Quartets and unrooted phylogenetic networks

Phylogenetic networks were introduced to describe evolution in the presence of exchanges of genetic material between coexisting species or individuals. Split networks in particular were introduced as a special kind of abstract network to visualize conflicts between phylogenetic trees which may correspond to such exchanges. More recently, methods were designed to reconstruct explicit phylogenetic networks (whose vertices can be interpreted as biological events) from triplet data. In this article, we link abstract and explicit networks through their combinatorial properties, by introducing the unrooted analog of level-k networks. In particular, we give an equivalence theorem between circular split systems and unrooted level-1 networks. We also show how to adapt to quartets some existing results on triplets, in order to reconstruct unrooted level-k phylogenetic networks. These results give an interesting perspective on the combinatorics of phylogenetic networks and also raise algorithmic and combinatorial questions.     

Coupling oscillations and switches in genetic networks

Switches (bistability) and oscillations (limit cycle) are omnipresent in biological networks. Synthetic genetic networks producing bistability and oscillations have been designed and constructed experimentally. However, in real biological systems, regulatory circuits are usually interconnected and the dynamics of those complex networks is often richer than the dynamics of simple modules. Here we couple the genetic Toggle switch and the Repressilator, two prototypic systems exhibiting bistability and oscillations, respectively. We study two types of coupling. In the first type, the bistable switch is under the control of the oscillator. Numerical simulation of this system allows us to determine the conditions under which a periodic switch between the two stable steady states of the Toggle switch occurs. In addition we show how birhythmicity characterized by the coexistence of two stable small-amplitude limit cycles, can easily be obtained in the system. In the second type of coupling, the oscillator is placed under the control of the Toggleswitch. Numerical simulation of this system shows that this construction could for example be exploited to generate a permanent transition from a stable steady state to self-sustained oscillations (and vice versa) after a transient external perturbation. Those results thus describe qualitative dynamical behaviors that can be generated through the coupling of two simple network modules. These results differ from the dynamical properties resulting from interlocked feedback loops systems in which a given variable is involved at the same time in both positive and negative feedbacks. Finally the models described here may be of interest in synthetic biology, as they give hints on how the coupling should be designed to get the required properties.     

Sampling strategy optimization to increase statistical power in landscape genomics: A simulation‐based approach

An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. Implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burden of logistical constraints. These important aspects are often underestimated in the literature dedicated to sampling strategies. In this study, we computed simulated genomic data sets to run against actual environmental data in order to trial landscape genomics experiments under distinct sampling strategies. These strategies differed by design approach (to enhance environmental and/or geographical representativeness at study sites), number of sampling locations and sample sizes. We then evaluated how these elements affected statistical performances (power and false discoveries) under two antithetical demographic scenarios. Our results highlight the importance of selecting an appropriate sample size, which should be modified based on the demographic characteristics of the studied population. For species with limited dispersal, sample sizes above 200 units are generally sufficient to detect most adaptive signals, while in random mating populations this threshold should be increased to 400 units. Furthermore, we describe a design approach that maximizes both environmental and geographical representativeness of sampling sites and show how it systematically outperforms random or regular sampling schemes. Finally, we show that although having more sampling locations (between 40 and 50 sites) increase statistical power and reduce false discovery rate, similar results can be achieved with a moderate number of sites (20 sites). Overall, this study provides valuable guidelines for optimizing sampling strategies for landscape genomics experiments.     

Synthetic immune signaling

A successful immune response against pathogens requires the activation of different cell types of the immune system. These activation processes are difficult to study by classical biochemical and genetic methods alone. In this review we describe how approaches of synthetic biology, such as rebuilding of minimal functional signaling systems and the design of new molecules acting as signaling switches, can be used to get a deeper insight into the signaling mechanism of immune cells. In particular, the interaction of receptors with signal-transducing elements can be studied in detail with these new methods. In addition, sophisticated synthetic immune receptors are being tested in the clinic for gene therapy against certain cancer types.     

GDSCalc: A Web-Based Application for Evaluating Discrete Graph Dynamical Systems

Discrete dynamical systems are used to model various realistic systems in network science, from social unrest in human populations to regulation in biological networks. A common approach is to model the agents of a system as vertices of a graph, and the pairwise interactions between agents as edges. Agents are in one of a finite set of states at each discrete time step and are assigned functions that describe how their states change based on neighborhood relations. Full characterization of state transitions of one system can give insights into fundamental behaviors of other dynamical systems. In this paper, we describe a discrete graph dynamical systems (GDSs) application called GDSCalc for computing and characterizing system dynamics. It is an open access system that is used through a web interface. We provide an overview of GDS theory. This theory is the basis of the web application; i.e., an understanding of GDS provides an understanding of the software features, while abstracting away implementation details. We present a set of illustrative examples to demonstrate its use in education and research. Finally, we compare GDSCalc with other discrete dynamical system software tools. Our perspective is that no single software tool will perform all computations that may be required by all users; tools typically have particular features that are more suitable for some tasks. We situate GDSCalc within this space of software tools.     

