Functional explanation
by Arno Wouters
To appear in Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho, Hiroki Yokota (Eds.) Encyclopedia of Systems Biology (Springer). (http://refworks.springer.com/mrw/index.php?id=3051)
Explanation in biology
by Arno Wouters
To appear in Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho, Hiroki Yokota (Eds.) Encyclopedia of Systems Biology (Springer). (http://refworks.springer.com/mrw/index.php?id=3051)
Biological function
by Arno Wouters
To appear in Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho, Hiroki Yokota (Eds.) Encyclopedia of Systems Biology (Springer). (http://refworks.springer.com/mrw/index.php?id=3051)
A Network Perspective on Metabolic Inconsistency
Sonnenschein N, Golib Dzib JF, Lesne A, Eilebrecht S, Boulkroun S, Zennaro MC, Benecke A, Hütt MT.
BMC Syst Biol. 2012 May 14;6(1):41. [Epub ahead of print]
PMID: 22583819 [PubMed - as supplied by publisher]
Background
Integrating gene expression profiles and metabolic pathways under different experimental conditions is... more
Background
Integrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation.
Results
Here, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns.
Conclusions
The methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical function. Finally, we demonstrate how physiologically relevant insights into the structure and dynamics of metabolic networks can be obtained using this novel approach.
De la biologie moléculaire à la biologie des systèmes. Le rôle des rythmes et du temps biologiques
The problem addressed in this paper concerns the epistemology of chronobiology, i.e. the study of biological rhythms,... more
The problem addressed in this paper concerns the epistemology of chronobiology, i.e. the study of biological rhythms, especially circadian ones, and their influence on organisms and ecosystems. I take this particular field of biology to be an instance of a scientific paradigm with two largely complementary concepts at its roots : the concepts of “program” (especially “genetic program”) and of “mechanism”. My analysis argues the case for this paradigm having emerged in the field of molecular biology, as a result of the interplay between biologists, physicists and philosophers. Hence, this paper defines it “the molecular paradigm”.
The first part of my work tracks the evolution of the concept of “genetic program” along a period stretching from the seminal work of Erwin Schrödinger (What is life ?) to the later writings of Jacques Monod and François Jacob. I argue as well that this concept is deeply connected to the notion of “mechanism” and that this special relation has been brought out in the last thirty years by a host of philosophical analyses, focusing mainly on molecular biology and neurobiology. These works have thus set a new trend in contemporary philosophy of science, “new mechanistic philosophy”, whose main concern is to describe and asses the way practicing biologists try to detect mechanisms and explain their workings.
The second part argues that chronobiology has assimilated this paradigm, thereby conceiving the study of biological rhythms as the search for molecular clocks. This claim is supported by the analysis of some founding papers in this field, like the one of Colin Pittendrigh on the existence of “free running” circadian rhythms in insects and microorganisms. To substantiate further my point, I review a paper of William Bechtel and Adele Abrahamsen, who account for the discovery of drosophila's “clock gene” per, located in some neurons of the Suprachiasmatic Nucleus (SCN), and of the corresponding system of regulation through a negative feedback loop in fully mechanical terms. I contend that this account is no more tenable, since the complex function of this gene cannot be reduced to the interaction of a cluster of independent mechanisms. Then, alternative accounts must be taken into consideration, such as the one of Denis Noble and the one of Pierre Bailly, Giuseppe Longo, Mael Montevil, the latter proposing to represent biological time not as an oscillatory pattern, but by use of a two-dimensional geometrical model. The paper interprets this model on a philosophical background that extensively draws on the tradition of french “historical
epistemology”, as well as on the “biological thought” of Immanuel Kant. Concerning the latter one the paper aims to highlight the link between his reflexions on finality and organization in the living world and the emergence of new trends in the biology of his time, such as the embryological theory of Caspar Friedrich Wolff. In doing so, the argument turns to the studies of Philippe Huneman, in order to show how Kant connects three fundamental concepts – finality, self organisation and contingence – in a coherent conception of the organism. This short detour into kantian philosophy proves useful to the paper's last conclusions, via the concept of “organisational closure” introduced by Matteo Mossio and Alvaro Moreno. In these conclusions, I propose a radically new conceptual interpretation of biological rhythms, not as properties of biological clocks, but as properties of “temporally closed systems”.
Plasticity of genetic interactions in metabolic networks of yeast
Harrison R, Papp B, Pál C, Oliver SG, Delneri D.
Proc Natl Acad Sci U S A. 2007 Feb 13;104(7):2307-12. Epub 2007 Feb 6.
Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence... more Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness.
