Cellular Subsystems Research Articles

Atmospheric Electric Field Sensors Network Integration in Brazil

This paper presents the integration details of an operational Atmospheric Electric Field Mill Sensor (EFM) Network for standardized and continuous operation in the region of Sao Jose dos Campos, State of Sao Paulo, Brazil, in order to collect and transmit real time data for studies of atmospheric electric field and support the development of a Lightning Warning System (LWS). Data collection equipments, including EFM sensors from different manufacturers, datalogger, telecommunication by 3G cellular modem and solar power energy subsystems and software were integrated. A set of graphs obtained by interpolating real time data collected by the integrated EFM network combined with lightning location data detected by the Brazilian Lightning Detection System (BrasilDAT) is also presented.

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

SummaryAccurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology’s hierarchical structure, we organize genotype data into an “ontotype,” that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

Common origins of RNA, protein and lipid precursors in a cyanosulfidic protometabolism.

A minimal cell can be thought of as comprising informational, compartment-forming and metabolic subsystems. To imagine the abiotic assembly of such an overall system, however, places great demands on hypothetical prebiotic chemistry. The perceived differences and incompatibilities between these subsystems have led to the widely held assumption that one or other subsystem must have preceded the others. Here we experimentally investigate the validity of this assumption by examining the assembly of various biomolecular building blocks from prebiotically plausible intermediates and one-carbon feedstock molecules. We show that precursors of ribonucleotides, amino acids and lipids can all be derived by the reductive homologation of hydrogen cyanide and some of its derivatives, and thus that all the cellular subsystems could have arisen simultaneously through common chemistry. The key reaction steps are driven by ultraviolet light, use hydrogen sulfide as the reductant and can be accelerated by Cu(I)-Cu(II) photoredox cycling.

On the Discovery of Cellular Subsystems in Gene Correlation Networks Using Measures of Centrality

On the Discovery of Cellular Subsystems in Gene Correlation Networks Using Measures of Centrality

In-silico identification of phenotype-biased functional modules

BackgroundPhenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.ResultsIn this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.ConclusionThus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (http://freescience.org/cs/phenotype-biased-biclusters/).

Effects of current localization in high-power microgate bipolar switches with nonideal interconnection between controlled cells

The influence of nonideal interconnection between controlled cells in bipolar microgate switches on current localization at the turn-off stage is analyzed. To estimate the resistance of the distributed electrodes, the entire device is represented as two parallel subsystems of controlled cells interconnected via the effective resistance of the gate circuit. Different scenarios of the turn-off process at nonideal couplings between the cells are numerically simulated for three turn-off circuit regimes by the example of an integrated thyristor with external field control. The turn-off scenarios are studied versus the effective resistance of the gate-off circuit and the ratio between the working surface areas of the cellular subsystems. Limitations on the ultimate switched current are compared. For small-scale inhomogeneities in the series resistance of the gate circuit, the maximum turn-off current in the cascode mode and using a negative voltage source remains an order of magnitude higher than that under the emitter short-circuit conditions.

Deconvolution of the Cellular Force-Generating Subsystems that Govern Cytokinesis Furrow Ingression

Cytokinesis occurs through the coordinated action of several biochemically-mediated stresses acting on the cytoskeleton. Here, we develop a computational model of cellular mechanics, and using a large number of experimentally measured biophysical parameters, we simulate cell division under a number of different scenarios. We demonstrate that traction-mediated protrusive forces or contractile forces due to myosin II are sufficient to initiate furrow ingression. Furthermore, we show that passive forces due to the cell's cortical tension and surface curvature allow the furrow to complete ingression. We compare quantitatively the furrow thinning trajectories obtained from simulation with those observed experimentally in both wild-type and myosin II null Dictyostelium cells. Our simulations highlight the relative contributions of different biomechanical subsystems to cell shape progression during cell division.

Dynamics of signaling, cytoskeleton and cell cycle regulation proteins in glioblastoma cells after sub-lethal photodynamic treatment: Antibody microarray study

