System Biology

Systems biology, the formal description of the structure and of the dynamics of biomolecular systems, is acquiring capital importance in the modern, "-omics" based, life sciences. But the classical mathematical approaches to modeling and simulation of inter-and intra-cellular processes reveal inadequate to tame the inherent complexity of life's molecular basis. Formal approaches from theoretical computer science appear more promising, permitting to describe the structure and the behavior of the complex biological systems composing the structure and the behavior of its components. We present a novel approach for modeling and simulation of metabolic pathways. Each enzyme, metabolite and complex involved in a pathway is represented by an autonomous software agent. We abstract the molecules at the mesoscale as spheres with a radius proportional to their weight. The molecules move with Brownian motion in a 3D virtual cell space. When an enzyme or a complex encounters an affine substrate for a metabolic reaction, than such reaction can take place, according to Michaelis-Menten kinetics. The system has been implemented as a society of agents on a mobile computing middleware and can be therefore executed on a network of computers.

In search of improving the ways to create meaningful systems from requirements specifications, this paper combines the showing and telling of how a system should behave. Using scenario-based programming and the language of live sequence charts, we suggest how user interaction with the system and user written requirements in natural language can interleave to create specifications through an interface that is both natural and agile.

The general architecture of a genetic regulatory network consists of strong connected components of its interaction graph, to which are attached three kinds of sub-structures: -a set of up-trees, rooted in the sources of the interaction graph, represented either by small RNAs like microRNAs: nuclear miRs or mitochondrial mitomiRs, i.e., translational inhibitors respectively of the messenger mRNAs and of the transfer tRNAs, or by gene repressors and/or inductors, -a set of circuits in the core (in graph sense) of the strong connected components of the interaction graph, -a set of down-trees going to the sinks of the interaction graph, i.e., to genes controlled, but not controlling any other gene. The various state configurations it is possible to observe in the above sub-structures correspond to different dynamical asymptotic behaviors. The network dynamics have in general a small number of attractors, corresponding in the Delbrück's paradigm to the functions of the tissue they represent. Examples of such dynamics will be given in embryology: cell proliferation control network in mammals and gastrulation control network in Drosophila melanogaster.

menaquinone. Deletion mutants have reduced oxygen consumption rates and electron transport efficiency, although membrane integrity, cellular ATP levels and rates of ATP synthesis are unaltered as are bacterial growth rates. However, in vitro infection studies using J774A.1 cells and the M. tuberculosis Rv0561c deletion mutant demonstrated that the mutant is unable to survive in macrophage-like cells. Thus, Rv0561c represents a novel, contextually essential enzyme that is a potential drug target in M. tuberculosis. Funded by NIH/NIAID grant # AI049151.

We propose a network structure-based model for heterosis, and investigate it relying on metabolite profiles from Arabidopsis. A simple feed-forward two-layer network model (the Steinbuch matrix) is used in our conceptual approach. It allows for directly relating structural network properties with biological function. Interpreting heterosis as increased adaptability, our model predicts that the biological networks involved show increasing connectivity of regulatory interactions. A detailed analysis of metabolite profile data reveals that the increasing-connectivity prediction is true for graphical Gaussian models in our data from early development. This mirrors properties of observed heterotic Arabidopsis phenotypes. Furthermore, the model predicts a limit for increasing hybrid vigor with increasing heterozygosity-a known phenomenon in the literature.

Abstract: The whole complex process to obtain a protein encoded by a gene is difficult to include in a mathematical model. There are many models for describing different aspects of a genetic network. Finding a better model is one of the most important and interesting ...

Background While functional genomics, focused on gene functions and gene-gene interactions, has become a very active field of research in molecular biology, equivalent methodologies embracing the environment and gene-environment interactions are relatively less developed. Understanding the function of environmental factors is, however, of paramount importance given the complex, interactive nature of environmental and genetic factors across multiple time scales. Results Here, we propose a systems biology framework, where the function of environmental factors is set at its core. We set forth a "reverse" functional analysis approach, whereby cellular functions are reconstructed from the analysis of dynamic envirome data. Our results show these data sets can be mapped to less than 20 core cellular functions in a typical mammalian cell culture, while explaining over 90% of flux data variance. A functional enviromics map can be created, which provides a template for manipulating...