An adaptive two-stage sequential design for sampling rare and clustered populations

How to design an efficient large-area survey continues to be an interesting question for ecologists. In sampling large areas, as is common in environmental studies, adaptive sampling can be efficient because it ensures survey effort is targeted to subareas of high interest. In two-stage sampling, higher density primary sample units are usually of more interest than lower density primary units when populations are rare and clustered. Two-stage sequential sampling has been suggested as a method for allocating second stage sample effort among primary units. Here, we suggest a modification: adaptive two-stage sequential sampling. In this method, the adaptive part of the allocation process means the design is more flexible in how much extra effort can be directed to higher-abundance primary units. We discuss how best to design an adaptive two-stage sequential sample.     

Numerical algorithms for estimation and calculation of parameters in modeling pest population dynamics and evolution of resistance

Computational simulation models can provide a way of understanding and predicting insect population dynamics and evolution of resistance, but the usefulness of such models depends on generating or estimating the values of key parameters. In this paper, we describe four numerical algorithms generating or estimating key parameters for simulating four different processes within such models. First, we describe a novel method to generate an offspring genotype table for one- or two-locus genetic models for simulating evolution of resistance, and how this method can be extended to create offspring genotype tables for models with more than two loci. Second, we describe how we use a generalized inverse matrix to find a least-squares solution to an over-determined linear system for estimation of parameters in probit models of kill rates. This algorithm can also be used for the estimation of parameters of Freundlich adsorption isotherms. Third, we describe a simple algorithm to randomly select initial frequencies of genotypes either without any special constraints or with some pre-selected frequencies. Also we give a simple method to calculate the “stable” Hardy–Weinberg equilibrium proportions that would result from these initial frequencies. Fourth we describe how the problem of estimating the intrinsic rate of natural increase of a population can be converted to a root-finding problem and how the bisection algorithm can then be used to find the rate. We implemented all these algorithms using MATLAB and Python code; the key statements in both codes consist of only a few commands and are given in the appendices. The results of numerical experiments are also provided to demonstrate that our algorithms are valid and efficient.     

Imbalance and balancing: Development of ecological flow networks

In this paper we address balancing process of ecological flow networks. In existing approaches, macroscopic objectives to which systems organize are assumed. Flow balance provides only constraints for the optimization. Since flow balance and objectives are separated from each other, it is impossible to address how the appearance of objectives is related to flow balance. Therefore, we take an alternative approach, in which we directly describe a dynamics of balancing process. We propose a simple mathematical formula for local balancing dynamics and show that it can generate a self-organizing property, which could be seen as a primitive objective.     

Modulating repeat protein stability: the effect of individual helix stability on the collective behavior of the ensemble

Repeat proteins are tandem arrays of a small structural motif, in which tertiary structure is stabilized by interactions within a repeat and between neighboring repeats. Several studies have shown that this modular structure is manifest in modular thermodynamic properties. Specifically, the global stability of a repeat protein can be described by simple linear models, considering only two parameters: the stability of the individual repeated units (H) and the coupling interaction between the units (J). If the repeat units are identical, single values of H and J, together with the number of repeated units, is sufficient to completely describe the thermodynamic behavior of any protein within a series. In this work, we demonstrate how the global stability of a repeat protein can be changed, in a predictable fashion, by modifying only the H parameter. Taking a previously characterized series of consensus tetratricopeptide repeats (TPR) (CTPRa) proteins, we introduced mutations into the basic repeating unit, such that the stability of the individual repeat unit was increased, but its interaction with neighboring units was unchanged. In other words, we increased H but kept J constant. We demonstrated that the denaturation curves for a series of such repeat proteins can be fit and additional curves can be predicted by the one-dimensional Ising model in which only H has changed from the original fit for the CTPRa series. Our results show that we can significantly increase the stability of a repeat protein by rationally increasing the stability of the units (H), whereas the interaction between repeats (J) remains unchanged.     