BackgroundPhotodynamic therapy (PDT) that induces oxidative stress and cell death is used for tumor destruction in oncology. To characterize early molecular events in photosensitized glioblastoma cells, we studied expression of 224 proteins after sublethal PDT that doesn't kill but wounds cells. MethodsCultured glioblastoma D54Mg cells were photosensitized with 5-aminolevulinic acid so that cell survival was 95–100%. At following 0.5–5.5h protein expression and phosphorylation was assayed using proteomic antibody microarrays. ResultsWithin the first post-treatment hour we observed phosphorylation of protein kinase Raf, adhesion-related kinases FAK and Pyk2, and microtubule-associated protein tau. Protein kinase Cγ and microtubule-associated protein MAP-1B were overexpressed. Dystrophin, calponin, and vinculin, components of the actin cytoskeleton scaffold, microtubule-associated proteins MAP2 and CNP, cytokeratins 4 and 7 were down-regulated that indicated changes in adhesion and cell shape. Down-regulation of cyclins A, D1 and D3, c-Myc, checkpoint proteins chk1/2 and up-regulation of Smad4 could arrest the cell cycle. Overexpression of Bcl-xL and down-regulation of caspase 9 demonstrated anti-apoptotic response. At 2h post-treatment protein expression changed lesser but at 5.5h levels of PKCγ and β-synuclein and phosphorylation of Raf, FAK, Pyk2, and tau increased again. ConclusionsSub-lethal PDT induces complex response of glioblastoma cells including changes in activity and expression of proteins involved in adhesion-mediated signaling, signal transduction, cytoskeleton remodeling, cell cycle regulation and anti-apoptotic processes. General significanceMultiple reactions of various cellular subsystems including adhesion, cytoskeleton, signal transduction, cell cycle, and apoptosis are integrated into the general cell response to a sublethal impact.

A quantitative analysis of monochromaticity in genetic interaction networks

BackgroundA genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed.ResultsIn this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.ConclusionIn summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).

Efficient α, β-motif finder for identification of phenotype-related functional modules

BackgroundMicrobial communities in their natural environments exhibit phenotypes that can directly cause particular diseases, convert biomass or wastewater to energy, or degrade various environmental contaminants. Understanding how these communities realize specific phenotypic traits (e.g., carbon fixation, hydrogen production) is critical for addressing health, bioremediation, or bioenergy problems.ResultsIn this paper, we describe a graph-theoretical method for in silico prediction of the cellular subsystems that are related to the expression of a target phenotype. The proposed (α, β)-motif finder approach allows for identification of these phenotype-related subsystems that, in addition to metabolic subsystems, could include their regulators, sensors, transporters, and even uncharacterized proteins. By comparing dozens of genome-scale networks of functionally associated proteins, our method efficiently identifies those statistically significant functional modules that are in at least α networks of phenotype-expressing organisms but appear in no more than β networks of organisms that do not exhibit the target phenotype. It has been shown via various experiments that the enumerated modules are indeed related to phenotype-expression when tested with different target phenotypes like hydrogen production, motility, aerobic respiration, and acid-tolerance.ConclusionThus, we have proposed a methodology that can identify potential statistically significant phenotype-related functional modules. The functional module is modeled as an (α, β)-clique, where α and β are two criteria introduced in this work. We also propose a novel network model, called the two-typed, divided network. The new network model and the criteria make the problem tractable even while very large networks are being compared. The code can be downloaded from http://www.freescience.org/cs/ABClique/

From Genomics to Scientomics: Expanding the Bioinformation Paradigm

Contemporary biological research (particularly in systems biology and the “omic” disciplines) is factually answering some of the poignant questions associated with the information concept and the limitations of information theory. Here, rather than emphasizing and persisting on a focalized discussion about the i-concept, an ampler conception of “informational entities” will be advocated. The way living cells self-produce, interact with their environment, and collectively organize multi-cell systems becomes a paradigmatic case of what such informational entities consist of. Starting with the fundamentals of molecular recognition, and continuing with the basic cellular processes and subsystems, a new interpretation of the global organization of the living cell must be assayed, so that the equivalents of meaning, value, and intelligence will be approached along an emerging “bioinformational” perspective. Further insights on the informational processes of brains, companies, institutions and human societies at large, and even the sciences themselves, could benefit from—and cross-fertilize with—the advancements derived from the informational approach to living systems. The great advantage fuelling the expansion of the bioinformation paradigm is that, today, cellular information processes may be defined almost to completion at the molecular scale (at least in the case of prokaryotic cells). This is not the case, evidently, with nervous systems and the variety of human organizational, cultural, and social developments. Concretely, the crucial evolutionary phenomenon of protein-domain recombination—knowledge recombination—will be analyzed here as a showcase of, and even as a model for, the interdisciplinary and multidisciplinary mixing of the sciences so prevalent in contemporary societies. Scientomics will be proposed as a new research endeavor to assist advancement. Informationally, the “society of enzymes” appears as a forerunner of the “society of neurons”, and even of the “society of individuals”.

