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Understanding the regulation of gene control networks and their ensuing dynamics will be a critical component in the understanding of the mountain of genomic data being currently collected. This paper reviews recent mathematical modeling work on the tryptophan and lactose operons which are, respectively, the classical paradigms for repressible and inducible operons.
Using evolutionary simulations, we develop autonomous agents controlled by artificial neural networks (ANNs). In simple lifelike tasks of foraging and navigation, high performance levels are attained by agents equipped with fully recurrent ANN controllers. In a set of experiments sharing the same behavioral task but differing in the sensory input available to the agents, we find a common structure of a command neuron switching the dynamics of the network between radically different behavioral modes. When sensory position information is available, the command neuron reflects a map of the environment, acting as a location-dependent cell sensitive to the location and orientation of the agent. When such information is unavailable, the command neuron's activity is based on a spontaneously evolving short-term memory mechanism, which underlies its apparent place-sensitive activity. A two-parameter stochastic model for this memory mechanism is proposed. We show that the parameter values emerging from the evolutionary simulations are near optimal; evolution takes advantage of seemingly harmful features of the environment to maximize the agent's foraging efficiency. The accessibility of evolved ANNs for a detailed inspection, together with the resemblance of some of the results to known findings from neurobiology, places evolved ANNs as an excellent candidate model for the study of structure and function relationship in complex nervous systems.
The vacuoles inhabited by viable Mycobacterium avium and Mycobacterium tuberculosis show limited fusion with endosomal and lysosomal compartments. This ability to regulate the maturation of their phagosomal compartments and restrict their differentiation into hydrolytically active vacuoles appears to correlate with the survival of the bacilli. Data presented in this current study demonstrate that despite the apparent isolation of mycobacterial vacuoles from the lysosomal network, they are dynamic, fusion-competent vesicles. Exploiting the ability of cholera toxin B subunit to bind to GM1 ganglioside on the macrophage plasmalemma, we demonstrate that these glycosphingolipids have ready access to the mycobacterial vacuoles. Entry into mycobacterial vacuoles is rapid, within 5 min of addition to the cells, and does not proceed through a brefeldin A-sensitive pathway. Furthermore, the gangliosides follow a route that differs from that taken by fluid-phase markers. TLC analysis gangliosides isolated from Mycobacterium-containing vacuoles, and IgG-bead phagosomes reveal similar profiles. These data indicate that rather than being fusion incompetent, mycobacterial vacuoles are actually highly dynamic, fusion-competent vesicles that behave like an extension of the recycling endosomal apparatus.
The propensity for synchronization of complex networks with directed and weighted links is considered. We show that a weighting procedure based upon the global structure of network pathways enhances complete synchronization of identical dynamical units in scale-free networks. Furthermore, we numerically show that very similar conditions hold also for phase synchronization of nonidentical chaotic oscillators.
The dynamics of a network of autocatalytically replicating species in a continuously stirred tank reactor can be described by a replicator equation in the limit of small flux rates.
Unlike the well-studied models of growing networks, where the dominant dynamics consist of insertions of new nodes and connections and rewiring of existing links, we study ad hoc networks, where one also has to contend with rapid and random deletions of existing nodes (and, hence, the associated links). We first show that dynamics based only on the well-known preferential attachments of new nodes do not lead to a sufficiently heavy-tailed degree distribution in ad hoc networks. In particular, the magnitude of the power-law exponent increases rapidly (from 3) with the deletion rate, becoming infinity in the limit of equal insertion and deletion rates. We then introduce a local and universal compensatory rewiring dynamic, and show that even in the limit of equal insertion and deletion rates true scale-free structures emerge, where the degree distributions obey a power law with a tunable exponent, which can be made arbitrarily close to 2. The dynamics reported in this paper can be used to craft protocols for designing highly dynamic peer-to-peer networks and also to account for the power-law exponents observed in existing popular services.
A continuous neural network coupled to a dynamical model of the urinary bladder is defined. The neural network is trained to control the bladder model to track a prescribed volume fluctuation, by adjusting weights and time constants. The gradients of the error in the output neurons of the neural network are unknown. Therefore, the learning procedure discussed here minimizes the error functional without using gradient descent.