Systems Biology has a mission that puts it at odds with traditional paradigms of physics and molecular biology, such as the simplicity requested by Occam's razor and minimum energy/maximal efficiency. By referring to biochemical experiments on control and regulation, and on flux balancing in yeast, we show that these paradigms are inapt. Systems Biology does not quite converge with biology either: Although it certainly requires accurate ‘stamp collecting’, it discovers quantitative laws. Systems Biology is a science of its own, discovering own fundamental principles, some of which we identify here.

Vascular endothelial growth factor (VEGF) is a family of cytokines for which the dysregulation of expression is involved in many diseases; for some, excess VEGF causes pathological hypervascularization, while for others VEGF-induced vascular remodeling may alleviate ischemia and/or hypoxia. Anti-angiogenic therapies attacking the VEGF pathway have begun to live up to their promise for treatment of certain cancers and of age-related macular degeneration. However, the corollary is not yet true: in coronary artery disease and peripheral artery disease, clinical trials of pro-angiogenic VEGF delivery have not, so far, proven successful. The VEGF and VEGF-receptor system is complex, with at least five ligand genes, some encoding multiple protein isoforms and five receptor genes. A systems biology approach for designing pro-angiogenic therapies, using a combination of quantitative experimental approaches and detailed computational models, is essential to deal with this complexity and to u...

Human nutrition and metabolism may serve as the paradigm for the complex interplay of the genome with its environment. The concept of nutrigenomics now enables science with new tools and comprehensive analytical techniques to investigate this interaction at all levels of the complexity of the organism. Moreover, nutrigenomics seeks to better define the homeostatic control mechanisms, identify the de-regulation in the early phases of diet-related diseases, and attempts to assess to what extent an individual's sensitizing genotype contributes to the overall health or disease state. In a comparative approach nutrigenomics uses biological systems of increasing complexity from yeast to mammalian models to define the general rules of metabolic and genetic mechanisms in adaptations to the nutritional environment. Powerful information technology, bioinformatics and knowledge management tools as well as new mathematical and computational approaches now make it possible to study these mol...

This paper is about state estimation in a timed probabilistic setting. The main contribution is a general procedure to design an observer for computing the probabilities of the states for labeled continuous time Markov models as functions of time, based on a sequence of observations and their associated time stamps that have been collected thus far. Two notions of state consistency with respect to such a timed observation sequence are introduced and related necessary and sucient conditions are derived. The method is then applied to the detection of cyber-attacks. The plant and the possible attacks are described in terms of a labeled continuous time Markov model that includes both observable and unobservable events, and where each attack corresponds to a particular subset of states. Consequently, attack detection is reformulated as a state estimation problem.

One aim of synthetic biology is to exert systematic control over cellular behavior, either for medical purposes or to "program" microorganisms. An engineering approach to the design of biological controllers demands a quantitative understanding of the dynamics of both the system to be controlled and the controllers themselves. Here we focus on a widely used method of exerting control in bacterial cells: plasmid vectors bearing gene-promoter pairs. We study two variants of the simplest such element, an unregulated promoter constitutively expressing its gene, against the varying genomic background of four Escherichia coli cell strains. Absolute protein numbers and rates of expression vary with both cell strain and plasmid type, as does the variability of expression across the population. Total variability is most strongly coupled to the cell division process, and after cell size is scaled away, plasmid copy number regulation emerges as a significant effect. We present simple models that capture the main features of the system behavior. Our results confirm that complex interactions between plasmids and their hosts can have significant effects on both expression and variability, even in deliberately simplified systems.

One aim of synthetic biology is to exert systematic control over cellular behavior, either for medical purposes or to "program" microorganisms. An engineering approach to the design of biological controllers demands a quantitative understanding of the dynamics of both the system to be controlled and the controllers themselves. Here we focus on a widely used method of exerting control in bacterial cells: plasmid vectors bearing gene-promoter pairs. We study two variants of the simplest such element, an unregulated promoter constitutively expressing its gene, against the varying genomic background of four Escherichia coli cell strains. Absolute protein numbers and rates of expression vary with both cell strain and plasmid type, as does the variability of expression across the population. Total variability is most strongly coupled to the cell division process, and after cell size is scaled away, plasmid copy number regulation emerges as a significant effect. We present simple models that capture the main features of the system behavior. Our results confirm that complex interactions between plasmids and their hosts can have significant effects on both expression and variability, even in deliberately simplified systems.

