Can we build synthetic,multicellular systems by controlling developmental signaling in space and time?

Using biological machinery to make new, functional molecules is an exciting area in chemical biology. Complex molecules containing both 'natural' and 'unnatural' components are made by processes ranging from enzymatic catalysis to the combination of molecular biology with chemical tools. Here, we discuss applying this approach to the next level of biological complexity -- building synthetic, functional biotic systems by manipulating biological machinery responsible for development of multicellular organisms. We describe recent advances enabling this approach, including first, recent developmental biology progress unraveling the pathways and molecules involved in development and pattern formation; second, emergence of microfluidic tools for delivering stimuli to a developing organism with exceptional control in space and time; third, the development of molecular and synthetic biology toolsets for redesigning or de novo engineering of signaling networks; and fourth, biological systems that are especially amendable to this approach.     

Biological sequences integrated: a relational database approach

Over the last decade the modeling and the storage of biological data has been a topic of wide interest for scientists dealing with biological and biomedical research. Currently most data is still stored in text files which leads to data redundancies and file chaos.In this paper we show how to use relational modeling techniques and relational database technology for modeling and storing biological sequence data, i.e. for data maintained in collections like EMBL or SWISS-PROT to better serve the needs for these application domains.For this reason we propose a two step approach. First, we model the structure (and therefore the meaning of the) data using an Entity-Relationship approach. The ER model leads to a clean design of a relational database schema for storing and retrieving the DNA and protein data extracted from various sources. Our approach provides the clean basis for building complex biological applications that are more amenable to changes and software ports than their file-base counterparts.     

A Chado case study: an ontology-based modular schema for representing genome-associated biological information

MOTIVATION: A few years ago, FlyBase undertook to design a new database schema to store Drosophila data. It would fully integrate genomic sequence and annotation data with bibliographic, genetic, phenotypic and molecular data from the literature representing a distillation of the first 100 years of research on this major animal model system. In developing this new integrated schema, FlyBase also made a commitment to ensure that its design was generic, extensible and available as open source, so that it could be employed as the core schema of any model organism data repository, thereby avoiding redundant software development and potentially increasing interoperability. Our question was whether we could create a relational database schema that would be successfully reused. RESULTS: Chado is a relational database schema now being used to manage biological knowledge for a wide variety of organisms, from human to pathogens, especially the classes of information that directly or indirectly can be associated with genome sequences or the primary RNA and protein products encoded by a genome. Biological databases that conform to this schema can interoperate with one another, and with application software from the Generic Model Organism Database (GMOD) toolkit. Chado is distinctive because its design is driven by ontologies. The use of ontologies (or controlled vocabularies) is ubiquitous across the schema, as they are used as a means of typing entities. The Chado schema is partitioned into integrated subschemas (modules), each encapsulating a different biological domain, and each described using representations in appropriate ontologies. To illustrate this methodology, we describe here the Chado modules used for describing genomic sequences. AVAILABILITY: GMOD is a collaboration of several model organism database groups, including FlyBase, to develop a set of open-source software for managing model organism data. The Chado schema is freely distributed under the terms of the Artistic License (http://www.opensource.org/licenses/artistic-license.php) from GMOD (www.gmod.org).     

Prey-catching and predator-avoidance in frog and toad: defining the schemas.

The present model postulates the construction of motor actions through the interaction of different motor schemas via a process of competition and co-operation wherein there is no need for a unique schema to win the competition (although that might well be the result) since two or more schemas may simultaneously be active and co-operate to yield a more complicated motor pattern. Based on lesion data, our model is structured on the principles of segregation of co-ordinate systems and participation of maps intermediate between sensory and motor schemas. The motor schemas are driven by specific internal maps which between them constitute a distributed internal representation of the world. These maps collectively provide the transition from topographically-coded sensory information to frequency-coded inputs to the diverse motor schemas that drive muscle activity. We stimulate data on approach and avoidance behavior of the frog or toad under normal conditions and under lesion of different brain centers. For example, the model generates different motor zones for prey-catching behavior which match those observed experimentally in normal conditions and in the medullary hemifield deficit, and offers predictions for new experiments on both approach and avoidance behaviors.     

Computation of optical motion by movement detectors

The first part of this paper deals with a system-theoretical approach for the decomposition of multi-input systems into the sum of simpler systems. This approach is applied here to analyse the algorithm which represents the computations underlying the extraction of motion information from the optical environment by biological movement detectors. The second part concentrates on a specific model for motion computation known to be realized by the visual system of insects and of man. These detectors provide the visual system with information on both the velocity and structural properties of a moving pattern. In the third part of this article the properties of two-dimensional arrays of movement detectors are analyzed and their relations to meaningful physiological responses are discussed.     

Evaluation of optical motion information by movement detectors

Summary The paper is dealing in its first part with a system-theoretical approach for the decomposition of multi-input systems into the sum of simpler systems. By this approach the algorithm for the computations underlying the extraction of motion information from the optical environment by biological movement detectors is analysed. In the second part it concentrates on a specific model for motion computation known to be realized by the visual system of insects and of man. These motion detectors provide the visual system with information on both, velocity and structural properties of a moving pattern. The last part of the paper deals with the functional properties of two-dimensional arrays of movement detectors. They are analyzed and their relations to meaningful physiological responses are discussed.     

