Immune systems biology: immunoprofiling of cells and molecules

White blood cells and their secreted products are key elements of immune systems biology that are important indicators of patient health and disease. We have developed the SurroScan microvolume laser scanning cytometer to immunoprofile hundreds of variables, including cell populations, cell surface antigens, and intracellular molecules in antibody-based assays on small samples (about 1 mL) of whole blood, processed blood, or other fluids without cell purification or washing steps. The system enables high-throughput, robust and automated data capture and analysis. We demonstrate the utility of this immunoprofiling technology platform by surveying patient samples before and after glucocorticosteroid administration and show both the expected and novel response characteristics. This system complements recent advances in genomic and proteomic approaches to disease prediction and monitoring.     

Emerging technologies: systems biology

The field of systems biology is based on the paradigm that the whole is greater than the sum of the parts. Through a combination of high-throughput experiments analyzing "-omic" scale phenomenon and the development of new computational techniques and algorithms, it is now feasible to study biological systems in a way that was previously not possible. During the 232nd National Meeting of the American Chemical Society, a session devoted to the emerging technology of Systems Biology was held. A number of talks on a wide variety of subjects covering cell signaling, network regulation and analysis, novel experimental procedures, synthetic biology, and metabolic flux analysis were presented. All of these approaches shared the common theme of using a systems biology approach to aid in the understanding of fundamental biology, with an eye toward applications for the benefit of society.     

Protease proteomics: revealing protease in vivo functions using systems biology approaches

Proteases irreversibly modify proteins by cleaving their amide bonds and are implicated in virtually every important biological process such as immunity, development and tissue repair. Accordingly, it is easy to see that deregulated proteolysis is a pathognomic feature of many diseases. Most of the current information available on proteases was acquired using in vitro methods, which reveals molecular structure, enzyme kinetics and active-site specificity. However, considerably less is known about the relevant biological functions and combined roles of proteases in moulding the proteome. Although models using genetically modified animals are powerful, they are slow to develop, they can be difficult to interpret, and while useful, they remain only models of human disease. Therefore, to understand how proteases accomplish their tasks in organisms and how they participate in pathology, we need to elucidate the protease degradome-the repertoire of proteases expressed by a cell, a tissue or an organism at a particular time-their expression level, activation state, their biological substrates, also known as the substrate degradome-the repertoire of substrates for each protease-and the effect of the activity of each protease on the pathways of the system under study. Achieving this goal is challenging because several proteases might cleave the same protein, and proteases also form pathways and interact to form the protease web [Overall, C.M., Kleifeld, O., 2006. Tumour microenvironment - opinion: validating matrix metalloproteinases as drug targets and anti-targets for cancer therapy. Nat. Rev. Cancer 6 (3), 227-239]. Hence, the net proteolytic potential of the degradome at a particular time on a substrate and pathway must also be understood. Proteomics offers one of the few routes to the understanding of proteolysis in complex in vivo systems and especially in man where genetic manipulations are impossible. The aim of this chapter is to review methods and tools that allow researchers to study protease biological functions using proteomics and mass spectrometry. We describe methods to assess protease expression at the messenger RNA level using DNA microarrays and at the protein level using mass spectrometry-based proteomics. We also review methods to reveal and quantify the activity state of proteases and to identify their biological substrates. The information acquired using these high throughput, high content techniques can then be interpreted with different bioinformatics approaches to reveal the effects of proteolysis on the system under study. Systems biology of the protease web-degradomics in the broadest sense-promises to reveal the functions of proteases in homeostasis and in disease states. This will indicate which proteases participate in defined pathologies and will help targeting specific proteases for disease treatments.     

Improved understanding of gene expression regulation using systems biology

This article reviews the current state of systems biology approaches, including the experimental tools used to generate ‘omic’ data and computational frameworks to interpret this data. Through illustrative examples, systems biology approaches to understand gene expression and gene expression regulation are discussed. Some of the challenges facing this field and the future opportunities in the systems biology era are highlighted.     

