Application Of Systems Biology Research Articles

What happens if you pose the wrong questions?

What happens if you pose the wrong questions?

Succinate production in Escherichia coli

Succinate has been recognized as an important platform chemical that can be produced from biomass. While a number of organisms are capable of succinate production naturally, this review focuses on the engineering of Escherichia coli for the production of four-carbon dicarboxylic acid. Important features of a succinate production system are to achieve an optimal balance of reducing equivalents generated by consumption of the feedstock, while maximizing the amount of carbon channeled into the product. Aerobic and anaerobic production strains have been developed and applied to production from glucose and other abundant carbon sources. Metabolic engineering methods and strain evolution have been used and supplemented by the recent application of systems biology and in silico modeling tools to construct optimal production strains. The metabolic capacity of the production strain, the requirement for efficient recovery of succinate, and the reliability of the performance under scaleup are important in the overall process. The costs of the overall biorefinery-compatible process will determine the economic commercialization of succinate and its impact in larger chemical markets.

Systems Biology: Confronting the Complexity of Cancer

The AACR-NCI Conference "Systems Biology: Confronting the Complexity of Cancer" took place from February 27 to March 2, 2011, in San Diego, CA. Several themes resonated during the meeting, notably (i) the need for better methods to distill insights from large-scale networks, (ii) the importance of integrating multiple data types in constructing more realistic models, (iii) challenges in translating insights about tumorigenic mechanisms into therapeutic interventions, and (iv) the role of the tumor microenvironment, at the physical, cellular, and molecular levels. The meeting highlighted concrete applications of systems biology to cancer, and the value of collaboration between interdisciplinary researchers in attacking formidable problems.

Live cell single‐molecule detection in systems biology

In this review, we describe technology and use of single-fluorophore imaging and detection in living cells with regard to application in systems biology and medicine. Because all biological reactions occur under aqueous conditions, the realization of single-fluorophore imaging using an optical microscope has led to the direct observation of biological molecules at work. Today, we can observe single molecules in individual living cells and even higher multicellular organisms. Using single-molecule imaging, we can determine the absolute values of kinetic and dynamic parameters of molecular reactions as a whole and during fluctuations and distribution. In addition, identification of the coordinate of single molecules has enabled super-localization techniques to virtually improve spatial resolution of optical microscopy. Single-molecule detection that depends on point detection instead of imaging is also useful in detecting concentrations, diffusive movements, and molecular interactions in living cells, especially in the cytoplasm. The precise and absolute values of positional, kinetic, and dynamic parameters that are determined by single-molecule imaging and detection in living cells constitute valuable data on unitary biological reactions, because they are obtained without destroying the integrity of complex cellular systems. Moreover, most parameters that are determined by single-molecule measurements can be substituted directly into equations that describe kinetic and dynamic models in systems biology and medicine.

A decrease of intracellular ATP is compensated by increased respiration and acidification at sub-lethal parathion concentrations in murine embryonic neuronal cells: Measurements in metabolic cell-culture chips

We present a label-free in vitro method for testing the toxic potentials of chemical substances using primary neuronal cells. The cells were prepared from 16-day-old NMRI mouse embryos and cultured on silicon chips (www.bionas.de) under the influence of different parathion concentrations with sensors for respiration (Clark-type oxygen electrodes), acidification (pH-ISFETs) and cell adhesion (interdigitated electrode structures, IDES). After 12 days in vitro, the sensor readouts were simultaneously recorded for 350min in the presence of parathion applying a serial 1:3 dilution. The parathion-dependent data was fitted by logistic functions. IC50 values of approximately 105μM, 65μM, and 54μM were found for respiration, acidification, and adhesion, respectively. An IC50 value of approximately 36μM was determined from the intracellular ATP-levels of cells, which were detected by an ATP-luminescence assay using micro-well plates. While the intracellular ATP level and cell adhesion showed no deviation from a simple logistic decay, increases of approximately 29% in the respiration and 15% in the acidification rates above the control values were found at low parathion concentrations, indicating hormesis. These increases could be fitted by a modified logistic function. We believe that the label-free, continuous, multi-parametric monitoring of cell-metabolic processes may have applications in systems-biology and biomedical research, as well as in environmental monitoring. The parallel characterization of IC50 values and hormetic effects may provide new insights into the metabolic mechanisms of toxic challenges to the cell.

Focus Issue: Series on Computational and Systems Biology

The application of computational biology and systems biology is yielding quantitative insight into cellular regulatory phenomena. For the month of September, Science Signaling highlights research featuring computational approaches to understanding cell signaling and investigation of signaling networks, a series of Teaching Resources from a course in systems biology, and various other articles and resources relevant to the application of computational biology and systems biology to the study of signal transduction.

