RNA aptamers are relatively short nucleic acid sequences that bind targets with high affinity, an... more RNA aptamers are relatively short nucleic acid sequences that bind targets with high affinity, and when combined with a riboswitch that initiates translation of a fluorescent reporter protein, can be used as a biosensor for chemical detection in various types of media. These processes span target binding at the molecular scale to fluorescence detection at the macroscale, which involves a number of intermediate rate-limiting physical (e.g., molecular conformation change) and biochemical changes (e.g., reaction velocity), which together complicate assay design. Here we describe a mathematical model developed to aid environmental detection of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) using the DsRed fluorescent reporter protein, but is general enough to potentially predict fluorescence from a broad range of water-soluble chemicals given the values of just a few kinetic rate constants as input. If we expose a riboswitch test population of Escherichia coli bacteria to a chemical diss...
Evolved biological network topologies may resist perturbances to allow for more robust informatio... more Evolved biological network topologies may resist perturbances to allow for more robust information transport across larger networks in which their network motifs may play a complex role. Although the abundance of individual motifs correlate with some metrics of biological robustness, the extent to which redundant regulatory interactions affect motif connectivity and how this connectivity affects robustness is unknown. To address this problem, we applied machine learning based regression modeling to evaluate how feed-forward loops interlinked by crosstalk altered information transport across a network in terms of packets successfully routed over networks of noisy channels via NS-2 simulation. We developed 233 topological features which distinctly account for the opportunities in which two feed-forward loops may exhibit crosstalk. Random forest regression modeling was used to infer significant features from this modest configuration space. The coefficient of determination was used as ...
Modeling, Methodologies and Tools for Molecular and Nano-scale Communications, 2017
A large section of the work in computational models of biological systems is based on classical c... more A large section of the work in computational models of biological systems is based on classical chemical kinetic (CCK) formalism based on a set of ordinary differential equations (ODE), also known as reaction rate equations or mass action kinetics [13]. Representing a homogeneous biological system as a set of biochemical reactions, the temporal dynamics of the molecular species is studied in the continuous-deterministic domain. While continuous-deterministic reaction models are
The transcriptional network determines a cell’s internal state by regulating protein expression i... more The transcriptional network determines a cell’s internal state by regulating protein expression in response to changes in the local environment. Due to the interconnected nature of this network, information encoded in the abundance of various proteins will often propagate across chains of noisy intermediate signaling events. The data-processing inequality (DPI) leads us to expect that this intracellular game of “telephone” should degrade this type of signal, with longer chains losing successively more information to noise. However, a previous modeling effort predicted that because the steps of these signaling cascades do not truly represent independent stages of data processing, the limits of the DPI could seemingly be surpassed, and the amount of transmitted information could actually increase with chain length. What that work did not examine was whether this regime of growing information transmission was attainable by a signaling system constrained by the mechanistic details of mo...
Large scale biological responses are inherently uncertain, in part as a consequence of noisy syst... more Large scale biological responses are inherently uncertain, in part as a consequence of noisy systems that do not respond deterministically to perturbations and measurement errors inherent to technological limitations. As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive (multiscale stochastic models), fail to capture the effects of noise across a system (chemical kinetic models), or fail to provide sufficient biological fidelity because of broad simplifying assumptions (stochastic differential equations). We use a new approach to modeling multiscale stationary biological processes that embraces the noise found in experimental data to provide estimates of the parameter uncertainties and the potential mis-specification of models. Our approach models the mean stationary response at each biological level given a particular expected response relationship, capturing variation around this mean using conditional Monte Carlo sampling that is statistically consistent with training data. A conditional probability distribution associated with a biological response can be reconstructed using this method for a subset of input values, which overcomes the parameter identification problem. Our approach could be applied in addition to dynamical modeling methods (see above) to predict uncertain biological responses over experimental time scales. To illustrate this point, we apply the approach to a test case in which we model the variation associated with measurements at multiple scales of organization across a reproduction-related Adverse Outcome Pathway described for teleosts.
A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of orga... more A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals. We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals. PSO reveals a relationship between chemical uptake and decomposition parameter values that predicts chemical-specific BCF values with moderate statistical agreement to a limited yet diverse chemical dataset, and all without a need to retrain the model to new data. The presented model requires only the oc...
Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to tr... more Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (≈20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically differe...
Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to i... more Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to in vivo extrapolation (IVIVE) using a top-down, compartmental approach, favoring architectural simplicity over physiological fidelity despite the lack of general guidelines relating model design to dynamical predictions. Here we explore the impact of design choice (high vs. low fidelity) on chemical distribution throughout an animal's organ system. We contrast transient dynamics and steady states of three previously proposed PBTK models of varying complexity in response to chemical exposure. The steady states for each model were determined analytically to predict exposure conditions from tissue measurements. Steady state whole-body concentrations differ between models, despite identical environmental conditions, which originates from varying levels of physiological fidelity captured by the models. These differences affect the relative predictive accuracy of the inverted models used in...
A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computa... more A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose-response and time course predictions that can support regulatory decision-making. Herein we describe several facets of qAOPs, including (a) motivation for development, (b) technical considerations, (c) evaluation of confidence, and (d) potential applications. The qAOP used as an illustrative example for these points describes the linkage between inhibition of cytochrome P450 19A aromatase (the MIE) and population-level decreases in the fathead minnow (FHM; Pimephales promelas). The qAOP consists of three linked computational models for: (a) the hypothalamic-pituitary-gonadal axis in female FHMs, where aromatase inhibition decreases the conversion of testosterone to 17β estradiol (E2), thereby reducing E2-dependent vitellogenin (VTG; egg yol...
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016
Given an aftermath of a cascade in the network, i.e. a set VI of "infected" nodes after an epidem... more Given an aftermath of a cascade in the network, i.e. a set VI of "infected" nodes after an epidemic outbreak or a propagation of rumors/worms/viruses, how can we infer the sources of the cascade? Answering this challenging question is critical for computer forensic, vulnerability analysis, and risk management. Despite recent interest towards this problem, most of existing works focus only on single source detection or simple network topologies, e.g. trees or grids. In this paper, we propose a new approach to identify infection sources by searching for a seed set S that minimizes the symmetric difference between the cascade from S and VI , the given set of infected nodes. Our major result is an approximation algorithm, called SISI, to identify infection sources without the prior knowledge on the number of source nodes. SISI, to our best knowledge, is the first algorithm with provable guarantee for the problem in general graphs. It returns a 2 (1−) 2 ∆-approximate solution with high probability, where ∆ denotes the maximum number of nodes in VI that may infect a single node in the network. Our experiments on real-world networks show the superiority of our approach and SISI in detecting true source(s), boosting the F1-measure from few percents, for the state-of-the-art NETSLEUTH, to approximately 50%.
Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2016
Biological network topologies are known to be robust despite internal and external perturbances. ... more Biological network topologies are known to be robust despite internal and external perturbances. Motifs such as feedforward loop and bifan have been marked to contribute to structural and functional significance. While network characteristics such as network density, average shortest path,
Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2016
Molecular communications is an emergent field of research that seeks to develop novel, nanoscale ... more Molecular communications is an emergent field of research that seeks to develop novel, nanoscale communications devices using design principles gleaned from studies of the topology and dynamic properties of biological signaling networks. To understand how these networks function as a whole, we must first identify and characterize the functional building blocks that compose them, and the best candidates for those are the topologically distinct subnetworks, or motifs, that appear in a statistically improbable abundance within these networks. In cellular transcriptional networks, one of the most prevalent motifs is the feed-forward loop, a three node motif wherein one top-level protein regulates the expression of a target gene either directly or indirectly through an intermediate regulator protein. Currently, no systematic effort has been made to treat an isolated feed-forward loop as a stand-alone signal amplifying/attenuating device and understand its communication capacity in terms of the diffusion of individual molecules. To address this issue, in this paper we derive a theorem that estimates the upper and lower bounds of the channel capacity for a relay channel, which structurally corresponds to a feed-forward loop, by using an additive inverse Gaussian noise channel model of protein-ligand binding. Our results are just a first step towards assessing the performance bounds of simplified biological circuits in order to guide the development and optimization of synthetic, bio-inspired devices that can be used as information processing and forwarding units.
Frontiers in Bioengineering and Biotechnology, 2015
Understanding relationships between architectural properties of gene-regulatory networks (GRNs) h... more Understanding relationships between architectural properties of gene-regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics, as it can provide insights into, e.g., disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs-i.e., small-node subgraphs that occur more abundantly in GRNs than expected from chance alone. Because these transcriptional modules represent "building blocks" of complex networks and exhibit a wide range of functional and dynamical properties, they may contribute to the remarkable robustness and dynamical stability associated with the whole of GRNs. Here, we developed network-construction models to better understand this relationship, which produce randomized GRNs by using transcriptional motifs as the fundamental growth unit in contrast to other methods that construct similar networks on a node-by-node basis. Because this model produces networks with a prescribed lower bound on the number of choice transcriptional motifs (e.g., downlinks, feed-forward loops), its fidelity to the motif distributions observed in model organisms represents an improvement over existing methods, which we validated by contrasting their resultant motif and degree distributions against existing networkgrowth models and data from the model organism of the bacterium Escherichia coli. These models may therefore serve as novel testbeds for further elucidating relationships between the topology of transcriptional motifs and network-wide dynamical properties.
