Jamming densities of random sequential adsorption on d-dimensional cubic lattices.

The rate of convergence of the jamming densities to their asymptotic high-dimensional tree approximation is studied, for two types of random sequential adsorption (RSA) processes on a d-dimensional cubic lattice. The first RSA process has an exclusion shell around a particle of nearest neighbors in all d dimensions (N1 model). In the second process the exclusion shell consists of a d-dimensional hypercube with length k=2 around a particle (N2 model). For the N1 model the deviation of the jamming density ρ_{r}(d) from its asymptotic high d value ρ_{asy}(d)=ln(1+2d)/2d vanishes as [ln(1+2d)/2d]^{3.41}. In addition, it has been shown that the coefficients a_{n}(d) of the short-time expansion of the occupation density of this model (at least up to n=6) are given for all d by a finite correction sum of order (n-2) in 1/d to their asymptotic high d limit. The convergence rate of the jamming densities of the N2 model to their high d limits ρ_{asy}(d)=dln3/3^{d} is slow. For 2≤d≤4 the generalized Palasti approximation provides by far a better approximation. For higher d values the jamming densities converge monotonically to the above asymptotic limits, and their decay with d is clearly faster than the decay as (0.432332...)^{d} predicted by the generalized Palasti approximation.

Commuting in metapopulation epidemic modeling.

The COVID-19 pandemic led authorities all over the world to imposing travel restrictions both on a national and on an international scale. Understanding the effect of such restrictions requires analysis of the role of commuting and calls for a metapopulation modeling that incorporates both local, intra-community infection and population exchange between different locations. Standard metapopulation models are formulated as markovian processes, and as such they do not label individuals according to their original location. However, commuting from home to work and backwards (reverse commuting) is the main pattern of transportation. Thus, it is important to be able to accurately model the effect of commuting on epidemic spreading. In this study we develop a methodology for modeling bidirectional commuting of individuals, without keeping track of each individual separately and with no need of proliferation of number of compartments beyond those defined by the epidemiologic model. We demonstrate the method using a city map of the state of Israel. The presented algorithm does not require any special computation resources and it may serve as a basis for intervention strategy examination in various levels of complication and resolution. We show how to incorporate an epidemiological model into a metapopulation commuting scheme while preserving the internal logic of the epidemiological modeling. The method is general and independent on the details of the epidemiological model under consideration.

Decoding information in cell shape.

Shape is an indicator of cell health. But how is the information in shape decoded? We hypothesize that decoding occurs by modulation of signaling through changes in plasma membrane curvature. Using analytical approaches and numerical simulations, we studied how elongation of cell shape affects plasma membrane signaling. Mathematical analyses reveal transient accumulation of activated receptors at regions of higher curvature with increasing cell eccentricity. This distribution of activated receptors is periodic, following the Mathieu function, and it arises from local imbalance between reaction and diffusion of soluble ligands and receptors in the plane of the membrane. Numerical simulations show that transient microdomains of activated receptors amplify signals to downstream protein kinases. For growth factor receptor pathways, increasing cell eccentricity elevates the levels of activated cytoplasmic Src and nuclear MAPK1,2. These predictions were experimentally validated by changing cellular eccentricity, showing that shape is a locus of retrievable information storage in cells.

Signaling network triggers and membrane physical properties control the actin cytoskeleton-driven isotropic phase of cell spreading.

Cell spreading is regulated by signaling from the integrin receptors that activate intracellular signaling pathways to control actin filament regulatory proteins. We developed a hybrid model of whole-cell spreading in which we modeled the integrin signaling network as ordinary differential equations in multiple compartments, and cell spreading as a three-dimensional stochastic model. The computed activity of the signaling network, represented as time-dependent activity levels of the actin filament regulatory proteins, is used to drive the filament dynamics. We analyzed the hybrid model to understand the role of signaling during the isotropic phase of fibroblasts spreading on fibronectin-coated surfaces. Simulations showed that the isotropic phase of spreading depends on integrin signaling to initiate spreading but not to maintain the spreading dynamics. Simulations predicted that signal flow in the absence of Cdc42 or WASP would reduce the spreading rate but would not affect the shape evolution of the spreading cell. These predictions were verified experimentally. Computational analyses showed that the rate of spreading and the evolution of cell shape are largely controlled by the membrane surface load and membrane bending rigidity, and changing information flow through the integrin signaling network has little effect. Overall, the plasma membrane acts as a damper such that only ∼5% of the actin dynamics capability is needed for isotropic spreading. Thus, the biophysical properties of the plasma membrane can condense varying levels of signaling network activities into a single cohesive macroscopic cellular behavior.