Ultra-performance liquid chromatography – mass spectrometry (UPLC-MS) is widely used for untargeted metabolomics in biomedical research. To optimize the quality and precision of UPLC-MS metabolomic analysis, evaluation of blank samples for the elimination of background features is required. Although blanks are usually run either at the beginning or at the end of a sequence of samples, a systematic analysis of their effect of the instrument performance has not been properly documented. Using the analysis of two common bio-fluids (plasma and urine), we describe how the injection of blank samples within a sequence of samples may affect both the chromatographic and MS detection performance depending on several factors, including the sample matrix and the physicochemical properties of the metabolites of interest. The analysis of blanks and post-blank conditioning samples using t-tests, PCA and guided-PCA provides useful information for the elimination of background UPLC-MS features, the ...

Bacterial spore formers are prime organisms of concern in the food industry. Spores from the genus Bacillus are extremely stress resistant, most notably exemplified by high thermotolerance. This sometimes allows surviving spores to germinate and grow out to vegetative cells causing food spoilage and possible intoxication. Similar issues though more pending toward spore toxigenicity are observed for the anaerobic Clostridia. The paper indicates the nature of stress resistance and highlights contemporary molecular approaches to analyze the mechanistic basis of it in Bacilli. A molecular comparison between a laboratory strain and a food borne isolate, very similar at the genomic level to the laboratory strain but generating extremely heat resistant spores, is discussed. The approaches cover genome-wide genotyping, proteomics and genome-wide expression analyses studies. The analyses aim at gathering sufficient molecular information to be able to put together an initial framework for dynamic modelling of spore germination and outgrowth behaviour. Such emerging models should be developed both at the population and at the single spore level. Tools and challenges in achieving the latter are succinctly discussed.

Electronic health records stored in primary care databases might be a valuable source to study the epidemiology of atopic disorders and their impact on health-care systems and costs. However, the prevalence of atopic disorders in such databases varies considerably and needs to be addressed. For this study, all children aged 0-18 years listed in a representative primary care database in the period 2002-2014, with sufficient data quality, were selected. The effects of four different strategies on the prevalences of atopic disorders were examined: (1) the first strategy examined the diagnosis as recorded in the electronic health records, whereas the (2) second used additional requirements (i.e., the patient had at least two relevant consultations and at least two relevant prescriptions). Strategies (3) and ( ) assumed the atopic disorders to be chronic based on strategy 1 and 2, respectively. When interested in cases with a higher probability of a clinically relevant disorder, strategy 2 yields a realistic estimation of the prevalence of atopic disorders derived from primary care data. Using this strategy, of the 478,076 included children, 28,946 (6.1%) had eczema, 29,182 (6.1%) had asthma, and 28,064 (5.9%) had allergic rhinitis; only 1251 (0.3%) children had all three atopic disorders. Prevalence rates are highly dependent on the clinical atopic definitions used. The strategy using cases with a higher probability of clinically relevant cases, yields realistic prevalences to establish the impact of atopic disorders on health-care systems. However, studies are needed to solve the problem of identifying atopic disorders that are missed or misclassified.

Electronic health records stored in primary care databases might be a valuable source to study the epidemiology of atopic disorders and their impact on health-care systems and costs. However, the prevalence of atopic disorders in such databases varies considerably and needs to be addressed. For this study, all children aged 0-18 years listed in a representative primary care database in the period 2002-2014, with sufficient data quality, were selected. The effects of four different strategies on the prevalences of atopic disorders were examined: (1) the first strategy examined the diagnosis as recorded in the electronic health records, whereas the (2) second used additional requirements (i.e., the patient had at least two relevant consultations and at least two relevant prescriptions). Strategies (3) and (4) assumed the atopic disorders to be chronic based on strategy 1 and 2, respectively. When interested in cases with a higher probability of a clinically relevant disorder, strategy...