There is great need to express the impacts of chemicals found in the environment in terms of effe... more There is great need to express the impacts of chemicals found in the environment in terms of effects from alternative chemicals of interest. Methods currently employed in fields such as life-cycle assessment, risk assessment, mixtures toxicology, and pharmacology rely mostly on heuristic arguments to justify the use of linear relationships in the construction of "equivalency factors," which aim to model these concentration-concentration correlations. However, the use of linear models, even at low concentrations, oversimplifies the nonlinear nature of the concentration-response curve, therefore introducing error into calculations involving these factors. We address this problem by reporting a method to determine a concentration-concentration relationship between two chemicals based on the full extent of experimentally derived concentration-response curves. Although this method can be easily generalized, we develop and illustrate it from the perspective of toxicology, in whi...
Social networks are often degree correlated between nearest neighbors, an effect termed homophily... more Social networks are often degree correlated between nearest neighbors, an effect termed homophily, wherein individuals connect to nearest neighbors of similar connectivity. Whether friendships or other associations are so correlated beyond the first-neighbors, and whether such correlations are an inherent property of the network or are dependent on other specifics of social interactions, remains unclear. Here we address these problems by examining long-range degree correlations in three undirected online social and three undirected nonsocial (airport, transcriptional-regulatory) networks. Degree correlations were measured using Pearson correlation scores and by calculating the average neighbor degrees for nodes separated by up to 5 sequential links. We found that the online social networks exhibited primarily weak anticorrelation at the first-neighbor level, and tended more strongly towards disassortativity as separation distances increased. In contrast, the nonsocial networks were disassortative among first-neighbors, but exhibited assortativity at longer separation distances. In addition, the average degrees of the separated neighbors approached the average network connectivity after approximately 3-4 steps. Finally, we observed that two algorithms designed to grow networks on a node-by-node basis failed to reproduce all the correlative features representative of the social networks studied here.
RNA aptamers are relatively short nucleic acid sequences that bind targets with high affinity, an... more RNA aptamers are relatively short nucleic acid sequences that bind targets with high affinity, and when combined with a riboswitch that initiates translation of a fluorescent reporter protein, can be used as a biosensor for chemical detection in various types of media. These processes span target binding at the molecular scale to fluorescence detection at the macroscale, which involves a number of intermediate rate-limiting physical (e.g., molecular conformation change) and biochemical changes (e.g., reaction velocity), which together complicate assay design. Here we describe a mathematical model developed to aid environmental detection of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) using the DsRed fluorescent reporter protein, but is general enough to potentially predict fluorescence from a broad range of water-soluble chemicals given the values of just a few kinetic rate constants as input. If we expose a riboswitch test population of Escherichia coli bacteria to a chemical diss...
Evolved biological network topologies may resist perturbances to allow for more robust informatio... more Evolved biological network topologies may resist perturbances to allow for more robust information transport across larger networks in which their network motifs may play a complex role. Although the abundance of individual motifs correlate with some metrics of biological robustness, the extent to which redundant regulatory interactions affect motif connectivity and how this connectivity affects robustness is unknown. To address this problem, we applied machine learning based regression modeling to evaluate how feed-forward loops interlinked by crosstalk altered information transport across a network in terms of packets successfully routed over networks of noisy channels via NS-2 simulation. We developed 233 topological features which distinctly account for the opportunities in which two feed-forward loops may exhibit crosstalk. Random forest regression modeling was used to infer significant features from this modest configuration space. The coefficient of determination was used as ...
Modeling, Methodologies and Tools for Molecular and Nano-scale Communications, 2017
A large section of the work in computational models of biological systems is based on classical c... more A large section of the work in computational models of biological systems is based on classical chemical kinetic (CCK) formalism based on a set of ordinary differential equations (ODE), also known as reaction rate equations or mass action kinetics [13]. Representing a homogeneous biological system as a set of biochemical reactions, the temporal dynamics of the molecular species is studied in the continuous-deterministic domain. While continuous-deterministic reaction models are
The transcriptional network determines a cell’s internal state by regulating protein expression i... more The transcriptional network determines a cell’s internal state by regulating protein expression in response to changes in the local environment. Due to the interconnected nature of this network, information encoded in the abundance of various proteins will often propagate across chains of noisy intermediate signaling events. The data-processing inequality (DPI) leads us to expect that this intracellular game of “telephone” should degrade this type of signal, with longer chains losing successively more information to noise. However, a previous modeling effort predicted that because the steps of these signaling cascades do not truly represent independent stages of data processing, the limits of the DPI could seemingly be surpassed, and the amount of transmitted information could actually increase with chain length. What that work did not examine was whether this regime of growing information transmission was attainable by a signaling system constrained by the mechanistic details of mo...