A systems approach to bone pathophysiology.

With evolving interest in multiscalar biological systems one could assume that reductionist approaches may not fully describe biological complexity. Instead, tools such as mathematical modeling, network analysis, and other multiplexed clinical- and research-oriented tests enable rapid analyses of high-throughput data parsed at the genomic, proteomic, metabolomic, and physiomic levels. A physiomic-level approach allows for recursive horizontal and vertical integration of subsystem coupling across and within spatiotemporal scales. Additionally, this methodology recognizes previously ignored subsystems and the strong, nonintuitively obvious and indirect connections among physiological events that potentially account for the uncertainties in medicine. In this review, we flip the reductionist research paradigm and review the concept of systems biology and its applications to bone pathophysiology. Specifically, a bone-centric physiome model is presented that incorporates systemic-level processes with their respective therapeutic implications.

Dimensional reduction of the master equation for stochastic chemical networks: The reduced-multiplane method.

Chemical reaction networks which exhibit strong fluctuations are common in microscopic systems in which reactants appear in low copy numbers. The analysis of these networks requires stochastic methods, which come in two forms: direct integration of the master equation and Monte Carlo simulations. The master equation becomes infeasible for large networks because the number of equations increases exponentially with the number of reactive species. Monte Carlo methods, which are more efficient in integrating over the exponentially large phase space, also become impractical due to the large amounts of noisy data that need to be stored and analyzed. The recently introduced multiplane method [A. Lipshtat and O. Biham, Phys. Rev. Lett. 93, 170601 (2004)] is an efficient framework for the stochastic analysis of large reaction networks. It is a dimensional reduction method, based on the master equation, which provides a dramatic reduction in the number of equations without compromising the accuracy of the results. The reduction is achieved by breaking the network into a set of maximal fully connected subnetworks (maximal cliques). A separate master equation is written for the reduced probability distribution associated with each clique, with suitable coupling terms between them. This method is highly efficient in the case of sparse networks, in which the maximal cliques tend to be small. However, in dense networks some of the cliques may be rather large and the dimensional reduction is not as effective. Furthermore, the derivation of the multiplane equations from the master equation is tedious and difficult. Here we present the reduced-multiplane method in which the maximal cliques are broken down to the fundamental two-vertex cliques. The number of equations is further reduced, making the method highly efficient even for dense networks. Moreover, the equations take a simpler form, which can be easily constructed using a diagrammatic procedure, for any desired network architecture. It is shown that the method provides accurate results for the population sizes of the reactive species and their reaction rates.

Mechanisms controlling cell size and shape during isotropic cell spreading.

Cell motility is important for many developmental and physiological processes. Motility arises from interactions between physical forces at the cell surface membrane and the biochemical reactions that control the actin cytoskeleton. To computationally analyze how these factors interact, we built a three-dimensional stochastic model of the experimentally observed isotropic spreading phase of mammalian fibroblasts. The multiscale model is composed at the microscopic levels of three actin filament remodeling reactions that occur stochastically in space and time, and these reactions are regulated by the membrane forces due to membrane surface resistance (load) and bending energy. The macroscopic output of the model (isotropic spreading of the whole cell) occurs due to the movement of the leading edge, resulting solely from membrane force-constrained biochemical reactions. Numerical simulations indicate that our model qualitatively captures the experimentally observed isotropic cell-spreading behavior. The model predicts that increasing the capping protein concentration will lead to a proportional decrease in the spread radius of the cell. This prediction was experimentally confirmed with the use of Cytochalasin D, which caps growing actin filaments. Similarly, the predicted effect of actin monomer concentration was experimentally verified by using Latrunculin A. Parameter variation analyses indicate that membrane physical forces control cell shape during spreading, whereas the biochemical reactions underlying actin cytoskeleton dynamics control cell size (i.e., the rate of spreading). Thus, during cell spreading, a balance between the biochemical and biophysical properties determines the cell size and shape. These mechanistic insights can provide a format for understanding how force and chemical signals together modulate cellular regulatory networks to control cell motility.

Design of versatile biochemical switches that respond to amplitude, duration, and spatial cues.

