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arXiv2013

Virtual-organism toy-model as a tool to develop bioinformatics approaches of Systems Biology for medical-target discovery

Systems Biology has emerged in the last years as a new holistic approach based on the global understanding of cells instead of only being focused on their individual parts (genes or proteins), to better understand the complexity of human cells. Since the Systems Biology still does not provide the most accurate answers to our questions due to the complexity of cells and the limited quality of available information to perform a good gene/protein map analysis, we have created simpler models to ensure easier analysis of the map that represents the human cell. Therefore, a virtual organism has been designed according to the main physiological rules for humans in order to replicate the human organism and its vital functions. This toy model was constructed by defining the topology of its genes/proteins and the biological functions associated to it. There are several examples of these toy models that emulate natural processes to perform analysis of the virtual life in order to design the best strategy to understand real life. The strategy applied in this study combines topological and functional analysis integrating the knowledge about the relative position of a node among the others in the map with the conclusions generated by mathematical models that reproduce functional data of the virtual organism. Our results demonstrate that the combination of both strategies allows better understanding of our virtual organism even with the lower input of information needed and therefore it can be a potential tool to better understand the real life.

q-bio.MN↗

arXiv2013

Integration of TNF induced apoptosis model and quantitative proteomics data of HeLa cells - our first experience

The TNF initiated processes are simulated using our integrative computational model with new quantitative proteomics data obtained on HeLa cell line. In spite of fact, that the model development is based on limited experimental information, with missing data estimated by simulations and parametrization using indirect experiments on different cell lines, the actual model with HeLa experimental data provides pleasant agreement between the computationally predicted behavior and experiments. Since no significant departures from experimentally determined functionality of HeLa cells has been observed, this can be taken as encouraging sign, that substantial features of the cell line behavior are reproduced correctly and indicate new possibilities for cell-line specific computational experiments.

q-bio.MN↗

arXiv2013

Increased Network Interdependency Leads to Aging

Although species longevity is subject to a diverse range of selective forces, the mortality curves of a wide variety of organisms are rather similar. We argue that aging and its universal characteristics may have evolved by means of a gradual increase in the systemic interdependence between a large collection of biochemical or mechanical components. Modeling the organism as a dependency network which we create using a constructive evolutionary process, we age it by allowing nodes to be broken or repaired according to a probabilistic algorithm that accounts for random failures/repairs and dependencies. Our simulations show that the network slowly accumulates damage and then catastrophically collapses. We use our simulations to fit experimental data for the time dependent mortality rates of a variety of multicellular organisms and even complex machines such as automobiles. Our study suggests that aging is an emergent finite-size effect in networks with dynamical dependencies and that the qualitative and quantitative features of aging are not sensitively dependent on the details of system structure.

q-bio.PE↗

arXiv2013

NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps

Molecular biology knowledge can be systematically represented in a computer-readable form as a comprehensive map of molecular interactions. There exist a number of maps of molecular interactions containing detailed description of various cell mechanisms. It is difficult to explore these large maps, to comment their content and to maintain them. Though there exist several tools addressing these problems individually, the scientific community still lacks an environment that combines these three capabilities together. NaviCell is a web-based environment for exploiting large maps of molecular interactions, created in CellDesigner, allowing their easy exploration, curation and maintenance. NaviCell combines three features: (1) efficient map browsing based on Google Maps engine; (2) semantic zooming for viewing different levels of details or of abstraction of the map and (3) integrated web-based blog for collecting the community feedback. NaviCell can be easily used by experts in the field of molecular biology for studying molecular entities of their interest in the context of signaling pathways and cross-talks between pathways within a global signaling network. NaviCell allows both exploration of detailed molecular mechanisms represented on the map and a more abstract view of the map up to a top-level modular representation. NaviCell facilitates curation, maintenance and updating the comprehensive maps of molecular interactions in an interactive fashion due to an imbedded blogging system. NaviCell provides an easy way to explore large-scale maps of molecular interactions, thanks to the Google Maps and WordPress interfaces, already familiar to many users. Semantic zooming used for navigating geographical maps is adopted for molecular maps in NaviCell, making any level of visualization meaningful to the user. In addition, NaviCell provides a framework for community-based map curation.

