Simulating the Dynamics of Scale-Free Networks via Optimization

We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of how information theory quantifiers can be applied to characterize networks evolution. With the objective of dynamically capture the topological changes of a network''s evolution, we propose a model able to quantify and reproduce several characteristics of a given network, by using the square root of the Jensen-Shannon divergence in combination with the mean degree and the clustering coefficient. To support our hypothesis, we test the model by copying the evolution of well-known models and real systems. The results show that the methodology was able to mimic the test-networks. By using this copycat model, the user is able to analyze the networks behavior over time, and also to conjecture about the main drivers of its evolution, also providing a framework to predict its evolution.     

A Model Simulating the Dynamics of Plant Mitochondrial Genomes

Molecular evolution of the plant mitochondrial genome involves rearrangements due to the presence of highly recombining repeated sequences. As a result, this genome is composed of a set of molecules of various sizes that generate each other through recombination. The model presented simulates the evolution of various frequencies of the different types of molecules over successive cell cycles. It considers the mitochondrial genome as a population of circular molecules evolving through recombination, replication and random segregation. The model parameters are the rates of recombination of each sequence, the frequency of each type of recombination, the replication rates of the circles and the total amount of mitochondrial DNA per cell. This model demonstrates that high recombination rates lead to rapid deletions of sequences in the absence of selection. The frequency of deletion is dependent on the simulated reproductive mechanism. The conditions leading to reversible or irreversible rearrangements were also investigated.     

Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression

The nonlinearity of dynamics in systems biology makes it hard to infer them from experimental data. Simple linear models are computationally efficient, but cannot incorporate these important nonlinearities. An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression. We combine this approach with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics. The approach is tested by inferring the dynamics of yeast glycolysis from simulated data. With little computing time, it produces dynamical models with high predictive power and with structural complexity adapted to the difficulty of the inference problem.     

A Novel Implicit Solvent Model for Simulating the Molecular Dynamics of RNA

Although molecular dynamics simulations can be accelerated by more than an order of magnitude by implicitly describing the influence of the solvent with a continuum model, most currently available implicit solvent simulations cannot robustly simulate the structure and dynamics of nucleic acids. The difficulties become exacerbated especially for RNAs, suggesting the presence of serious physical flaws in the prior continuum models for the influence of the solvent and counter ions on the nucleic acids. We present a novel, to our knowledge, implicit solvent model for simulating nucleic acids by combining the Langevin–Debye model and the Poisson–Boltzmann equation to provide a better estimate of the electrostatic screening of both the water and counter ions. Tests of the model involve comparisons of implicit and explicit solvent simulations for three RNA targets with 20, 29, and 75 nucleotides. The model provides reasonable agreement with explicit solvent simulations, and directions for future improvement are noted.     

Simulating the Dynamics of Leaf Physiology and Morphology with an Extended Optimality Approach

On both a seasonal and single canopy scale several physiologicaland morphological leaf characteristics show significant variationsfor a wide range of species. Aiming at a better understanding,as well as predictions of spatial and temporal leaf adjustments,a recently proposed dynamic optimality approach is adapted toa set of relevant leaf variables. The underlying formalism explicitlyincludes the adaptive dynamics of an arbitrary growth controllingtrait. The approach is based on the calculation of growth gradients,and hence requires functional growth dependencies on the traitsas well as on the different environmental conditions withina crown. These dependencies are quantified using a standardphotosynthesis model (PGEN). It is found that the simple gradientapproach fails in describing patterns originating from leafsenescence. To include aging phenomena the model is completedby a simple evolution equation which is applicable for a widerange of species. Simulation results of the combined model forshaded and for sunlit leaves are compared to an existing datasetfor beech phenology collected during 3 years at the Sollingsite in central Germany. The good overall correspondence, togetherwith the small number of uncertain parameters, demonstrate ahigh efficacy of the model. It can thus be regarded as a valuabletool for assessing the annual canopy carbon assimilation aswell as the adaptive significance of leaf variability. Copyright2000 Annals of Botany Company Leaf, phenology, model, adaptation, gradient dynamics, Fagus sylvatica, senescence, photosynthesis, rubisco, chlorophyll, leaf mass per area     

Toward a Phenomenological Account of Embodied Subjectivity in Autism

Sensorimotor research is currently challenging the dominant understanding of autism as a deficit in the cognitive ability to ‘mindread’. This marks an emerging shift in autism research from a focus on the structure and processes of the mind to a focus on autistic behavior as grounded in the body. Contemporary researchers in sensorimotor differences in autism call for a reconciliation between the scientific understanding of autism and the first-person experience of autistic individuals. I argue that fulfilling this ambition requires a phenomenological understanding of the body as it presents itself in ordinary experience, namely as the subject of experience rather than a physical object. On this basis, I investigate how the phenomenology of Maurice Merleau-Ponty can be employed as a frame of understanding for bodily experience in autism. Through a phenomenological analysis of Tito Mukhopadhyay’s autobiographical work, How can I talk if my lips don’t move (2009), I illustrate the relevance and potential of phenomenological philosophy in autism research, arguing that this approach enables a deeper understanding of bodily and subjective experiences related to autism.     

