Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
494,219
result(s) for
"system biology"
Sort by:
Environments that Induce Synthetic Microbial Ecosystems
Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interactions constitutes an open challenge in microbial ecology, with applications ranging from the human microbiome to environmental sustainability. In parallel to natural communities, it is possible to explore interactions in artificial microbial ecosystems, e.g. pairs of genetically engineered mutualistic strains. Here we computationally generate artificial microbial ecosystems without re-engineering the microbes themselves, but rather by predicting their growth on appropriately designed media. We use genome-scale stoichiometric models of metabolism to identify media that can sustain growth for a pair of species, but fail to do so for one or both individual species, thereby inducing putative symbiotic interactions. We first tested our approach on two previously studied mutualistic pairs, and on a pair of highly curated model organisms, showing that our algorithms successfully recapitulate known interactions, robustly predict new ones, and provide novel insight on exchanged molecules. We then applied our method to all possible pairs of seven microbial species, and found that it is always possible to identify putative media that induce commensalism or mutualism. Our analysis also suggests that symbiotic interactions may arise more readily through environmental fluctuations than genetic modifications. We envision that our approach will help generate microbe-microbe interaction maps useful for understanding microbial consortia dynamics and evolution, and for exploring the full potential of natural metabolic pathways for metabolic engineering applications.
Journal Article
Pi-Pi contacts are an overlooked protein feature relevant to phase separation
Protein phase separation is implicated in formation of membraneless organelles, signaling puncta and the nuclear pore. Multivalent interactions of modular binding domains and their target motifs can drive phase separation. However, forces promoting the more common phase separation of intrinsically disordered regions are less understood, with suggested roles for multivalent cation-pi, pi-pi, and charge interactions and the hydrophobic effect. Known phase-separating proteins are enriched in pi-orbital containing residues and thus we analyzed pi-interactions in folded proteins. We found that pi-pi interactions involving non-aromatic groups are widespread, underestimated by force-fields used in structure calculations and correlated with solvation and lack of regular secondary structure, properties associated with disordered regions. We present a phase separation predictive algorithm based on pi interaction frequency, highlighting proteins involved in biomaterials and RNA processing.
Journal Article
The long evolution of brains and minds
On the basis of evolutionary and behavioral biology, neuroscience and anthropology, this book investigates to which extent it is possible to reconstruct the evolution of nervous systems and brains as well as of mental-cognitive abilities, in short \"intelligence\", and to which extent we can correlate the one with the other. One central question is, whether or not abilities exist that make humans truly unique, or whether the evolution of the human mind was a gradual process. Exactly which neural features make animals and humans intelligent and creative? Is it absolute or relative brain size or the size of \"intelligence centers\" inside the brains, the number of nerve cells inside the brain in total or in such \"intelligence centers\" decisive for the degree of intelligence, of mind and eventually consciousness? Which are the driving forces behind these processes? Here, many different answers exist. For some experts the driving force for brains and minds are the conditions for biological survival: the more complex these conditions, the more effective need to be sense organs, nervous systems and brains, and the stronger is the tendency to an increase in learning abilities, behavioral flexibility and innovation power of animals. This is the ecological intelligence hypothesis. Other authors believe that the true driving force is the challenge from social life of an animal: the more complex the social conditions, the more sophisticated are abilities such as social learning, imitation, empathy, knowledge transfer, consciousness and the development of a theory of mind and meta-cognition. This, again, needs progressive changes inside the brains. This is the social intelligence hypothesis. Again other authors distinguish physical intelligence as a third form of cognitive functions mostly related to tool use, tool fabrication and understanding of the principles of how things work. Finally, some experts believe that the decisive factor in the evolution of brains and minds consisted in an increase in the speed and efficacy of information processing in cognitive brain centers. This is the general intelligence or information processing hypothesis. It is discussed, which of these hypotheses is the most convincing one. At its end, the book deals with the eminent question of whether we can arrive at a naturalistic concept of mind and consciousness. Is it possible to explain mind and intelligence within the framework of the natural science, or do mind and intelligence as found in humans, transcend nature? -- Back cover.
