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At least 343 records · Page 19Linked to original sources

arXiv2012

Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm

This article presents an algorithm that allows modeling of biological networks in a qualitative framework with continuous time. Mathematical modeling is used as a systems biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and to predict the effect of perturbations. We propose a modeling approach that is intrinsically continuous in time. The algorithm presented here fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. The values of transition rates have a natural interpretation: it is the inverse of the time for the transition to occur. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions; therefore, it can be seen as an approach in between quantitative and qualitative. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) in the Boolean state space. This software, parallelized and optimized, computes temporal evolution of probability distributions and can also estimate stationary distributions. Applications of Boolean Kinetic Monte-Carlo have been demonstrated for two qualitative models: a toy model and a published p53/Mdm2 model. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.

q-bio.MN↗

arXiv2012

Dynamic phenotypes as criteria for model discrimination: fold-change detection in R. sphaeroides

The chemotaxis pathway of the bacterium Rhodobacter sphaeroides has many similarities to the well-studied pathway in Escherichia coli. It exhibits robust adaptation and has several homologues of the latter's chemotaxis proteins. Recent theoretical results have been able to correctly predict that the chemotactic response of Escherichia coli exhibits the same output behavior in response to scaled ligand inputs, a dynamic property known as fold-change detection (FCD), or input-scale invariance. In this paper, we present theoretical assumptions on the R. sphaeroides chemotaxis sensing dynamics that can be analytically shown to yield FCD behavior in a specific ligand concentration range. Based on these assumptions, we construct two models of the full chemotaxis pathway that are able to reproduce experimental time-series data from earlier studies. To test the validity of our assumptions, we propose a series of experiments in which our models predict robust FCD behavior where earlier models do not. In this way, we illustrate how a dynamic phenotype such as FCD can be used for the purposes of discriminating between two models that reproduce the same experimental time-series data.

q-bio.MN↗

arXiv2012

Probabilistic and Flux Landscapes of the Phage $λ$ Genetic Switch

The phage $λ$ infection of an \textit{E. coli} cell has become a paradigm for understanding the molecular processes involved in gene expression and cell signaling. This system provides an example of a genetic switch, as cells with identical DNA choose either of two cell cycles: a lysogenic cycle, in which the phage genome is incorporated into the host and copied by the host; or a lytic cycle, resulting in the death of the cell and a burst of viruses. The robustness of this switch is remarkable; although the first stages of the lysogenic and lytic cycles are identical, a lysogen rarely spontaneously flips, and external stressors or instantaneous cell conditions are required to induce flipping. In particular, the cell fate decision can depend on the populations of two proteins, cI and Cro, as well as their oligomerization and subsequent binding affinities to three DNA sites. These processes in turn govern the rates at which RNAp transcribes the cI and Cro genes to produce more of their respective proteins. In this work, a dynamical model of the non-equilibrium statistical mechanics is revisited and generalized. The low number of proteins and other sources of noise are non-negligble and corrections to the kinetics are essential to understanding the stability. To this end, general integral forms for advection-diffusion equations have been developed and numerically solved for a variety of mutants and assumptions about the state of the cells. These solutions quantify the probabilistic and flux landscapes of the ensembles' evolution in concentration space and are used to predict the populations of the cell states, entropy production, passage times, and potential barriers of wild type and mutant bacteria to illuminate some structure of the configuration space from which Nature naturally selects.

physics.bio-ph↗

arXiv2012

Attribute Exploration of Gene Regulatory Processes

This thesis aims at the logical analysis of discrete processes, in particular of such generated by gene regulatory networks. States, transitions and operators from temporal logics are expressed in the language of Formal Concept Analysis. By the attribute exploration algorithm, an expert or a computer program is enabled to validate a minimal and complete set of implications, e.g. by comparison of predictions derived from literature with observed data. Here, these rules represent temporal dependencies within gene regulatory networks including coexpression of genes, reachability of states, invariants or possible causal relationships. This new approach is embedded into the theory of universal coalgebras, particularly automata, Kripke structures and Labelled Transition Systems. A comparison with the temporal expressivity of Description Logics is made. The main theoretical results concern the integration of background knowledge into the successive exploration of the defined data structures (formal contexts). Applying the method a Boolean network from literature modelling sporulation of Bacillus subtilis is examined. Finally, we developed an asynchronous Boolean network for extracellular matrix formation and destruction in the context of rheumatoid arthritis.

