On Systems Thinking, Systems Biology, and the in Silico Plant

The recent summary report of a Department of Energy Workshop on Plant Systems Biology (P.V. Minorsky [2003] Plant Physiol 132: 404-409) offered a welcomed advocacy for systems analysis as essential in understanding plant development, growth, and production. The goal of the Workshop was to consider methods for relating the results of molecular research to real-world challenges in plant production for increased food supplies, alternative energy sources, and environmental improvement. The rather surprising feature of this report, however, was that the Workshop largely overlooked the rich history of plant systems analysis extending over nearly 40 years (Sinclair and Seligman, 1996) that has considered exactly those challenges targeted by the Workshop. Past systems research has explored and incorporated biochemical and physiological knowledge into plant simulation models from a number of perspectives. The research has resulted in considerable understanding and insight about how to simulate plant systems and the relative contribution of various factors in influencing plant production. These past activities have contributed directly to research focused on solving the problems of increasing biomass production and crop yields. These modeling approaches are also now providing an avenue to enhance integration of molecular genetic technologies in plant improvement (Hammer et al., 2002).

Mass Spectrometry Informatics in Systems Biology

Mass spectrometry (MS) became a standard tool for identifying metabolites in biological tissues, and metabolomics is slowly acknowledged as a legitimate research discipline for characterizing biological conditions. The computational analyses of metabolomics, however, lag behind compared with the rapid advances in analytical aspects for two reasons. First is the lack of standardized data repository for mass spectra: each research institution is flooded with gigabytes of mass-spectral data from its own analytical groups and cannot host a world-class repository for mass spectra. The second reason is the lack of informatics experts that are fully experienced with spectral analyses. The two barriers must be overcome to establish a publicly free data server for MS analysis in metabolomics as does GenBank in genomics and UniProt in proteomics. The workshop brought together bioinformaticians working on mass spectral analyses in Finland and Japan with the goal to establish a consortium to freely exchange and publicize mass spectra of metabolites measured on various platforms computational tools to analyze spectra spectral knowledge that are computationally predicted from standardized data. This book contains the abstracts of the presentations given in the workshop. The programme of the workshop consisted of oral presentations from Japan and Finland, invited lectures from Steffen Neumann (Leibniz Institute of Plant Biochemistry), Matej Oresic (VTT), Merja Penttila (VTT) and Nicola Zamboni (ETH Zurich) as well as free form discussion among the participants. The event was funded by Academy of Finland (grants 139203 and 118653), Japan Society for the Promotion of Science (JSPS Japan-Finland Bilateral Semi- nar Program 2010) and Department of Computer Science University of Helsinki. We would like to thank all the people contributing to the technical pro- gramme and the sponsors for making the workshop possible. Helsinki, October 2010 Masanori Arita, Markus Heinonen and Juho Rousu

Systems biology of tuberculosis

Systems biology seeks to study biological systems as a whole, by adopting an integrated approach to study and understand the function of biological systems, particularly, the response of such systems to various perturbations. In this article, we focus on the Indian efforts towards systems-level studies of Mycobacterium tuberculosis and its interaction with the host. Availability of a variety of genome-scale experimental data, providing first level `omics' descriptions of the pathogen, render it feasible to study it at a systems level. Various aspects of the pathogen, from metabolic pathways to protein-protein interaction networks have been modelled and simulated, while host-pathogen interactions have been studied experimentally using siRNA-based techniques. These studies have been useful in obtaining a global perspective of the pathogen and its interactions with the host in many ways. For example, significant insights have been gained about different aspects such as proteins essential for bacterial survival, proteins that are highly influential in the network, pathways that are highly connected, host factors responsible for maintaining the TB infection and key factors involved in autophagy and pathogenesis. A rational pipeline developed for drug target identification incorporating analyses of the interactome, reactome, genome, pocketome and the transcriptome is discussed. Finally, exploring host factors as drug targets and insights about the emergence of drug resistance are also discussed. (C) 2011 Elsevier Ltd. All rights reserved.

Towards in situ Single Cell Systems Biology

Systems-level studies of biological systems rely on observations taken at a resolution lower than the essential unit of biology, the cell. Recent technical advances in DNA sequencing have enabled measurements of the transcriptomes in single cells excised from their environment, but it remains a daunting technical problem to reconstruct in situ gene expression patterns from sequencing data. In this thesis I develop methods for the routine, quantitative in situ measurement of gene expression using fluorescence microscopy.

The number of molecular species that can be measured simultaneously by fluorescence microscopy is limited by the pallet of spectrally distinct fluorophores. Thus, fluorescence microscopy is traditionally limited to the simultaneous measurement of only five labeled biomolecules at a time. The two methods described in this thesis, super-resolution barcoding and temporal barcoding, represent strategies for overcoming this limitation to monitor expression of many genes in a single cell. Super-resolution barcoding employs optical super-resolution microscopy (SRM) and combinatorial labeling via-smFISH (single molecule fluorescence in situ hybridization) to uniquely label individual mRNA species with distinct barcodes resolvable at nanometer resolution. This method dramatically increases the optical space in a cell, allowing a large numbers of barcodes to be visualized simultaneously. As a proof of principle this technology was used to study the S. cerevisiae calcium stress response. The second method, sequential barcoding, reads out a temporal barcode through multiple rounds of oligonucleotide hybridization to the same mRNA. The multiplexing capacity of sequential barcoding increases exponentially with the number of rounds of hybridization, allowing over a hundred genes to be profiled in only a few rounds of hybridization.

The utility of sequential barcoding was further demonstrated by adapting this method to study gene expression in mammalian tissues. Mammalian tissues suffer both from a large amount of auto-fluorescence and light scattering, making detection of smFISH probes on mRNA difficult. An amplified single molecule detection technology, smHCR (single molecule hairpin chain reaction), was developed to allow for the quantification of mRNA in tissue. This technology is demonstrated in combination with light sheet microscopy and background reducing tissue clearing technology, enabling whole-organ sequential barcoding to monitor in situ gene expression directly in intact mammalian tissue.

The methods presented in this thesis, specifically sequential barcoding and smHCR, enable multiplexed transcriptional observations in any tissue of interest. These technologies will serve as a general platform for future transcriptomic studies of complex tissues.

Control theory and systems biology

Presenting a control-theoretic treatment of stoichiometric systems, ... local parametric sensitivity analysis, the two approaches yield identical results. ...

Networks for systems biology: conceptual connection of data and function

The purpose of this study is to survey the use of networks and network-based methods in systems biology. This study starts with an introduction to graph theory and basic measures allowing to quantify structural properties of networks. Then, the authors present important network classes and gene networks as well as methods for their analysis. In the last part of this study, the authors review approaches that aim at analysing the functional organisation of gene networks and the use of networks in medicine. In addition to this, the authors advocate networks as a systematic approach to general problems in systems biology, because networks are capable of assuming multiple roles that are very beneficial connecting experimental data with a functional interpretation in biological terms.