Cells often mount ultrasensitive (switch-like) responses to stimuli. The design principles underlying many switches are not known. We computationally studied the switching behavior of GTPases, and found that this first-order kinetic system can show ultrasensitivity. Analytical solutions indicate that ultrasensitive first-order reactions can yield switches that respond to signal amplitude or duration. The three-component GTPase system is analogous to the physical fermion gas. This analogy allows for an analytical understanding of the functional capabilities of first-order ultrasensitive systems. Experiments show amplitude- and time-dependent Rap GTPase switching in response to Cannabinoid-1 receptor signal. This first-order switch arises from relative reaction rates and the concentrations ratios of the activator and deactivator of Rap. First-order ultrasensitivity is applicable to many systems where threshold for transition between states is dependent on the duration, amplitude, or location of a distal signal. We conclude that the emergence of ultrasensitivity from coupled first-order reactions provides a versatile mechanism for the design of biochemical switches.

Effect of desired speed variability on highway traffic flow.

Traffic flow is a function of many natural, environmental, and human factors. Not only that weather and road condition can vary, but drivers' decisions and policies also can affect the flow. Here we analyze the effect of distribution of desired speeds. We show that a broader distribution can reduce the flow efficiency and increase congestions. Since different drivers react differently to changes in weather or road conditions, such a change leads to a change in desired speed distribution as well. As a result, nonintuitive changes in traffic flow may occur. Besides providing insight and analyzing the underlying mechanism of a collective phenomenon, this example sheds light on a fundamental aspect of computational modeling. Although "mean-field" models that deal with average values only and ignore variability are simpler and easier to analyze, they can very easily turn into oversimplifications and miss relevant qualitative phenomena.

Specification of spatial relationships in directed graphs of cell signaling networks.

Graph theory provides a useful and powerful tool for the analysis of cellular signaling networks. Intracellular components such as cytoplasmic signaling proteins, transcription factors, and genes are connected by links, representing various types of chemical interactions that result in functional consequences. However, these graphs lack important information regarding the spatial distribution of cellular components. The ability of two cellular components to interact depends not only on their mutual chemical affinity but also on colocalization to the same subcellular region. Localization of components is often used as a regulatory mechanism to achieve specific effects in response to different receptor signals. Here we describe an approach for incorporating spatial distribution into graphs and for the development of mixed graphs where links are specified by mutual chemical affinity as well as colocalization. We suggest that such mixed graphs will provide more accurate descriptions of functional cellular networks and their regulatory capabilities and aid in the development of large-scale predictive models of cellular behavior.

Functions of bifans in context of multiple regulatory motifs in signaling networks.

Representation of intracellular signaling networks as directed graphs allows for the identification of regulatory motifs. Regulatory motifs are groups of nodes with the same connectivity structure, capable of processing information. The bifan motif, made of two source nodes directly crossregulating two target nodes, is an overrepresented motif in a mammalian cell signaling network and in transcriptional networks. One example of a bifan is the two MAP-kinases, p38, and JNK that phosphorylate and activate the two transcription factors ATF2 and Elk-1. We have used a system of coupled ordinary differential equations to analyze the regulatory capability of this bifan motif by itself, and when it interacts with other motifs such as positive and negative feedback loops. Our results indicate that bifans provide temporal regulation of signal propagation and act as signal sorters, filters, and synchronizers. Bifans that have OR gate configurations show rapid responses whereas AND gate bifans can introduce delays and allow prolongation of signal outputs. Bifans that have AND gates can filter noisy signal inputs. The p38/JNK-ATF2/Elk-1bifan synchronizes the output of activated transcription factors. Synchronization is a robust property of bifans and is exhibited even when the bifan is adjacent to a positive feedback loop. The presence of the bifan promotes the transcription and translation of the dual specificity protein phosphatase MKP-1 that inhibits p38 and JNK thus enabling a negative feedback loop. These results indicate that bifan motifs in cell signaling networks can contribute to signal processing capability both intrinsically and by enabling the functions of other regulatory motifs.

"All possible steps" approach to the accelerated use of Gillespie's algorithm.

Many physical and biological processes are stochastic in nature. Computational models and simulations of such processes are a mathematical and computational challenge. The basic stochastic simulation algorithm was published by Gillespie about three decades ago [J. Phys. Chem. 81, 2340 (1977)]. Since then, intensive work has been done to make the algorithm more efficient in terms of running time. All accelerated versions of the algorithm are aimed at minimizing the running time required to produce a stochastic trajectory in state space. In these simulations, a necessary condition for reliable statistics is averaging over a large number of simulations. In this study the author presents a new accelerating approach which does not alter the stochastic algorithm, but reduces the number of required runs. By analysis of collected data the author demonstrates high precision levels with fewer simulations. Moreover, the suggested approach provides a good estimation of statistical error, which may serve as a tool for determining the number of required runs.