q-bio.MN↗

arXiv2012

Identifying edge clusters in networks via edge graphlet degree vectors (edge-GDVs) and edge-GDV-similarities

Inference of new biological knowledge, e.g., prediction of protein function, from protein-protein interaction (PPI) networks has received attention in the post-genomic era. A popular strategy has been to cluster the network into functionally coherent groups of proteins and predict protein function from the clusters. Traditionally, network research has focused on clustering of nodes. However, why favor nodes over edges, when clustering of edges may be preferred? For example, nodes belong to multiple functional groups, but clustering of nodes typically cannot capture the group overlap, while clustering of edges can. Clustering of adjacent edges that share many neighbors was proposed recently, outperforming different node clustering methods. However, since some biological processes can have characteristic "signatures" throughout the network, not just locally, it may be of interest to consider edges that are not necessarily adjacent. Hence, we design a sensitive measure of the "topological similarity" of edges that can deal with edges that are not necessarily adjacent. We cluster edges that are similar according to our measure in different baker's yeast PPI networks, outperforming existing node and edge clustering approaches.

q-bio.MN↗

arXiv2013

Stochastic oscillations induced by intrinsic fluctuations in a self-repressing gene: a deterministic approach

Biochemical reaction networks are subjected to large fluctuations attributable to small molecule numbers, yet underlie reliable biological functions. Most theoretical approaches describe them as purely deterministic or stochastic dynamical systems, depending on which point of view is favored. Here, we investigate the dynamics of a self-repressing gene using an intermediate approach based on a moment closure approximation of the master equation, which allows us to take into account the binary character of gene activity. We thereby obtain deterministic equations that describe how nonlinearity feeds back fluctuations into the mean-field equations, providing insight into the interplay of determinism and stochasticity. This allows us to identify regions of parameter space where fluctuations induce relatively regular oscillations.

q-bio.MN↗

arXiv2012

Universal protein fluctuations in populations of microorganisms

The copy number of any protein fluctuates among cells in a population; characterizing and understanding these fluctuations is a fundamental problem in biophysics. We show here that protein distributions measured under a broad range of biological realizations collapse to a single non-Gaussian curve under scaling by the first two moments. Moreover in all experiments the variance is found to depend quadratically on the mean, showing that a single degree of freedom determines the entire distribution. Our results imply that protein fluctuations do not reflect any specific molecular or cellular mechanism, and suggest that some buffering process masks these details and induces universality.

physics.bio-ph↗

arXiv2012

The Landscape of Complex Networks

Topological landscape is introduced for networks with functions defined on the nodes. By extending the notion of gradient flows to the network setting, critical nodes of different indices are defined. This leads to a concise and hierarchical representation of the network. Persistent homology from computational topology is used to design efficient algorithms for performing such analysis. Applications to some examples in social and biological networks are demonstrated, which show that critical nodes carry important information about structures and dynamics of such networks.

stat.ME↗

arXiv2012

Origins of Binary Gene Expression in Post-transcriptional Regulation by MicroRNAs

MicroRNA-mediated regulation of gene expression is characterised by some distinctive features that set it apart from unregulated and transcription factor-regulated gene expression. Recently, a mathematical model has been proposed to describe the dynamics of post-transcriptional regulation by microRNAs. The model explains the observations made in single cell experiments quite well. In this paper, we introduce some additional features into the model and consider two specific cases. In the first case, a non-cooperative positive feedback loop is included in the transcriptional regulation of the target gene expression. In the second case, a stochastic version of the original model is considered in which there are random transitions between the inactive and active expression states of the gene. In the first case we show that bistability is possible in a parameter regime, due to the presence of a non-linear protein decay term in the gene expression dynamics. In the second case, we derive the conditions for obtaining stochastic binary gene expression. We find that this type of gene expression is more favourable in the case of regulation by microRNAs as compared to the case of unregulated gene expression. The theoretical predictions relating to binary gene expression are experimentally testable.

q-bio.QM↗

arXiv2015

The channel capacity and information density of biochemical signaling cascades

Living cell signaling systems include multistep biochemical signaling reaction cascades (BSCs) comprising modifications of molecular signaling proteins. Substantial data on BSCs have been accumulated in the field of molecular biology and the analysis of signaling systems requires qualitative evaluation. However, quantification of the information and channel capacity of BSCs has not been focused on from the perspective of information theory. In the current study, we aimed to derive basic equations for describing the channel capacity and information density of BSCs using the fluctuation theorem. From the results, channel capacity and information density can be described using the average entropy production rate when the signaling system is minimally redundant. The channel capacity could actually be calculated for the mitogen-activated protein kinase BSC when it was minimally redundant. This quantitative method of examination is applicable to the quantitative analysis of BSCs.

