Network-based functional modeling of genomics, transcriptomics and metabolism in bacteria

Molecular entities present in a cell (mRNA, proteins, metabolites,…) do not act in isolation, but rather in cooperation with each other to define an organisms form and function. Their concerted action can be viewed as networks of interacting entities that are active under certain conditions within the cell or upon certain environmental signals. A main challenge in systems biology is to model these networks, or in other words studying which entities interact to form cellular systems or accomplish similar functions. On the contrary, viewing a single entity or an experimental dataset in the light of an interaction network can reveal previous unknown insights in biological processes. In this review we give an overview of how integrated networks can be reconstructed from multiple omics data and how they can subsequently be used for network-based modeling of cellular function in bacteria.     

Copper resistance determinants in bacteria.

Copper is an essential trace element that is utilized in a number of oxygenases and electron transport proteins, but it is also a highly toxic heavy metal, against which all organisms must protect themselves. Known bacterial determinants of copper resistance are plasmid-encoded. The mechanisms which confer resistance must be integrated with the normal metabolism of copper. Different bacteria have adopted diverse strategies for copper resistance, and this review outlines what is known about bacterial copper resistance mechanisms and their genetic regulation.     

Genes, genome and Gestalt

According to Gestalt thinking, biological systems cannot be viewed as the sum of their elements, but as processes of the whole. To understand organisms we must start from the whole, observing how the various parts are related. In genetics, we must observe the genome over and above the sum of its genes. Either loss or addition of one gene in a genome can change the function of the organism. Genomes are organized in networks of genes, which need to be well integrated. In the case of genetically modified organisms (GMOs), for example, soybeans, rats, Anopheles mosquitoes, and pigs, the insertion of an exogenous gene into a receptive organism generally causes disturbance in the networks, resulting in the breakdown of gene interactions. In these cases, genetic modification increased the genetic load of the GMO and consequently decreased its adaptability (fitness). Therefore, it is hard to claim that the production of such organisms with an increased genetic load does not have ethical implications.     

Light-controlled synthetic gene circuits

Highly complex synthetic gene circuits have been engineered in living organisms to develop systems with new biological properties. A precise trigger to activate or deactivate these complex systems is desired in order to tightly control different parts of a synthetic or natural network. Light represents an excellent tool to achieve this goal as it can be regulated in timing, location, intensity, and wavelength, which allows for precise spatiotemporal control over genetic circuits. Recently, light has been used as a trigger to control the biological function of small molecules, oligonucleotides, and proteins involved as parts in gene circuits. Light activation has enabled the construction of unique systems in living organisms such as band-pass filters and edge-detectors in bacterial cells. Additionally, light also allows for the regulation of intermediate steps of complex dynamic pathways in mammalian cells such as those involved in kinase networks. Herein we describe recent advancements in the area of light-controlled synthetic networks.     

Total bacterial diversity in soil and sediment communities—A review

Advances in microbial methods have demonstrated that microorganisms globally are the dominating organisms both concerning biomass and diversity. Their functional and genetic potential may exceed that of higher organisms. Studies of bacterial diversity have been hampered by their dependence on phenotypic characterization of bacterial isolates. Molecular techniques have provided the tools for analyzing the entire bacterial community including those which we are not able to grow in the laboratory. Reassociation analysis of DNA isolated directly from the bacteria in pristine soil and marine sediment samples revealed that such environments contained in the order of 10 000 bacterial types. The diversity of the total bacterial community was approximately 170 times higher than the diversity of the collection of bacterial isolates from the same soil. The culturing conditions therefore select for a small and probably skewed fraction of the organisms present in the environment. Environmental stress and agricultural management reduce the bacterial diversity. With the reassociation technique it was demonstrated that in heavily polluted fish farm sediments the diversity was reduced by a factor of 200 as compared to pristine sediments. Here we discuss some molecular mechanisms and environmental factors controlling the bacterial diversity in soil and sediments.     