The Department of Justice (DOJ) and the Federal Trade Commission (FTC) have taken a recent interest in collaborations among hospitals that do not entail common ownership, despite the lack of scientific evidence regarding effects of these hospital "networks" on market competition. This paper explores the relationship between hospital networks and pricing behavior in California during a period of dynamic increase in hospital network activity there. Using Herfindahl-Hirschman Indexes (HHI) market definitions based on patient origin and accounting for network relationships, we find a positive network effect on hospital pricing. However, this result appears to be generated primarily by system-owned hospitals that form networks to provide particular services jointly.
Cholesterol 24-hydroxylase (CYP46A1) regulates brain cholesterol homeostasis and synaptic plasticity, playing a crucial role in ischemic stroke. Although previous studies have reported post-ischemic CYP46A1 upregulation, its spatiotemporal dynamics remain poorly defined. To elucidate these dynamics, we investigated the expression of CYP46A1 and other essential cholesterol homeostasis-related genes from 6 h to 3 days after permanent middle cerebral artery occlusion (pMCAO) in CB-17 mice. We utilized single-cell and single-nucleus transcriptomics, regional quantitative PCR, and high-resolution immunohistochemistry. CYP46A1 is predominantly expressed in neurons. Following ischemia, the cholesterol network exhibited a dynamic spatiotemporal divergence. Acutely (6 h post-ischemia), surviving regions transiently upregulated cell-autonomous cholesterol synthesis genes and CYP46A1. Subacutely (3 days), this response shifted toward a widespread upregulation of glia-dependent cholesterol transport genes and general CYP46A1 downregulation. At 24 h, CYP46A1 protein was substantially reduced in the necrotic core and superficial layer II/III of the peri-infarct cortex, but upregulated in deeper layer V, hippocampus, and lateral striatum. Notably, this localized upregulation spatially coincided with reactive microglial hypertrophy. These findings indicate that CYP46A1 is dynamically modulated in viable tissues following ischemic stress. This spatial divergence likely reflects a synergistic interaction between inflammatory propagation and neural circuit-mediated oxidative stress. Resolving these spatiotemporal profiles provides a rigorous foundation for evaluating CYP46A1 functionality and developing stage-specific therapeutic interventions.
The N-terminal domain of the Tn916 integrase protein (INT-DBD) is responsible for DNA binding in the process of strand cleavage and joining reactions required for transposition of the Tn916 conjugative transposon. Site-specific association is facilitated by numerous protein-DNA contacts from the face of a three-stranded beta-sheet inserted into the major groove. The protein undergoes a subtle conformational transition and is slightly unfolded in the protein-DNA complex. The conformation of many charged residues is poorly defined by NMR data but mutational studies have indicated that removal of polar side chains decreases binding affinity, while non-polar contacts are malleable. Based on analysis of the binding enthalpy and binding heat capacity, we have reasoned that dehydration of the protein-DNA interface is incomplete. This study presents results from a molecular dynamics investigation of the INT-DBD-DNA complex aimed at a more detailed understanding of the role of conformational dynamics and hydration in site-specific binding. Comparison of simulations (total of 13 ns) of the free protein and of the bound protein conformation (in isolation or DNA-bound) reveals intrinsic flexibility in certain parts of the molecule. Conformational adaptation linked to partial unfolding appears to be induced by protein-DNA contacts. The protein-DNA hydrogen-bonding network is highly dynamic. The simulation identifies protein-DNA interactions that are poorly resolved or only surmised from the NMR ensemble. Single water molecules and water clusters dynamically optimize the complementarity of polar interactions at the 'wet' protein-DNA interface. The simulation results are useful to establish a qualitative link between experimental data on individual residue's contribution to binding affinity and thermodynamic properties of INT-DBD alone and in complex with DNA.
