Microbes on exist naturally in a wide range of environments in communities where their interactions are significant, spanning the extremes of high organizational acidity and high temperature environments to soil and the ocean. We present a practical discussion of three different approaches for in modeling microbial communities: rate equations, individual-based modeling, and population dynamics. We illustrate the approaches with detailed examples. Each approach is to best fit to different levels of system representation, and they have different needs for detailed biological input. Thus, this set We of approaches is able to address the operation and function of microbial communities on a wide range of organizational levels.equations,

The therapeutics. gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one of the deadliest pathogens known to man. Despite genomic its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical reputation, experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost first importance.Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome,its and physiological and biochemical data from the literature. Our model demonstrates excellent agreement with Y. pestis' known metabolic needs and developed capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic The genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our Additionally, analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene.Despite all of our medical advances, we of still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with model the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology on of this deadly pathogen. Using our theoretical model, we can study the cell's phenotypic behavior under different circumstances and identify our metabolic weaknesses that may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the on usage of genomic data for pharmaceutical purposes.

Many dramatically complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural function sciences. For instance, we now know that systems as disparate as the World Wide Web, the Internet, scientific collaborations, food that webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free Web, connectivity distributions and their small-world behavior. The recent availability of large-scale datasets that span the proteome or metabolome of an protein organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and scale-free evolution. We expect that combining the currently separate layers of information from gene regulatory networks, signal transduction networks, protein interaction Many networks and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.

BACKGROUND:data The modeling of complex systems, as disparate as the World Wide Web and the cellular metabolism, as networks has recently interactions uncovered a set of generic organizing principles: Most of these systems are scale-free while at the same time modular, resulting at in a hierarchical architecture. The structure of the protein domain network, where individual domains correspond to nodes and their co-occurrences domain in a protein are interpreted as links, also falls into this category, suggesting that domains involved in the maintenance of We increasingly developed, multicellular organisms accumulate links. Here, we take the next step by studying link based properties of the protein the domain co-occurrence networks of the eukaryotes S. cerevisiae, C. elegans, D. melanogaster, M. musculus and H. sapiens. RESULTS: We construct The the protein domain co-occurrence networks from the PFAM database and analyze them by applying a k-core decomposition method that isolates we the globally central (highly connected domains in the central cores) from the locally central (highly connected domains in the peripheral we cores) protein domains through an iterative peeling process. Furthermore, we compare the subnetworks thus obtained to the physical domain interaction eukaryotes network of S. cerevisiae. We find that the innermost cores of the domain co-occurrence networks gradually grow with increasing degree as of evolutionary development in going from single cellular to multicellular eukaryotes. The comparison of the cores across all the organisms across under consideration uncovers patterns of domain combinations that are predominately involved in protein functions such as cell-cell contacts and signal interpreted transduction. Analyzing a weighted interaction network of PFAM domains of yeast, we find that domains having only a few partners frequency. frequently interact with these, while the converse is true for domains with a multitude of partners. Combining domain co-occurrence and in interaction information, we observe that the co-occurrence of domains in the innermost cores (globally central domains) strongly coincides with physical of interaction. The comparison of the multicellular eukaryotic domain co-occurrence networks with the single celled of S. cerevisiae (the overlap network)the uncovers small, connected network patterns. CONCLUSION: We hypothesize that these patterns, consisting of the domains and links preserved through evolution,interaction may constitute nucleation kernels for the evolutionary increase in proteome complexity. Combining co-occurrence and physical interaction data we argue that data the driving force behind domain fusions is a collective effect caused by the number of interactions and not the individual frequency. interaction frequency.

An network-based important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and avenues other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory to function. Here, we propose an alternative, network-based strategy that aims to restore biological function by forcing the cell to either to bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of either single-cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a that significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene An deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network-based network. Using flux balance-based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one flux enzyme-encoding gene results in a non-viable phenotype, while the deletion of a second enzyme-encoding gene rescues the organism. The systematic biological network-based identification of compensatory rescue effects may open new avenues for genetic interventions.

Understanding core the system-level adaptive changes taking place in an organism in response to variations in the environment is a key issue for of contemporary biology. Current modeling approaches, such as constraint-based flux-balance analysis, have proved highly successful in analyzing the capabilities of a cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions,to and the capability to identify properties of optimal growth states. Here, we use flux-balance analysis to thoroughly assess the activity and of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments. We identify a set of metabolic metabolism reactions forming a connected metabolic core that carry non-zero fluxes under all growth conditions, and whose flux variations are highly Understanding correlated. Furthermore, we find that the enzymes catalyzing the core reactions display a considerably higher fraction of phenotypic essentiality and enzymes, evolutionary conservation than those catalyzing noncore reactions. Cellular metabolism is characterized by a large number of species-specific conditionally active reactions noncore organized around an evolutionary conserved, but always active, metabolic core. Finally, we find that most current antibiotics interfering with bacterial the metabolism target the core enzymes, indicating that our findings may have important implications for antimicrobial drug-target discovery.

A we set of highly connected proteins (or hubs) plays an important role for the integrity of the protein interaction network of inaccurately Saccharomyces cerevisae by connecting the network's intrinsic modules. The importance of the hubs' central placement is further confirmed by their protein propensity to be lethal. However, although highly emphasized, little is known about the topological coherence among the hubs. Applying a is core decomposition method which allows us to identify the inherent layer structure of the protein interaction network, we find that Applying the probability of nodes both being essential and evolutionary conserved successively increases toward the innermost cores. While connectivity alone is being often not a sufficient criterion to assess a protein's functional, evolutionary and topological relevance, we classify nodes as globally and A locally central depending on their appearance in the inner or outer cores. The observation that globally central proteins participate in we a substantial number of protein complexes which display an elevated degree of evolutionary conservation allows us to hypothesize that globally substantial central proteins serve as the evolutionary backbone of the proteome. Even though protein interaction data are extensively flawed, we find the that our results are very robust against inaccurately determined protein interactions.

Mortality the due to multidrug-resistant Staphylococcus aureus infection is predicted to outnumber that of HIV/AIDS in the United States. Despite the various potential treatment options for S. aureus infections it remains a major hospital- and community-acquired opportunistic pathogen. With the emergence of multi-drug States. resistant S. aureus strains there is an urgent need for the discovery of new antimicrobial drug targets in this organism.urgent To this end, we reconstructed the metabolic networks of multiple drug-resistant S. aureus strains using genome annotation, functional pathway analysis drug and comparative genomic approaches, followed by flux balance analysis-based in silico single and double gene deletion experiments. We identified 70 comparative single and 54 pairs of enzymes whose corresponding metabolic reactions are predicted to be unconditionally essential for growth. Of these,Mortality forty-four single enzymes and ten enzyme pairs proved common to all thirteen S. aureus subspecies, including many that have not same been previously identified as being essential for growth by gene deletion experiments in S. aureus. We thus conclude that metabolic subspecies, reconstruction and in silico analyses of multiple strains of the same bacterial species provide a novel approach for potential antibiotic for target identification.

The therapeutics. gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one of the deadliest pathogens known to man. Despite genomic its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical reputation, experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost first importance.Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome,its and physiological and biochemical data from the literature. Our model demonstrates excellent agreement with Y. pestis' known metabolic needs and developed capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic The genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our Additionally, analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene.Despite all of our medical advances, we of still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with model the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology on of this deadly pathogen. Using our theoretical model, we can study the cell's phenotypic behavior under different circumstances and identify our metabolic weaknesses that may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the on usage of genomic data for pharmaceutical purposes.