Complete genome sequences together with high throughput technologies have made comprehensive characterizations of gene expression patterns possible. While genome-wide measurement of mRNA levels was one of the first applications of these advances, other important aspects of gene expression are also amenable to a genomic approach, for example, the translation of message into protein. Earlier we reported a high throughput technology for simultaneously studying mRNA level and translation, which we termed translation state array analysis, or TSAA. The current studies test the proposition that TSAA can identify novel instances of translation regulation at the genome-wide level. As a biological model, cultures of Saccharomyces cerevisiae were cell cycle-arrested using either ␣-factor or the temperature-sensitive cdc15-2 allele. Forty-eight mRNAs were found to change significantly in translation state following release from ␣-factor arrest, including genes involved in pheromone response and cell cycle arrest such as BAR1, SST2, and FAR1. After the shift of the cdc15-2 strain from 37 °C to 25 °C, 54 mRNAs were altered in translation state, including the products of the stress genes HSP82, HSC82, and SSA2. Thus, regulation at the translational level seems to play a significant role in the response of yeast cells to external physical or biological cues. In contrast, surprisingly few genes were found to be translationally controlled as cells progressed through the cell cycle. Additional refinements of TSAA should allow characterization of both transcriptional and translational regulatory networks on a genomic scale, providing an additional layer of information that can be integrated into models of system biology and function.

Features inside living cells are complex and crowded, and in such complex environments diffusion processes exhibit three different behaviours; Fickian diffusion, subdiffusion, and superdiffusion. This study aims to investigate the phenomenon of subdiffusion, which occurs when there is molecular crowding, by proposing a new continuous spatial model involving fractional differential equations. The anomalous diffusion parameter is introduced to represent the spatial crowdedness in the media. The equations are solved numerically using an implicit fractional trapezoidal method. Simulations applied to the particular case of the Michaelis-Menten reaction demonstrate that, as a result of anomalous diffusion or a crowded situation in low dimensional biological media, kinetics are of the fractal type. The model also predicts that increasing obstacle density results in reactant segregation, which is similar to that observed in in vivo conditions in cells.

Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been shown to be an effective alternative. Extending our earlier work, we present the first algorithm for performing statistical Model Checking using Bayesian Sequential Hypothesis Testing. We show that our Bayesian approach outperforms current statistical Model Checking techniques, which rely on tests from Classical (aka Frequentist) statistics, by requiring fewer system simulations. Another advantage of our approach is the ability to incorporate prior Biological knowledge about the model being verified. We demonstrate our algorithm on a variety of models from the Systems Biology literature and show that it enables faster verification than state-of-the-art techniques, even when no prior knowledge is available.

We present an exact algorithm, based on techniques from the field of Model Checking, for finding control policies for Boolean networks (BN) with control nodes. Given a BN, a set of starting states, I, a set of goal states, F , and a target time, t, our algorithm automatically finds a sequence of control signals that deterministically drives the BN from I to F at, or before time t, or else guarantees that no such policy exists. Despite recent hardness-results for finding control policies for BNs, we show that, in practice, our algorithm runs in seconds to minutes on over 13,400 BNs of varying sizes and topologies, including a BN model of embryogenesis in D. melanogaster with 15,360 Boolean variables. We then extend our method to automatically identify a set of Boolean transfer functions that reproduce the qualitative behavior of gene regulatory networks. Specifically, we automatically (re)learn a BN model of D. melanogaster embryogenesis in 5.3 seconds, from a space containing 6.9 × 10 10 possible models.

Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been shown to be an effective alternative. Extending our earlier work, we present the first algorithm for performing statistical Model Checking using Bayesian Sequential Hypothesis Testing. We show that our Bayesian approach outperforms current statistical Model Checking techniques, which rely on tests from Classical (aka Frequentist) statistics, by requiring fewer system simulations. Another advantage of our approach is the ability to incorporate prior Biological knowledge about the model being verified. We demonstrate our algorithm on a variety of models from the Systems Biology literature and show that it enables faster verification than state-of-the-art techniques, even when no prior knowledge is available.

Building complex computational models of the spread of epidemics is a problem that has seen renewed interest in recent years. Such models are being used for understanding real-time disease evolution prediction and are also proving useful in the prevention, monitoring and control of contagious diseases. There is a pressing need to ensure reliability of epidemiological models since they are widely used in safety-critical applications. In this paper, we present a new spatiotemporal specification language, EpiSpec, for describing detailed properties of agent-based computational epidemiological models. We describe the formal syntax of EpiSpec and demonstrate its use by describing various spatio-temporal properties related to disease evolution, and propose the use of statistical model checking as an algorithmic technique for verification and validation of large computational epidemiological models.