Hybrid modeling of cell signaling and transcriptional reprogramming and its application in C. elegans development
by Elana Fertig
EJ Fertig*, LV Danilova*, AV Favorov, and MF Ochs (2011) Frontiers in Bioinformatics and Computational Biology, 2:77. (* Co-first author)
Modeling of signal driven transcriptional reprogramming is critical for understanding of organism development, human... more Modeling of signal driven transcriptional reprogramming is critical for understanding of organism development, human disease, and cell biology. Many current modeling techniques discount key features of the biological sub-systems when modeling multi-scale, organism level processes. We present a mechanistic hybrid model, GESSA, which integrates a novel pooled probabilistic Boolean network model of cell signaling and a stochastic simulation of transcription and translation responding to a diffusion model of extra-cellular signals. We apply the model to simulate the well studied cell fate decision process of the vulval precursor cells (VPCs) in C. elegans, using experimentally derived rate constants wherever possible and shared parameters to avoid overfitting. We demonstrate that GESSA recovers (1) the effects of varying scaffold protein concentration on signal strength, (2) amplification of signals in expression, (3) the relative external ligand concentration in a known geometry, and (4) feedback in biochemical networks. We demonstrate that setting model parameters based on wild-type and LIN-12 loss-of-function mutants in C. elegans leads to correct prediction of a wide variety of mutants including partial penetrance of phenotypes. Moreover, the model is relatively insensitive to parameters, retaining the wild-type phenotype for a wide range of cell signaling rate parameters.
Cancer Systems Biology
by Elana Fertig
Springer Handbooks of Computational Statistics, 2011, Part 3, 533-565, DOI: 10.1007/978-3-642-16345-6_25
Cancer is a complex disease, resulting from system-wide interactions of biological processes rather than from any... more Cancer is a complex disease, resulting from system-wide interactions of biological processes rather than from any single underlying cause. The processes that drive all cancer development and progression have been termed the ‘hallmarks of cancer’. With the growth of large-scale measurements of numerous molecular and cellular properties, a new approach, cancer systems biology, to understanding the interrelationship between the hallmarks is presently being developed. Cancer systems biology focuses on systems-level analysis and presently strives to develop novel data integration and analysis techniques to model and infer cancer biology and treatment response.
CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data
by Elana Fertig
EJ Fertig, J Ding, AV Favorov, G Parmigiani, and MF Ochs. (2010) Bioinformatics, 26 (21): 2792-2793.
Summary: Coordinated Gene Activity in Pattern Sets (CoGAPS) provides an integrated package for isolating gene... more
Summary: Coordinated Gene Activity in Pattern Sets (CoGAPS) provides an integrated package for isolating gene expression driven by a biological process, enhancing inference of biological processes from transcriptomic data. CoGAPS improves on other enrichment measurement methods by combining a Markov chain Monte Carlo (MCMC) matrix factorization algorithm (GAPS) with a threshold-independent statistic inferring activity on gene sets. The software is provided as open source C++ code built on top of JAGS software with an R interface.
Availability: The R package CoGAPS and the C++ package GAPS-JAGS are provided open source under the GNU Lesser Public License (GLPL) with a users manual containing installation and operating instructions. CoGAPS is available through Bioconductor and depends on the rjags package available through CRAN to interface CoGAPS with GAPS-JAGS.
URL: http://www.cancerbiostats.onc.jhmi.edu/cogaps.cfm
Cancer Systems Biology
by Elana Fertig
Springer Handbooks of Computational Statistics, 2011, Part 3, 533-565, DOI: 10.1007/978-3-642-16345-6_25
Cancer is a complex disease, resulting from system-wide interactions of biological processes rather than from any... more Cancer is a complex disease, resulting from system-wide interactions of biological processes rather than from any single underlying cause. The processes that drive all cancer development and progression have been termed the ‘hallmarks of cancer’. With the growth of large-scale measurements of numerous molecular and cellular properties, a new approach, cancer systems biology, to understanding the interrelationship between the hallmarks is presently being developed. Cancer systems biology focuses on systems-level analysis and presently strives to develop novel data integration and analysis techniques to model and infer cancer biology and treatment response.
Global optimization in systems biology: stochastic methods and their applications
by Alejandro Fernández Villaverde
E Balsa-Canto, JR Banga, JA Egea, A Fernandez Villaverde, GM de Hijas-Liste (2012) Advances in Experimental Medicine and Biology, vol. 736 (special issue "Advances in Systems Biology"), pp. 409--425, doi:10.1007/978-1-4419-7210-1\_24
Mathematical optimization is at the core of many problems in systems biology: (1) as the underlying hypothesis for... more Mathematical optimization is at the core of many problems in systems biology: (1) as the underlying hypothesis for model development, (2) in model identification, or (3) in the computation of optimal stimulation procedures to synthetically achieve a desired biological behavior. These problems are usually formulated as nonlinear programing problems (NLPs) with dynamic and algebraic constraints. However the nonlinear and highly constrained nature of systems biology models, together with the usually large number of decision variables, can make their solution a daunting task, therefore calling for efficient and robust optimization techniques. Here, we present novel global optimization methods and software tools such as cooperative enhanced scatter search (eSS), AMIGO, or DOTcvpSB, and illustrate their possibilities in the context of modeling including model identification and stimulation design in systems biology.