Assignment of protein function in the postgenomic era

Genome sequencing projects have provided researchers with an unprecedented boon of molecular information that promises to revolutionize our understanding of life and lead to new treatments of its disorders. However, genome sequences alone offer only limited insights into the biochemical pathways that determine cell and tissue function. These complex metabolic and signaling networks are largely mediated by proteins. The vast number of uncharacterized proteins found in prokaryotic and eukaryotic systems suggests that our knowledge of cellular biochemistry is far from complete. Here, we highlight a new breed of 'postgenomic' methods that aim to assign functions to proteins through the integrated application of chemical and biological techniques.     

Multiple Phenotypic Changes Associated with Large-Scale Horizontal Gene Transfer

Horizontal gene transfer often leads to phenotypic changes within recipient organisms independent of any immediate evolutionary benefits. While secondary phenotypic effects of horizontal transfer (i.e., changes in growth rates) have been demonstrated and studied across a variety of systems using relatively small plasmids and phage, little is known about the magnitude or number of such costs after the transfer of larger regions. Here we describe numerous phenotypic changes that occur after a large-scale horizontal transfer event (∼1 Mb megaplasmid) within Pseudomonas stutzeri including sensitization to various stresses as well as changes in bacterial behavior. These results highlight the power of horizontal transfer to shift pleiotropic relationships and cellular networks within bacterial genomes. They also provide an important context for how secondary effects of transfer can bias evolutionary trajectories and interactions between species. Lastly, these results and system provide a foundation to investigate evolutionary consequences in real time as newly acquired regions are ameliorated and integrated into new genomic contexts.     

Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity

Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment''s causal structure. We investigated the evolution of small, adaptive logic-gate networks (“animats”) in task environments where falling blocks of different sizes have to be caught or avoided in a ‘Tetris-like’ game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats'' adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks (“brains”) with many concepts, leading to an increase in their internal complexity.     

Glutathione transferases in the genomics era: new insights and perspectives

In the last decade the tumultuous development of "omics" greatly improved our ability to understand protein structure, function and evolution, and to define their roles and networks in complex biological processes. This fast accumulating knowledge holds great potential for biotechnological applications, from the development of biomolecules with novel properties of industrial and medical importance, to the creation of transgenic organisms with new, favorable characteristics. This review focuses on glutathione transferases (GSTs), an ancient protein superfamily with multiple roles in all eukaryotic organisms, and attempts to give an overview of the new insights and perspectives provided by omics into the biology of these proteins. Among the aspects considered are the redefinition of GST subfamilies, their evolution in connection with structurally related families, present and future biotechnological outcomes.     

Coherent activation of a synthetic mammalian gene network

A quantitative analysis of naturally-occurring regulatory networks, especially those present in mammalian cells, is difficult due to their high complexity. Much simpler gene networks can be engineered in model organisms and analyzed as isolated regulatory modules. Recently, several synthetic networks have been constructed in mammalian systems. However, most of these engineered mammalian networks have been characterized using steady-state population level measurements. Here, we use an integrated experimental-computational approach to analyze the dynamical response of a synthetic positive feedback network in individual mammalian cells. We observe a switch-like activation of the network with variable delay times in individual cells. In agreement with a stochastic model of the network, we find that increasing the strength of the positive feedback results in a decrease in the mean delay time and a more coherent activation of individual cells. Our results are important for gaining insight into biological processes which rely on positive feedback regulation.     

Planning and design of ecological networks in urban areas

Urban ecological networks are defined differently in ecology, urban planning and landscape ecology, but they all have linearity and linkage in common. Early urban representations evolved from the constraints of deep ecological structure in the landscape to built elements that must work around natural linear obstacles—rivers, coastlines, dunes, cliffs, hills and valley swamps. Village commons were linked by roads. The Industrial Revolution led to accelerating urban growth, where the role of open space focussed on public health and transport. The Renaissance, Baroque and Picturesque movements accentuated networks in wooded parks, boulevards and sweeping riverine vistas. These provided a new aesthetic and sense of grandeur in the urban centres of European empires and later their colonies. Grafted onto this visual connectivity has been an awakened ecological understanding of spatial dynamics. The emergent notion of ecological corridor functionality provided support for green linear features, although initially this was based on untested theory. The idea of organisms moving along green highways seemed logical, but only recently has unequivocal empirical evidence emerged that demonstrates this functionality. Nevertheless, the main role of corridors may be to provide habitat rather than to act as connectors of nodal habitats. Most organisms can utilise stepping stones, and these may accommodate desired meta-populations while deterring pest movement. Swale drains and treatment wetlands provide riparian services and serve as biodiversity corridors. However, to most people the obvious function is visual—providing green fingers through what would otherwise be urban grey. The health benefits of these are have been demonstrated to be psychological as much as biophysical.     

