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In this thirteenth piece to the Ubiquity symposium discussing What is computation? Melanie Mitchell discusses the idea that biological computation is a process that occurs in nature, not merely in computer simulations of nature. --Editor

A proliferation of new computational methods and software tools for synthetic biology design has emerged in recent years but the field has not yet reached the stage where the design and construction of novel synthetic biology systems has become routine. To a large degree this is due to the inherent complexity of biological systems. However, advances in biotechnology and our scientific understanding have already enabled a number of significant achievements in this area. A key concept in engineering is the ability to assemble simpler standardised modules into systems of increasing complexity but it has yet to be adequately addressed how this approach can be applied to biological systems. In particular, the use of computer aided design tools is common in other engineering disciplines and it should eventually become centrally important to the field of synthetic biology if the challenge of dealing with the stochasticity and complexity of biological systems can be overcome.

In the past few decades major advances in the fields of molecular biology and genomic technology have led to an explosive growth in the biological information generated by the scientific community. Bioinformatics has evolved as an emerging research direction in response to this deluge of information. It is viewed as the use of computational methods to make biological discoveries, and is almost synonymous with computational biology.

Biological systems are inherently noisy. Predicting the outcome of a perturbation is extremely challenging. Traditional reductionist approach of describing properties of parts, vis-a-vis higher level behaviour has led to enormous understanding of fundamental molecular level biology. This approach typically consists of converting genes into junk (knock-down) and garbage (knock-out) and observe how a system responds. To enable broader understanding of biological dynamics, an integrated computational and experimental strategy was formally proposed in mid 1990s leading to the re-emergence of Systems Biology. However, soon it became clear that natural systems were far more complex than expected. A new strategy to address biological complexity was proposed at MIT (Massachusetts Institute of Technology) in June 2004, when the first meeting of synthetic biology was held. Though the term ‘synthetic biology’ was proposed during 1970s (Szybalski in Control of gene expression, Plenum Press, New York, 1974), the usage of the original concept found an experimental proof in 2000 with the demonstration of a three-gene circuit called repressilator (Elowitz and Leibler in Nature, 403:335–338, 2000). This encouraged people to think of forward engineering biology from a set of well described parts.

Life is one of the most complex phenomena in the universe. To understand complex biological systems, it requires the integration of experimental and computational research -- in other words a systems biology approach. Many theoretical methods and models exist for exploring systems biology including well-known examples such as statistical inference, graph analysis, network inference, and dynamic modeling. These systems play a key role in the development of systems biology. The trend in the development of these methods and models gives an integrative framework to acquire a global perspective beyond the traditional reductionistic views of molecular biology. We will here present our review which specifically focuses on network theory to analyze systems biology with two goals in mind: to aid researchers in efficiently understanding the network theory for systems biology analysis; and to illustrate the necessary and realistic goals how complex networks can be integrated into systems biology research.

Background Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods. Results We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed. Conclusions Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.

A report of the 3rd International Symposium on Optimization and Systems Biology, 20-22 September 2009, Zhangjiajie, China.

There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

Welcome to the 9th International Workshop on High Performance Computational Biology (HiCOMB). Computational Biology and related disciplines are fast emerging as an important area for academic research and industrial application. The large size of biological data sets, the inherent complexity of biological problems, and the ability to deal with error-prone data require the development of novel parallel algorithms in order to address the underlying computational and memory requirements. The goal of this workshop is to provide a forum for discussion of latest research in developing high-performance computing solutions to problems arising from molecular biology and related life sciences areas.

Interactions among cellular constituents play a crucial role in overall cellular function and organization. These interactions can be viewed as being complementary to the usual "parts list" of genes and proteins and, in conjunction with the expression states of these parts, are key to a systems level understanding of the cell. Here, we review computational approaches to the understanding of the functional roles of cellular networks, ranging from "static" models of network topology to dynamical and stochastic simulations.

Natural Computing (see, e.g., [5] or [6]) is concerned with human-designed computing inspired by nature as well as with computation taking place in nature. In other words, natural computing investigates models and computational techniques inspired by nature as well as it investigates, in terms of information processing, processes taking place in nature.

Biological systems perform pattern recognition using interconnections of large numbers of cells called neurons. The large number of parallel neural connections makes the human information processing system adaptable, context-sensitive, error-tolerant, large in memory capacity, and real-time responsive. These characteristics of the human brain provide an alternative model to the more common serial, single-processor signal processing architecture. Although each human neuron is relatively slow in processing information (on the order of milliseconds), the overall processing of information in the human brain is completed in a few hundred milliseconds. The processing speed of the human brain suggests that biological computation involves a small number of serial steps, each massively parallel. Artificial neural networks attempt to mimic the perceptual or cognitive power of humans using the parallel-processing paradigm. Table 7.1 compares the features of artificial neural networks and the more conventional von Neumann serial signal processing architecture.

