Heuristic approaches to models and modeling in systems biology

Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must meet to achieve these objectives as predictive robustness—predictive reliability over large domains. Drawing on the results of an ethnographic investigation and various studies in the systems biology literature, I explore four current obstacles to achieving predictive robustness; data constraints, parameter uncertainty, collaborative constraints and system-scale requirements. I use a case study and the commentary of systems biologists themselves to show that current practices in the field, rather than pursuing these goals, frequently use models heuristically to investigate and build understanding of biological systems that do not meet standards of predictive robustness but are nonetheless effective uses of computation. A more heuristic conception of modeling allows us to interpret current practices as ways that manage these obstacles more effectively, particularly collaborative constraints, such that modelers can in the long-run at least work towards prediction and control.     

Functional and systems biology approaches to Huntington's disease

Huntington's disease (HD) is a hereditary, progressively degenerative and fatal brain disorder classified as a rare, or 'orphan', disease. HD is caused by the extension of trinucleotide repeats encoding a stretch of glutamine residues at the amino-terminal end of the large huntingtin (HTT) protein. Since the discovery of the mutated HTT gene in 1993, the mechanisms by which the mutant HTT protein induces neurodegeneration remain poorly understood and no disease-modifying therapy is currently available. Several functional approaches combining different experimental models and experimental technologies have been used to shed some light on the mechanisms underlying this disease. This review presents these functional approaches, highlights their potential and limitations.     

From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

     

Standards requirements for systems biology approaches to health care: mitochondrial proteomics

A review of the standards needs of the mitochondrial proteomics communities is presented based on the presentations and discussions at National Institute of Standards and Technology (NIST) workshop, Systems Biology Approaches to Health Care: Mitochondrial Proteomics, held on September 17-18, 2002. The mitochondrial proteomics areas addressed for standards needs are model systems, methods and data. This review outlines the challenges in the field, proposes standards efforts that the community would like to see pursued to meet those challenges, and is followed by a summary and NIST's planned efforts to address these standards requirements.     

From systems biology to systems biomedicine

Systems Biology is about combining theory, technology, and targeted experiments in a way that drives not only data accumulation but knowledge as well. The challenge in Systems Biomedicine is to furthermore translate mechanistic insights in biological systems to clinical application, with the central aim of improving patients' quality of life. The challenge is to find theoretically well-chosen models for the contextually correct and intelligible representation of multi-scale biological systems. In this review, we discuss the current state of Systems Biology, highlight the emergence of Systems Biomedicine, and highlight some of the topics and views that we think are important for the efficient application of Systems Theory in Biomedicine.     

Extracellular matrix bioengineering and systems biology approaches in liver disease

The extracellular matrix (ECM) in the liver as well as in many organs comprises a peripheral network linking numerous macromolecules typically classified into collagens, microfibrillar proteins, proteoglycans, chemokines, growth factors and glycoproteins. In addition to its role as an essential structural and physiological component, it plays a vital role in driving key cellular events such as cell adhesion, migration, proliferation, differentiation and survival. Any structural inherited or acquired defect and/or metabolic or pathologic alteration in the hepatic ECM may cause cellular and organ responses leading to the development or progression of liver disease. Therefore, the ECM molecules are key players in tissue engraftment and in the pathophysiology of liver disease. In this review we provide a snapshot on current efforts for understanding its role in physiological and non-physiological states, by describing how tissue engineering platforms can enhance in vitro and in vivo models of liver disease, by providing examples where bioengineered ECM can serve as systems biology approaches to study the ECM, and then by evaluating pathological protein regulatory networks in the liver using systems biology tools. These approaches hold great promise for future research.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Lipidomics: a global approach to lipid analysis in biological systems

Lipids are water-insoluble molecules that have a wide variety of functions within cells, including: 1) maintenance of electrochemical gradients; 2) subcellular partitioning; 3) first- and second-messenger cell signaling; 4) energy storage; and 5) protein trafficking and membrane anchoring. The physiological importance of lipids is illustrated by the numerous diseases to which lipid abnormalities contribute, including atherosclerosis, diabetes, obesity, and Alzheimer's disease. Lipidomics, a branch of metabolomics, is a systems-based study of all lipids, the molecules with which they interact, and their function within the cell. Recent advances in soft-ionization mass spectrometry, combined with established separation techniques, have allowed the rapid and sensitive detection of a variety of lipid species with minimal sample preparation. A "lipid profile" from a crude lipid extract is a mass spectrum of the composition and abundance of the lipids it contains, which can be used to monitor changes over time and in response to particular stimuli. Lipidomics, integrated with genomics, proteomics, and metabolomics, will contribute toward understanding how lipids function in a biological system and will provide a powerful tool for elucidating the mechanism of lipid-based disease, for biomarker screening, and for monitoring pharmacologic therapy.     