Prokaryotic Ancestry of Eukaryotic Protein Networks Mediating Innate Immunity and Apoptosis

Protein domains characteristic of eukaryotic innate immunity and apoptosis have many prokaryotic counterparts of unknown function. By reconstructing interactomes computationally, we found that bacterial proteins containing these domains are part of a network that also includes other domains not hitherto associated with immunity. This network is connected to the network of prokaryotic signal transduction proteins, such as histidine kinases and chemoreceptors. The network varies considerably in domain composition and degree of paralogy, even between strains of the same species, and its repetitive domains are often amplified recently, with individual repeats sharing up to 100% sequence identity. Both phenomena are evidence of considerable evolutionary pressure and thus compatible with a role in the “arms race” between host and pathogen. In order to investigate the relationship of this network to its eukaryotic counterparts, we performed a cluster analysis of organisms based on a census of its constituent domains across all fully sequenced genomes. We obtained a large central cluster of mainly unicellular organisms, from which multicellular organisms radiate out in two main directions. One is taken by multicellular bacteria, primarily cyanobacteria and actinomycetes, and plants form an extension of this direction, connected via the basal, unicellular cyanobacteria. The second main direction is taken by animals and fungi, which form separate branches with a common root in the α-proteobacteria of the central cluster. This analysis supports the notion that the innate immunity networks of eukaryotes originated from their endosymbionts and that increases in the complexity of these networks accompanied the emergence of multicellularity.     

A cellular automaton model of morphogenesis in Arabidopsis thaliana

Development of organisms is a very complex process in that a lot of gene networks of different cell types are to be integrated. Development of cellular automata that model the morphodynamics of different cell types is the first step in understanding and analyzing the regulatory mechanisms that underlie the developmental gene networks. We have developed a model of a cellular automaton that simulates the embryonic development of the shoot meristem in Arabidopsis thaliana. The model adequately describes the basic stages in the development of this organ in wild type and mutants.

     

Effects of deception in social networks

Honesty plays a crucial role in any situation where organisms exchange information or resources. Dishonesty can thus be expected to have damaging effects on social coherence if agents cannot trust the information or goods they receive. However, a distinction is often drawn between prosocial lies (‘white’ lies) and antisocial lying (i.e. deception for personal gain), with the former being considered much less destructive than the latter. We use an agent-based model to show that antisocial lying causes social networks to become increasingly fragmented. Antisocial dishonesty thus places strong constraints on the size and cohesion of social communities, providing a major hurdle that organisms have to overcome (e.g. by evolving counter-deception strategies) in order to evolve large, socially cohesive communities. In contrast, white lies can prove to be beneficial in smoothing the flow of interactions and facilitating a larger, more integrated network. Our results demonstrate that these group-level effects can arise as emergent properties of interactions at the dyadic level. The balance between prosocial and antisocial lies may set constraints on the structure of social networks, and hence the shape of society as a whole.     

Developmental evolution in social insects: regulatory networks from genes to societies

The evolution and development of complex phenotypes in social insect colonies, such as queen-worker dimorphism or division of labor, can, in our opinion, only be fully understood within an expanded mechanistic framework of Developmental Evolution. Conversely, social insects offer a fertile research area in which fundamental questions of Developmental Evolution can be addressed empirically. We review the concept of gene regulatory networks (GRNs) that aims to fully describe the battery of interacting genomic modules that are differentially expressed during the development of individual organisms. We discuss how distinct types of network models have been used to study different levels of biological organization in social insects, from GRNs to social networks. We propose that these hierarchical networks spanning different organizational levels from genes to societies should be integrated and incorporated into full GRN models to elucidate the evolutionary and developmental mechanisms underlying social insect phenotypes. Finally, we discuss prospects and approaches to achieve such an integration.     

A survey on methods for modeling and analyzing integrated biological networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics" technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.