EvoluCode: Evolutionary Barcodes as a Unifying Framework for Multilevel Evolutionary Data
Evolutionary Bioinformatics
Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological... more Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological networks. An essential part of this process is the reconstruction and analysis of the evolutionary histories of these complex, dynamic networks. Unfortunately, the methodologies for representing and exploiting such complex evolutionary histories in large scale studies are currently limited. Here, we propose a new formalism, called EvoluCode (Evolutionary barCode), which allows the integration of different evolutionary parameters (eg, sequence conservation, orthology, synteny …) in a unifying format and facilitates the multilevel analysis and visualization of complex evolutionary histories at the genome scale. The advantages of the approach are demonstrated by constructing barcodes representing the evolution of the complete human proteome. Two large-scale studies are then described: (i) the mapping and visualization of the barcodes on the human chromosomes and (ii) automatic clustering of the barcodes to highlight protein subsets sharing similar evolutionary histories and their functional analysis. The methodologies developed here open the way to the efficient application of other data mining and knowledge extraction techniques in evolutionary systems biology studies.
Quantifying the dynamics of coupled networks of switches and oscillators
by Elana Fertig
Matthew R Francis and EJ Fertig* (2012) PLoS One, 7:e29497. *Corresponding author
Complex network dynamics have been analyzed with models of systems of coupled switches or systems of coupled... more Complex network dynamics have been analyzed with models of systems of coupled switches or systems of coupled oscillators. However, many complex systems are composed of components with diverse dynamics whose interactions drive the system's evolution. We, therefore, introduce a new modeling framework that describes the dynamics of networks composed of both oscillators and switches. Both oscillator synchronization and switch stability are preserved in these heterogeneous, coupled networks. Furthermore, this model recapitulates the qualitative dynamics for the yeast cell cycle consistent with the hypothesized dynamics resulting from decomposition of the regulatory network into dynamic motifs. Introducing feedback into the cell-cycle network induces qualitative dynamics analogous to limitless replicative potential that is a hallmark of cancer. As a result, the proposed model of switch and oscillator coupling provides the ability to incorporate mechanisms that underlie the synchronized stimulus response ubiquitous in biochemical systems.
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Seen by:Biology and the systems view.
“Is There a Turn to Systems Approaches in Life Sciences?”, in: European Molecular Biology Organisation (EMBO) Reports 10 (2009), pp. 37-42.
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Seen by: and 9 moreAdaptable Functionality of Transcriptional Feedback in Bacterial Two-Component Systems
J. Christian J. Ray, Oleg A. Igoshin
A widespread mechanism of bacterial signaling occurs through two-component systems, comprised of a sensor histidine... more A widespread mechanism of bacterial signaling occurs through two-component systems, comprised of a sensor histidine kinase (SHK) and a transcriptional response regulator (RR). The SHK activates RR by phosphorylation. The most common two-component system structure involves expression from a single operon, the transcription of which is activated by its own phosphorylated RR. The role of this feedback is poorly understood, but it has been associated with an overshooting kinetic response and with fast recovery of previous interrupted signaling events in different systems. Mathematical models show that overshoot is only attainable with negative feedback that also improves response time. Our models also predict that fast recovery of previous interrupted signaling depends on high accumulation of SHK and RR, which is more likely in a positive feedback regime. We use Monte Carlo sampling of the parameter space to explore the range of attainable model behaviors. The model predicts that the effective feedback sign can change from negative to positive depending on the signal level. Variations in two-component system architectures and parameters may therefore have evolved to optimize responses in different bacterial lifestyles. We propose a conceptual model where low signal conditions result in a responsive system with effectively negative feedback while high signal conditions with positive feedback favor persistence of system output.
Non-transcriptional regulatory processes shape transcriptional network dynamics
J. Christian J. Ray, Jeffrey J. Tabor & Oleg A. Igoshin (2011) Nature Reviews Microbiology doi:10.1038/nrmicro2667
Information about the extra- or intracellular environment is often captured as biochemical signals that propagate... more Information about the extra- or intracellular environment is often captured as biochemical signals that propagate through regulatory networks. These signals eventually drive phenotypic changes, typically by altering gene expression programmes in the cell. Reconstruction of transcriptional regulatory networks has given a compelling picture of bacterial physiology, but transcriptional network maps alone often fail to describe phenotypes. Cellular response dynamics are ultimately determined by interactions between transcriptional and non-transcriptional networks, with dramatic implications for physiology and evolution. Here, we provide an overview of non-transcriptional interactions that can affect the performance of natural and synthetic bacterial regulatory networks.

