References of "Westerhoff, Hans V"
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See detailMacromolecular networks and intelligence in microorganisms
Westerhoff, Hans V.; Brooks, Aaron; Simeonidis, Vangelis UL et al

in Frontiers in Microbiology (2014)

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks ... [more ▼]

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of Information and Communication Technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity – particularly activity of the human brain – with a phenomenon we call “intelligence”. Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as “human” and “brain” out of the defining features of “intelligence”, all forms of life – from microbes to humans – exhibit some or all characteristics consistent with “intelligence”. We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo. [less ▲]

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See detailA model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes
Smallbone, Kieran; Messiha, Hanan L.; Carroll, Kathleen M. et al

in FEBS Letters (2013), 587(17), 2832-2841

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an ... [more ▼]

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a ‘‘cycle of knowledge’’ strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. [less ▲]

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See detailOptimization of stress response through the nuclear receptor-mediated cortisol signalling network
Kolodkin, Alexey UL; Sahin, Nilgun; Phillips, Anna et al

in Nature Communications (2013), 4

It is an accepted paradigm that extended stress predisposes an individual to pathophysiology. However, the biological adaptations to minimize this risk are poorly understood. Using a computational model ... [more ▼]

It is an accepted paradigm that extended stress predisposes an individual to pathophysiology. However, the biological adaptations to minimize this risk are poorly understood. Using a computational model based upon realistic kinetic parameters we are able to reproduce the interaction of the stress hormone cortisol with its two nuclear receptors, the high-affinity glucocorticoid receptor and the low-affinity pregnane X-receptor. We demonstrate that regulatory signals between these two nuclear receptors are necessary to optimize the body’s response to stress episodes, attenuating both the magnitude and duration of the biological response. In addition, we predict that the activation of pregnane X-receptor by multiple, low-affinity endobiotic ligands is necessary for the significant pregnane X-receptor-mediated transcriptional response observed following stress episodes. This integration allows responses mediated through both the high and low-affinity nuclear receptors, which we predict is an important strategy to minimize the risk of disease from chronic stress. [less ▲]

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See detailComputing life: Add logos to biology and bios to physics
Kolodkin, Alexey UL; Simeonidis, Evangelos UL; Westerhoff, Hans V.

in Progress in Biophysics & Molecular Biology (2012), 111(2-3), 69-74

This paper discusses the interrelations between physics and biology. Particularly, we analyse the approaches for reconstructing the emergent properties of physical or biological systems. We propose ... [more ▼]

This paper discusses the interrelations between physics and biology. Particularly, we analyse the approaches for reconstructing the emergent properties of physical or biological systems. We propose approaches to scale emergence according to the degree of state-dependency of the system's component properties. Since the component properties of biological systems are state-dependent to a high extent, biological emergence should be considered as very strong emergence – i.e. its reconstruction would require a lot of information about state-dependency of its component properties. However, due to its complexity and volume, this information cannot be handled in the naked human brain, or on the back of an envelope. To solve this problem, biological emergence can be reconstructed in silico based on experimentally determined rate laws and parameter values of the living cell. According to some rough calculations, the silicon human might comprise the mathematical descriptions of around 105 interactions. This is not a small number, but taking into account the exponentially increase of computational power, it should not prove to be our principal limitation. The bigger challenges will be located in different areas. For example they may be related to the observer effect – the limitation to measuring a system's component properties without affecting the system. Another obstacle may be hidden in the tradition of "shaving away" all “unnecessary” assumptions (the so-called Occam's razor) that, in fact, reflects the intention to model the system as simply as possible and thus to deem the emergence to be less strong than it possibly is. We argue here that that Occam's razor should be replaced with the law of completeness. [less ▲]

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See detailEmergence of the silicon human and network targeting drugs
Kolodkin, Alexey UL; Boogerda, Fred C.; Plantb, Nick et al

in European Journal of Pharmaceutical Sciences (2012), 46(4), 190-197

The development of disease may be characterized as a pathological shift of homeostasis; the main goal of contemporary drug treatment is, therefore, to return the pathological homeostasis back to the ... [more ▼]