DENSE: efficient and prior knowledge-driven discovery of phenotype-associated protein functional modules

BackgroundIdentifying cellular subsystems that are involved in the expression of a target phenotype has been a very active research area for the past several years. In this paper, cellular subsystem refers to a group of genes (or proteins) that interact and carry out a common function in the cell. Most studies identify genes associated with a phenotype on the basis of some statistical bias, others have extended these statistical methods to analyze functional modules and biological pathways for phenotype-relatedness. However, a biologist might often have a specific question in mind while performing such analysis and most of the resulting subsystems obtained by the existing methods might be largely irrelevant to the question in hand. Arguably, it would be valuable to incorporate biologist's knowledge about the phenotype into the algorithm. This way, it is anticipated that the resulting subsytems would not only be related to the target phenotype but also contain information that the biologist is likely to be interested in.ResultsIn this paper we introduce a fast and theoretically guranteed method called DENSE (Dense and ENriched Subgraph Enumeration) that can take in as input a biologist's prior knowledge as a set of query proteins and identify all the dense functional modules in a biological network that contain some part of the query vertices. The density (in terms of the number of network egdes) and the enrichment (the number of query proteins in the resulting functional module) can be manipulated via two parameters γ and μ, respectively.ConclusionThis algorithm has been applied to the protein functional association network of Clostridium acetobutylicum ATCC 824, a hydrogen producing, acid-tolerant organism. The algorithm was able to verify relationships known to exist in literature and also some previously unknown relationships including those with regulatory and signaling functions. Additionally, we were also able to hypothesize that some uncharacterized proteins are likely associated with the target phenotype. The DENSE code can be downloaded from http://www.freescience.org/cs/DENSE/

Cell design in bacteria as a convex optimization problem

In this paper, we investigate the prediction of the cell composition of bacteria with respect to their medium. By modeling the bacterium as an interconnection of subsystems, the problem is written as a non-smooth convex optimization problem equivalent to a Linear Programming feasibility problem. We then obtain a new method, called Resource Balance Analysis (RBA), predicting the distribution of the available resources in the medium among the various cellular subsystems. Beyond its predictive capability, the proposed approach grasps some fundamental aspects of the bacterium physiology by including a refined model. This method reveals the existence of an intrinsic bottleneck in the system resource distribution of the bacterium, leading to the existence of a structural limitation of its growth rate which can be predicted. RBA is also able to predict the configuration of the metabolic network for a given medium at steady-state regimen which nicely fits the available experimental results for the gram-positive model bacterium Bacillus subtilis.

A Dynamic Model of Furrow Ingression during Cytokinesis

Cytokinesis is one of the most elegant transformations in nature, whereby a mother cell is separated into two identical daughter cells. This complex process relies not only on biochemical reactions, but also on the mechanics and geometry. We create a computational model to examine the role geometry, cellular mechanics and forces influence cytokinesis. Using the level set method, an effective tool for studying shape changes, coupled with high fidelity, experimentally obtained stress profiles and material models, we model cell division under a variety of conditions. We carry out a systematic, reverse engineering of the cellular subsystems, and recreate experimentally measured furrow-thinning trajectories measured for several genotypes in Dictyostelim cells. In doing so, we identify the contributions of motor proteins, substrate adhesion and surface tension in shaping the furrow-thinning trajectory. This model allows for the modular addition and removal of cellular subsystems and provides an attractive framework for the study of altered genotypes and conceptual models. In particular, it provides an effective way to study the effects of temporally and spatially molecular and mechanical inhomogeneities have on the furrow trajectory.

Deep epistasis in human metabolism.

We extend and apply a method that we have developed for deriving high-order epistatic relationships in large biochemical networks to a published genome-scale model of human metabolism. In our analysis we compute 33,328 reaction sets whose knockout synergistically disables one or more of 43 important metabolic functions. We also design minimal knockouts that remove flux through fumarase, an enzyme that has previously been shown to play an important role in human cancer. Most of these knockout sets employ more than eight mutually buffering reactions, spanning multiple cellular compartments and metabolic subsystems. These reaction sets suggest that human metabolic pathways possess a striking degree of parallelism, inducing "deep" epistasis between diversely annotated genes. Our results prompt specific chemical and genetic perturbation follow-up experiments that could be used to query in vivo pathway redundancy. They also suggest directions for future statistical studies of epistasis in genetic variation data sets.