q-bio.MN↗

arXiv2012

The Structure of Autocatalytic Sets: Evolvability, Enablement, and Emergence

This paper presents new results from a detailed study of the structure of autocatalytic sets. We show how autocatalytic sets can be decomposed into smaller autocatalytic subsets, and how these subsets can be identified and classified. We then argue how this has important consequences for the evolvability, enablement, and emergence of autocatalytic sets. We end with some speculation on how all this might lead to a generalized theory of autocatalytic sets, which could possibly be applied to entire ecologies or even economies.

q-bio.MN↗

arXiv2012

Evolutionary algorithms in genetic regulatory networks model

Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes. Modeling GRNs is significantly important in order to reveal fundamental cellular processes, examine gene functions and understanding their complex relationships. Understanding the interactions between genes gives rise to develop better method for drug discovery and diagnosis of the disease since many diseases are characterized by abnormal behaviour of the genes. In this paper we have reviewed various evolutionary algorithms-based approach for modeling GRNs and discussed various opportunities and challenges.

cs.CE↗

arXiv2012

Predicting Phenotype from Genotype Through Automatically Composed Petri Nets

We describe a modular modelling approach permitting curation, updating, and distributed development of modules through joined community effort overcoming the problem of keeping a combinatorially exploding number of monolithic models up to date. For this purpose, the effects of genes and their mutated alleles on downstream components are modeled by composable, metadata-containing Petri net models organized in a database with version control, accessible through a web interface. Gene modules can be coupled to protein modules through mRNA modules by specific interfaces designed for the automatic, database-assisted composition. Automatically assembled executable models may then consider cell type-specific gene expression patterns and the resulting protein concentrations. Gene modules and allelic interference modules may represent effects of gene mutation and predict their pleiotropic consequences or uncover complex genotype/phenotype relationships. Forward and reverse engineered modules are fully compatible.

q-bio.MN↗

arXiv2012

Data Gathering in Networks of Bacteria Colonies: Collective Sensing and Relaying Using Molecular Communication

The prospect of new biological and industrial applications that require communication in micro-scale, encourages research on the design of bio-compatible communication networks using networking primitives already available in nature. One of the most promising candidates for constructing such networks is to adapt and engineer specific types of bacteria that are capable of sensing, actuation, and above all, communication with each other. In this paper, we describe a new architecture for networks of bacteria to form a data collecting network, as in traditional sensor networks. The key to this architecture is the fact that the node in the network itself is a bacterial colony; as an individual bacterium (biological agent) is a tiny unreliable element with limited capabilities. We describe such a network under two different scenarios. We study the data gathering (sensing and multihop communication) scenario as in sensor networks followed by the consensus problem in a multi-node network. We will explain as to how the bacteria in the colony collectively orchestrate their actions as a node to perform sensing and relaying tasks that would not be possible (at least reliably) by an individual bacterium. Each single bacterium in the colony forms a belief by sensing external parameter (e.g., a molecular signal from another node) from the medium and shares its belief with other bacteria in the colony. Then, after some interactions, all the bacteria in the colony form a common belief and act as a single node. We will model the reception process of each individual bacteria and will study its impact on the overall functionality of a node. We will present results on the reliability of the multihop communication for data gathering scenario as well as the speed of convergence in the consensus scenario.

cs.IT↗

arXiv2012

JAK/STAT signalling - an executable model assembled from molecule-centred modules demonstrating a module-oriented database concept for systems- and synthetic biology

We describe a molecule-oriented modelling approach based on a collection of Petri net models organized in the form of modules into a prototype database accessible through a web interface. The JAK/STAT signalling pathway with the extensive cross-talk of its components is selected as case study. Each Petri net module represents the reactions of an individual protein with its specific interaction partners. These Petri net modules are graphically displayed, can be executed individually, and allow the automatic composition into coherent models containing an arbitrary number of molecular species chosen ad hoc by the user. Each module contains metadata for documentation purposes and can be extended to a wiki-like minireview. The database can manage multiple versions of each module. It supports the curation, documentation, version control, and update of individual modules and the subsequent automatic composition of complex models, without requiring mathematical skills. Modules can be (semi-) automatically recombined according to user defined scenarios e.g. gene expression patterns in given cell types, under certain physiological conditions, or states of disease. Adding a localisation component to the module database would allow to simulate models with spatial resolution in the form of coloured Petri nets. As synthetic biology application we propose the fully automated generation of synthetic or synthetically rewired network models by composition of metadata-guided automatically modified modules representing altered protein binding sites. Petri nets composed from modules can be executed as ODE system, stochastic, hybrid, or merely qualitative models and exported in SMBL format.