q-bio.MN↗

arXiv2013

Noise reduction by coupling of stochastic processes and canalization in biology

Randomness is an unavoidable feature of the intracellular environment due to chemical reactants being present in low copy number. That phenomenon, predicted by Delbrück long ago \cite{delbruck40}, has been detected in both prokaryotic \cite{elowitz02,cai06} and eukaryotic \cite{blake03} cells after the development of the fluorescence techniques. On the other hand, developing organisms, e.g. {\em D. melanogaster}, exhibit strikingly precise spatio-temporal patterns of protein/mRNA concentrations \cite{gregor07b,manu09a,manu09b,boettiger09}. Those two characteristics of living organisms are in apparent contradiction: the precise patterns of protein concentrations are the result of multiple mutually interacting random chemical reactions. The main question is to establish biochemical mechanisms for coupling random reactions so that canalization, or fluctuations reduction instead of amplification, takes place. Here we explore a model for coupling two stochastic processes where the noise of the combined process can be smaller than that of the isolated ones. Such a canalization occurs if, and only if, there is negative covariance between the random variables of the model. Our results are obtained in the framework of a master equation for a negatively self-regulated -- or externally regulated -- binary gene and show that the precise control due to negative self regulation \cite{becskei00} is because it may generate negative covariance. Our results suggest that negative covariance, in the coupling of random chemical reactions, is a theoretical mechanism underlying the precision of developmental processes.

q-bio.MN↗

arXiv2013

On prediction of regulatory genes by analysis of C.elegans functional networks

Connectivity networks have recently become widely used in biology due to increasing amounts of information on the physical and functional links between individual proteins. This connectivity data provides valuable material for expanding our knowledge far beyond the experimentally validated via mathematical analysis and theoretical predictions of new functional interactions. In this paper we demonstrate an application of several algorithms developed for the ranking of potential gene-expression regulators within the context of an associated network. We analyze how different types of connectivity between genes and proteins affect the topology of the integral C.elegans functional network and thereby validate algorithmic performance. We demonstrate the possible definition of co-expression gene clusters within a network context from their specific motif distribution signatures. We also show that the method based on the shortest path function (SPF) applied to gene interactions sub-network of the co-expression gene cluster, efficiently predicts novel regulatory transcription factors (TFs). Simultaneous application of other methods, including only interactions with neighborhood genes, allows rapid ranking of potential regulators that could be functionally linked with the group of co-expressed genes. Predicting functions of regulators for a cluster of ribosomal/mRNA metabolic genes we highlight a role of mRNA translation and decay in a longevity of organisms.

q-bio.GN↗

arXiv2013

Note on the Complex Networks and Epidemiology Part I: Complex Networks

Complex networks describe a wide range of systems in nature and society. Frequently cited examples include Internet, WWW, a network of chemicals linked by chemical reactions, social relationship networks, citation networks, etc. The research of complex networks has attracted many scientists' attention. Physicists have shown that these networks exhibit some surprising characters, such as high clustering coefficient, small diameter, and the absence of the thresholds of percolation. Scientists in mathematical epidemiology discovered that the threshold of infectious disease disappears on contact networks that following Scale-Free distribution. Researchers in economics and public health also find that the imitation behavior could lead to cluster phenomena of vaccination and un-vaccination. In this note, we will review the basic concepts of complex networks; Basic epidemic models; the development of complex networks and epidemiology.

physics.soc-ph↗

arXiv2013

The protein-protein interaction network of human Sirtuin family

Protein-protein interaction networks are useful for studying human diseases and to look for possible health care through a holistic approach. Networks are playing an increasing and important role in the understanding of physiological processes such as homeostasis, signaling, spatial and temporal organizations, and pathological conditions. In this article we show the complex system of interactions determined by human Sirtuins (Sirt) largely involved in many metabolic processes as well as in different diseases. The Sirtuin family consists of seven homologous Sirt-s having structurally similar cores but different terminal segments, being rather variable in length and/or intrinsically disordered. Many studies have determined their cellular location as well as biological functions although molecular mechanisms through which they act are actually little known. Therefore, the aim of this work was to define, explore and understand the Sirtuin-related human interactome. As a first step, we have integrated the experimentally determined protein-protein interactions of the Sirtuin-family as well as their first and second neighbors to a Sirtuin-related sub-interactome. Our data showed that the second-neighbor network of Sirtuins encompasses 25% of the entire human interactome, exhibits a scale-free degree distribution and interconnectedness among top degree nodes. Moreover, the Sirtuin sub interactome showed a modular structure around the core comprising mixed functions. Finally, we extracted from the Sirtuin sub-interactome subnets related to cancer, aging and post-translational modifications for information on key nodes and topological space of the subnets in Sirt family network.