We present algorithms for finding control strategies in Boolean Networks (BN). Our approach uses symbolic techniques from the field of model checking. We show that despite recent hardness-results for finding control policies, a model checking-based approach is often capable of scaling to extremely large and complex models. We demonstrate the effectiveness of our approach by applying it to a BN model of embryogenesis in D. melanogaster with 15,360 Boolean variables.

In this article, a model is presented of a network whose structure was inspired by the 'five elements law' of Chinese medicine. Computer simulations illustrate the dynamic behavior of this system, that can be set in different attractors of Boolean type and can be differentially modified by delays and perturbations. We suggest that this model may contribute to the understanding of the 'logic' of the regulation of biological systems by means of small and carefully selected perturbations, a major line of thought of complementary therapies.

In recent years, bacterial infections have become a major public health concern due to their ability to cooperate between single and multiple species resisting to various forms of treatments (e.g., antibiotics). One form of protection is through biofilms, where the bacteria produce a protective medium known as the Extracellular Polymeric Substances (EPS ). Researchers are pursuing new multi-disciplinary approaches to treating and kerb the evolving process of these infections through the biofilms, to lower the humans' antibiotic dependence that can result in the so-called "superbugs". Although various solutions have been proposed to break biofilms, they are based on applying drugs or using nanoparticles. In this paper, we propose an alternative approach, where bacteria will cooperate and surround the biofilms to consume the nutrients. By hijacking the nutrients in the environment and blocking the flow from reaching the biofilms, this will lead to starvation, forcing them to break their structure. Preliminary simulations show that a small action radius of quorum sensing molecules is needed to maximise bacteria attraction to a particular location and create the protective wall. Therefore, this formation is capable of speeds up biofilm dispersal process by two hours.

Model organisms are an essential component of biological and biomedical research that can be used to study specific biological processes. These organisms are in part selected for facile experimental study. However, just as importantly, intensive study of a small number of model organisms yields important synergies as discoveries in one area of science for a given organism shed light on biological processes in other areas, even for other organisms. Furthermore, the extensive knowledge bases compiled for each model organism enable systems-level understandings of these species, which enhance the overall biological and biomedical knowledge for all organisms, including humans. Building upon extensive genomics research, we argue that the time is now right to focus intensively on model organism metabolomes. We propose a grand challenge for metabolomics studies of model organisms: to identify and map all metabolites onto metabolic pathways, to develop quantitative metabolic models for model...

The genome of tomato (Solanum lycopersicum) is being sequenced by an international consortium of 10 countries (Korea, China, the United Kingdom, India, The Netherlands, France, Japan, Spain, Italy and the United States) as part of a larger initiative called the ‘International Solanaceae Genome Project (SOL): Systems Approach to Diversity and Adaptation’. The goal of this grassroots initiative, launched in November 2003, is to establish a network of information, resources and scientists to ultimately tackle two of the most significant questions in plant biology and agriculture: (1) How can a common set of genes/proteins give rise to a wide range of morphologically and ecologically distinct organisms that occupy our planet? (2) How can a deeper understanding of the genetic basis of plant diversity be harnessed to better meet the needs of society in an environmentally friendly and sustainable manner? The Solanaceae and closely related species such as coffee, which are included in the s...

This time the meeting had two main themes -the integration of genomic and chemical information in the analysis of the dynamics and topology of cellular regulatory networks, and the development of more accurate computational tools for the analysis of gene expression and the prediction of transcription-factor binding sites. Full papers accepted for the fifth workshop have been published in the Genome Informatics Series of the Japanese Society of Bioinformatics, edited by Satoru Miyano (

In several model-based system maintenance problems, parameters are used to represent unknown characteristics of a component, equipment degradation, etc. This allows for modelling constant, slow-varying terms. The identifiability of these parameters is an important condition to estimate them. Linear Parameter Varying (LPV) models are being increasingly used in the industries as a bridge between linear and nonlinear models. Techniques exist that can rewrite some nonlinear models in LPV form. However, the problem of identifiability of these models is still at a nascent stage. In this paper, we propose an approach to verify identifiability of unknown parameters for LPV state-space models. It makes use of a parity-space like formulation to eliminate the states of the model. The resulting input-output-parameter equation is analysed to verify the identifiability of the original model or a subset of unknown parameters. This approach provides a framework for both continuous-time and discrete-time models and we illustrate it using examples.