Large scale biological responses are inherently uncertain, in part as a consequence of noisy syst... more Large scale biological responses are inherently uncertain, in part as a consequence of noisy systems that do not respond deterministically to perturbations and measurement errors inherent to technological limitations. As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive (multiscale stochastic models), fail to capture the effects of noise across a system (chemical kinetic models), or fail to provide sufficient biological fidelity because of broad simplifying assumptions (stochastic differential equations). We use a new approach to modeling multiscale stationary biological processes that embraces the noise found in experimental data to provide estimates of the parameter uncertainties and the potential mis-specification of models. Our approach models the mean stationary response at each biological level given a particular expected response relationship, capturing variation around this mean using conditional Monte Carlo sampling that is statistically consistent with training data. A conditional probability distribution associated with a biological response can be reconstructed using this method for a subset of input values, which overcomes the parameter identification problem. Our approach could be applied in addition to dynamical modeling methods (see above) to predict uncertain biological responses over experimental time scales. To illustrate this point, we apply the approach to a test case in which we model the variation associated with measurements at multiple scales of organization across a reproduction-related Adverse Outcome Pathway described for teleosts.
A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of orga... more A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals. We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals. PSO reveals a relationship between chemical uptake and decomposition parameter values that predicts chemical-specific BCF values with moderate statistical agreement to a limited yet diverse chemical dataset, and all without a need to retrain the model to new data. The presented model requires only the oc...
Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to tr... more Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (≈20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically differe...
Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to i... more Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to in vivo extrapolation (IVIVE) using a top-down, compartmental approach, favoring architectural simplicity over physiological fidelity despite the lack of general guidelines relating model design to dynamical predictions. Here we explore the impact of design choice (high vs. low fidelity) on chemical distribution throughout an animal's organ system. We contrast transient dynamics and steady states of three previously proposed PBTK models of varying complexity in response to chemical exposure. The steady states for each model were determined analytically to predict exposure conditions from tissue measurements. Steady state whole-body concentrations differ between models, despite identical environmental conditions, which originates from varying levels of physiological fidelity captured by the models. These differences affect the relative predictive accuracy of the inverted models used in...
A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computa... more A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose-response and time course predictions that can support regulatory decision-making. Herein we describe several facets of qAOPs, including (a) motivation for development, (b) technical considerations, (c) evaluation of confidence, and (d) potential applications. The qAOP used as an illustrative example for these points describes the linkage between inhibition of cytochrome P450 19A aromatase (the MIE) and population-level decreases in the fathead minnow (FHM; Pimephales promelas). The qAOP consists of three linked computational models for: (a) the hypothalamic-pituitary-gonadal axis in female FHMs, where aromatase inhibition decreases the conversion of testosterone to 17β estradiol (E2), thereby reducing E2-dependent vitellogenin (VTG; egg yol...
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016
Given an aftermath of a cascade in the network, i.e. a set VI of "infected" nodes after an epidem... more Given an aftermath of a cascade in the network, i.e. a set VI of "infected" nodes after an epidemic outbreak or a propagation of rumors/worms/viruses, how can we infer the sources of the cascade? Answering this challenging question is critical for computer forensic, vulnerability analysis, and risk management. Despite recent interest towards this problem, most of existing works focus only on single source detection or simple network topologies, e.g. trees or grids. In this paper, we propose a new approach to identify infection sources by searching for a seed set S that minimizes the symmetric difference between the cascade from S and VI , the given set of infected nodes. Our major result is an approximation algorithm, called SISI, to identify infection sources without the prior knowledge on the number of source nodes. SISI, to our best knowledge, is the first algorithm with provable guarantee for the problem in general graphs. It returns a 2 (1−) 2 ∆-approximate solution with high probability, where ∆ denotes the maximum number of nodes in VI that may infect a single node in the network. Our experiments on real-world networks show the superiority of our approach and SISI in detecting true source(s), boosting the F1-measure from few percents, for the state-of-the-art NETSLEUTH, to approximately 50%.
Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2016
Biological network topologies are known to be robust despite internal and external perturbances. ... more Biological network topologies are known to be robust despite internal and external perturbances. Motifs such as feedforward loop and bifan have been marked to contribute to structural and functional significance. While network characteristics such as network density, average shortest path,
Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2016
Molecular communications is an emergent field of research that seeks to develop novel, nanoscale ... more Molecular communications is an emergent field of research that seeks to develop novel, nanoscale communications devices using design principles gleaned from studies of the topology and dynamic properties of biological signaling networks. To understand how these networks function as a whole, we must first identify and characterize the functional building blocks that compose them, and the best candidates for those are the topologically distinct subnetworks, or motifs, that appear in a statistically improbable abundance within these networks. In cellular transcriptional networks, one of the most prevalent motifs is the feed-forward loop, a three node motif wherein one top-level protein regulates the expression of a target gene either directly or indirectly through an intermediate regulator protein. Currently, no systematic effort has been made to treat an isolated feed-forward loop as a stand-alone signal amplifying/attenuating device and understand its communication capacity in terms of the diffusion of individual molecules. To address this issue, in this paper we derive a theorem that estimates the upper and lower bounds of the channel capacity for a relay channel, which structurally corresponds to a feed-forward loop, by using an additive inverse Gaussian noise channel model of protein-ligand binding. Our results are just a first step towards assessing the performance bounds of simplified biological circuits in order to guide the development and optimization of synthetic, bio-inspired devices that can be used as information processing and forwarding units.
Frontiers in Bioengineering and Biotechnology, 2015
Understanding relationships between architectural properties of gene-regulatory networks (GRNs) h... more Understanding relationships between architectural properties of gene-regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics, as it can provide insights into, e.g., disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs-i.e., small-node subgraphs that occur more abundantly in GRNs than expected from chance alone. Because these transcriptional modules represent "building blocks" of complex networks and exhibit a wide range of functional and dynamical properties, they may contribute to the remarkable robustness and dynamical stability associated with the whole of GRNs. Here, we developed network-construction models to better understand this relationship, which produce randomized GRNs by using transcriptional motifs as the fundamental growth unit in contrast to other methods that construct similar networks on a node-by-node basis. Because this model produces networks with a prescribed lower bound on the number of choice transcriptional motifs (e.g., downlinks, feed-forward loops), its fidelity to the motif distributions observed in model organisms represents an improvement over existing methods, which we validated by contrasting their resultant motif and degree distributions against existing networkgrowth models and data from the model organism of the bacterium Escherichia coli. These models may therefore serve as novel testbeds for further elucidating relationships between the topology of transcriptional motifs and network-wide dynamical properties.
There is great need to express the impacts of chemicals found in the environment in terms of effe... more There is great need to express the impacts of chemicals found in the environment in terms of effects from alternative chemicals of interest. Methods currently employed in fields such as life-cycle assessment, risk assessment, mixtures toxicology, and pharmacology rely mostly on heuristic arguments to justify the use of linear relationships in the construction of "equivalency factors," which aim to model these concentration-concentration correlations. However, the use of linear models, even at low concentrations, oversimplifies the nonlinear nature of the concentration-response curve, therefore introducing error into calculations involving these factors. We address this problem by reporting a method to determine a concentration-concentration relationship between two chemicals based on the full extent of experimentally derived concentration-response curves. Although this method can be easily generalized, we develop and illustrate it from the perspective of toxicology, in whi...
Social networks are often degree correlated between nearest neighbors, an effect termed homophily... more Social networks are often degree correlated between nearest neighbors, an effect termed homophily, wherein individuals connect to nearest neighbors of similar connectivity. Whether friendships or other associations are so correlated beyond the first-neighbors, and whether such correlations are an inherent property of the network or are dependent on other specifics of social interactions, remains unclear. Here we address these problems by examining long-range degree correlations in three undirected online social and three undirected nonsocial (airport, transcriptional-regulatory) networks. Degree correlations were measured using Pearson correlation scores and by calculating the average neighbor degrees for nodes separated by up to 5 sequential links. We found that the online social networks exhibited primarily weak anticorrelation at the first-neighbor level, and tended more strongly towards disassortativity as separation distances increased. In contrast, the nonsocial networks were disassortative among first-neighbors, but exhibited assortativity at longer separation distances. In addition, the average degrees of the separated neighbors approached the average network connectivity after approximately 3-4 steps. Finally, we observed that two algorithms designed to grow networks on a node-by-node basis failed to reproduce all the correlative features representative of the social networks studied here.
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Papers by Michael Mayo