Review of uses of network and graph theory concepts within proteomics

The size and nature of data collected on gene and protein interactions has led to a rapid growth of interest in graph theory and modern techniques for describing, characterizing and comparing networks. Simultaneously, this is a field of growth within mathematics and theoretical physics, where the global properties, and emergent behavior of networks, as a function of the local properties has long been studied. In this review, a number of approaches for exploiting modern network theory to help describe and analyze different data sets and problems associated with proteomic data are considered. This review aims to help biologists find their way towards useful ideas and references, yet may also help scientists from a mathematics and physics background to understand where they may apply their expertise.     

Network thinking in ecology and evolution

Although pairwise interactions have always had a key role in ecology and evolutionary biology, the recent increase in the amount and availability of biological data has placed a new focus on the complex networks embedded in biological systems. The increased availability of computational tools to store and retrieve biological data has facilitated wide access to these data, not just by biologists but also by specialists from the social sciences, computer science, physics and mathematics. This fusion of interests has led to a burst of research on the properties and consequences of network structure in biological systems. Although traditional measures of network structure and function have started us off on the right foot, an important next step is to create biologically realistic models of network formation, evolution, and function. Here, we review recent applications of network thinking to the evolution of networks at the gene and protein level and to the dynamics and stability of communities. These studies have provided new insights into the organization and function of biological systems by applying existing techniques of network analysis. The current challenge is to recognize the commonalities in evolutionary and ecological applications of network thinking to create a predictive science of biological networks.     

Imaging and modeling growth and morphogenesis in plants

The growth of tissues, organs or organisms derives from the coordinated activities of complex genetic regulatory networks. In addition to its molecular underpinnings, growth also generally involves significant changes in geometry. To fully understand morphogenesis in its molecular and physical contexts the development of an interdisciplinary approach is required associating biology, mathematics, and physics, which held together by computer science. Growth quantitation and digital simulations have been developed to generate and test the plausibilities of complex hypotheses. Increasingly, real-time live imaging protocols are becoming an essential part of this process. In this review, I discuss the evolution of imaging techniques in plant developmental biology and briefly examine the different ways in which these studies have shed light on growth and morphogenesis in plants.     

Recent advances in i-Gene tools and analysis: microarrays, next generation sequencing and mass spectrometry

Recent advances in technology and associated methodology have made the current period one of the most exciting in molecular biology and medicine. Underlying these is an appreciation that modern research is driven by increasing large amounts of data being interpreted by interdisciplinary collaborative teams which are often geographically dispersed. The availability of cheap computing power, high speed informatics networks and high quality analysis software has been essential to this as has the application of modern quality assurance methodologies. In this review, we discuss the application of modern 'High-Throughput' molecular biological technologies such as 'Microarrays' and 'Next Generation Sequencing' to scientific and biomedical research as we have observed. Furthermore in this review, we also offer some guidance that enables the reader as to understand certain features of these as well as new strategies and help them to apply these i-Gene tools in their endeavours successfully. Collectively, we term this 'i-Gene Analysis'. We also offer predictions as to the developments that are anticipated in the near and more distant future.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.     

Network structure and the biology of populations

A scientific growth area in recent years has been the study of networks of interacting entities within a population, including species in food webs, human or other animals transmitting infection, proteins in cells, cells in organisms (e.g. neuronal networks), the internet and the World Wide Web. Here, I review some of the differing network patterns that arise in theory and in practice, with an emphasis on their dynamical implications, particularly for resistance to deliberate or accidental disturbance. I offer caveats and opinionated comment about some excesses of enthusiasm and suggest some areas where these network ideas might find further application.     

Algorithms for effective querying of compound graph-based pathway databases

Background  

Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.     

Theoretical neuroscience rising

Theoretical neuroscience has experienced explosive growth over the past 20 years. In addition to bringing new researchers into the field with backgrounds in physics, mathematics, computer science, and engineering, theoretical approaches have helped to introduce new ideas and shape directions of neuroscience research. This review presents some of the developments that have occurred and the lessons they have taught us.     

Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

Background  

Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory.     

Unraveling spurious properties of interaction networks with tailored random networks

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erd?s-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.     

Pleistocene megafaunal interaction networks became more vulnerable after human arrival

The end of the Pleistocene was marked by the extinction of almost all large land mammals worldwide except in Africa. Although the debate on Pleistocene extinctions has focused on the roles of climate change and humans, the impact of perturbations depends on properties of ecological communities, such as species composition and the organization of ecological interactions. Here, we combined palaeoecological and ecological data, food-web models and community stability analysis to investigate if differences between Pleistocene and modern mammalian assemblages help us understand why the megafauna died out in the Americas while persisting in Africa. We show Pleistocene and modern assemblages share similar network topology, but differences in richness and body size distributions made Pleistocene communities significantly more vulnerable to the effects of human arrival. The structural changes promoted by humans in Pleistocene networks would have increased the likelihood of unstable dynamics, which may favour extinction cascades in communities facing extrinsic perturbations. Our findings suggest that the basic aspects of the organization of ecological communities may have played an important role in major extinction events in the past. Knowledge of community-level properties and their consequences to dynamics may be critical to understand past and future extinctions.     

