Synthetic biology of minimal systems

“Synthetic biology” is a concept that has developed together with, or slightly after, “systems biology”. But while systems biology aims at the full understanding of large systems by integrating more and more details into their models, synthetic biology phrases different questions, namely: what particular biological function could be obtained with a certain known subsystem of reduced complexity; can this function be manipulated or engineered in artificial environments or genetically modified organisms; and if so, how? The most prominent representation of synthetic biology has so far been microbial engineering by recombinant DNA technology, employing modular concepts known from information technology. However, there are an increasing number of biophysical groups who follow similar strategies of dissecting cellular processes and networks, trying to identify functional minimal modules that could then be combined in a bottom-up approach towards biology. These modules are so far not as particularly defined by their impact on DNA processing, but rather influenced by core fields of biophysics, such as cell mechanics and membrane dynamics. This review will give an overview of some classical and some quite new biophysical strategies for constructing minimal systems of certain cellular modules. We will show that with recent advances in understanding of cytoskeletal and membrane elements, the time might have come to experimentally challenge the concept of a minimal cell.     

Wnt signaling and the regulation of stem cell function

Canonical Wnt signaling plays a crucial role in controlling cell expansion in many types of stem cells. Recent studies, however, demonstrated that Wnt is not only a general stem cell growth factor but can also influence cell lineage decisions in certain stem cell types by promoting specific fates at the expense of others. Thus, Wnt signaling elicits multiple functions in stem cells. Wnt activity appears to depend on cell-intrinsic properties that might change with time during development, thereby altering the cellular response to Wnt. Moreover, the spatial context of a stem cell also determines how the cell interprets Wnt signal activity, in that synergistic or antagonistic signaling pathways can modulate Wnt signaling. How a stem cell integrates Wnt and other signals and how such signaling networks regulate stem cell function on the molecular level remains to be elucidated.     

How cells process information: quantification of spatiotemporal signaling dynamics

Arguably, one of the foremost distinctions between life and non-living matter is the ability to sense environmental changes and respond appropriately—an ability that is invested in every living cell. Within a single cell, this function is largely carried out by networks of signaling molecules. However, the details of how signaling networks help cells make complicated decisions are still not clear. For instance, how do cells read graded, analog stress signals but convert them into digital live-or-die responses? The answer to such questions may originate from the fact that signaling molecules are not static but dynamic entities, changing in numbers and activity over time and space. In the past two decades, researchers have been able to experimentally monitor signaling dynamics and use mathematical techniques to quantify and abstract general principles of how cells process information. In this review, the authors first introduce and discuss various experimental and computational methodologies that have been used to study signaling dynamics. The authors then discuss the different types of temporal dynamics such as oscillations and bistability that can be exhibited by signaling systems and highlight studies that have investigated such dynamics in physiological settings. Finally, the authors illustrate the role of spatial compartmentalization in regulating cellular responses with examples of second-messenger signaling in cardiac myocytes.     

Representing and analysing molecular and cellular function using the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model.     

Modeling the topological organization of cellular processes

The cell as a dynamical system presents the characteristics of having a dynamical structure. That is, the exact phase space of the system cannot be fixed before the evolution and integrative cell models must state the evolution of the structure jointly with the evolution of the cell state. This kind of dynamical systems is very challenging to model and simulate. New programming concepts must be developed to ease their modeling and simulation. In this context, the goal of the MGS project is to develop an experimental programming language dedicated to the simulation of this kind of systems. MGS proposes a unified view on several computational mechanisms (CHAM, Lindenmayer systems, Paun systems, cellular automata) enabling the specification of spatially localized computations on heterogeneous entities. The evolution of a dynamical structure is handled through the concept of transformation which relies on the topological organization of the system components. An example based on the modeling of spatially distributed biochemical networks is used to illustrate how these notions can be used to model the spatial and temporal organization of intracellular processes.     

Combining many interaction networks to predict gene function and analyze gene lists

In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems.     

Extraction of biological interaction networks from scientific literature

Biology can be regarded as a science of networks: interactions between various biological entities (eg genes, proteins, metabolites) on different levels (eg gene regulation, cell signalling) can be represented as graphs and, thus, analysis of such networks might shed new light on the function of biological systems. Such biological networks can be obtained from different sources. The extraction of networks from text is an important technique that requires the integration of several different computational disciplines. This paper summarises the most important steps in network extraction and reviews common approaches and solutions for the extraction of biological networks from scientific literature.     

