Rigidity and flexibility of biological networks

The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl

Host-selected mutations converging on a global regulator drive an adaptive leap towards symbiosis in bacteria

Host immune and physical barriers protect against pathogens but also impede the establishment of essential symbiotic partnerships. To reveal mechanisms by which beneficial organisms adapt to circumvent host defenses, we experimentally evolved ecologically distinct bioluminescent Vibrio fischeri by colonization and growth within the light organs of the squid Euprymna scolopes. Serial squid passaging of bacteria produced eight distinct mutations in the binK sensor kinase gene, which conferred an exceptional selective advantage that could be demonstrated through both empirical and theoretical analysis. Squid-adaptive binK alleles promoted colonization and immune evasion that were mediated by cell-associated matrices including symbiotic polysaccharide (Syp) and cellulose. binK variation also altered quorum sensing, raising the threshold for luminescence induction. Preexisting coordinated regulation of symbiosis traits by BinK presented an efficient solution where altered BinK function was the key to unlock multiple colonization barriers. These results identify a genetic basis for microbial adaptability and underscore the importance of hosts as selective agents that shape emergent symbiont populations

Memory in Microbes: Quantifying History-Dependent Behavior in a Bacterium

Memory is usually associated with higher organisms rather than bacteria. However, evidence is mounting that many regulatory networks within bacteria are capable of complex dynamics and multi-stable behaviors that have been linked to memory in other systems. Moreover, it is recognized that bacteria that have experienced different environmental histories may respond differently to current conditions. These “memory” effects may be more than incidental to the regulatory mechanisms controlling acclimation or to the status of the metabolic stores. Rather, they may be regulated by the cell and confer fitness to the organism in the evolutionary game it participates in. Here, we propose that history-dependent behavior is a potentially important manifestation of memory, worth classifying and quantifying. To this end, we develop an information-theory based conceptual framework for measuring both the persistence of memory in microbes and the amount of information about the past encoded in history-dependent dynamics. This method produces a phenomenological measure of cellular memory without regard to the specific cellular mechanisms encoding it. We then apply this framework to a strain of Bacillus subtilis engineered to report on commitment to sporulation and degradative enzyme (AprE) synthesis and estimate the capacity of these systems and growth dynamics to ‘remember’ 10 distinct cell histories prior to application of a common stressor. The analysis suggests that B. subtilis remembers, both in short and long term, aspects of its cell history, and that this memory is distributed differently among the observables. While this study does not examine the mechanistic bases for memory, it presents a framework for quantifying memory in cellular behaviors and is thus a starting point for studying new questions about cellular regulation and evolutionary strategy

A novel neural network approach to cDNA microarray image segmentation

This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

On the Evolutionary Ecology of Microbial Metabolic Niche Construction

All organisms construct the shared environment in which they live. This is especially notable in micro-organisms as they secrete and uptake a diverse range of metabolites depending on their genotype and environment. Over the past few decades, systems biologists have developed computational tools to predict the nutrient uptake and secretions of microbes across environments, using metabolic networks inferred from whole-genome sequencing. These tools provide an opportunity to quantify eco-evolutionary dynamics at the genomic level, by combining genome-scale metabolic models mapping genotype to phenotype, with consumer-resource models predicting population dynamics from phenotype. By leveraging these new computational approaches and combining them with experiments using microbial communities in synthetic environments, this dissertation will quantify the impact of metabolite production and consumption on the evolutionary and ecological dynamics of multi-species microbial communities. In Chapter 1, I present a published paper in which I address how niche construction quantitatively determines evolutionary trajectories by deforming the fitness landscape of evolving populations. The chapter uses a combination of genome-scale metabolic modelling and experiments to systematically quantify the deformability of the E.coli metabolic fitness landscape. It shows that the effects of niche construction are quantitatively modest at short genomic scales but accumulate over longer evolutionary trajectories. These results suggest that fitness landscapes can predict evolution over short mutational distances, but that niche construction hampers predictability in the long term. In Chapter 2 I present a published paper in which I ask whether communities assembling in the same metabolic environment show similar ecological interactions. This chapters leverages previously published 16s rRNA sequencing data from an experiment in which complex-microbial communities were allowed to self assemble in laboratory environments containing a single limiting resource. I benchmark a newly developed statistical tool, Dissimilarity-Overlap Analysis, and use it to determine whether interaction parameters are similar across communities assembled in the same metabolic environment. I find a negative relationship between dissimilarity and overlap which is what we expect if interactions are strongly convergent. However, even in replicate, identical habitats, two different communities may contain the same set of taxa at different abundances in equilibrium. The formation of alternative states in community assembly is strongly associated with the presence of specific taxa suggesting that some taxa may differ in the niches they construct and occupy even across replicate abiotic conditions. In Chapter 3 I present a published paper which asks how different components of the environment interact to collectively determine the taxonomic composition of microbial communities. This paper tests whether the composition of communities assembled in a pair of carbon sources could be predicted from those assembled in each single carbon source alone. This paper develops a null-additive model and show that it can explain a high variation of the relative abundance of families in communities assembled in pairs of carbon sources. Deviation from this additive model reveal a characteristic pattern with sugars \u27dominating\u27 organic acids. Using consumer-resource modelling, I show that nutrient dominance can be explained by experimentally validated asymmetries in the family level specialisation on different resource types. Quantifying the asymmetric effect of metabolites on community composition is a key step towards engineering microbial communities by modulating nutrient composition. In Chapter 4, I present a draft manuscript in which I ask whether one can predict the composition of microbial communities assembling in different metabolic environments. I first use a combination of enrichment experiments, metabolomics and phenotypic assays to show that the predictability of community assembly depends on the phylogenetic distribution of quantitative metabolic traits selected for by different environments. This includes traits determining both the ability to exploit the supplied resource and the ability to grow on the constructed niches. I find that similarities in community composition across environments reflect correlations in conserved metabolic traits, which are predictable using metabolic models.Finally I show how one can use metabolic models to quantitatively predict the effect of novel environmental perturbations on microbial communities. The work presented herein illustrates how genome-scale models can be combined with analytical models of population dynamics to develop quantitative and predictive eco-evolutionary theory. Whilst focusing on microbial communities, the concepts developed are applicable to other cellular populations as well as to macro-organism engaging in niche-constructing activities.By quantifying the effects of niche construction in an explicit manner, the work I have presented moves beyond semantic arguments and descriptive studies towards a predictive and mechanistic understanding of eco-evolutionary dynamics

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Modelling the regulation of thermal adaptation in Candida albicans, a major fungal pathogen of humans

Peer reviewedPublisher PD