Process algebra-based computational tools in ecological modelling

Ecological systems and processes are inherently variable. The multiplicity of interacting processes and the variety of components call for multi-level, integrative models that keep track of stochasticity and quantify its consequences. Mathematics and physics already helped biologists a lot, but the composability feature of process algebra-based computational tools may provide additional help in modelling interacting stochastic processes. Compositional models can be built in a modular way, and can be easily standardized and evaluated rigorously. Following initial applications in systems biology, the concepts of parallelism and concurrency can also be of key importance to future systems ecology. We provide an overview of process algebra-based approaches in ecology, discuss the perspectives of this research line and provide a toy and a real example for illustration.

Application of Computational Systems Biology to Explore Environmental Toxicity Hazards

Background: Computer-based modeling is part of a new approach to predictive toxicology.Objectives: We investigated the usefulness of an integrated computational systems biology approach in a case study involving the isomers and metabolites of the pesticide dichlorodiphenyltrichloroethane (DDT) to ascertain their possible links to relevant adverse effects.Methods: We extracted chemical–protein association networks for each DDT isomer and its metabolites using ChemProt, a disease chemical biology database that includes both binding and gene expression data, and we explored protein–protein interactions using a human interactome network. To identify associated dysfunctions and diseases, we integrated protein–disease annotations into the protein complexes using the Online Mendelian Inheritance in Man database and the Comparative Toxicogenomics Database.Results: We found 175 human proteins linked to p,p´-DDT, and 187 to o,p´-DDT.Dichlorodiphenyldichloroethylene (p,p´-DDE) was the metabolite with the highest number of links, with 52. We grouped proteins for each compound based on their disease annotations. Although the two data sources differed in linkage to diseases, integrated results predicted that most diseases were linked to the two DDT isomers. Asthma was uniquely linked with p,p´-DDT, and autism with o,p´-DDT. Several reproductive and neurobehavioral outcomes and cancer types were linked to all three compounds.Conclusions: Computer-based modeling relies on available information. Although differences in linkages to proteins may be due to incomplete data, our results appear meaningful and suggest that the parent DDT compounds may be responsible for more disease connections than the metabolites. The findings illustrate the potential use of computational approaches to toxicology.

Applications and trends in systems biology in biochemistry

Systems biology has received an ever increasing interest during the last decade. A large amount of third-party funding is spent on this topic, which involves quantitative experimentation integrated with computational modeling. Industrial companies are also starting to use this approach more and more often, especially in pharmaceutical research and biotechnology. This leads to the question of whether such interest is wisely invested and whether there are success stories to be told for basic science and/or technology/biomedicine. In this review, we focus on the application of systems biology approaches that have been employed to shed light on both biochemical functions and previously unknown mechanisms. We point out which computational and experimental methods are employed most frequently and which trends in systems biology research can be observed. Finally, we discuss some problems that we have encountered in publications in the field.

Modeling autonomic regulation of cardiac function and heart rate variability in human endotoxemia

Heart rate variability (HRV), the quantification of beat-to-beat variability, has been studied as a potential prognostic marker in inflammatory diseases such as sepsis. HRV normally reflects significant levels of variability in homeostasis, which can be lost under stress. Much effort has been placed in interpreting HRV from the perspective of quantitatively understanding how stressors alter HRV dynamics, but the molecular and cellular mechanisms that give rise to both homeostatic HRV and changes in HRV have received less focus. Here, we develop a mathematical model of human endotoxemia that incorporates the oscillatory signals giving rise to HRV and their signal transduction to the heart. Connections between processes at the cellular, molecular, and neural levels are quantitatively linked to HRV. Rhythmic signals representing autonomic oscillations and circadian rhythms converge to modulate the pattern of heartbeats, and the effects of these oscillators are diminished in the acute endotoxemia response. Based on the semimechanistic model developed herein, homeostatic and acute stress responses of HRV are studied in terms of these oscillatory signals. Understanding the loss of HRV in endotoxemia serves as a step toward understanding changes in HRV observed clinically through translational applications of systems biology based on the relationship between biological processes and clinical outcomes.

