Information Content,Complexity, and Uncertainty in Material Flow Analysis

Material Flow Analysis (MFA) is a useful method for modeling, understanding, and optimizing sociometabolic systems. Among others, MFAs can be distinguished by two general system properties: First, they differ in their complexity, which depends on system structure and size. Second, they differ in their inherent uncertainty, which arises from limited data quality. In this article, uncertainty and complexity in MFA are approached from a systems perspective and expressed as formally linked phenomena. MFAs are, in a graph‐theoretical sense, understood as networks. The uncertainty and complexity of these networks are computed by use of information measures from the field of theoretical ecology. The size of a system is formalized as a function of its number of flows. It defines the potential information content of an MFA system and holds as a reference against which complexity and uncertainty are gauged. Integrating data quality measures, the uncertainty of an MFA before and after balancing is determined. The actual information content of an MFA is measured by relating its uncertainty to its potential information content. The complexity of a system is expressed based on the configuration of each individual flow in relation to its neighboring flows. The proposed metrics enable different material flow systems to be compared to one another and the role of individual flows within a system to be assessed. They provide information useful for the design of MFAs and for the communication of MFA results. For exemplification, the regional MFAs of aluminum and plastics in Austria are analyzed in this article.     

Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics

The epidemic spread of infectious diseases is ubiquitous and often has a considerable impact on public health and economic wealth. The large variability in the spatio-temporal patterns of epidemics prohibits simple interventions and requires a detailed analysis of each epidemic with respect to its infectious agent and the corresponding routes of transmission. To facilitate this analysis, we introduce a mathematical framework which links epidemic patterns to the topology and dynamics of the underlying transmission network. The evolution, both in disease prevalence and transmission network topology, is derived from a closed set of partial differential equations for infections without allowing for recovery. The predictions are in excellent agreement with complementarily conducted agent-based simulations. The capacity of this new method is demonstrated in several case studies on HIV epidemics in synthetic populations: it allows us to monitor the evolution of contact behavior among healthy and infected individuals and the contributions of different disease stages to the spreading of the epidemic. This gives both direction to and a test bed for targeted intervention strategies for epidemic control. In conclusion, this mathematical framework provides a capable toolbox for the analysis of epidemics from first principles. This allows for fast, in silico modeling--and manipulation--of epidemics and is especially powerful if complemented with adequate empirical data for parameterization.     

Interactions between Seagrass Complexity,Hydrodynamic Flow and Biomixing Alter Food Availability for Associated Filter-Feeding Organisms

Seagrass shoots interact with hydrodynamic forces and thereby a positively or negatively influence the survival of associated species. The modification of these forces indirectly alters the physical transport and flux of edible particles within seagrass meadows, which will influence the growth and survivorship of associated filter-feeding organisms. The present work contributes to gaining insight into the mechanisms controlling the availability of resources for filter feeders inhabiting seagrass canopies, both from physical (influenced by seagrass density and patchiness) and biological (regulated by filter feeder density) perspectives. A factorial experiment was conducted in a large racetrack flume, which combined changes in hydrodynamic conditions, chlorophyll a concentration in the water and food intake rate (FIR) in a model active filter-feeding organism (the cockle). Results showed that seagrass density and patchiness modified both hydrodynamic forces and availability of resources for filter feeders. Chlorophyll a water content decreased to 50% of the initial value when densities of both seagrass shoots and cockles were high. Also, filter feeder density controlled resource availability within seagrass patches, depending on its spatial position within the racetrack flume. Under high density of filter-feeding organisms, chlorophyll a levels were lower between patches. This suggests that the pumping activity of cockles (i.e. biomixing) is an emergent key factor affecting both resource availability and FIR for filter feeders in dense canopies. Applying our results to natural conditions, we suggest the existence of a direct correlation between habitat complexity (i.e. shoot density and degree of patchiness) and filter feeders density. Fragmented and low-density patches seem to offer both greater protection from hydrodynamic forces and higher resource availability. In denser patches, however, resources are allocated mostly within the canopy, which would benefit filter feeders if they occurred at low densities, but would be limiting when filter feeder were at high densities.     

Relating Diseases by Integrating Gene Associations and Information Flow through Protein Interaction Network

Identifying similar diseases could potentially provide deeper understanding of their underlying causes, and may even hint at possible treatments. For this purpose, it is necessary to have a similarity measure that reflects the underpinning molecular interactions and biological pathways. We have thus devised a network-based measure that can partially fulfill this goal. Our method assigns weights to all proteins (and consequently their encoding genes) by using information flow from a disease to the protein interaction network and back. Similarity between two diseases is then defined as the cosine of the angle between their corresponding weight vectors. The proposed method also provides a way to suggest disease-pathway associations by using the weights assigned to the genes to perform enrichment analysis for each disease. By calculating pairwise similarities between 2534 diseases, we show that our disease similarity measure is strongly correlated with the probability of finding the diseases in the same disease family and, more importantly, sharing biological pathways. We have also compared our results to those of MimMiner, a text-mining method that assigns pairwise similarity scores to diseases. We find the results of the two methods to be complementary. It is also shown that clustering diseases based on their similarities and performing enrichment analysis for the cluster centers significantly increases the term association rate, suggesting that the cluster centers are better representatives for biological pathways than the diseases themselves. This lends support to the view that our similarity measure is a good indicator of relatedness of biological processes involved in causing the diseases. Although not needed for understanding this paper, the raw results are available for download for further study at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/DiseaseRelations/.     