Parameter estimation in systems biology models using spline approximation

Background  

Mathematical models for revealing the dynamics and interactions properties of biological systems play an important role in computational systems biology. The inference of model parameter values from time-course data can be considered as a "reverse engineering" process and is still one of the most challenging tasks. Many parameter estimation methods have been developed but none of these methods is effective for all cases and can overwhelm all other approaches. Instead, various methods have their advantages and disadvantages. It is worth to develop parameter estimation methods which are robust against noise, efficient in computation and flexible enough to meet different constraints.     

Improved understanding of gene expression regulation using systems biology

This article reviews the current state of systems biology approaches, including the experimental tools used to generate 'omic' data and computational frameworks to interpret this data. Through illustrative examples, systems biology approaches to understand gene expression and gene expression regulation are discussed. Some of the challenges facing this field and the future opportunities in the systems biology era are highlighted.     

Peptide and protein building blocks for synthetic biology: from programming biomolecules to self-organized biomolecular systems.

There are several approaches to creating synthetic-biological systems. Here, we describe a molecular-design approach. First, we lay out a possible synthetic-biology space, which we define with a plot of complexity of components versus divergence from nature. In this scheme, there are basic units, which range from natural amino acids to totally synthetic small molecules. These are linked together to form programmable tectons, for example, amphipathic alpha-helices. In turn, tectons can interact to give self-assembled units, which can combine and organize further to produce functional assemblies and systems. To illustrate one path through this vast landscape, we focus on protein engineering and design. We describe how, for certain protein-folding motifs, polypeptide chains can be instructed to fold. These folds can be combined to give structured complexes, and function can be incorporated through computational design. Finally, we describe how protein-based systems may be encapsulated to control and investigate their functions.     

Approximate parameter inference in systems biology using gradient matching: a comparative evaluation

Background

A challenging problem in current systems biology is that of parameter inference in biological pathways expressed as coupled ordinary differential equations (ODEs). Conventional methods that repeatedly numerically solve the ODEs have large associated computational costs. Aimed at reducing this cost, new concepts using gradient matching have been proposed, which bypass the need for numerical integration. This paper presents a recently established adaptive gradient matching approach, using Gaussian processes (GPs), combined with a parallel tempering scheme, and conducts a comparative evaluation with current state-of-the-art methods used for parameter inference in ODEs. Among these contemporary methods is a technique based on reproducing kernel Hilbert spaces (RKHS). This has previously shown promising results for parameter estimation, but under lax experimental settings. We look at a range of scenarios to test the robustness of this method. We also change the approach of inferring the penalty parameter from AIC to cross validation to improve the stability of the method.

Methods

Methodology for the recently proposed adaptive gradient matching method using GPs, upon which we build our new method, is provided. Details of a competing method using RKHS are also described here.

Results

We conduct a comparative analysis for the methods described in this paper, using two benchmark ODE systems. The analyses are repeated under different experimental settings, to observe the sensitivity of the techniques.

Conclusions

Our study reveals that for known noise variance, our proposed method based on GPs and parallel tempering achieves overall the best performance. When the noise variance is unknown, the RKHS method proves to be more robust.

     

Dupuytren's: a systems biology disease

Dupuytren's disease (DD) is an ill-defined fibroproliferative disorder of the palm of the hands leading to digital contracture. DD commonly occurs in individuals of northern European extraction. Cellular components and processes associated with DD pathogenesis include altered gene and protein expression of cytokines, growth factors, adhesion molecules, and extracellular matrix components. Histology has shown increased but varying levels of particular types of collagen, myofibroblasts and myoglobin proteins in DD tissue. Free radicals and localised ischaemia have been suggested to trigger the proliferation of DD tissue. Although the existing available biological information on DD may contain potentially valuable (though largely uninterpreted) information, the precise aetiology of DD remains unknown. Systems biology combines mechanistic modelling with quantitative experimentation in studies of networks and better understanding of the interaction of multiple components in disease processes. Adopting systems biology may be the ideal approach for future research in order to improve understanding of complex diseases of multifactorial origin. In this review, we propose that DD is a disease of several networks rather than of a single gene, and show that this accounts for the experimental observations obtained to date from a variety of sources. We outline how DD may be investigated more effectively by employing a systems biology approach that considers the disease network as a whole rather than focusing on any specific single molecule.     

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.