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms.

This talk is divided to two parts. First comes Dr. Luc Varin with an introduction to the molecular biology fundamentals necessary for the full appreciation of the second part given by Dr. Nawwaf Kharma. He will will speak about the use of Synthetic Biology ideas and methods to build computational devices, with the aim of building larger and more reliable systems.

Thediscovery by designparadigm driving research in synthetic biology entails the engineering of de novo biological constructs with well-characterized input–output behaviours and interfaces. The construction of biological circuits requires iterative phases of design, simulation and assembly, leading to the fabrication of a biological device. In order to represent engineered models in a consistent visual format and further simulating themin silico, standardization of representation and model formalism is imperative. In this article, we review different efforts for standardization, particularly standards for graphical visualization and simulation/annotation schemata adopted in systems biology. We identify the importance of integrating the different standardization efforts and provide insights into potential avenues for developing a common framework for model visualization, simulation and sharing across various tools. We envision that such a synergistic approach would lead to the development of global, standardized schemata in biology, empowering deeper understanding of molecular mechanisms as well as engineering of novel biological systems.

Corporate and advanced information and database technologies make it possible to solve potential and hidden problems, such as uncertainty data interactions, disputable resolutions, unclear processes, etc. In this case, a rough set method can be used to grasp characteristics of the classified objects included in those problems. The rough set method is often used for classifying data while figuring out the distinctive features of the given objects in problem solutions. These given object problems that emerge, especially in database handling and resolving discernibility relations with the rough set method, are often computed by electronic computations. On the other hand, in this paper, we basically focus on taking advantage of biological molecular functions to create a novel biological computation method with which we proposed to derive and resolve all the discernibility relations.

A key component of any synthetic biology effort is the use of quantitative models. These models and their corresponding simulations allow optimization of a system design, as well as guiding their subsequent analysis. Once a domain mostly reserved for experts, dynamical modelling of gene regulatory and reaction networks has been an area of growth over the last decade. There has been a concomitant increase in the number of software tools and standards, thereby facilitating model exchange and reuse. We give here an overview of the model creation and analysis processes as well as some software tools in common use. Using markup language to encode the model and associated annotation, we describe the mining of components, their integration in relational models, formularization and parametrization. Evaluation of simulation results and validation of the model close the systems biology ‘loop’.

A strategy for making layout decisions is an important element in developing operating systems in manufacturing factories or other industrial plants. In this paper, we look at fixed factory facilities and propose a method for designing different sorts of layouts related to factories running at high-volume and producing a low-variety of products. Where many tasks are called, each with a different task time, it can be difficult to arrange a fixed factory facility in the optimal way. Therefore, we propose a computational method using DNA molecules for designing production systems by determining all the feasible workstation groups in a fixed factory facility, and we show that this computation method can be generally applied to layout decisions.

A huge number of different product types are managed through various processes in facilities with different approaches to scheduling. In this paper, we concentrate mainly on process-focused facilities. Sample groups of such facilities and processes were selected: its orders and times were investigated using both biological computation and genetic algorithms. First, biological computation was used to determine practical schedules. Second, genetic algorithms were used to identify which of the schedules determined by biological computation worked best. Here, we examine how combining these methods can be applied to solving process-focused scheduling problems.

The recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing, and computational processing power has driven a reinvention and expansion of the way we identify, infer, model, and store relationships between molecules, pathways, and cells in living organisms. In Computational Systems Biology, expert investigators contribute chapters which bring together biological data and computational and/or mathematical models of the data to aid researchers striving to create a system that provides both predictive and mechanistic information for a model organism. The volume is organized into five major sections involving network components, network inference, network dynamics, function and evolutionary system biology, and computational infrastructure for systems biology. As a volume of the highly successful Methods in Molecular Biology series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Comprehensive and up-to-date, Computational Systems Biology serves to motivate and inspire all those who wish to develop a complete description of a biological system.

Chapters 4 and 5 covered the systematic method to model and simulate pathways. In this chapter, we first introduce a method for visualizing and analyzing large-scale gene networks, and then discuss further functionalities required for the research and development in Systems Biology.