q-bio.MN↗

arXiv2012

Autocatalytic Sets Extended: Dynamics, Inhibition, and a Generalization

Background: Autocatalytic sets are often considered a necessary (but not sufficient) condition for the origin and early evolution of life. Although the idea of autocatalytic sets was already conceived of many years ago, only recently have they gained more interest, following advances in creating them experimentally in the laboratory. In our own work, we have studied autocatalytic sets extensively from a computational and theoretical point of view. Results: We present results from an initial study of the dynamics of self-sustaining autocatalytic sets (RAFs). In particular, simulations of molecular flow on autocatalytic sets are performed, to illustrate the kinds of dynamics that can occur. Next, we present an extension of our (previously introduced) algorithm for finding autocatalytic sets in general reaction networks, which can also handle inhibition. We show that in this case detecting autocatalytic sets is fixed parameter tractable. Finally, we formulate a generalized version of the algorithm that can also be applied outside the context of chemistry and origin of life, which we illustrate with a toy example from economics. Conclusions: Having shown theoretically (in previous work) that autocatalytic sets are highly likely to exist, we conclude here that also in terms of dynamics such sets are viable and outcompete non-autocatalytic sets. Furthermore, our dynamical results confirm arguments made earlier about how autocatalytic subsets can enable their own growth or give rise to other such subsets coming into existence. Finally, our algorithmic extension and generalization show that more realistic scenarios (e.g., including inhibition) can also be dealt with within our framework, and that it can even be applied to areas outside of chemistry, such as economics.

q-bio.MN↗

arXiv2012

Quasi-potential landscape in complex multi-stable systems

Developmental dynamics of multicellular organism is a process that takes place in a multi-stable system in which each attractor state represents a cell type and attractor transitions correspond to cell differentiation paths. This new understanding has revived the idea of a quasi-potential landscape, first proposed by Waddington as a metaphor. To describe development one is interested in the "relative stabilities" of N attractors (N>2). Existing theories of state transition between local minima on some potential landscape deal with the exit in the transition between a pair attractor but do not offer the notion of a global potential function that relate more than two attractors to each other. Several ad hoc methods have been used in systems biology to compute a landscape in non-gradient systems, such as gene regulatory networks. Here we present an overview of the currently available methods, discuss their limitations and propose a new decomposition of vector fields that permit the computation of a quasi-potential function that is equivalent to the Freidlin-Wentzell potential but is not limited to two attractors. Several examples of decomposition are given and the significance of such a quasi-potential function is discussed.

q-bio.MN↗

arXiv2012

Gene autoregulation via intronic microRNAs and its functions

Background: MicroRNAs, post-transcriptional repressors of gene expression, play a pivotal role in gene regulatory networks. They are involved in core cellular processes and their dysregulation is associated to a broad range of human diseases. This paper focus on a minimal microRNA-mediated regulatory circuit, in which a protein-coding gene (host gene) is targeted by a microRNA located inside one of its introns. Results: Autoregulation via intronic microRNAs is widespread in the human regulatory network, as confirmed by our bioinformatic analysis, and can perform several regulatory tasks despite its simple topology. Our analysis, based on analytical calculations and simulations, indicates that this circuitry alters the dynamics of the host gene expression, can induce complex responses implementing adaptation and Weber's law, and efficiently filters fluctuations propagating from the upstream network to the host gene. A fine-tuning of the circuit parameters can optimize each of these functions. Interestingly, they are all related to gene expression homeostasis, in agreement with the increasing evidence suggesting a role of microRNA regulation in conferring robustness to biological processes. In addition to model analysis, we present a list of bioinformatically predicted candidate circuits in human for future experimental tests. Conclusions: The results presented here suggest a potentially relevant functional role for negative self-regulation via intronic microRNAs, in particular as a homeostatic control mechanism of gene expression. Moreover, the map of circuit functions in terms of experimentally measurable parameters, resulting from our analysis, can be a useful guideline for possible applications in synthetic biology.

q-bio.MN↗