Redox Control of the Survival of Healthy and Diseased Cells

Abstract Cellular redox homeostasis is the first line of defense against diverse stimuli and is crucial for various biological processes. Reactive oxygen species (ROS), byproducts of numerous cellular events, may serve in turn as signaling molecules to regulate cellular processes such as proliferation, differentiation, and apoptosis. However, when overproduced ROS fail to be scavenged by the antioxidant system, they may damage cellular components, giving rise to senescent, degenerative, or fatal lesions in cells. Accordingly, this review not only covers general mechanisms of ROS production under different conditions, but also focuses on various types of ROS-involved diseases, including atherosclerosis, ischemia/reperfusion injury, diabetes mellitus, neurodegenerative diseases, and cancer. In addition, potentially therapeutic agents and approaches are reviewed in a relatively comprehensive manner. However, due to the complexity of ROS and their cellular impacts, we believe that the goal to design more effective approaches or agents may require a better understanding of mechanisms of ROS production, particularly their multifaceted impacts in disease at biochemical, molecular, genetic, and epigenetic levels. Thus, it requires additional tools of omics in systems biology to achieve such a goal. Antioxid. Redox Signal. 15, 2867-2908.

Tumor microenvironment in cancer treatment and metastasis

Tumor microenvironment in cancer treatment and metastasis

Multi-comparative systems biology analysis reveals time-course biosignatures of in vivo bovine pathway responses to B. melitensis, S. enterica Typhimurium and M. avium paratuberculosis

BackgroundTo decipher the complexity and improve the understanding of host-pathogen interactions, biologists must adopt new system level approaches in which the hierarchy of biological interactions and dynamics can be studied. This paper presents the application of systems biology for the cross-comparative analysis and interactome modeling of three different infectious agents, leading to the identification of novel, unique and common molecular host responses (biosignatures).MethodsA computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Salmonella enterica Typhimurium (STM) and Mycobacterium avium paratuberculosis (MAP). A bovine ligated ileal loop biological model was employed to capture the host gene expression response at four time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a systematic comparative analysis of pathway and Gene Ontology category perturbations.ResultsA cross-comparative assessment of 219 pathways and 1620 gene ontology (GO) categories was performed on each pathogen-host condition. Both unique and common pathway and GO perturbations indicated remarkable temporal differences in pathogen-host response profiles. Highly discriminatory pathways were selected from each pathogen condition to create a common system level interactome model comprised of 622 genes. This model was trained with data from each pathogen condition to capture unique and common gene expression features and relationships leading to the identification of candidate host-pathogen points of interactions and discriminatory biosignatures.ConclusionsOur results provide deeper understanding of the overall complexity of host defensive and pathogen invasion processes as well as the identification of novel host-pathogen interactions. The application of advanced computational methods for developing interactome models based on DBN has proven to be instrumental in conducting multi-conditional cross-comparative analyses. Further, this approach generates a fully simulateable model with capabilities for predictive analysis as well as for diagnostic pattern recognition. The resulting biosignatures may represent future targets for identification of emerging pathogens as well as for development of antimicrobial drugs, immunotherapeutics, or vaccines for prevention and treatment of diseases caused by known, emerging/re-emerging infectious agents.

Characterization of Interactions between PilA from Pseudomonas aeruginosa Strain K and a Model Membrane

Type IV pili are important adhesion and motility factors in both gram-negative and gram-positive bacterial species, making pilus assembly from pilin subunits an important biophysical mechanism to understand at an atomic level. Knowledge of the pilus assembly mechanism has applications in antibiotic development, microbial physiology, and systems biology. We applied molecular dynamics simulations to investigate the position and orientation of Pseudomonas aeruginosa strain K PilA in a model membrane as well as the interactions of PilA with the surrounding lipids, identifying several key residues that stabilize the position of PilA within the membrane. Furthermore, we performed umbrella sampling to determine the free energy of extracting this protein from the membrane. Our results provide insight into the molecular events and energetics associated with pilus assembly, thereby initiating a detailed examination of the dynamics of this process.

Deep and Highly Sensitive Proteome Coverage by LC-MS/MS Without Prefractionation

In-depth MS-based proteomics has necessitated fractionation of either proteins or peptides or both, often requiring considerable analysis time. Here we employ long liquid chromatography runs with high resolution coupled to an instrument with fast sequencing speed to investigate how much of the proteome is directly accessible to liquid chromatography-tandem MS characterization without any prefractionation steps. Triplicate single-run analyses identified 2990 yeast proteins, 68% of the total measured in a comprehensive yeast proteome. Among them, we covered the enzymes of the glycolysis and gluconeogenesis pathway targeted in a recent multiple reaction monitoring study. In a mammalian cell line, we identified 5376 proteins in a triplicate run, including representatives of 173 out of 200 KEGG metabolic and signaling pathways. Remarkably, the majority of proteins could be detected in the samples at sub-femtomole amounts and many in the low attomole range, in agreement with absolute abundance estimation done in previous works (Picotti et al. Cell, 138, 795–806, 2009). Our results imply an unexpectedly large dynamic range of the MS signal and sensitivity for liquid chromatography-tandem MS alone. With further development, single-run analysis has the potential to radically simplify many proteomic studies while maintaining a systems-wide view of the proteome.