Getting to synaptic complexes through systems biology

Large numbers of synaptic components have been identified, but the effect so far on our understanding of synaptic function is limited. Now, network maps and annotated functions of individual components have been used in a systems biology approach to analyzing the function of NMDA receptor complexes at synapses, identifying biologically relevant modular networks within the complex.     

Systems biology approaches to metabolic and cardiovascular disorders: network perspectives of cardiovascular metabolism

In this review, we examine cardiovascular metabolism from three different, but highly complementary, perspectives. First, from the abstract perspective of a metabolite network, composed of nodes and links. We present fundamental concepts in network theory, including emergence, to illustrate how nature has designed metabolism with a hierarchal modular scale-free topology to provide a robust system of energy delivery. Second, from the physical perspective of a modular spatially compartmentalized network. We review evidence that cardiovascular metabolism is functionally compartmentalized, such that oxidative phosphorylation, glycolysis, and glycogenolysis preferentially channel ATP to ATPases in different cellular compartments, using creatine kinase and adenylate kinase to maximize efficient energy delivery. Third, from the dynamics perspective, as a network of dynamically interactive metabolic modules capable of self-oscillation. Whereas normally, cardiac metabolism exists in a regime in which excitation-metabolism coupling closely matches energy supply and demand, we describe how under stressful conditions, the network can be pushed into a qualitatively new dynamic regime, manifested as cell-wide oscillations in ATP levels, in which the coordination between energy supply and demand is lost. We speculate how this state of "metabolic fibrillation" leads to cell death if not corrected and discuss the implications for cardioprotection.     

Peroxisystem: Harnessing systems cell biology to study peroxisomes

In recent years, high‐throughput experimentation with quantitative analysis and modelling of cells, recently dubbed systems cell biology, has been harnessed to study the organisation and dynamics of simple biological systems. Here, we suggest that the peroxisome, a fascinating dynamic organelle, can be used as a good candidate for studying a complete biological system. We discuss several aspects of peroxisomes that can be studied using high‐throughput systematic approaches and be integrated into a predictive model. Such approaches can be used in the future to study and understand how a more complex biological system, like a cell and maybe even ultimately a whole organism, works.     

Modeling and simulation of molecular biology systems using petri nets: modeling goals of various approaches

Petri nets are a discrete event simulation approach developed for system representation, in particular for their concurrency and synchronization properties. Various extensions to the original theory of Petri nets have been used for modeling molecular biology systems and metabolic networks. These extensions are stochastic, colored, hybrid and functional. This paper carries out an initial review of the various modeling approaches based on Petri net found in the literature, and of the biological systems that have been successfully modeled with these approaches. Moreover, the modeling goals and possibilities of qualitative analysis and system simulation of each approach are discussed.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.     

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.     

Biorobotic approaches to the study of motor systems

Biorobotics is a promising new area of research at the interface between biology and robotics. Robots can either be used as physical models of biological systems or be directly inspired by biological studies. A great deal of progress has recently been made in biorobotic studies of locomotion, orientation, and vertebrate arm control.     

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.     

Game-theoretical approaches to studying the evolution of biochemical systems

Evolutionary optimization has been successfully used to increase our understanding of key properties of biochemical systems. Traditional optimization is, however, often insufficient for gaining deeper insights into the evolution of such systems because usually there is a mutual relationship between the properties optimized by evolution and the properties of the environment. Thus, by evolving towards optimal properties, organisms change their environment, which in turn alters the optimum. Evolutionary game theory provides an appropriate framework for analyzing evolution in such 'dynamic fitness landscapes'. We therefore argue that it is a promising approach to studying the evolution of biochemical systems. Indeed, recent studies have applied evolutionary game theory to key issues in the evolution of energy metabolism.