The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3–7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

Much progress has been achieved in recent years in molecular biology and genetics. The sheer volume of data in the form of biological sequences has been enormous and efficient methods for dealing with these huge amounts of data are needed. In addition, the data alone does not provide information on the workings of biological systems; hence much research effort has focused on designing mathematical and computational models to address problems from molecular biology. Often, the terms bioinformatics and computational biology are used to refer to the research fields concerning themselves with designing solutions to molecular problems in biology. However, there is a slight distinction between bioinformatics and computational biology: the former is concerned with managing the enormous amounts of biological data and extracting information from it, while the latter is more concerned with the design and development of new algorithms to address problems such as protein or RNA folding. However, the boundary is blurry, and there is no consistent usage of the terms. We will use the term bioinformatics to encompass both fields. To cover all areas of research in bioinformatics is beyond the scope of this section and we refer the interested reader to [2] for a general introduction. A large part of what bioinformatics is concerned about is evolution and function of biological systems on a molecular level. Evolutionary computation and evolutionary design are concerned with developing computational systems that “mimic” certain aspects of natural evolution (mutation, crossover, selection, fitness). Much of the inner workings of natural evolutionary systems have been copied, sometimes in modified format into evolutionary computation systems. Artificial neural networks mimic the functioning of simple brain cell clusters. Fuzzy systems are concerned with the “fuzzyness” in decision making, similar to a human expert. These three computational paradigms fall into the category of computational intelligence (CI). While biological systems have helped to develop many of the computational paradigms in CI, CI is now returning the favor to help solve some of the most challenging biological mysteries itself. In many cases these probabilistic methods can produce biologically relevant results where exact deterministic methods fail. For an extensive overview of successful applications of CI algorithms to problems in bioinformatics please refer to [1].

Systems biology represents an experimental approach to biology that attempts to study biological systems in a holistic rather than an atomistic manner. Ideally this involves gathering dynamic and global data sets as well as phenotypic data from different levels of the biological information hierarchy, integrating them and modeling them graphically and/or mathematically to generate mechanistic explanations for the emergent systems properties. This requires that the biological frontiers drive the development of new measurement and visualization technologies and the pioneering of new computational and mathematical tools-all of which requires a cross-disciplinary environment composed of biologists, chemists, computer scientists, engineers, mathematicians, physicists, and physicians speaking common discipline languages. The Institute for Systems Biology has aspired to pioneer and seamlessly integrate each of these concepts.

Synthetic biology is a rapidly growing field that has emerged in a global, multidisciplinary effort among biologists, chemists, engineers, physicists, and mathematicians. Broadly, the field has two complementary goals: To improve understanding of biological systems through mimicry and to produce bio-orthogonal systems with new functions. Here we review the area specifically with reference to the concept of synthetic biology space, that is, a hierarchy of components for, and approaches to generating new synthetic and functional systems to test, advance, and apply our understanding of biological systems. In keeping with this issue of Current Opinion in Structural Biology, we focus largely on the design and engineering of biomolecule-based components and systems.

Motivated by the many stochastic processes found in biology that allow for ultra-efficient computing, this paper explores circuit implementations for stochastic computation in hardware. Several novel contributions are presented in this paper, namely a dynamically controllable system of random number generators that produces Bernoulli random variables, exponentially distributed random variables, and allows for random variables of an arbitrary distribution to be generated. This system is implemented on a reconfigurable analog chipset allowing for the first time ever a hardware stochastic process with a user input to control the probability distribution. The utility of this system is demonstrated by implementing the well-known Gillespie algorithm for simulating an arbitrary biological system trajectory of sufficiently small molecules where over a 127times performance improvement over current software approaches is shown.

Biological computation may or may not be Turing Complete, but it is clearly organized differently from traditional von Neumann architectures. Computation (whether in a brain or an ant colony) is distributed, self-organising, autonomous and embodied in its environments. Systemic computation is a model of computation designed to follow the "biological way" of computation: it relies on the notion that systems are transformed through interaction in some context, with all computation equivalent to controlled transformations. This model implies a distributed, stochastic architecture, and in this work it is proposed that a physical implementation of this architecture could be achieved through the use of wireless devices produced for sensor networks. A useful, fault-tolerant and autonomous computer could exploit all the features of sensor networks, providing benefits for our understanding of "natural computing" and robust wireless networking.

Swarm-based systems are a class of multi-agent systems (MAS) of particular interest because they exhibit emergent behaviour through self-organisation. They are biology-inspired but find themselves applicable to a wide range of domains, with some of them characterised as mission critical. It is therefore implied that the use of a formal framework and methods would facilitate modelling of a MAS in such a way that the final product is fully tested and safety properties are verified. One way to achieve this is by defining a new formalism to specify MAS, something which could precisely fit the purpose but requires significant period to formally prove the validation power of the method. The alternative is to use existing formal methods thus exploiting their legacy. In this paper, we follow the latter approach. We present OPERAS, an open framework that facilitates formal modelling of MAS through employing existing formal methods. We describe how a particular instance of this framework, namely OPERAS XC , could integrate the most prominent characteristics of finite state machines and biological computation systems, such as X-machines and P Systems respectively. We demonstrate how the resulting method can be used to formally model a swarm system and discuss the flexibility and advantages of this approach.