Bio-Ontology and text: bridging the modeling gap

MOTIVATION: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. RESULTS: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86-93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). AVAILABILITY: http://zellig.cpmc.columbia.edu/PGschema     

Dynamic optimization of distributed biological systems using robust and efficient numerical techniques

ABSTRACT: BACKGROUND: Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations. This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. RESULTS: Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. CONCLUSIONS: In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems.     

Bifurcation Analysis of Reaction Diffusion Systems on Arbitrary Surfaces

In this paper, we present computational techniques to investigate the effect of surface geometry on biological pattern formation. In particular, we study two-component, nonlinear reaction–diffusion (RD) systems on arbitrary surfaces. We build on standard techniques for linear and nonlinear analysis of RD systems and extend them to operate on large-scale meshes for arbitrary surfaces. In particular, we use spectral techniques for a linear stability analysis to characterise and directly compose patterns emerging from homogeneities. We develop an implementation using surface finite element methods and a numerical eigenanalysis of the Laplace–Beltrami operator on surface meshes. In addition, we describe a technique to explore solutions of the nonlinear RD equations using numerical continuation. Here, we present a multiresolution approach that allows us to trace solution branches of the nonlinear equations efficiently even for large-scale meshes. Finally, we demonstrate the working of our framework for two RD systems with applications in biological pattern formation: a Brusselator model that has been used to model pattern development on growing plant tips, and a chemotactic model for the formation of skin pigmentation patterns. While these models have been used previously on simple geometries, our framework allows us to study the impact of arbitrary geometries on emerging patterns.     

Mechanobiology in cortical waves and oscillations

Cortical actin waves have emerged as a widely prevalent phenomena and brought pattern formation to many fields of cell biology. Cortical excitabilities, reminiscent of the electric excitability in neurons, are likely fundamental property of the cell cortex. Although they have been mostly considered to be biochemical in nature, accumulating evidence support the role of mechanics in the pattern formation process. Both pattern formation and mechanobiology approach biological phenomena at the collective level, either by looking at the mesoscale dynamical behavior of molecular networks or by using collective physical properties to characterize biological systems. As such they are very different from the traditional reductionist, bottom-up view of biology, which brings new challenges and potential opportunities. In this essay, we aim to provide our perspectives on what the proposed mechanochemical feedbacks are and open questions regarding their role in cortical excitable and oscillatory dynamics.     

Relax with CouchDB--into the non-relational DBMS era of bioinformatics

With the proliferation of high-throughput technologies, genome-level data analysis has become common in molecular biology. Bioinformaticians are developing extensive resources to annotate and mine biological features from high-throughput data. The underlying database management systems for most bioinformatics software are based on a relational model. Modern non-relational databases offer an alternative that has flexibility, scalability, and a non-rigid design schema. Moreover, with an accelerated development pace, non-relational databases like CouchDB can be ideal tools to construct bioinformatics utilities. We describe CouchDB by presenting three new bioinformatics resources: (a) geneSmash, which collates data from bioinformatics resources and provides automated gene-centric annotations, (b) drugBase, a database of drug-target interactions with a web interface powered by geneSmash, and (c) HapMap-CN, which provides a web interface to query copy number variations from three SNP-chip HapMap datasets. In addition to the web sites, all three systems can be accessed programmatically via web services.     

Studying Gene Expression System Regulation at the Program Level

Understanding how gene expression systems influence biological outcomes is an important goal for diverse areas of research. Gene expression profiling allows for the simultaneous measurement of expression levels for thousands of genes and the opportunity to use this information to increase biological understanding. Yet, the best way to relate this immense amount of information to biological outcomes is far from clear. Here, a novel approach to gene expression systems research is presented that focuses on understanding gene expression systems at the level of gene expression program regulation. It is suggested that such an approach has important advantages over current techniques and may provide novel insights into how gene expression systems are regulated to shape biological outcomes such as the development of disease or response to treatment.     

Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance

In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.     

Pattern recognition software and techniques for biological image analysis

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.     

Genomics: an in vitro toxicology point of view

Genomics, and in particular its derived discipline, toxicogenomics, are rapidly developing technologies, which permit studies on the impact of chemicals and drugs on gene expression in particular biological systems. Enormous amounts of data will be provided in the context of mechanistic and predictive toxicology from the use of the DNA microarray approach for the simultaneous analysis of the expression pattern of multiple genes. The high-throughput requirement of these approaches necessitate in vitro cell culture systems. This article will give a short overview of the areas of ECVAM's research in which this technology will initially be applied.     

Coordination of many oscillators and generation of locomotory patterns

This paper considers a synthesis approach to a decentralized autonomous system in which the functional order of the entire system is generated by cooperative interaction among its subsystems, each of which has the autonomy to control a part of the state of the system, and its application to pattern generators of animal locomotion. First, biological locomotory rhythms and their generators, swimming patterns of aquatic animals and gait patterns of quadrupeds, are reviewed briefly. Then, a design principle for autonomous coordination of many oscillators is proposed. Using these results, we synthesize a swimming pattern generator and a gait pattern generator. Finally, it is shown using computer simulations that the proposed systems generate desirable patterns.