Validation and discovery from computational biology models

Simulation software is often a fundamental component in systems biology projects and provides a key aspect of the integration of experimental and analytical techniques in the search for greater understanding and prediction of biology at the systems level. It is important that the modelling and analysis software is reliable and that techniques exist for automating the analysis of the vast amounts of data which such simulation environments generate. A rigorous approach to the development of complex modelling software is needed. Such a framework is presented here together with techniques for the automated analysis of such models and a process for the automatic discovery of biological phenomena from large simulation data sets. Illustrations are taken from a major systems biology research project involving the in vitro investigation, modelling and simulation of epithelial tissue.     

Modelling strategies for the industrial exploitation of lactic acid bacteria

Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications has increased considerably over the years. Traditionally, process optimization for these applications involved both strain selection and trial and error. More recently, metabolic engineering has emerged as a discipline that focuses on the rational improvement of industrially useful strains. In the post-genomic era, metabolic engineering increasingly benefits from systems biology, an approach that combines mathematical modelling techniques with functional-genomics data to build models for biological interpretation and--ultimately--prediction. In this review, the industrial applications of LAB are mapped onto available global, genome-scale metabolic modelling techniques to evaluate the extent to which functional genomics and systems biology can live up to their industrial promise.     

Development and validation of computational models of cellular interaction

In this paper we take the view that computational models of biological systems should satisfy two conditions – they should be able to predict function at a systems biology level, and robust techniques of validation against biological models must be available. A modelling paradigm for developing a predictive computational model of cellular interaction is described, and methods of providing robust validation against biological models are explored, followed by a consideration of software issues.     

Gene regulatory network inference: Data integration in dynamic models—A review

Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein–DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling

Regional conservation planning can often make more effective use of sparse biological data by linking these data to remotely mapped environmental variables through statistical modelling. While modelling distributions of individual species is the best known and most widely used approach to such modelling, there are many situations in which more information can be extracted from available data by supplementing, or replacing, species-level modelling with modelling of communities or assemblages. This paper provides an overview of approaches to community-level modelling employed in a series of major land-use planning processes in the northeast New South Wales region of Australia, and evaluates how well communities and assemblages derived using these techniques function as surrogates in regional conservation planning. We also outline three new directions that may enhance the effectiveness of community-level modelling by: (1) more closely integrating modelling with traditional ecological mapping (e.g. vegetation mapping); (2) more tightly linking numerical classification and spatial modelling through application of canonical classification techniques; and (3) enhancing the applicability of modelling to data-poor regions through employment of a new technique for modelling spatial pattern in compositional dissimilarity.     

Network modelling of gene regulation

Gene regulatory network (GRN) modelling has gained increasing attention in the past decade. Many computational modelling techniques have been proposed to facilitate the inference and analysis of GRN. However, there is often confusion about the aim of GRN modelling, and how a gene network model can be fully utilised as a tool for systems biology. The aim of the present article is to provide an overview of this rapidly expanding subject. In particular, we review some fundamental concepts of systems biology and discuss the role of network modelling in understanding complex biological systems. Several commonly used network modelling paradigms are surveyed with emphasis on their practical use in systems biology research.     

Miniaturized analytical assays in biotechnology

Biotechnology today is a well-established paradigm in many areas of human endeavor, such as the pharmaceutical industry, agriculture, management of the environment and many others. Meanwhile, biology is undergoing a spectacular transition: whereas systematic biology was replaced gradually by molecular biology, the latter is rapidly being transformed into a new systematic era in which entire genomes are being charted by ever more sophisticated analytical techniques.In the wake of this onslaught of data, new fields are germinating, such as bioinformatics in an attempt to find answers to fundamental questions, answers that may be hidden in the massive amounts of data already available today.     

Software for systems biology: from tools to integrated platforms

Understanding complex biological systems requires extensive support from software tools. Such tools are needed at each step of a systems biology computational workflow, which typically consists of data handling, network inference, deep curation, dynamical simulation and model analysis. In addition, there are now efforts to develop integrated software platforms, so that tools that are used at different stages of the workflow and by different researchers can easily be used together. This Review describes the types of software tools that are required at different stages of systems biology research and the current options that are available for systems biology researchers. We also discuss the challenges and prospects for modelling the effects of genetic changes on physiology and the concept of an integrated platform.     