ABC SMC for parameter estimation and model selection with applications in systems biology

AbstractApproximate Bayesian Computation (ABC) methods can be used in situations where the evaluation of the likelihood is computationally prohibitive. They are thus ideally suited for analyzing the complex dynamical models encountered in systems biology, where knowledge of the full (approximate) posterior is often essential.This talk gives an overview of an ABC algorithm based on Sequential Monte Carlo (ABC SMC). Different uses of the algorithm will be presented, depending on the application question of interest. The first is the general parameter estimation framework, where the interest lies in estimating the posterior parameter distribution from available experimental data. In the second context we ask whether the model can reproduce a desired qualitative or semi-quantitative behavior, and what dynamic behaviors the system can achieve. The third context discussed here for use of the ABC SMC algorithm is that of model selection. Here we ask which of the models (i.e. model topologies) from a pool of proposed candidate models represents the most suitable hypothesis about the biological system of interest.The focus of the presentation is applications of ABC SMC to questions from systems biology. ABC SMC is applied to a variety of biological models, including stochastic and deterministic descriptions of eukaryotic signaling pathways and prokaryotic stress response pathways. In particular, the algorithm is employed to understand understand the qualitative behavior of the phage shock protein response in bacteria Escherichia coli, and the model selection algorithm is applied to distinguish between differential equation models of MAP kinase phosphorylation dynamics and the JAK-STAT signaling pathway.

ABC SMC for parameter estimation and model selection with applications in systems biology

ABC SMC for parameter estimation and model selection with applications in systems biology

Applying systems biology to develop T cell tolerance: Jurkat cell responses to knockdown hub genes using siRNA in-vitro transfection (169.47)

Abstract Introduction: Systems biology is increasingly gaining importance in studying complex genome wise associations and will, in future, integrate into personalized medicine. Aim: To study the effect of T cell lipotransfection by certain hub genes and evaluate their role in inducing T cell anergy. Methods: Using publically available gene-gene interactions, we constructed a network using an open bioinformatics software platform for visualizing molecular interaction networks to identify the following upregulated hub genes: ATP5C1, IL6ST, PRKCZ, MYC, FOS, JUN and MAPK1. We hypothesized that suppression of hub genes in the gene network would result in significant phenotypic effects on T cells resulting in T cell hyporesponsiveness and examined this in-vitro. Jurkat and HeLa cells were transfected with siRNA for the selected hub genes. Cell proliferation was measured using ATP luminescence and BRDU labeling. Results: We found a significant decrease in ATP production by Jurkat cells (P<0.05) when the hub genes ATP5C1 and PRKCZ were knocked down using siRNA transfection. No difference in ATP production was observed in siRNA transfected HELA cells. However, these cells showed a significant (P<0.05) decrease in cell proliferation when the genes MAPK1, IL6ST, ATP5C1, JUN and FOS were knocked down using siRNA. This experiment provides proof of principle of applying a systems biology approach in identifying hub genes for targeted therapeutics in transplant immunology.

On network entropy and bio-interactome applications

Abstract Networks represent a main methodological instrument in systems biology applications. In particular, modularity is widely investigated in the attempt to elucidate the regulative and correlative nature of gene and protein associations, respectively. However, modular maps are only approximate representations due to two main factors. First, the resolution spectrum that has to be covered is wide and method-dependent. Second, the randomness underlying network dynamics and influencing them through fluctuations and system perturbations, is difficult to measure. We investigate both aspects by an application to the yeast protein interactome network, and suggest that a non-extensive characterization of entropy may play a role for elucidating both random and biological variation.

Multi-scale modelling and simulation in systems biology

The aim of systems biology is to describe and understand biology at a global scale where biological functions are recognised as a result of complex mechanisms that happen at several scales, from the molecular to the ecosystem. Modelling and simulation are computational tools that are invaluable for description, prediction and understanding these mechanisms in a quantitative and integrative way. Therefore the study of biological functions is greatly aided by multi-scale methods that enable the coupling and simulation of models spanning several spatial and temporal scales. Various methods have been developed for solving multi-scale problems in many scientific disciplines, and are applicable to continuum based modelling techniques, in which the relationship between system properties is expressed with continuous mathematical equations or discrete modelling techniques that are based on individual units to model the heterogeneous microscopic elements such as individuals or cells. In this review, we survey these multi-scale methods and explore their application in systems biology.