Dynamics of Nephron-Vascular Network

The paper presents a modeling study of the spatial dynamics of a nephro-vascular network consisting of individual nephrons connected via a tree-like vascular branching structure. We focus on the effects of nonlinear mechanisms that are?responsible for the formation of synchronous patterns in order to learn about processes not directly amenable to experimentation. We demonstrate that: (i) the nearest nephrons are synchronized in-phase due to a vascular propagated electrical coupling, (ii) the next few branching levels display a formation of phase-shifted patterns due to hemodynamic coupling and mode elimination, and (iii) distantly located areas show asynchronous behavior or, if all nephrons and branches are perfectly identical, an infinitely long transient behavior. These results contribute to the understanding of mechanisms responsible for the highly dynamic and limited synchronization observed among groups of nephrons despite of the fairly strong interaction between the individual units.     

Relationships between Cerebral Blood Flow Dynamics and Bioelectric Activity of the Human Brain in Experimental Acute Hypoxia

The relationships between the parameters of oxygen content in the body (hemoglobin saturation with oxygen and trancutaneous oxygen tension), central hemodynamics (cardiac output), and cerebral hemodynamics (cerebral blood flow rate) were studied during a hypoxic test (inhalation of an oxygen–nitrogen mixture containing 8% oxygen for 15 min). Special attention was paid to the relationships between the dynamics of cerebral blood flow and cerebral bioelectric activity measured by EEG parameters. It was demonstrated that the trancutaneous oxygen tension decreased to a greater extent than the hemoglobin saturation with oxygen and the cerebral blood flow increased to a greater extent than the cardiac output. The increase in cerebral blood flow and the increase in the indices and power of and EEG waves in the course of hypoxia were strongly positively correlated with each other in most subjects. However, if these parameters were considered in the series of subjects, the degree of the increase in the indices and power of and waves in different subjects was negatively correlated with the increase in the cerebral blood flow. The results are explained in terms of redistribution of blood flow in the body to provide a better oxygen supply to the brain and optimization of the ratios between the cerebral oxygen consumption and the functional load on the system of oxygen supply.     

Cell Shape Dynamics Reveal Balance of Elasticity and Contractility in Peripheral Arcs

The mechanical interaction between adherent cells and their substrate relies on the formation of adhesion sites and on the stabilization of contractile acto-myosin bundles, or stress fibers. The shape of the cell and the orientation of these fibers can be controlled by adhesive patterning. On nonadhesive gaps, fibroblasts develop thick peripheral stress fibers, with a concave curvature. The radius of curvature of these arcs results from the balance of the line tension in the arc and of the surface tension in the cell bulk. However, the nature of these forces, and in particular the contribution of myosin-dependent contractility, is not clear. To get insight into the force balance, we inhibit myosin activity and simultaneously monitor the dynamics of peripheral arc radii and traction forces. We use these measurements to estimate line and surface tension. We found that myosin inhibition led to a decrease in the traction forces and an increase in arc radius, indicating that both line tension and surface tension dropped, but the line tension decreased to a lesser extent than surface tension. These results suggest that myosin-independent force contributes to tension in the peripheral arcs. We propose a simple physical model in which the peripheral arc line tension is due to the combination of myosin II contractility and a passive elastic component, while surface tension is largely due to active contractility. Numerical solutions of this model reproduce well the experimental data and allow estimation of the contributions of elasticity and contractility to the arc line tension.     

Chaotic Dynamics of Neuromuscular System Parameters and the Problems of the Evolution of Complexity

The evolution rate v(t) varies among diverse biosystems, but a general theory can be formulated when the dynamics of the biosystem stater x = x(t) = (x₁, x₂, x _m ) ^T is considered in the m-dimensional space of states. A mathematical approach is proposed for evaluating such processes and describes the processes in terms of particular chaos of the statistical distribution functions f(x). In the case of complex multicomponent systems with a high dimension number m (m ?1) of the phase space of states, we propose using pairwise comparison matrices of samples x(t) when homeostasis is constant and calculating the parameters of quasiattractors. The Glensdorff–Prigogine thermodynamic approach to estimating evolution is inefficient in assessing the third-type systems, while it is applicable and the Prigogine theorem works at the level of molecular systems. Alterations in the state of the human neuromuscular system were found to lead to chaotic changes in the statistical functions f(x) in tremor recording samples, while quasiattractor parameters demonstrate a certain regularity.     