In several model-based system maintenance problems, parameters are used to represent unknown characteristics of a component, equipment degradation, etc. This allows for modelling constant, slow-varying terms. The identifiability of these parameters is an important condition to estimate them. Linear Parameter Varying (LPV) models are being increasingly used in the industries as a bridge between linear and nonlinear models. Techniques exist that can rewrite some nonlinear models in LPV form. However, the problem of identifiability of these models is still at a nascent stage. In this paper, we propose an approach to verify identifiability of unknown parameters for LPV state-space models. It makes use of a parity-space like formulation to eliminate the states of the model. The resulting input-output-parameter equation is analysed to verify the identifiability of the original model or a subset of unknown parameters. This approach provides a framework for both continuous-time and discrete...

Most genetic networks, such as that for the biological clock, are part of much larger modules controlling fundamental processes in the cell, such as metabolism, development, and response to environmental signals. For example, the biological clock is part of a much larger network controlling the circadian rhythms of about 2418 distinct genes in the genome (with 11 000 genes) of the model system, Neurospora crassa. Predicting and understanding the dynamics of all of these genes and their products in a genetic network describing how the clock functions is a challenge and beyond the current capability of the fastest serial computers. We have implemented a novel variable-topology supernet ensemble method using Markov chain Monte Carlo simulations to fit and discover a regulatory network of unknown topology composed of 2418 genes describing the entire clock circadian network, a network that is found in organisms ranging from bacteria to humans, by harnessing the power of the general-purpose graphics processing unit and exploiting the hierarchical structure of that genetic network. The result is the construction of a genetic network that explains mechanistically how the biological clock functions in the filamentous fungus N. crassa and is validated against over 31 000 data points from microarray experiments. Two transcription factors are identified targeting ribosome biogenesis in the clock network. INDEX TERMS Biological clock, general-purpose graphical processing unit, ensemble method, supernet, systems biology, and regulatory network topologies.

This article presents a brief overview of label-free optical biosensor-based cell assay technologies. Theoretical analysis suggests that a resonant waveguide grating (RWG) biosensor detects ligand-induced dynamic mass redistribution (DMR) within the bottom portion of an adherent cell layer. Pharmacological studies suggest that the DMR signal can serve as a novel physiological readout for monitoring receptor activation, and examining ligand pharmacology. Chemical biology studies support the hypothesis that the DMR signal is an integrated response that consists of contributions from many cellular events induced by the ligand, thus providing alternative means to study cell systems biology. Orthogonal confirmations using conventional cell biology approaches have led to identifying specific cellular event(s) that dominate the DMR signals observed for three classes of receptors: epidermal growth factor receptor, Gq-coupled receptors and Gs-coupled receptors.

Measuring precise concentrations of proteins can provide insights into biological processes. Here, we use efficient protein extraction and sample fractionation and state-of-the-art quantitative mass spectrometry techniques to generate a comprehensive, condition-dependent protein abundance map of Escherichia coli. We measure cellular protein concentrations for 55% of predicted E. coli genes (>2300 proteins) under 22 different experimental conditions and identify methylation and N-terminal protein acetylations previously not known to be prevalent in bacteria. We uncover system-wide proteome allocation, expression regulation, and post-translational adaptations. These data provide a valuable resource for the systems biology and broader E. coli research communities.

G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the antitargets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our inhouse algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.

Introduction Interleukin-1 receptor-associated kinases (IRAKs) are serine-threonine kinases involved in toll-like receptor and interleukin-1 signaling pathways. They play a key role in inflammation and innate immunity. IRAKs have been previously incriminated in autoimmune diseases such as systemic lupus erythematosus (SLE) and rheumatoid arthritis and inhibition of IRAKs has been recently regarded as a potential therapeutic strategy for SLE. Objectives The aim of the present study was to test the association between IRAK2 rs708035 and rs3844283 with SLE. Material and methods IRAK2 rs708035 and rs3844283 were genotyped by mutagenically separated polymerase chain reaction (MS-PCR) in 142 SLE patients and 149 age-and gender-matched controls. Results The hyperfunctional IRAK2 rs708035 A allele was more frequent among SLE patients than controls (62.9% versus 54.7%, p = 0.046). IRAK2 rs3844283 C allele was present in 66.5% of patients and 75.5% of controls. The CC genotype was the most frequently exhibited genotype. It was carried by 45.1% of patients with SLE and 57.7% of controls. The G allele was associated with an increased risk of SLE (OR = 1.54, 95%, CI = 1.07-2.22, p = 0.017). IRAK2 rs708035 and IRAK2 rs3844283 were in linkage disequilibrium (D′ = 0.64). The AG haplotype was more frequently observed in SLE patients than in controls (0.292 versus 0.194, p = 0.008). Conclusion This study for the first time ever reveals the association of IRAK2 rs708035 and IRAK2 rs3844283 and the corresponding haplotypes with SLE. Our findings give additional rationale to target IRAKs in the treatment of SLE. • IRAK2 rs708035 A allele is more frequent in SLE patients than in controls and IRAK2 rs3844283 G allele is associated with SLE susceptibility. • These two alleles are in linkage disequilibrium. • The AG haplotype is associated with SLE.