Swarm-based systems are biology-inspired systems which can be directly mapped to multi-agent systems (MAS), possessing characteristics such as local control over the decisions taken by the agents and a highly dynamic structure which continuously changes. This class of MAS is of a particular interest because it exhibits emergent behavior through self-organization and finds itself applicable to a wide range of domains. In this paper, we present OPERAS, an open formal framework that facilitates modeling of MAS, we describe how a particular instance of this framework, namely OPERASCC, could employ existing biological computation systems, such as population P systems, and demonstrate how the resulting method can be used to formally model a swarm-based system of autonomous spacecrafts.

Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models--mathematical and computational--which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.

Statistical machine learning is a field that combines algorithmic ideas with foundational concepts from probability and statistics. This combination makes statistical machine learning an essential tool for computational biology, in part because probabilistic notions are inherent in biology (arising, e.g., via thermodynamics, recombination and germline mutation) and in part because of the incomplete nature of most biological data sets. I will present several examples of applications of statistical machine learning to problems in biology, in the areas of protein functional annotation, protein structural modeling, protein structure prediction and multipopulation linkage and association analysis.

This paper first gives an introduction to the research area of parameterized computation, a new subfield in theoretical computer science. Then it presents the applications of some important parameterized graph problems in computational biology.

In solving a scientific problem, one of the most helpful possibilities is that you will see a pattern in your data. It is almost the definition of an interesting scientific problem that it contains some sort of pattern. The patterns that arise in nature are often subtle and escape notice until cleverness or hard work un-cover them. The field of machine learning is a collection of techniques intended to automate the process of pattern discovery. A broad survey of machine learning techniques applied to bioinformatics is given in Pierre Baldi and Soren Brunak (2001). This document introduces a single, relatively versatile machine learning technique called evolutionary computation. A collection of applications of evolutionary computation to bioinformatics is given in Fogel and Corne (2003). Both machine learning and evolutionary computation have applications far beyond bioinformatics but almost all of the techniques in the domain of machine learning and evolutionary computation have useful applications within bioinformatics. The introduction to evolutionary computation given here is in the form of three examples intended to showcase three substantially different applications of evolutionary computation. The first, while it solves a real problem, is an almost trivial instance of evolutionary computation. It seeks a gapless alignment of 315 sequences in a fashion that permits the discovery of a motif associated with

The data from a ChIP-on-chip experiment is produced by a conventional array scanner using conventional microarrays. However the analysis of this data is often not well supported by traditional expression array analysis tools. In particular, the positional nature of the measurements is not normally important in expression arrays but is critical in ChIP-on-chip. Likewise ChIP-on-chip data is best visualised in the context of an annotated genome, yet this sort of visualisation is not well supported in expression array analysis packages.

We are witnessing the emergence of the "data rich" era in biology. The myriad data in biology ranging from sequence strings to complex phenotypic and disease-relevant data pose a huge challenge to modern biology. The standard paradigm in biology that deals with hypothesis to experimentation (low throughput data) to models is being gradually replaced by data to hypothesis to models and experimentation to more data and models. And unlike data in physical sciences, that in biological sciences is almost guaranteed to be highly heterogeneous and incomplete. In order to make significant advances in this data rich era, it is essential that there be robust data repositories that allow interoperable navigation, query and analysis across diverse data, and a plug-and-play tools environment that will facilitate seamless interplay of tools and data. Further, the integrated data will enable the reconstruction and modeling of biological systems. This talk with address several of the challenges posed by enormous need for scientific data integration and modeling in biology with specific exemplars and possible strategies. The issues addressed will include--Architecture of Data and Knowledge Repositories--Databases Flat, Relational and Object-Oriented; what is most appropriate? The imminent need for Ontologies in biology--Reduction and Analysis of Data the largest challenge! How to integrate legacy knowledge with data? How can we carry out systems level modeling in biology?

Advances in experimental techniques have transformed biology into a data-intensive science, with a rapid explosion of data at the genomic and proteomic level. Few comprehensive suites of computationally-intensive life science applications are available to the computer science community for optimization of current high-performance architectures specifically targeted towards the computational biology applications. BioSplash represents a wide variety of open-source codes spanning the heterogeneity of algorithms, biological problems, popularity among biologists, and memory traits, gearing the suite to be of importance to both biologists and computer scientists.