The interpretation of selection coefficients

Evolutionary biologists have an array of powerful theoretical techniques that can accurately predict changes in the genetic composition of populations. Changes in gene frequencies and genetic associations between loci can be tracked as they respond to a wide variety of evolutionary forces. However, it is often less clear how to decompose these various forces into components that accurately reflect the underlying biology. Here, we present several issues that arise in the definition and interpretation of selection and selection coefficients, focusing on insights gained through the examination of selection coefficients in multilocus notation. Using this notation, we discuss how its flexibility—which allows different biological units to be identified as targets of selection—is reflected in the interpretation of the coefficients that the notation generates. In many situations, it can be difficult to agree on whether loci can be considered to be under “direct” versus “indirect” selection, or to quantify this selection. We present arguments for what the terms direct and indirect selection might best encompass, considering a range of issues, from viability and sexual selection to kin selection. We show how multilocus notation can discriminate between direct and indirect selection, and describe when it can do so.     

DNA repair: models for damage and mismatch recognition

Maintaining the integrity of the genome is critical for the survival of any organism. To achieve this, many families of enzymatic repair systems which recognize and repair DNA damage have evolved. Perhaps most intriguing about the workings of these repair systems is the actual damage recognition process. What are the chemical characteristics which are common to sites of nucleic acid damage that DNA repair proteins may exploit in targeting sites? Importantly, thermodynamic and kinetic principles, as much as structural factors, make damage sites distinct from the native DNA bases, and indeed, in many cases, these are the features which are believed to be exploited by repair enzymes. Current proposals for damage recognition may not fulfill all of the demands required of enzymatic repair systems given the sheer size of many genomes, and the efficiency with which the genome is screened for damage. Here we discuss current models for how DNA damage recognition may occur and the chemical characteristics, shared by damaged DNA sites, of which repair proteins may take advantage. These include recognition based upon the thermodynamic and kinetic instabilities associated with aberrant sites. Additionally, we describe how small changes in base pair structure can alter also the unique electronic properties of the DNA base pair pi-stack. Further, we describe photophysical, electrochemical, and biochemical experiments in which mismatches and other local perturbations in structure are detected using DNA-mediated charge transport. Finally, we speculate as to how this DNA electron transfer chemistry might be exploited by repair enzymes in order to scan the genome for sites of damage.     

Cancer: a Systems Biology disease

Cancer research has focused on the identification of molecular differences between cancerous and healthy cells. The emerging picture is overwhelmingly complex. Molecules out of many parallel signal transduction pathways are involved. Their activities appear to be controlled by multiple factors. The action of regulatory circuits, cross-talk between pathways and the non-linear reaction kinetics of biochemical processes complicate the understanding and prediction of the outcome of intracellular signaling. In addition, interactions between tumor and other cell types give rise to a complex supra-cellular communication network. If cancer is such a complex system, how can one ever predict the effect of a mutation in a particular gene on a functionality of the entire system? And, how should one go about identifying drug targets? Here, we argue that one aspect is to recognize, where the essence resides, i.e. recognize cancer as a Systems Biology disease. Then, more cancer biologists could become systems biologists aiming to provide answers to some of the above systemic questions. To this aim, they should integrate the available knowledge stemming from quantitative experimental results through mathematical models. Models that have contributed to the understanding of complex biological systems are discussed. We show that the architecture of a signaling network is important for determining the site at which an oncologist should intervene. Finally, we discuss the possibility of applying network-based drug design to cancer treatment and how rationalized therapies, such as the application of kinase inhibitors, may benefit from Systems Biology.     

Bending continuous structures with SMAs: a novel robotic fish design

In this paper, we describe our research on bio-inspired locomotion systems using deformable structures and smart materials, concretely shape memory alloys (SMAs). These types of materials allow us to explore the possibility of building motor-less and gear-less robots. A swimming underwater fish-like robot has been developed whose movements are generated using SMAs. These actuators are suitable for bending the continuous backbone of the fish, which in turn causes a change in the curvature of the body. This type of structural arrangement is inspired by fish red muscles, which are mainly recruited during steady swimming for the bending of a flexible but nearly incompressible structure such as the fishbone. This paper reviews the design process of these bio-inspired structures, from the motivations and physiological inspiration to the mechatronics design, control and simulations, leading to actual experimental trials and results. The focus of this work is to present the mechanisms by which standard swimming patterns can be reproduced with the proposed design. Moreover, the performance of the SMA-based actuators' control in terms of actuation speed and position accuracy is also addressed.     