Simulating complex intracellular processes using object-oriented computational modelling

The aim of this paper is to give an overview of computer modelling and simulation in cellular biology, in particular as applied to complex biochemical processes within the cell. This is illustrated by the use of the techniques of object-oriented modelling, where the computer is used to construct abstractions of objects in the domain being modelled, and these objects then interact within the computer to simulate the system and allow emergent properties to be observed. The paper also discusses the role of computer simulation in understanding complexity in biological systems, and the kinds of information which can be obtained about biology via simulation.     

Resources, standards and tools for systems biology.

Modelling and simulation techniques are valuable tools for the understanding of complex biological systems. The design of a computer model necessarily has many diverse inputs, such as information on the model topology, reaction kinetics and experimental data, derived either from the literature, databases or direct experimental investigation. In this review, we describe different data resources, standards and modelling and simulation tools that are relevant to integrative systems biology.     

Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics

Functional genomics has been driven greatly by emerging experimental technologies. Its development as a scientific discipline will be enhanced by systems biology, which generates novel, quantitative hypotheses via modelling. However, in order to better assist crop improvement, the impact of developing functional genomics needs to be assessed at the crop level, given a projected diminishing effect of genetic alteration on phenotypes from the molecule to crop levels. This review illustrates a recently proposed research field, crop systems biology, which is located at the crossroads of crop physiology and functional genomics, and intends to promote communications between the two. Past experiences with modelling whole-crop physiology indicate that the layered structure of biological systems should be taken into account. Moreover, modelling not only plays a role in data synthesis and quantitative prediction, but certainly also in heuristics and system design. These roles of modelling can be applied to crop systems biology to enhance its contribution to our understanding of complex crop phenotypes and subsequently to crop improvement. The success of crop systems biology needs commitments from scientists along the entire knowledge chain of plant biology, from molecule or gene to crop and agro-ecosystem.     

Redefining plant systems biology: from cell to ecosystem

Molecular biologists typically restrict systems biology to cellular levels. By contrast, ecologists define biological systems as communities of interacting individuals at different trophic levels that process energy, nutrient and information flows. Modern plant breeding needs to increase agricultural productivity while decreasing the ecological footprint. This requires a holistic systems biology approach that couples different aggregation levels while considering the variables that affect these biological systems from cell to community. The challenge is to generate accurate experimental data that can be used together with modelling concepts and techniques that allow experimentally verifying in silico predictions. The coupling of aggregation levels in plant sciences, termed Integral Quantification of Biological Organization (IQ(BiO)), might enhance our abilities to generate new desired plant phenotypes.     

A systems biology approach to optimising hosts for industrial protein production

The vast number of expression hosts available for recombinant protein production have a variety of advantages and disadvantages; none, however, is globally optimal and host selection is frequently a compromise. Strain development requires a holistic approach, which systems biology can supply by delineating experimental data sets with computational modelling. Here, we review recent advances in computational models, in parallel with an expansion of the molecular toolbox, in the pursuit of optimal host strains for industrial protein production.     

The simulation approach in synthetic biology

Synthetic biology and systems biology are often highlighted as antagonistic strategies for dealing with the overwhelming complexity of biology (engineering versus understanding; tinkering in the lab versus modelling in the computer). However, a closer view of contemporary engineering methods (inextricably interwoven with mathematical modelling and simulation) and of the situation in biology (inextricably confronted with the intrinsic complexity of biomolecular environments) demonstrates that tinkering in the lab is increasingly supported by rational design methods. In other words: Synthetic biology and systems biology are merged by the use of computational techniques. These computational techniques are needed because the intrinsic complexity of biomolecular environments (stochasticity, non-linearities, system-level organization, evolution, independence, etc.) require advanced concepts of bio bricks and devices. A philosophical investigation of the history and nature of bio parts and devices reveals that these objects are imitating generic objects of engineering (switches, gates, oscillators, sensors, etc.), but the well-known design principles of generic objects are not sufficient for complex environments like cells. Therefore, the rational design methods have to be used to create more advanced generic objects, which are not only generic in their use, but also adaptive in their behavior. Case studies will show how simulation-based rational design methods are used to identify adequate parameters for synthesized designs (stability analyses), to improve lab experiments by ‘looking through noise’ (estimation of hidden variables and parameters), and to replace laborious and time-consuming post hoc tweaking in the lab by in-silico guidance (in-silico variation of bio brick properties). The overall aim of these developments, as will be argued in the discussion, is to achieve adaptive-generic instrumentality for bio parts and devices and thus increasingly merging systems and synthetic biology.     