q-bio.MN↗

arXiv2013

A statistical mechanics description of environmental variability in metabolic networks

Many of the chemical reactions that take place within a living cell are irreversible. Due to evolutionary pressures, the number of allowable reactions within these systems are highly constrained and thus the resulting metabolic networks display considerable asymmetry. In this paper, we explore possible evolutionary factors pertaining to the reduced symmetry observed in these networks, and demonstrate the important role environmental variability plays in shaping their structural organization. Interpreting the returnability index as an equilibrium constant for a reaction network in equilibrium with a hypothetical reference system, enables us to quantify the extent to which a metabolic network is in disequilibrium. Further, by introducing a new directed centrality measure via an extension of the subgraph centrality metric to directed networks, we are able to characterise individual metabolites by their participation within metabolic pathways. To demonstrate these ideas, we study 116 metabolic networks of bacteria. In particular, we find that the equilibrium constant for the metabolic networks decreases significantly in-line with variability in bacterial habitats, supporting the view that environmental variability promotes disequilibrium within these biochemical reaction systems.

q-bio.MN↗

arXiv2013

Warburg Effect due to Exposure to Different Types of Radiation

Cancer cells maintain a high level of aerobic glycolysis (the Warburg effect), which is associated with their rapid proliferation. Many studies have reported that the suppression of glycolysis and activation of oxidative phosphorylation can repress the growth of cancer cells through regulation of key regulators. Whether Warburg effect of cancer cells could be switched by some other environmental stimulus? Herein, we report an interesting phenomenon in which cells alternated between glycolysis and mitochondrial respiration depending on the type of radiation they were exposed to. We observed enhanced glycolysis and mitochondrial respiration in HeLa cells exposed to 2-Gy X-ray and 2-Gy carbon ion radiation, respectively. This discovery may provide novel insights for tumor therapy.

q-bio.TO↗

arXiv2013

Functional redundancy in the NF-κB signalling pathway

The ability to represent intracellular biochemical dynamics via deterministic and stochastic modelling is one of the crucial components to move biological sciences in the observe-predict-control-design knowledge ladder. Compared to the engineering or physics problems, dynamical models in quantitative biology typically dependent on a relatively large number of parameters. Therefore, the relationship between model parameters and dynamics is often prohibitively difficult to determine. We developed a method to depict the input-output relationship for multi-parametric stochastic and deterministic models via information-theoretic quantification of similarity between model parameters and modules. Identification of most information-theoretically orthogonal biological components, provided mathematical language to precisely communicate and visualise compensation like phenomena such as biological robustness, sloppiness and statistical non-identifiability. A comprehensive analysis of the multi-parameter NF-$κ$B signalling pathway demonstrates that the information-theoretic similarity reflects a topological structure of the network. Examination of the currently available experimental data on this system reveals the number of identifiable parameters and suggests informative experimental protocols.

q-bio.QM↗

arXiv2015

Toggling a Genetic Switch Using Reinforcement Learning

In this paper, we consider the problem of optimal exogenous control of gene regulatory networks. Our approach consists in adapting an established reinforcement learning algorithm called the fitted Q iteration. This algorithm infers the control law directly from the measurements of the system's response to external control inputs without the use of a mathematical model of the system. The measurement data set can either be collected from wet-lab experiments or artificially created by computer simulations of dynamical models of the system. The algorithm is applicable to a wide range of biological systems due to its ability to deal with nonlinear and stochastic system dynamics. To illustrate the application of the algorithm to a gene regulatory network, the regulation of the toggle switch system is considered. The control objective of this problem is to drive the concentrations of two specific proteins to a target region in the state space.

eess.SY↗

arXiv2013

Modular dynamical systems on networks

We propose a new framework for the study of continuous time dynamical systems on networks. We view such dynamical systems as collections of interacting control systems. We show that a class of maps between graphs called graph fibrations give rise to maps between dynamical systems on networks. This allows us to produce conjugacy between dynamical systems out of combinatorial data. In particular we show that surjective graph fibrations lead to synchrony subspaces in networks. The injective graph fibrations, on the other hand, give rise to surjective maps from large dynamical systems to smaller ones. One can view these surjections as a kind of "fast/slow" variable decompositions or as "abstractions" in the computer science sense of the word.