A recent breakthrough in application of experimental methods to biomedicine not only has already resulted in accumulation of massive amounts of data, but also created new challenges in the field of data mining. This paper addresses the issue by combining advanced data mining techniques with the novel application of systems biology t o study axonal regeneration and neurogenesis. To obtain the data for analysis, a series of biological experiments are conducted, in which the rat pheochromocytoma cell PC-12 was used as an appropriate model for neuronal differentiation. The resulting DNA microarray data set is studied using a combination of methods -a statistical procedure for feature selection together with a dimensionality reduction technique. First, we apply feature selection, which can be based either on the Wilcoxon rank-sum test, or on the two-sample t-test, depending on the statistical properties of the data. Next, we utilized an efficient dimensionality reduction procedure called correspondence analysis to obtain a two-dimensional projection of the data, which allows us to perform a visual exploration of the patterns as well as to select the features corresponding to the top down-regulated and up-regulated genes.

The prevalence and morbidity of asthma, a chronic inflammatory airway disease, is increasing. Animal models provide a meaningful but limited view of the mechanisms of asthma in humans. A systemslevel view of asthma that integrates multiple levels of molecular and functional information is needed. For this, we compiled a gene expression compendium from five publicly available mouse microarray datasets and a gene knowledge base of 4,305 gene annotation sets. Using this collection we generated a high-level map of the functional themes that characterize animal models of asthma, dominated by innate and adaptive immune response. We used Module Networks analysis to identify co-regulated gene modules. The resulting modules reflect four distinct responses to treatment, including early response, general induction, repression, and IL-13-dependent response. One module with a persistent induction in response to treatment is mainly composed of genes with suggested roles in asthma, suggesting a similar role for other module members. Analysis of IL-13-dependent response using protein interaction networks highlights a role for TGF-b1 as a key regulator of asthma. Our analysis demonstrates the discovery potential of systems-level approaches and provides a framework for applying such approaches to asthma.

Pentose phosphate pathway Tricarboxilic acid gDCW Grams of dry cell weight AOX Alcohol oxidase Fab Antibody fragment NMR Nuclear magnetic resonance MFA Metabolic flux analysis HPLC High performance liquid chromatography LC-MS Liquid chromatography coupled with mass spectrometry GC-MS Gas chromatography coupled with mass spectrometry MAR Organic acids Glucose Unknown compound

and Dave Kotterman presented "Artificial Closed Ecosystems for Human Habitation of Space" at the Rotary club of Lafayette on October 11. We received the second lighting array from Orbitec at the end of September. At that time the issue of light uniformity changing with increasing number of engines energized was addressed when the chip containing the software controlling driver function was upgraded with better software coding. This eliminated the problems encountered during preliminary testing of the first system. The first lighting system was upgraded to be identical to the performance of the second system at the time the second system was received. Trial 6 of the first system was ended prematurely due to mistakes with CO2 programming of the growth chamber, which led to uninterpretable results. Construction of a new mounting system to suspend the updated lightsicles (with eight that are 7" longer than the other eight to allow closer, staggered spacing) is underway. The new intracanopy mounting schematic will feature two Y-arrangements in the middle of the tub, and the remaining 10 lightsicles will be dispersed around the perimeter. Mounts have been constructed and mounting is underway. Following installation, new hardware calibration analyses will be performed and a side-by-side study with cowpea stands growing either with intracanopy or overhead LED lights will be conducted. 'Norland' potato explants are being cultivated for the second side-by-side IC vs. OH LED lighting experiment. Experiments with strawberry and sweetpotato are ongoing in the greenhouse. Strawberry fruit are harvested from the greenhouse and saved for antioxidant studies. Strawberry fruit from the temperature studies in the growth chambers are being harvested and eaten, and fruit are rated for flavor and quality. Ten types of carrots of varying color have been planted in tubs in the greenhouse. These will be used for antioxidant studies by Lisa Mauer's students. Preparations for testing of the pH control system are underway. A computer is being set up to log data of the control and monitoring system. Side-by-side tubs of Waldmann's green lettuce will be grown hydroponically in a walk-in growth chamber. One tub will be continuously adjusted for pH at intervals of 5 minutes automatically, with the duration (which can be converted to volume) of added solution (acid or base) logged. The second hydroponic system will be manually adjusted for pH every 48 hours. ESM for both procedures (power, crewtime) will be calculated and edible lettuce biomass produced will be compared after 30 days of growth.

