Orkun S. Soyer

Challenges in microbial ecology: building predictive understanding of community function and dynamics

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Synthetic microbial communities

Highlights • Microbial interactions and system function are two ways to study communities.• Natural microbial communities are difficult to define and to study.• Synthetic microbial communities are comprehensible systems of reduced complexity.• Synthetic communities keep key features of natural ones and are amenable to modelling.• Synthetic microbial communities are gaining importance in biotechnology.

Evolution of complexity in signaling pathways

It is not clear how biological pathways evolve to mediate a certain physiological response and why they show a level of complexity that is generally above the minimum required to achieve such a response. One possibility is that pathway complexity increases due to the nature of evolutionary mechanisms. Here, we analyze this possibility by using mathematical models of biological pathways and evolutionary simulations. Starting with a population of small pathways of three proteins, we let the population evolve with mutations that affect pathway structure through duplication or deletion of existing proteins, deletion or creation of interactions among them, or addition of new proteins. Our simulations show that such mutational events, coupled with a selective pressure, leads to growth of pathways. These results indicate that pathways could be driven toward complexity via simple evolutionary mechanisms and that complexity can arise without any specific selective pressure for it. Furthermore, we find that the level of complexity that pathways evolve toward depends on the selection criteria. In general, we find that final pathway size tends to be lower when pathways evolve under stringent selection criteria. This leads to the counterintuitive conclusion that simple response requirements on a pathway would facilitate its evolution toward higher complexity.

The roles of integration in molecular systems biology

A common way to think about scientific practice involves classifying it as hypothesis- or data-driven. We argue that although such distinctions might illuminate scientific practice very generally, they are not sufficient to understand the day-to-day dynamics of scientific activity and the development of programmes of research. One aspect of everyday scientific practice that is beginning to gain more attention is integration. This paper outlines what is meant by this term and how it has been discussed from scientific and philosophical points of view. We focus on methodological, data and explanatory integration, and show how they are connected. Then, using some examples from molecular systems biology, we will show how integration works in a range of inquiries to generate surprising insights and even new fields of research. From these examples we try to gain a broader perspective on integration in relation to the contexts of inquiry in which it is implemented. In todays environment of data-intensive large-scale science, integration has become both a practical and normative requirement with corresponding implications for meta-methodological accounts of scientific practice. We conclude with a discussion of why an understanding of integration and its dynamics is useful for philosophy of science and scientific practice in general.

Evolutionary Systems Biology

Preface.- Evolutionary Systems Biology: Historical and Philosophical Persepctives on an Emerging Synthesis.- Metabolic Networks and their Evolution.- Organization Principles and their Evolution.- Organization principles in genetic interaction networks.- Evolution of regulatory networks: Nematode vulva induction as an example of developmental systems drift.- Lifes attractors understanding developmental systems through reverse engineering and in silico evolution.- Evolutionary characteristics of bacterial two-component systems.- Comparative interaction networks - bridging genotype to phenotype.- Evolution in silico: from network structure to bifurcation theory.- On the search for design principles in biological systems.- Towards a theory of multilevel evolution long term information integration shapes the mutational landscape and enhances evolvability.- Evolutionary principles underlying structure and response dynamics of cellular networks.- Phenotypic plasticity and robustness: Evolutionary stability theory, gene expression dynamics model, and laboratory experiments.- Genetic redundancies and their evolutionary maintenance.- Evolution of resource and energy management in biologically realistic gene regulatory network models.- Reverse ecology: From systems to environments and back.- Bacteria-virus coevolution.- The genotype-phenotype maps of systems biology and quantitative genetics: distinct and complementary.- How evolutionary systems biology will help understand adaptive landscapes and distributions of mutational effects.- Building synthetic systems to learn Natures design principles.- The robustness continuum.- Index.

Metabolic tinker: an online tool for guiding the design of synthetic metabolic pathways

One of the primary aims of synthetic biology is to (re)design metabolic pathways towards the production of desired chemicals. The fast pace of developments in molecular biology increasingly makes it possible to experimentally redesign existing pathways and implement de novo ones in microbes or using in vitro platforms. For such experimental studies, the bottleneck is shifting from implementation of pathways towards their initial design. Here, we present an online tool called ‘Metabolic Tinker’, which aims to guide the design of synthetic metabolic pathways between any two desired compounds. Given two user-defined ‘target’ and ‘source’ compounds, Metabolic Tinker searches for thermodynamically feasible paths in the entire known metabolic universe using a tailored heuristic search strategy. Compared with similar graph-based search tools, Metabolic Tinker returns a larger number of possible paths owing to its broad search base and fast heuristic, and provides for the first time thermodynamic feasibility information for the discovered paths. Metabolic Tinker is available as a web service at http://osslab.ex.ac.uk/tinker.aspx. The same website also provides the source code for Metabolic Tinker, allowing it to be developed further or run on personal machines for specific applications.

