreal lake (Lake 221, Experimental Lakes Area) containing abundant populations of two planktivorous fish species. After the introduction, pearl dace were extirpated and yellow perch abundance was greatly reduced. Daphnia species shifted from D. galeata mendota to larger bodied Daphnia catawba, but the total zooplankton biomass did not increase, nor did the biomass of large grazers such as Daphnia. Phytoplankton biomass decreased after the northern pike introduction, but increased when northern pike were partially removed from the lake. Phosphorus (P) excretion by fish was ∼0.18 mg P m⁻² d⁻¹ before pike addition, declined rapidly to approximately 0.03–0.10 as planktivorous perch and dace populations were reduced by pike, and increased back to premanipulation levels after the pike were partially removed and the perch population recovered. When perch were abundant, P excretion by fish supported about 30% of the P demand by primary producers, decreasing to 6–14% when pike were abundant. Changes in phytoplankton abundance in Lake 221 appear to be driven by changes in P cycling by yellow perch, whose abundance was controlled by the addition and removal of pike. These results confirm the role of nutrient cycling in mediating trophic cascades and are consistent with previous enclosure experiments conducted in the same lake.