Synthetic cell biology

Synthesis of data into formal models of cellular function is rapidly becoming a necessary industry. The complexity of the interactions among cellular constituents and the quantity of data about these interactions hinders the ability to predict how cells will respond to perturbation and how they can be engineered for industrial or medical purposes. Models provide a systematic framework to describe and analyze these complex systems. In the past few years, models have begun to have an impact on mainstream biology by creating deeper insight into the design rules of cellular signal processing, providing a basis for rational engineering of cells, and for resolving debates about the root causes of certain cellular behaviors. This review covers some of the recent work and challenges in developing these "synthetic cell" models and their growing practical applications.     

Identifying responsive functional modules from protein-protein interaction network

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.     

Estimating the Stochastic Bifurcation Structure of Cellular Networks

High throughput measurement of gene expression at single-cell resolution, combined with systematic perturbation of environmental or cellular variables, provides information that can be used to generate novel insight into the properties of gene regulatory networks by linking cellular responses to external parameters. In dynamical systems theory, this information is the subject of bifurcation analysis, which establishes how system-level behaviour changes as a function of parameter values within a given deterministic mathematical model. Since cellular networks are inherently noisy, we generalize the traditional bifurcation diagram of deterministic systems theory to stochastic dynamical systems. We demonstrate how statistical methods for density estimation, in particular, mixture density and conditional mixture density estimators, can be employed to establish empirical bifurcation diagrams describing the bistable genetic switch network controlling galactose utilization in yeast Saccharomyces cerevisiae. These approaches allow us to make novel qualitative and quantitative observations about the switching behavior of the galactose network, and provide a framework that might be useful to extract information needed for the development of quantitative network models.     

Sets2Networks: network inference from repeated observations of sets

Background

The skeleton of complex systems can be represented as networks where vertices represent entities, and edges represent the relations between these entities. Often it is impossible, or expensive, to determine the network structure by experimental validation of the binary interactions between every vertex pair. It is usually more practical to infer the network from surrogate observations. Network inference is the process by which an underlying network of relations between entities is determined from indirect evidence. While many algorithms have been developed to infer networks from quantitative data, less attention has been paid to methods which infer networks from repeated co-occurrence of entities in related sets. This type of data is ubiquitous in the field of systems biology and in other areas of complex systems research. Hence, such methods would be of great utility and value.

Results

Here we present a general method for network inference from repeated observations of sets of related entities. Given experimental observations of such sets, we infer the underlying network connecting these entities by generating an ensemble of networks consistent with the data. The frequency of occurrence of a given link throughout this ensemble is interpreted as the probability that the link is present in the underlying real network conditioned on the data. Exponential random graphs are used to generate and sample the ensemble of consistent networks, and we take an algorithmic approach to numerically execute the inference method. The effectiveness of the method is demonstrated on synthetic data before employing this inference approach to problems in systems biology and systems pharmacology, as well as to construct a co-authorship collaboration network. We predict direct protein-protein interactions from high-throughput mass-spectrometry proteomics, integrate data from Chip-seq and loss-of-function/gain-of-function followed by expression data to infer a network of associations between pluripotency regulators, extract a network that connects 53 cancer drugs to each other and to 34 severe adverse events by mining the FDA’s Adverse Events Reporting Systems (AERS), and construct a co-authorship network that connects Mount Sinai School of Medicine investigators. The predicted networks and online software to create networks from entity-set libraries are provided online at http://www.maayanlab.net/S2N.

Conclusions

The network inference method presented here can be applied to resolve different types of networks in current systems biology and systems pharmacology as well as in other fields of research.     

RNA synthetic biology

RNA molecules play important and diverse regulatory roles in the cell by virtue of their interaction with other nucleic acids, proteins and small molecules. Inspired by this natural versatility, researchers have engineered RNA molecules with new biological functions. In the last two years efforts in synthetic biology have produced novel, synthetic RNA components capable of regulating gene expression in vivo largely in bacteria and yeast, setting the stage for scalable and programmable cellular behavior. Immediate challenges for this emerging field include determining how computational and directed-evolution techniques can be implemented to increase the complexity of engineered RNA systems, as well as determining how such systems can be broadly extended to mammalian systems. Further challenges include designing RNA molecules to be sensors of intracellular and environmental stimuli, probes to explore the behavior of biological networks and components of engineered cellular control systems.     