math.DS↗

arXiv2013

Tropicalization and tropical equilibration of chemical reactions

Systems biology uses large networks of biochemical reactions to model the functioning of biological cells from the molecular to the cellular scale. The dynamics of dissipative reaction networks with many well separated time scales can be described as a sequence of successive equilibrations of different subsets of variables of the system. Polynomial systems with separation are equilibrated when at least two monomials, of opposite signs, have the same order of magnitude and dominate the others. These equilibrations and the corresponding truncated dynamics, obtained by eliminating the dominated terms, find a natural formulation in tropical analysis and can be used for model reduction.

q-bio.MN↗

arXiv2014

Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data

Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influence the rate and timing of gene expression. In this work we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are estimated using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome-wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. We find that rapidly induced genes are enriched for both estrogen receptor alpha (ER$α$) and FOXA1 binding in their proximal promoter regions.

q-bio.QM↗

arXiv2013

The coupling of thermodynamics with the organizational water-protein intra-dynamics driven by the H-bonds dissipative potential of cluster water

The Red cell-Hb-CSF functions as a sensor adapting response to Hb heterotropic equilibriums. At the lungs O2 and Mg2+, each one increasing affinity for the other stabilize the relax (R) form [(O2)4Hb(Mg)2].(H2O)R. At tissue level, the inclusion of H+ and 2,3-DPG excludes O2 and Mg2+ to stabilize the tense (T) form 2,3-DPG-deoxyHb-(H2O)T. Both senses are integrated into a cycle T into R and R into T, without involving a direct reversal. The dissipative potential of water cluster (H2O)n interacts with the hydrophilic asymmetries of Hb, to restrict randomness of the kinetic sense implicated in a single peak for activation energy (Ea). The hydration shells could sequence an enhanced Ea into several peaks, to sequentially activate transitions states. Hence, changes in dipole state, sliding, pKa, n-H-bonds, etc., could became concatenated for vectoriality. (H2O)n by the loss of H-bonds couple with to the hydration turnover of proteins and ions to result in incomplete water cluster (H2O)n*, with a lower-n. (H2O)n* became a carrier of heat/entropy into the cerebrospinal fluid (CSF) which has to be replaced 3.7 times per day. OxyHb formation involves sliding-down of alpha vs beta chains, to shift alpha1 and alpha2 Pro 44 into allowing the entrance of a fully hydrated [Mg.(H2O)6](H2O)12-14(2+) (or Zn2+) into the hydrophilic beta2-alpha1 and beta1-alpha2 interfaces. OxyHb pKa of 6.4 leads to H+-dissociation increasing negative charge of R-groups. This at beta2-alpha1 sequence two tetradentate chelates, first an Mg2+, bonding with beta2 His 92 and a second Mg2+ with alpha1 His 87, to cooperatively release hindrance. The interconversion of oxy-to-deoxyHb, pKa=8, leads to the amphoteric imidazole to became positively charged and proximal histidines return into hindrance position, releasing the incomplete hydrated Mg.(H2O)inc(2+) and O2 into CSF.

q-bio.MN↗

arXiv2013

Properties of Random Complex Chemical Reaction Networks and Their Relevance to Biological Toy Models

We investigate the properties of large random conservative chemical reaction networks composed of elementary reactions endowed with either mass-action or saturating kinetics, assigning kinetic parameters in a thermodynamically-consistent manner. We find that such complex networks exhibit qualitatively similar behavior when fed with external nutrient flux. The nutrient is preferentially transformed into one specific chemical that is an intrinsic property of the network. We propose a self-consistent proto-cell toy model in which the preferentially synthesized chemical is a precursor for the cell membrane, and show that such proto-cells can exhibit sustainable homeostatic growth when fed with any nutrient diffusing through the membrane, provided that nutrient is metabolized at a sufficient rate.

q-bio.MN↗

arXiv2013

Synthetic Biology in Leishmaniasis: Design,simulation and validation of constructed Genetic circuit

Building circuits and studying their behavior in cells is a major goal of systems and synthetic biology. Synthetic biology enables the precise control of cellular states for systems studies, the discovery of novel parts, control strategies, and interactions for the design of robust synthetic systems. To the best of our knowledge,there are no literature reports for the synthetic circuit construction for protozoan parasites. This paper describes the construction of genetic circuit for the targeted enzyme inositol phosphorylceramide synthase belonging to the protozoan parasite Leishmania. To explore the dynamic nature of the circuit designed, simulation was done followed by circuit validation by qualitative and quantitative approaches. The genetic circuit designed for inositol phosphorylceramide synthase shows responsiveness, oscillatory and bistable behavior, together with intrinsic robustness.

q-bio.MN↗