Impact of Noise on Molecular Network Inference

Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a) common-effect, (b) three-chain, and (c) coherent type-I feed-forward loop (FFL) are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS) and Incremental Association Markov Blanket (IAMB) that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the results presented clearly elucidate their non-trivial impact on the conclusions that in turn can challenge the biological significance of the findings. The analytical treatment and arguments presented are generic and not restricted to molecular data sets.     

A Trade-Off between Sample Complexity and Computational Complexity in Learning Boolean Networks from Time-Series Data

A key problem in molecular biology is to infer regulatory relationships between genes from expression data. This paper studies a simplified model of such inference problems in which one or more Boolean variables, modeling, for example, the expression levels of genes, each depend deterministically on a small but unknown subset of a large number of Boolean input variables. Our model assumes that the expression data comprises a time series, in which successive samples may be correlated. We provide bounds on the expected amount of data needed to infer the correct relationships between output and input variables. These bounds improve and generalize previous results for Boolean network inference and continuous-time switching network inference. Although the computational problem is intractable in general, we describe a fixed-parameter tractable algorithm that is guaranteed to provide at least a partial solution to the problem. Most interestingly, both the sample complexity and computational complexity of the problem depend on the strength of correlations between successive samples in the time series but in opposing ways. Uncorrelated samples minimize the total number of samples needed while maximizing computational complexity; a strong correlation between successive samples has the opposite effect. This observation has implications for the design of experiments for measuring gene expression.     

Dynamics of Nucleotides in VDAC Channels: Structure-Specific Noise Generation

Nucleotide penetration into the voltage-dependent mitochondrial ion channel (VDAC) reduces single-channel conductance and generates excess current noise through a fully open channel. VDAC channels were reconstituted into planar phospholipid membranes bathed in 1.0 M NaCl. At a given nucleotide concentration, the average decrease in small-ion channel conductance induced by mononucleotides ATP, ADP, AMP, and UTP and dinucleotides β- and α-NADH, NAD, and NADPH are very close. However, the excess current noise is about seven times higher in the presence of NADPH than in the presence of ATP and is about 40 times higher than in the presence of UTP. The nucleotide-generated low-frequency noise obeys the following sequence: β-NADPH > β-NADH = α-NADH > ATP > ADP > β-NAD ≥ AMP > UTP. Measurements of bulk-phase diffusion coefficients and of the effective charge of the nucleotides in 1.0 M NaCl suggest that differences in size and charge cannot be the major factors responsible for the ability to generate current noise. Thus, although the ability of nucleotides to partition into the channel’s pore, as assessed by the reduction in conductance, is very similar, the ability to generate current noise involves a detailed recognition of the three-dimensional structure of the nucleotide by the VDAC channel. A possible mechanism for this selectivity is two noise-generating processes operating in parallel.     

Kinetic Modeling of Mitochondrial-Reticular Network Dynamics

Biophysics - This study involves the mitochondrial-reticular network, which is functionally integrated into the processes that provide energy to all systems of an organism as an open, dynamic,...     

Molecular Dynamics Simulations of Molecules in Uniform Flow

Flow at the molecular level induces shear-induced unfolding of single proteins and can drive their assembly, the mechanisms of which are not completely understood. To be able to analyze the role of flow on molecules, we present uniform-flow molecular dynamics simulations at atomic level. The pull module of the GRoningen MAchine for Chemical Simulations package was extended to be able to force-group atoms within a defined layer of the simulation box. Application of this external enforcement to explicit water molecules, together with the coupling to a thermostat, led to a uniform terminal velocity of the solvent water molecules. We monitored the density of the whole system to establish the conditions under which the simulated flow is well-behaved. A maximal velocity of 1.3 m/s can be generated if a pull slice of 8 nm is used, and high velocities would require larger pull slices to still maintain a stable density. As expected, the target velocity increases linearly with the total external force applied. Finally, we suggest an appropriate setup to stretch a protein by uniform flow, in which protein extensions depend on the flow conditions. Our implementation provides an efficient computational tool to investigate the effect of the flow at the molecular level.     

Information Properties of Naturally-Occurring Proteins: Fourier Analysis and Complexity Phase Plots

In previous work from this lab, the information in natural proteins was investigated with Ribonuclease A (RNase A) serving as the source. The signature traits were investigated at three structure levels: primary through tertiary. The present paper travels further by charting the primary structure information of about half a million molecules. This was feasible given abundant sequence archives for both living and viral systems. Notably, a method is presented for evaluating primary structure information, based on Fourier analysis and spectral complexity. Significantly, the results show certain complexity traits to be universal for living sources. Viruses, by contrast, encode protein collections which are case-specific and complexity-divergent. The results have ramifications for discriminating collections on the basis of sequence information. This discrimination offers new strategies for selecting drug targets.