Programming the Grid: Distributed Software Components, P2P and Grid Web Services for Scientific Applications

Computational Grids [17,25] have become an important asset in large-scale scientific and engineering research. By providing a set of services that allow a widely distributed collection of resources to be tied together into a relatively seamless computing framework, teams of researchers can collaborate to solve problems that they could not have attempted before. Unfortunately the task of building Grid applications remains extremely difficult because there are few tools available to support developers. To build reliable and re-usable Grid applications, programmers must be equipped with a programming framework that hides the details of most Grid services and allows the developer a consistent, non-complex model in which applications can be composed from well tested, reliable sub-units. This paper describes experiences with using a software component framework for building Grid applications. The framework, which is based on the DOE Common Component Architecture (CCA) [1,2,3,8], allows individual components to export function/service interfaces that can be remotely invoked by other components. The framework also provides a simple messaging/event system for asynchronous notification between application components. The paper also describes how the emerging Web-services [52] model fits with a component-oriented application design philosophy. To illustrate the connection between Web services and Grid application programming we describe a simple design pattern for application factory services which can be used to simplify the task of building reliable Grid programs. Finally we address several issues of Grid programming that better understood from the perspective of Peer-to-Peer (P2P) systems. In particular we describe how models for collaboration and resource sharing fit well with many Grid application scenarios.     

Crop proteomics: aim at sustainable agriculture of tomorrow

The advent of proteomics has made it possible to identify a broad spectrum of proteins in living systems. This capability is especially useful for crops as it may give clues not only about nutritional value, but also about yield and how these factors are affected by adverse conditions. In this review, we describe the recent progress in crop proteomics and highlight the achievements made in understanding the proteomes of major crops. The major emphasis will be on crop responses to abiotic stresses. Rigorous genetic testing of the role of possibly important proteins can be conducted. The increasing ease with the DNA, mRNA and protein levels can be conducted and connected suggests that proteomics data will not be difficult to apply to practical crop breeding.     

Sequence structure of van der Waals locks in proteins

Recent works has suggested that proteins in early evolution have gone through a stage of closed loop elements with a typical contour size of 25-35 residues. These closed loops are still the elementary protein units to these days, and can be used to spell out protein sequence/structure relationship through a relatively small number of protein prototypes. In this study we aimed to identify the sequences that are used to lock the loop ends to one another, and to show how an extensive dictionary of such locking pairs can be created using positional correlation data from a large proteome database, and structural data from PDB databases. Such a dictionary can be used in reconstructing the evolutionary pathway the modern proteins have gone through, and in identifying closed loop elements in modern proteins with yet unknown 3D structure.     

The nature of protein domain evolution: shaping the interaction network

The proteomes that make up the collection of proteins in contemporary organisms evolved through recombination and duplication of a limited set of domains. These protein domains are essentially the main components of globular proteins and are the most principal level at which protein function and protein interactions can be understood. An important aspect of domain evolution is their atomic structure and biochemical function, which are both specified by the information in the amino acid sequence. Changes in this information may bring about new folds, functions and protein architectures. With the present and still increasing wealth of sequences and annotation data brought about by genomics, new evolutionary relationships are constantly being revealed, unknown structures modeled and phylogenies inferred. Such investigations not only help predict the function of newly discovered proteins, but also assist in mapping unforeseen pathways of evolution and reveal crucial, co-evolving inter- and intra-molecular interactions. In turn this will help us describe how protein domains shaped cellular interaction networks and the dynamics with which they are regulated in the cell. Additionally, these studies can be used for the design of new and optimized protein domains for therapy. In this review, we aim to describe the basic concepts of protein domain evolution and illustrate recent developments in molecular evolution that have provided valuable new insights in the field of comparative genomics and protein interaction networks.     

Three approaches to assembling nano-bio-machines using molecular motors

Efforts to use protein molecular motors as nanoactuators are making rapid progress. For instance, it is now possible to carry out directional transport of small cargo along microtracks or microchannels using kinesin-microtubule systems, which could be the basis of micro-conveyor belts or molecular shuttles. However, the applicability of protein-based devices is limited by their poor stability in artificial environments. In addition, assembly of complex, intelligent microdevices or systems will likely require bottom-up self-assembly, and we still do not have sufficient knowledge to rationally design self-assembling protein-based microdevices or systems. One approach to solving the problems associated with protein-based systems is to use DNA-based nanodevices, which are amenable to rational design. Indeed, ingenious design has enabled realization of DNA-based nanoactuators and self-assembled micropatterns of various shapes. One also could use cells, organelles, or tissues as preassembled motile units, and several motile devices have already been realized using this approach. In addition to being less prone to the assemaly problems, cell-based microdevices have the advantage that the motile units reproduce themselves, and genetically encoded functional modifications can be replicated effortlessly. These protein-based, DNA-based, and cell-based systems each have distinct advantages and disadvantages, so that hybrid devices combining the best characteristics of all three would seem the most likely to succeed.