Ariadne's ChemEffect and Pathway Studio knowledge base

Importance of the field: Drug discovery and development is a very complex and costly process. Understanding the detailed molecular mechanisms of a disease and drug actions can make it more efficient not only for new target discovery but also for lead prioritization, drug repositioning and development of biomarkers for drug efficacy and safety. Access to formalized knowledge about functions of proteins and small molecules is crucial for rationalization of the drug development process, and scientific publications are the main source of this knowledge. Protein knowledge networks capturing protein functions, protein–protein relations and organization of proteins in complex cellular sub-systems are making their way into modern drug discovery. Chemical networks representing multiple aspects of chemical functional information integrated into a protein systems biology network is even more advanced and promising paradigm.Areas covered in this review: This review describes utilization of literature-derived protein and chemical functional knowledge bases in drug development.What the reader will gain: Readers will gain an understanding of how integrated protein and chemical knowledge networks can be used for understanding and building the models of cellular events, disease mechanisms, and drug actions, finding biomarkers of drug efficacy and safety, as well as interpretation of high-throughput gene expression, proteomic and metabolomic experiments.Take home message: Integrated literature-derived protein and chemical knowledge bases can rationalize many aspects of drug development process including drug repositioning and biomarker design.

Host Cell Factors in HIV Replication: Meta-Analysis of Genome-Wide Studies

We have analyzed host cell genes linked to HIV replication that were identified in nine genome-wide studies, including three independent siRNA screens. Overlaps among the siRNA screens were very modest (<7% for any pairwise combination), and similarly, only modest overlaps were seen in pairwise comparisons with other types of genome-wide studies. Combining all genes from the genome-wide studies together with genes reported in the literature to affect HIV yields 2,410 protein-coding genes, or fully 9.5% of all human genes (though of course some of these are false positive calls). Here we report an “encyclopedia” of all overlaps between studies (available at http://www.hostpathogen.org), which yielded a more extensively corroborated set of host factors assisting HIV replication. We used these genes to calculate refined networks that specify cellular subsystems recruited by HIV to assist in replication, and present additional analysis specifying host cell genes that are attractive as potential therapeutic targets.

The harmony of the cell: the regulatory design of cellular processes

The living cell is a complex system of interacting processes. The properties of the agents that facilitate these processes, such as enzymes, transporters and receptors, must be tuned to each other if the system is to behave harmoniously. The present chapter describes how the regulatory design of cellular subsystems that makes this harmonious behaviour possible can be visualized on a graph that combines the so-called log-log rate characteristics of these subsystems. The tools that are needed to create and analyse these graphs are metabolic control analysis, supply-demand analysis, enzyme kinetics and computer simulation.

Growth and ligninolytic system production dynamics of the Phanerochaete chrysosporium fungus : A modelling and optimization approach

The well-documented ability to degrade lignin and a variety of complex chemicals showed by the white-rot fungus Phanerochaete chrysosporium has made it the subject of many studies in areas of environmental concern, including pulp bioleaching and bioremediation technologies. However, until now, most of the work in this field has been focused on the ligninolytic sub-system but, due to the great complexity of the involved processes, less progress has been made in understanding the biochemical regulatory structure that could explain growth dynamics, the substrate utilization and the ligninolytic system production itself. In this work we want to tackle this problem from the perspectives and approaches of systems biology, which have been shown to be effective in the case of complex systems. We will use a top-down approach to the construction of this model aiming to identify the cellular sub-systems that play a major role in the whole process. We have investigated growth dynamics, substrate consumption and lignin peroxidase production of the P. chrysosporium wild type under a set of definite culture conditions. Based on data gathered from different authors and in our own experimental determinations, we built a model using a GMA power-law representation, which was used as platform to make predictive simulations. Thereby, we could assess the consistency of some current assumptions about the regulatory structure of the overall process. The model parameters were estimated from a time series experimental measurements by means of an algorithm previously adapted and optimized for power-law models. The model was subsequently checked for quality by comparing its predictions with the experimental behavior observed in new, different experimental settings and through perturbation analysis aimed to test the robustness of the model. Hence, the model showed to be able to predict the dynamics of two critical variables such as biomass and lignin peroxidase activity when in conditions of nutrient deprivation and after pulses of veratryl alcohol. Moreover, it successfully predicts the evolution of the variables during both, the active growth phase and after the deprivation shock. The close agreement between the predicted and observed behavior and the advanced understanding of its kinetic structure and regulatory features provides the necessary background for the design of a biotechnological set-up designed for the continuous production of the ligninolityc system and its optimization.

New SIP compression mechanism in IMS

Session Initiation Protocol(SIP)was used in 3G cellular IP Multimedia Subsystems(IMS)to perform multimedia session setup and maintenance.However,it is text-based and has large messages with size frequently exceeding several hundred bytes during the setup session;consequently,it causes more setup session delay.In this paper,a new algorithm combining LZW with HUFFMAN and static dictionary was proposed,which was named as S-LZW-HUFFMAN.Simulation results prove that the new algorithm can effectively reduce the message size and shorten the session setup delay in IMS.