XANNpred: neural nets that predict the propensity of a protein to yield diffraction-quality crystals

Production of diffracting crystals is a critical step in determining the three-dimensional structure of a protein by X-ray crystallography. Computational techniques to rank proteins by their propensity to yield diffraction-quality crystals can improve efficiency in obtaining structural data by guiding both protein selection and construct design. XANNpred comprises a pair of artificial neural networks that each predict the propensity of a selected protein sequence to produce diffraction-quality crystals by current structural biology techniques. Blind tests show XANNpred has accuracy and Matthews correlation values ranging from 75% to 81% and 0.50 to 0.63 respectively; values of area under the receiver operator characteristic (ROC) curve range from 0.81 to 0.88. On blind test data XANNpred outperforms the other available algorithms XtalPred, PXS, OB-Score, and ParCrys. XANNpred also guides construct design by presenting graphs of predicted propensity for diffraction-quality crystals against residue sequence position. The XANNpred-SG algorithm is likely to be most useful to target selection in structural genomics consortia, while the XANNpred-PDB algorithm is more suited to the general structural biology community. XANNpred predictions that include sliding window graphs are freely available from http://www.compbio.dundee.ac.uk/xannpred     

PATHLOGIC-S: A Scalable Boolean Framework for Modelling Cellular Signalling

Curated databases of signal transduction have grown to describe several thousand reactions, and efficient use of these data requires the development of modelling tools to elucidate and explore system properties. We present PATHLOGIC-S, a Boolean specification for a signalling model, with its associated GPL-licensed implementation using integer programming techniques. The PATHLOGIC-S specification has been designed to function on current desktop workstations, and is capable of providing analyses on some of the largest currently available datasets through use of Boolean modelling techniques to generate predictions of stable and semi-stable network states from data in community file formats. PATHLOGIC-S also addresses major problems associated with the presence and modelling of inhibition in Boolean systems, and reduces logical incoherence due to common inhibitory mechanisms in signalling systems. We apply this approach to signal transduction networks including Reactome and two pathways from the Panther Pathways database, and present the results of computations on each along with a discussion of execution time. A software implementation of the framework and model is freely available under a GPL license.     

Using functional–structural plant models to study, understand and integrate plant development and ecophysiology

Functional–structural plant models (FSPMs) explore and integrate relationships between a plant’s structure and processes that underlie its growth and development. In recent years, the range of topics being addressed by scientists interested in functional–structural plant modelling has expanded greatly. FSPM techniques are now being used to dynamically simulate growth and development occurring at the microscopic scale involving cell division in plant meristems to the macroscopic scales of whole plants and plant communities. The plant types studied also cover a broad spectrum from algae to trees. FSPM is highly interdisciplinary and involves scientists with backgrounds in plant physiology, plant anatomy, plant morphology, mathematics, computer science, cellular biology, ecology and agronomy. This special issue of Annals of Botany features selected papers that provide examples of comprehensive functional–structural models, models of key processes such as partitioning of resources, software for modelling plants and plant environments, data acquisition and processing techniques and applications of functional–structural plant models for agronomic purposes.     

Correlation and studies of habitat selection: problem,red herring or opportunity?

With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses that explicitly model correlation rather than consider it a nuisance, like mixed effects and state-space models, offer potentially novel insights into the process of resource selection, but additional work is needed to make them more generally applicable to large datasets based on the use–availability designs. Until then, variance inflation techniques and two-stage approaches should offer pragmatic and flexible approaches to modelling correlated data.     

Understanding systems-level properties: timely stories from the study of clocks

After several decades dominated by reductionist approaches in biology, researchers are returning to the study of complex biology with a litany of new and old techniques--this paradigm has been termed systems biology. Here we detail how systems biology is being used to uncover complex systems-level properties of the circadian clock. These properties include robustness, periodicity and temperature compensation. We describe how clock researchers are using systems-biology techniques, such as genetic perturbations, kinetic luminescence imaging, synthetic biology and mathematical modelling, to untangle these complex properties in mammals, fungi and bacteria. The strategies developed in the context of circadian clocks may prove useful for tackling similar problems in other systems.