conducted a tour of the facilities and answered questions for an elementary school class researching advanced life support on December 1 st . Yang Yang, Gioia Massa, and Cary Mitchell met with Jerry Shephard in the Central Machine shop to discuss plans for the development of the cropcanopy gas-exchange cuvette system, Minitron III. In addition, discussions were pursued with Al Heber and Connie Li to determine what, if any, gas contaminants may be produced by the LED lighting system. Plans are underway to capture and analyze the cooling air passing through the lightsicles. Following an analysis of light output and current levels for both LED lighting systems, a side-by-side cowpea experiment was planted December 20 th in the growth chamber comparing intracanopy and overhead LED lighting. In addition, this experiment will be part of a collaboration with systems analyst Jim Russell to examine transpirational burdens under different lighting conditions. The conditions set for this experiment are 30 plants per growth area (0.23 m 2 ) with light levels set to approximately 300 µmols/m 2 /s at 2.5 cm from the light engines. Plants are being grown for 32 days, with pH and conductivity adjusted every other day. The treatments will be harvested January 20th. Lettuce plants were planted Dec. 21 st in a side-by-side comparison of manual versus automated hydroponic pH adjustment. This will be the first test with plants of the automated pH control system developed by Moeed Muhktar in George Chiu's lab. Harvests of experiments occurred November 29 th and December 15 th for carrot and December 7th for sweetpotato. Harvested biomass was sent to Lisa Mauer in Food Science for use in antioxidant studies. A second batch of carrots and sweetpotatoes has been replanted for replication. Final dry weight of paired basil/wheat straw residues composted with P. ostreatus 'Grey Dove' was taken and data analyzed. Samples were prepared for analyses of residual lignin content, cellulose and hemicellulose following 80-90 days of fungal colonization. In continuation of our collaborative work with the Food Safety team at AAMU, growth and maintenance of radish and lettuce using a nutrient film technique is on-going. Water samples from the nutrient film including leaf samples are being examined for waterborne food pathogens.

The genome of tomato (Solanum lycopersicum) is being sequenced by an international consortium of 10 countries (Korea, China, the United Kingdom, India, The Netherlands, France, Japan, Spain, Italy and the United States) as part of a larger initiative called the ‘International Solanaceae Genome Project (SOL): Systems Approach to Diversity and Adaptation’. The goal of this grassroots initiative, launched in November 2003, is to establish a network of information, resources and scientists to ultimately tackle two of the most significant questions in plant biology and agriculture: (1) How can a common set of genes/proteins give rise to a wide range of morphologically and ecologically distinct organisms that occupy our planet? (2) How can a deeper understanding of the genetic basis of plant diversity be harnessed to better meet the needs of society in an environmentally friendly and sustainable manner? The Solanaceae and closely related species such as coffee, which are included in the s...

The natural environment for plants is composed of a complex set of abiotic stresses and biotic stresses. Plant responses to these stresses are equally complex. Systems biology approaches facilitate a multi-targeted approach by allowing one to identify regulatory hubs in complex networks. Systems biology takes the molecular parts (transcripts, proteins and metabolites) of an organism and attempts to fit them into functional networks or models designed to describe and predict the dynamic activities of that organism in different environments. In this review, research progress in plant responses to abiotic stresses is summarized from the physiological level to the molecular level. New insights obtained from the integration of omics datasets are highlighted. Gaps in our knowledge are identified, providing additional focus areas for crop improvement research in the future.