Classical theories of sensory processing view the brain as a passive, stimulus-driven device. By contrast, more recent approaches emphasize the constructive nature of perception, viewing it as an active and highly selective process. Indeed, there is ample evidence that the processing of stimuli is controlled by top-down influences that strongly shape the intrinsic dynamics of thalamocortical networks and constantly create predictions about forthcoming sensory events. We discuss recent experiments indicating that such predictions might be embodied in the temporal structure of both stimulus-evoked and ongoing activity, and that synchronous oscillations are particularly important in this process. Coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.
We calculate the statistical properties of the energy landscape of a minimal model for strong network-forming liquids. Dynamic and thermodynamic properties of this model can be computed with arbitrary precision even at low temperatures. A degenerate disordered ground state and logarithmic statistics for the local minima energy distribution are the landscape signatures of strong liquid behavior. Differences from fragile liquid properties are attributed to the presence of a discrete energy scale, provided by the particle bonds, and to the intrinsic degeneracy of topologically disordered networks.
We discuss a generic scenario along which complex spiking behavior evolves in biologically realistic neural networks. Our nonlinear dynamics approach is based directly on rat neocortical in vitro recordings. Using this experimental data, we obtain a full overview on the possible spiking behaviors of pyramidal neurons that are engaged in binary interactions. Universality arguments imply that the observed spiking behaviors are largely independent from the specific properties of individual neurons; theoretical arguments and numerical experiments indicate that they should be observable in in vivo neocortical neuron networks.
This review culls disparate elements from the theoretical and research literature on human migration to argue for the construction of a theory of migration that simultaneously incorporates multiple levels of analysis within a longitudinal perspective. A detailed review of interconnections among individual behavior, household strategies, community structures, and national political economies indicates that inter-level and inter-temporal dependencies are inherent to the migration process and give it a strong internal momentum. The dynamic interplay between network growth and individual migration labor, migration remittances, and local income distributions all create powerful feedback mechanisms that lead to the cumulative causation of migration. These mechanisms are reinforced and shaped by macrolevel relationships within the larger political economy.
The dynamics following charge transfer to solvent from iodide to a water cluster are studied using time-resolved photoelectron imaging of I-(H2O)n and I-(D2O)n clusters with n< or =28. The results show spontaneous conversion, on a time scale of approximately 1 ps, from water cluster anions with surface-bound electrons to structures in which the excess electron is more strongly bound and possibly more internalized within the solvent network. The resulting dynamics provide valuable insight into the electron solvation dynamics in water clusters and the relative stabilities between recently observed isomers of water cluster anions.
We use series expansions to study dynamics of equilibrium and nonequilibrium systems on networks. This analytical method enables us to include detailed nonuniversal effects of the network structure. We show that even low order calculations produce results which compare accurately to numerical simulation, while the results can be systematically improved. We show that certain commonly accepted analytical results for the critical point on networks with a broad degree distribution need to be modified in certain cases due to disassortativity; the present method is able to take into account the assortativity at sufficiently high order, while previous results correspond to leading and second order approximations in this method. Finally, we apply this method to real-world data.
In this letter, we discuss the dynamics of the Cohen-Grossberg neural networks. We provide a new and relaxed set of sufficient conditions for the Cohen-Grossberg networks to be absolutely stable and exponentially stable globally. We also provide an estimate of the rate of convergence.
Pollen tubes expand by tip growth and extend directionally toward the ovule to deliver sperms during pollination. They provide an excellent model system for the study of cell polarity control and tip growth, because they grow into uniformly shaped cylindrical cells in culture. Mechanisms underlying tip growth are poorly understood in pollen tubes. It has been demonstrated that ROP1, a pollen-specific member of the plant-specific Rop subfamily of Rho GTPases, is a central regulator of pollen tube tip growth. Recent studies in pollen from Arabidopsis and other species have revealed a ROP-mediated signalling network that is localized to the apical PM region of pollen tubes. The results provide evidence that the localization of this signalling network establishes the site for tip growth and the localized activation of this signalling network regulates the dynamics of tip F-actin. These results have shown that the ROP1-mediated dynamics of tip F-actin is a key cellular mechanism behind tip growth in pollen tubes. Current understanding of the molecular basis for the regulation of the tip actin dynamics will be discussed.