Simulating food web dynamics along a gradient: quantifying human influence

Realistically parameterized and dynamically simulated food-webs are useful tool to explore the importance of the functional diversity of ecosystems, and in particular relations between the dynamics of species and the whole community. We present a stochastic dynamical food web simulation for the Kelian River (Borneo). The food web was constructed for six different locations, arrayed along a gradient of increasing human perturbation (mostly resulting from gold mining activities) along the river. Along the river, the relative importance of grazers, filterers and shredders decreases with increasing disturbance downstream, while predators become more dominant in governing eco-dynamics. Human activity led to increased turbidity and sedimentation which adversely impacts primary productivity. Since the main difference between the study sites was not the composition of the food webs (structure is quite similar) but the strengths of interactions and the abundance of the trophic groups, a dynamical simulation approach seemed to be useful to better explain human influence. In the pristine river (study site 1), when comparing a structural version of our model with the dynamical model we found that structurally central groups such as omnivores and carnivores were not the most important ones dynamically. Instead, primary consumers such as invertebrate grazers and shredders generated a greater dynamical response. Based on the dynamically most important groups, bottom-up control is replaced by the predominant top-down control regime as distance downstream and human disturbance increased. An important finding, potentially explaining the poor structure to dynamics relationship, is that indirect effects are at least as important as direct ones during the simulations. We suggest that our approach and this simulation framework could serve systems-based conservation efforts. Quantitative indicators on the relative importance of trophic groups and the mechanistic modeling of eco-dynamics could greatly contribute to understanding various aspects of functional diversity.     

Dynamics of hydrocephalus: a physical approach

As brain ventricles lose their ability to regulate the cerebrospinal fluid (CSF) pressure, serious brain conditions collectively named hydrocephalus can appear. By modelling ventricular dynamics with the laws of physics, dynamical instabilities are evidenced, caused by either CSF transport dysregulations or abnormal properties of the elasticity of the ependyma. We show that these instabilities would lead, in most cases, to dilation of the ventricles, establishing a close connection to hydrocephalus, or in some other cases to a ventricular contraction as observed in the slit ventricle syndrome. Signs seem to indicate the possibility of phase transitions occurring as a result of these instabilities, which might have important clinical consequences, such as the inability to recover a healthy state. Even so, our dynamical approach could allow the development of a unified view of these complex intracranial conditions along with a classification that might be clinically relevant.     

Dynamics of Small Molecules in a Dense Polymer Matrix: Molecular Dynamics Studies

Abstract

The anomalous diffusion regime appearing in the self-diffusion of small molecules in bulk amorphous polymers has been extensively studied by molecular dynamics simulations. A rather long simulation of duration ～ 10 ^?8 s is performed on a polyethylene-like simple polymer model containing either oxygen molecules or helium atoms as a diffusant. Dynamic properties evaluated for these diffusants are the mean-square displacement, the van Hove self correlation function, and the self part of the density autocorrelation. It is first confirmed that the anomalous diffusion regime appears in a few hundred picoseconds for oxygen molecule, while the Einstein relation adopted beyond this regime results in the self-diffusion coefficient of the order of ～ 10^?5 cm²/s. This anomaly is still observed for helium that diffuses much faster than oxygen. In the anomalous diffusion regime, it is found that the correlation functions for the two diffusants show characteristic features and become essentially the same as time is scaled appropriately. These features allow the estimation of the two characteristic spatial scales which are probably dominated by the microstructure of the polymer matrix, namely, the cage size and the distance between adjacent cages. The time dependence of the mean-square displacements of the two diffusants can be well interpreted by these characteristic spatial scales as time is scaled with the self-diffusion coefficients. It is shown that the anomalous diffusion regime arises from the inhomogeneous microstructure of the polymer matrix.     