Book
A Catalog of Reference Genomes from the Human Microbiome
The human microbiome refers to the community of microorganisms, including prokaryotes, viruses, and microbial eukaryotes, that populate the human body. The National Institutes of Health launched an initiative that focuses on describing the diversity of microbial species that are associated with health and disease. The first phase of this initiative includes the sequencing of hundreds of microbial reference genomes, coupled to metagenomic sequencing from multiple body sites. Here we present results from an initial reference genome sequencing of 178 microbial genomes. From 547,968 predicted polypeptides that correspond to the gene complement of these strains, previously unidentified (\"novel\") polypeptides that had both unmasked sequence length greater than 100 amino acids and no BLASTP match to any nonreference entry in the nonredundant subset were defined. This analysis resulted in a set of 30,867 polypeptides, of which 29,987 (̃97%) were unique. In addition, this set of microbial genomes allows for ̃40% of random sequences from the microbiome of the gastrointestinal tract to be associated with organisms based on the match criteria used. Insights into pan-genome analysis suggest that we are still far from saturating microbial species genetic data sets. In addition, the associated metrics and standards used by our group for quality assurance are presented.
Journal Article
Missing microbes : how the overuse of antibiotics is fueling our modern plagues
\"A critically important and startling look at the harmful effects of overusing antibiotics, from the field's leading expert Tracing one scientist's journey toward understanding the crucial importance of the microbiome, this revolutionary book will take readers to the forefront of trail-blazing research while revealing the damage that overuse of antibiotics is doing to our health: contributing to the rise of obesity, asthma, diabetes, and certain forms of cancer. In Missing Microbes, Dr. Martin Blaser invites us into the wilds of the human microbiome where for hundreds of thousands of years bacterial and human cells have existed in a peaceful symbiosis that is responsible for the health and equilibrium of our body. Now, this invisible eden is being irrevocably damaged by some of our most revered medical advances--antibiotics--threatening the extinction of our irreplaceable microbes with terrible health consequences. Taking us into both the lab and deep into the fields where these troubling effects can be witnessed firsthand, Blaser not only provides cutting edge evidence for the adverse effects of antibiotics, he tells us what we can do to avoid even more catastrophic health problems in the future. \"-- Provided by publisher.
Book
Systems biology informed deep learning for inferring parameters and hidden dynamics
Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems.
Journal Article
Sick! : the twists and turns behind animal germs
\"Follow the scientists, around the world and into their labs, who are studying animals and the germs that attack them\"-- Provided by publisher.
Book
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Key Points
When applying high-throughput molecular methods to the study of neurodevelopmental disorders, major challenges include the spatial and temporal heterogeneity of the brain, a lack of appropriate tissue available for studies and poorly defined phenotypes.
Transcriptomics assays are currently the most widely used functional genomic assays in neurobiology owing to their ability to efficiently capture tissue-specific spatial and temporal heterogeneity in a high-throughput manner. Principles from transcriptomic studies will aid in evaluating additional molecular and cellular levels of regulation.
We review the principles of network analysis and describe how gene networks provide a framework to organize, integrate and analyse large-scale genomic data sets in neurobiology.
We review representative differential expression and gene network studies in neurodevelopmental disorders and neurodegenerative diseases and identify some next steps in data generation and integration that are necessary for progress in the field.
We provide guidelines for designing, analysing and evaluating high-throughput transcriptomic studies in the brain in order to improve study quality and reproducibility.
The study of the genetic basis of neurodevelopmental disorders and neurodegenerative diseases has progressed through recent large-scale association studies as well as the application of a range of high-throughput molecular methods. In this Review, the authors examine systems biology approaches and demonstrate how gene networks provide an organizing framework to integrate the analysis of large-scale genetic and molecular profiling data sets to characterize the genetic basis of phenotypes that affect the central nervous system.
Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology.
Journal Article