Evolution of Osmosensory MAP Kinase Signaling Pathways

Mitogen-activated protein (MAP) kinases constitute a large familyof proteins with many functions. They are represented by a multitudeof paralogous isoforms in yeast, vertebrates, and other eukaryotes.A phylogenetically conserved function of MAP kinases is to carryosmotic signals from sensory to target elements of cells. Eventhough this function of MAP kinases is ubiquitous and characteristicof unicellular and multicellular eukaryotes alike the contingenciesbetween individual MAP kinases, sensor elements, and targetelements have been subject to vast modification during evolution.Extensive networking of MAP kinase cascades with other signalingpathways is reflected by the large number of diverse signalsthat can be carried by a single MAP kinase pathway and flexibleactivation kinetics. It is emerging that the most importantfunction of MAP kinase networks may not be signal amplificationbut integration of information about the setpoint of environmentalparameters (including osmolality) with other physiological processesto control cell function. Insight into how this cellular integrationof information is achieved by MAP kinase networks will shedlight on the principles of cell dynamics and adaptation.     

Interplay between gene expression noise and regulatory network architecture

Complex regulatory networks orchestrate most cellular processes in biological systems. Genes in such networks are subject to expression noise, resulting in isogenic cell populations exhibiting cell-to-cell variation in protein levels. Increasing evidence suggests that cells have evolved regulatory strategies to limit, tolerate or amplify expression noise. In this context, fundamental questions arise: how can the architecture of gene regulatory networks generate, make use of or be constrained by expression noise? Here, we discuss the interplay between expression noise and gene regulatory network at different levels of organization, ranging from a single regulatory interaction to entire regulatory networks. We then consider how this interplay impacts a variety of phenomena, such as pathogenicity, disease, adaptation to changing environments, differential cell-fate outcome and incomplete or partial penetrance effects. Finally, we highlight recent technological developments that permit measurements at the single-cell level, and discuss directions for future research.     

Intercellular communication via gap junctions affected by mechanical load in the bovine annulus fibrosus

Cells in the intervertebral disc, as in other connective tissues including tendon, ligament and bone, form interconnected cellular networks that are linked via functional gap junctions. These cellular networks may be necessary to affect a coordinated response to mechanical and environmental stimuli. Using confocal microscopy with fluorescence recovery after photobleaching methods, we explored the in situ strain environment of the outer annulus of an intact bovine disc and the effect of high-level flexion on gap junction signalling. The in situ strain environment in the extracellular matrix of the outer annulus under high flexion load was observed to be non-uniform with the extensive cellular processes remaining crimped sometimes at flexion angles greater than 25°. A significant transient disruption of intercellular communication via functional gap junctions was measured after 10 and 20 min under high flexion load. This study illustrates that in healthy annulus fibrosus tissue, high mechanical loads can impede the functioning of the gap junctions. Future studies will explore more complex loading conditions to determine whether losses in intercellular communication can be permanent and whether gap junctions in aged and degenerated tissues become more susceptible to load. The current research suggests that cellular structures such as gap junctions and intercellular networks, as well as other cell–cell and cell–matrix interconnections, need to be considered in computational models in order to fully understand how macroscale mechanical signals are transmitted across scales to the microscale and ultimately into a cellular biosynthetic response in collagenous tissues.     

Modeling cell signaling networks

Cell signaling pathways interact with one another to form networks in mammalian systems. Such networks are complex in their organization and exhibit emergent properties such as bistability and ultrasensitivity. Analysis of signaling networks requires a combination of experimental and theoretical approaches including the development and analysis of models. This review focuses on theoretical approaches to understanding cell signaling networks. Using heterotrimeric G protein pathways an example, we demonstrate how interactions between two pathways can result in a network that contains a positive feedback loop and function as a switch. Different mathematical approaches that are currently used to model signaling networks are described, and future challenges including the need for databases as well as enhanced computing environments are discussed.     

Watching the grin fade: Tracing the effects of polyploidy on different evolutionary time scales

Polyploidy, or whole-genome duplication (WGD), is a recurrent mutation both in cell lineages and over evolutionary time. By globally changing the relationship between gene copy number and other cellular entities, it can induce dramatic changes at the cellular and phenotypic level. Perhaps surprisingly, then, the insights that these events can bring to understanding other cellular features are not as well appreciated as they could be. In this review, we draw on examples of polyploidy from animals, plants and yeast to explore how investigations of polyploid cells have improved our understanding of the cell cycle, biological network complexity, metabolic phenotypes and tumor biology. We argue that the study of polyploidy across organisms, cell types, and time scales serves not only as a window into basic cell biology, but also as a basis for a predictive biology with applications ranging from crop improvement to treating cancer.