Evolution of Taxis Responses in Virtual Bacteria: Non-Adaptive Dynamics

Bacteria are able to sense and respond to a variety of external stimuli, with responses that vary from stimuli to stimuli and from species to species. The best-understood is chemotaxis in the model organism Escherichia coli, where the dynamics and the structure of the underlying pathway are well characterised. It is not clear, however, how well this detailed knowledge applies to mechanisms mediating responses to other stimuli or to pathways in other species. Furthermore, there is increasing experimental evidence that bacteria integrate responses from different stimuli to generate a coherent taxis response. We currently lack a full understanding of the different pathway structures and dynamics and how this integration is achieved. In order to explore different pathway structures and dynamics that can underlie taxis responses in bacteria, we perform a computational simulation of the evolution of taxis. This approach starts with a population of virtual bacteria that move in a virtual environment based on the dynamics of the simple biochemical pathways they harbour. As mutations lead to changes in pathway structure and dynamics, bacteria better able to localise with favourable conditions gain a selective advantage. We find that a certain dynamics evolves consistently under different model assumptions and environments. These dynamics, which we call non-adaptive dynamics, directly couple tumbling probability of the cell to increasing stimuli. Dynamics that are adaptive under a wide range of conditions, as seen in the chemotaxis pathway of E. coli, do not evolve in these evolutionary simulations. However, we find that stimulus scarcity and fluctuations during evolution results in complex pathway dynamics that result both in adaptive and non-adaptive dynamics depending on basal stimuli levels. Further analyses of evolved pathway structures show that effective taxis dynamics can be mediated with as few as two components. The non-adaptive dynamics mediating taxis responses provide an explanation for experimental observations made in mutant strains of E. coli and in wild-type Rhodobacter sphaeroides that could not be explained with standard models. We speculate that such dynamics exist in other bacteria as well and play a role linking the metabolic state of the cell and the taxis response. The simplicity of mechanisms mediating such dynamics makes them a candidate precursor of more complex taxis responses involving adaptation. This study suggests a strong link between stimulus conditions during evolution and evolved pathway dynamics. When evolution was simulated under conditions of scarce and fluctuating stimulus conditions, the evolved pathway contained features of both adaptive and non-adaptive dynamics, suggesting that these two types of dynamics can have different advantages under distinct environmental circumstances.

NMR structure of the second intracellular loop of the α2A adrenergic receptor: Evidence for a novel cytoplasmic helix

A major, unresolved question in signal transduction by G protein coupled receptors (GPCRs) is to understand how, at atomic resolution, a GPCR activates a G protein. A step toward answering this question was made with the determination of the high-resolution structure of rhodopsin; we now know the intramolecular interactions that characterize the resting conformation of a GPCR. To what degree does this structure represent a structural paradigm for other GPCRs, especially at the cytoplasmic surface where GPCR-G protein interaction occurs and where the sequence homology is low among GPCRs? To address this question, we performed NMR studies on approximately 35-residue-long peptides including the critical second intracellular loop (i2) of the alpha 2A adrenergic receptor (AR) and of rhodopsin. To stabilize the secondary structure of the peptide termini, 4-12 residues from the adjacent transmembrane helices were included and structures determined in dodecylphosphocholine micelles. We also characterized the effects on an alpha 2A AR peptide of a D130I mutation in the conserved DRY motif. Our results show that in contrast to the L-shaped loop in the i2 of rhodopsin, the i2 of the alpha 2A AR is predominantly helical, supporting the hypothesis that there is structural diversity within GPCR intracellular loops. The D130I mutation subtly modulates the helical structure. The spacing of nonpolar residues in i2 with helical periodicity is a predictor of helical versus loop structure. These data should lead to more accurate models of the intracellular surface of GPCRs and of receptor-mediated G protein activation.

Depicting a protein's two faces: GPCR classification by phylogenetic tree-based HMMs.

Related proteins with similar biological functions generally share common features, allowing us to extract the common sequence features. These common features enable us to build statistical models that can be used to classify proteins, to predict new members, and to study the sequence–function relationship of this protein function group. Although evolution underlies the basis of multiple sequence analysis methods, most methods ignore phylogenetic relationships and the evolutionary process in building these statistical models. Previously we have shown that a phylogenetic tree‐based profile hidden Markov model (T‐HMM) is superior in generating a profile for a group of similar proteins. In this study we used the method to generate common features of G protein‐coupled receptors (GPCRs). The profile generated by T‐HMM gives high accuracy in GPCR function classification, both by ligand and by coupled G protein.

Bistability in feedback circuits as a byproduct of evolution of evolvability

Noisy bistable dynamics in gene regulation can underlie stochastic switching and is demonstrated to be beneficial under fluctuating environments. It is not known, however, if fluctuating selection alone can result in bistable dynamics. Using a stochastic model of simple feedback networks, we apply fluctuating selection on gene expression and run in silico evolutionary simulations. We find that independent of the specific nature of the environment–fitness relationship, the main outcome of fluctuating selection is the evolution of increased evolvability in the network; system parameters evolve toward a nonlinear regime where phenotypic diversity is increased and small changes in genotype cause large changes in expression level. In the presence of noise, the evolution of increased nonlinearity results in the emergence and maintenance of bistability. Our results provide the first direct evidence that bistability and stochastic switching in a gene regulatory network can emerge as a mechanism to cope with fluctuating environments. They strongly suggest that such emergence occurs as a byproduct of evolution of evolvability and exploitation of noise by evolution.