     

Review: Artificial liver support systems

Despite recent advances in medical therapy, patients with fulminant hepatic failure (FHF) have a mortality rate approaching 90%. Many patients die because of failure to arrest the progression of cerebral edema. Liver transplantation has improved survival to 65% to 75%. However, there is a shortage of donors and approximately one half of the patients with FHF will die while awaiting liver transplantation. There is thus a need to develop an extracorporeal liver assist system to help keep these patients alive and neurologically intact until either an organ becomes available for transplantation or the native liver recovers from injury. Such a system could also be used during the period of functional recovery from massive liver resection or to assist patients with decompensated chronic liver disease. Over the years, various methods utilizing charcoal and resin hemoperfusion, dialysis, plasma exchange, and other methods of blood detoxification have been developed and tested, but none have gained wide acceptance. This was due to: (i) incomplete understanding of the pathophysiology of liver failure; (ii) lack of accurate methods of assessment, quantitation, and stratification of the degree of liver dysfunction; and (iii) inadequate numbers of prospective controlled clinical trials examining the effects of specific therapeutic modalities. Liver support systems utilizing liver tissue preparations were developed in the 1950s, but it was not until recently that advances in hepatocyte isolation and culture, better understanding of hepatocyte-matrix interactions, and improved hollow-fiber technology have resulted in the development of a new generation of liver assist devices. Some of these devices are currently being tested in the clinical setting. In a preliminary clinical study, we have used a porcine hepatocyte-based liver support system to treat patients with acute liver failure as well as patients with acute exacerbation of chronic liver disease. Patients in the first group, who were candidates for transplantation, were successfully bridged to a transplant with excellent survival. No obvious benefit from bioartifical liver treatments was seen in the second group. It is possible that, in this group, patients will have to be treated earlier and for longer periods of time. Prospective controlled trials will be initiated as soon as the current phase I study is concluded to determine the efficacy of this system in both patients populations. (c) 1996 John Wiley & Sons, Inc.     

Interfacing systems biology and synthetic biology

A report of BioSysBio 2009, the IET conference on Synthetic Biology, Systems Biology and Bioinformatics, Cambridge, UK, 23-25 March 2009.     

Small RNA biology is systems biology

During the last decade small regulatory RNA (srRNA) emerged as central players in the regulation of gene expression in all kingdoms of life. Multiple pathways for srRNA biogenesis and diverse mechanisms of gene regulation may indicate that srRNA regulation evolved independently multiple times. However, small RNA pathways share numerous properties, including the ability of a single srRNA to regulate multiple targets. Some of the mechanisms of gene regulation by srRNAs have significant effect on the abundance of free srRNAs that are ready to interact with new targets. This results in indirect interactions among seemingly unrelated genes, as well as in a crosstalk between different srRNA pathways. Here we briefly review and compare the major srRNA pathways, and argue that the impact of srRNA is always at the system level. We demonstrate how a simple mathematical model can ease the discussion of governing principles. To demonstrate these points we review a few examples from bacteria and animals.     

Towards classifying species in systems biology papers using text mining

Background

In recent years high throughput methods have led to a massive expansion in the free text literature on molecular biology. Automated text mining has developed as an application technology for formalizing this wealth of published results into structured database entries. However, database curation as a task is still largely done by hand, and although there have been many studies on automated approaches, problems remain in how to classify documents into top-level categories based on the type of organism being investigated. Here we present a comparative analysis of state of the art supervised models that are used to classify both abstracts and full text articles for three model organisms.

Results

Ablation experiments were conducted on a large gold standard corpus of 10,000 abstracts and full papers containing data on three model organisms (fly, mouse and yeast). Among the eight learner models tested, the best model achieved an F-score of 97.1% for fly, 88.6% for mouse and 85.5% for yeast using a variety of features that included gene name, organism frequency, MeSH headings and term-species associations. We noted that term-species associations were particularly effective in improving classification performance. The benefit of using full text articles over abstracts was consistently observed across all three organisms.

Conclusions

By comparing various learner algorithms and features we presented an optimized system that automatically detects the major focus organism in full text articles for fly, mouse and yeast. We believe the method will be extensible to other organism types.

     

Planetary systems biology

Combining paleogenetics, protein engineering, synthetic biology, and metabolic modeling, a planetary biology perspective is brought to bear on adaptive evolutionary events in ancient bacteria.