Stochastic simulations of genetic switch systems.

Genetic switch systems with mutual repression of two transcription factors are studied using deterministic methods (rate equations) and stochastic methods (the master equation and Monte Carlo simulations). These systems exhibit bistability, namely two stable states such that spontaneous transitions between them are rare. Induced transitions may take place as a result of an external stimulus. We study several variants of the genetic switch and examine the effects of cooperative binding, exclusive binding, protein-protein interactions, and degradation of bound repressors. We identify the range of parameters in which bistability takes place, enabling the system to function as a switch. Numerous studies have concluded that cooperative binding is a necessary condition for the emergence of bistability in these systems. We show that a suitable combination of network structure and stochastic effects gives rise to bistability even without cooperative binding. The average time between spontaneous transitions is evaluated as a function of the biological parameters.

Topology of resultant networks shaped by evolutionary pressure.

Understanding the topology of complex systems abstracted to networks is important for unraveling their functional capabilities. Many such networks follow the small-world and scale-free regimes. Several models of artificially growing networks lead to this observed network topology. Most previously proposed models for growing networks, such as rich-get-richer and duplication-divergence, produce realistic network topologies but do not consider the effects of exogenous forces such as optimization for adaptation in shaping network topology. It is likely that such forces have shaped complex systems throughout their evolution. To develop further insights into possible mechanisms that shape networks, a model that uses several previously proposed network growth algorithms was developed to grow networks that adapt under exogenous stress. A decision tree problem was used to generate a complex Boolean function. Growing networks were required to adapt to correctly decode this function using an evolutionary selection process. Under this growth regimen all growing network models are similarly adaptable. The newly added nodes tend to cluster into pathways emanating from few inputs, regardless of the growth algorithm. Distribution of redundant pathways from inputs to the output follow a power-law function with a scaling exponent (approximately 1.3). Similar distribution of redundant pathways was observed from inputs in a cell signaling network and an air traffic control network. A flat distribution of redundant pathways from inputs was observed in growing networks that do not attempt to adapt. This analysis provides initial insights into distribution of pathways in naturally evolving complex systems that have defined input-output relationships.

Genetic toggle switch without cooperative binding.

Genetic switch systems with mutual repression of two transcription factors are studied using deterministic and stochastic methods. Numerous studies have concluded that cooperative binding is a necessary condition for the emergence of bistability in these systems. Here we show that, for a range of biologically relevant conditions, a suitable combination of network structure and stochastic effects gives rise to bistability even without cooperative binding.

Modeling of negative autoregulated genetic networks in single cells.

We discuss recent developments in the modeling of negative autoregulated genetic networks. In particular, we consider the temporal evolution of the population of mRNA and proteins in simple networks using rate equations. In the limit of low copy numbers, fluctuation effects become significant and more adequate modeling is then achieved using the master equation formalism. The analogy between regulatory gene networks and chemical reaction networks on dust grains in the interstellar medium is discussed. The analysis and simulation of complex reaction networks are also considered.

Reaction kinetics in a tight spot.

The standard analysis of reaction networks based on deterministic rate equations fails in confined geometries, commonly encountered in fields such as astrochemistry, thin-film growth and cell biology. In these systems the small reactant population implies anomalous behavior of reaction rates, which can be accounted for only by following the full distribution of reactant numbers.

Efficient simulations of gas-grain chemistry in interstellar clouds.

Chemical reactions on dust grains are of crucial importance in interstellar chemistry because they produce molecular hydrogen and various organic molecules. Because of the submicron size of the grains and the low flux, the surface populations of reactive species are small and strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations grows exponentially with the number of reactive species, severely limiting its feasibility. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in models of interstellar chemistry.

Scaling anomalies in the coarsening dynamics of fractal viscous fingering patterns.

We analyze a recent experiment of Sharon et al. (2003) on the coarsening, due to surface tension, of fractal viscous fingering patterns (FVFPs) grown in a radial Hele-Shaw cell. We argue that an unforced Hele-Shaw model, a natural model for that experiment, belongs to the same universality class as model B of phase ordering. Two series of numerical simulations with model B are performed, with the FVFPs grown in the experiment and with diffusion limited aggregates as the initial conditions. We observed Lifshitz-Slyozov scaling t(1/3) at intermediate distances and slow convergence to this scaling at small distances. Dynamic scale invariance breaks down at large distances.