Simulating Microdosimetry in a Virtual Hepatic Lobule

The liver plays a key role in removing harmful chemicals from the body and is therefore often the first tissue to suffer potentially adverse consequences. To protect public health it is necessary to quantitatively estimate the risk of long-term low dose exposure to environmental pollutants. Animal testing is the primary tool for extrapolating human risk but it is fraught with uncertainty, necessitating novel alternative approaches. Our goal is to integrate in vitro liver experiments with agent-based cellular models to simulate a spatially extended hepatic lobule. Here we describe a graphical model of the sinusoidal network that efficiently simulates portal to centrilobular mass transfer in the hepatic lobule. We analyzed the effects of vascular topology and metabolism on the cell-level distribution following oral exposure to chemicals. The spatial distribution of metabolically inactive chemicals was similar across different vascular networks and a baseline well-mixed compartment. When chemicals were rapidly metabolized, concentration heterogeneity of the parent compound increased across the vascular network. As a result, our spatially extended lobule generated greater variability in dose-dependent cellular responses, in this case apoptosis, than were observed in the classical well-mixed liver or in a parallel tubes model. The mass-balanced graphical approach to modeling the hepatic lobule is computationally efficient for simulating long-term exposure, modular for incorporating complex cellular interactions, and flexible for dealing with evolving tissues.     

Cooperativity in Binding Processes: New Insights from Phenomenological Modeling

Cooperative binding is one of the most interesting and not fully understood phenomena involved in control and regulation of biological processes. Here we analyze the simplest phenomenological model that can account for cooperativity (i.e. ligand binding to a macromolecule with two binding sites) by generating equilibrium binding isotherms from deterministically simulated binding time courses. We show that the Hill coefficients determined for cooperative binding, provide a good measure of the Gibbs free energy of interaction among binding sites, and that their values are independent of the free energy of association for empty sites. We also conclude that although negative cooperativity and different classes of binding sites cannot be distinguished at equilibrium, they can be kinetically differentiated. This feature highlights the usefulness of pre-equilibrium time-resolved strategies to explore binding models as a key complement of equilibrium experiments. Furthermore, our analysis shows that under conditions of strong negative cooperativity, the existence of some binding sites can be overlooked, and experiments at very high ligand concentrations can be a valuable tool to unmask such sites.     

Things as They Are: New Directions in Phenomenological Anthroplogy

Things as They Are: New Directions in Phenomenological Anthroplogy. Michael Jackson. ed. Bloomington and Indianapolis: Indiana University Press, 1996. 278 pp.     

Phenomenological and neurocognitive perspectives on delusions: A critical overview

There is considerable overlap between phenomenological and neurocognitive perspectives on delusions. In this paper, we first review major phenomenological accounts of delusions, beginning with Jaspers’ ideas regarding incomprehensibility, delusional mood, and disturbed “cogito” (basic, minimal, or core self‐experience) in what he termed “delusion proper” in schizophrenia. Then we discuss later studies of decontextualization and delusional mood by Matussek, changes in self and world in delusion formation according to Conrad's notions of “apophany” and “anastrophe”, and the implications of ontological transformations in the felt sense of reality in some delusions. Next we consider consistencies between: a) phenomenological models stressing minimal‐self (ipseity) disturbance and hyperreflexivity in schizophrenia, and b) recent neurocognitive models of delusions emphasizing salience dysregulation and prediction error. We voice reservations about homogenizing tendencies in neurocognitive explanations of delusions (the “paranoia paradigm”), given experiential variations in states of delusion. In particular we consider shortcomings of assuming that delusions necessarily or always involve “mistaken beliefs” concerning objective facts about the world. Finally, we offer some suggestions regarding possible neurocognitive factors. Current models that stress hypersalience (banal stimuli experienced as strange) might benefit from considering the potential role of hyposalience in delusion formation. Hyposalience – associated with experiencing the strange as if it were banal, and perhaps with activation of the default mode network – may underlie a kind of delusional derealization and an “anything goes” attitude. Such an attitude would be conducive to delusion formation, yet differs significantly from the hypersalience emphasized in current neurocognitive theories.     

Simulating dynamic activities using a five-axis knee simulator

This work describes the design and capabilities of the Purdue Knee Simulator: Mark II and a sagittal-plane model of the machine. This five-axis simulator was designed and constructed to simulate dynamic loading activities on either cadaveric knee specimens or total knee prostheses mounted on fixtures. The purpose of the machine was to provide a consistent, realistic loading of the knee joint, allowing the kinematics and specific loading of the structures of the knee to be determined based on condition, articular geometry, and simulated activity. The sagittal-plane model of the knee simulator was developed both to predict the loading at the knee from arbitrary inputs and to generate the necessary inputs required to duplicate specified joint loading. Measured tibio-femoral compressive force and quadriceps tension were shown to be in good agreement with the predicted loads from the model. A controlled moment about the ankle-flexion axis was also shown to change the loading on the quadriceps.