Taking the Relational Turn: Biosemiotics and Some New Trends in Biology

A cluster of similar trends emerging in separate fields of science and philosophy points to new opportunities to apply biosemiotic ideas as tools for conceptual integration in theoretical biology. I characterize these developments as the outcome of a “relational turn” in these disciplines. They signal a shift of attention away from objects and things and towards relational structures and processes. Increasingly sophisticated research technologies of molecular biology have generated an enormous quantity of experimental data, sparking a need for relational approaches that could help to find recurrent patterns in the mass of data. Earlier conceptions of relational biology and cybernetics, once deemed too abstract and speculative, are now resurrected and applied by means of new computational and simulation tools. I think this receptivity should be extended to incorporate nets of semiotic relations as heuristic guides for discerning global patterns of interactions in living systems. In this article I review aspects of systems biology and new directions in evolutionary theory, focusing on the role of circular and downward causation in relational structures and dynamical networks. I also indicate promising avenues of integration of some ideas of biosemiotics with those emerging from these new currents in biology. Relational developments in biology bear a telling similarity to a parallel relational turn presently manifest in the philosophy of science, rooted in the philosophy of physics and mathematics and in different varieties of structural and informational realism. The recognition of the relational nature of reality within these disciplines entails a tacit repudiation of nominalistic biases in science that have hindered the reception of semitiotic conceptions in biology. In previous investigations I explored connections between two kinds of relational structures: the networks of self-referential circular loops that appear pervasively in living systems, and the triadic relational structures that Peircean semiotics places at the basis of all semiotic transactions. Current relational views in the sciences seem oblivious to the difference between dyadic and triadic relations. Incorporating this essential distinction from biosemiotics into other fields could be a first step in seizing the opportunities opened by the relational turn for a renewal of biology and of natural philosophy in general, across disciplinary boundaries.     

Metabolic networks of microbial systems

In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors.     

Patterns of genetic variation in populations of infectious agents

Background  

The analysis of genetic variation in populations of infectious agents may help us understand their epidemiology and evolution. Here we study a model for assessing the levels and patterns of genetic diversity in populations of infectious agents. The population is structured into many small subpopulations, which correspond to their hosts, that are connected according to a specific type of contact network. We considered different types of networks, including fully connected networks and scale free networks, which have been considered as a model that captures some properties of real contact networks. Infectious agents transmit between hosts, through migration, where they grow and mutate until elimination by the host immune system.     

Gold nanoparticles and bioconjugation: a pathway for proteomic applications

In the last few years gold nanoparticles (AuNPs) became extremely interesting materials due to their enhanced optical, chemical and electrical properties. With the intention of taking advantage of those properties, the use of AuNPs has spread into a wide variety of areas such as physics, chemistry, biology, industry and medicine. More interestingly, their ability to form robust conjugates with biomolecules has given proteomics a new tool to improve aspects where the current methods to study proteins and their interactions in living cells cannot achieve the success required. In this review we present some of the current methods for AuNPs synthesis, the tailoring of their surface with ligands to improve stability and strategies to conjugate with biomolecules. Lastly, we also discuss their application in proteomic methods and recent developments in clinical diagnosis.     

Compartments in a marine food web associated with phylogeny, body mass, and habitat structure

A long-standing question in community ecology is whether food webs are organized in compartments, where species within the same compartment interact frequently among themselves, but show fewer interactions with species from other compartments. Finding evidence for this community organization is important since compartmentalization may strongly affect food web robustness to perturbation. However, few studies have found unequivocal evidence of compartments, and none has quantified the suite of mechanisms generating such a structure. Here, we combine computational tools from the physics of complex networks with phylogenetic statistical methods to show that a large marine food web is organized in compartments, and that body size, phylogeny, and spatial structure are jointly associated with such a compartmentalized structure. Sharks account for the majority of predatory interactions within their compartments. Phylogenetically closely related shark species tend to occupy different compartments and have divergent trophic levels, suggesting that competition may play an important role structuring some of these compartments. Current overfishing of sharks has the potential to change the structural properties, which might eventually affect the stability of the food web.     

Model answers to lipid membrane questions

Ever since it was discovered that biological membranes have a core of a bimolecular sheet of lipid molecules, lipid bilayers have been a model laboratory for investigating physicochemical and functional properties of biological membranes. Experimental and theoretical models help the experimental scientist to plan experiments and interpret data. Theoretical models are the theoretical scientist's preferred toys to make contact between membrane theory and experiments. Most importantly, models serve to shape our intuition about which membrane questions are the more fundamental and relevant ones to pursue. Here we review some membrane models for lipid self-assembly, monolayers, bilayers, liposomes, and lipid-protein interactions and illustrate how such models can help answering questions in modern lipid cell biology.     

On evaluating models in Computational Morphodynamics

Recent advances in experimental plant biology have led to an increased potential to investigate plant development at a systems level. The emerging research field of Computational Morphodynamics has the aim to lead this development by combining dynamic spatial experimental data with computational models of molecular networks, growth, and mechanics in a multicellular context. The increased number of published models may lead to a diversification of our understanding of the systems, and methods for evaluating, comparing, and sharing models are main challenges for the future. We will discuss this problem using ideas originating from physics and use recent computational models of plant development as examples.     

Generating Attributed Networks with Communities

In many modern applications data is represented in the form of nodes and their relationships, forming an information network. When nodes are described with a set of attributes we have an attributed network. Nodes and their relationships tend to naturally form into communities or clusters, and discovering these communities is paramount to many applications. Evaluating algorithms or comparing algorithms for automatic discovery of communities requires networks with known structures. Synthetic generators of networks have been proposed for this task but most solely focus on connectivity and their properties and overlook attribute values and the network properties vis-à-vis these attributes. In this paper, we propose a new generator for attributed networks with community structure that dependably follows the properties of real world networks.