The development of disease may be characterized as a pathological shift of homeostasis; the main goal of contemporary drug treatment is, therefore, to return the pathological homeostasis back to the normal physiological range. From the view point of systems biology, homeostasis emerges from the interactions within the network of biomolecules (e.g. DNA, mRNA, proteins), and, hence, understanding how drugs impact upon the entire network should improve their efficacy at returning the network (body) to physiological homeostasis. Large, mechanism-based computer models, such as the anticipated human whole body models (silicon or virtual human), may help in the development of such network-targeting drugs. Using the philosophical concept of weak and strong emergence, we shall here take a more general look at the paradigm of network-targeting drugs, and propose our approaches to scale the strength of strong emergence. We apply these approaches to several biological examples and demonstrate their utility to reveal principles of bio-modeling. We discuss this in the perspective of building the silicon human. [less ▲]

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See detailUnderstanding complexity in neurodegenerative diseases: in silico reconstruction of emergence.
Kolodkin, Alexey UL; Simeonidis, Evangelos UL; Balling, Rudi UL et al

in Frontiers in Physiology (2012), 3

Healthy functioning is an emergent property of the network of interacting biomolecules that comprise an organism. It follows that disease (a network shift that causes malfunction) is also an emergent ... [more ▼]

Healthy functioning is an emergent property of the network of interacting biomolecules that comprise an organism. It follows that disease (a network shift that causes malfunction) is also an emergent property, emerging from a perturbation of the network. On the one hand, the biomolecular network of every individual is unique and this is evident when similar disease-producing agents cause different individual pathologies. Consequently, a personalized model and approach for every patient may be required for therapies to become effective across mankind. On the other hand, diverse combinations of internal and external perturbation factors may cause a similar shift in network functioning. We offer this as an explanation for the multi-factorial nature of most diseases: they are "systems biology diseases," or "network diseases." Here we use neurodegenerative diseases, like Parkinson's disease (PD), as an example to show that due to the inherent complexity of these networks, it is difficult to understand multi-factorial diseases with simply our "naked brain." When describing interactions between biomolecules through mathematical equations and integrating those equations into a mathematical model, we try to reconstruct the emergent properties of the system in silico. The reconstruction of emergence from interactions between huge numbers of macromolecules is one of the aims of systems biology. Systems biology approaches enable us to break through the limitation of the human brain to perceive the extraordinarily large number of interactions, but this also means that we delegate the understanding of reality to the computer. We no longer recognize all those essences in the system's design crucial for important physiological behavior (the so-called "design principles" of the system). In this paper we review evidence that by using more abstract approaches and by experimenting in silico, one may still be able to discover and understand the design principles that govern behavioral emergence. [less ▲]

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See detailChasing the flux: selecting target pathways through flux analysis of carbon metabolism
Simeonidis, Vangelis UL; Murabito, Ettore; Smallbone, Kieran et al

Poster (2011, June 26)

One of the goals of Systems Biology is to develop and utilise high-throughput methods for the measurement of parameters and concentrations on a genome-wide scale, while at the same time generating ... [more ▼]

One of the goals of Systems Biology is to develop and utilise high-throughput methods for the measurement of parameters and concentrations on a genome-wide scale, while at the same time generating predictive models for system behaviour. In studying genome-scale metabolic networks, the task of exhaustively assaying and measuring all reaction components can be daunting, because hundreds or even thousands of enzymes (activities and concentrations) need to be considered for the construction of a full-scale, detailed model. There is a clear need for strategies that allow us to systematically select the subsets of pathways and reactions which should be prioritized when studying metabolism. We present a methodology for selecting those reactions that carry the overwhelming majority of the carbon flux through the metabolic network. The recent community-driven reconstruction of the metabolic network of baker’s yeast [1] provides the basis for our analysis. Flux Balance Analysis provides a theoretical flux distribution. Results are constrained with GC-MS exometabolomic measurements of the carbon flux. Flux calculations can also be improved by using 13C measurements to determine intracellular metabolic fluxes. The solution of the constrained FBA problem gives us a ranked list of reactions, based on the amount of carbon flux through each reaction. We improve the specificity of the method further by performing an Elementary Flux Mode analysis, which provides us with target pathways consisting of the reactions that carry the most carbon flux. Our methodology allows us to cover more than 95% of the carbon flux by studying but a small subset of the reactions of the genome-scale metabolic network of baker’s yeast. [less ▲]

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See detailDesign principles of nuclear receptor signalling: How complex networking improves signal transduction
Kolodkin, Alexey UL; Bruggeman, Frank J.; Plant, Nick et al

in Toxicology (2011), 290(2-3), 131-132

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See detailA systems biology approach towards understanding nuclear receptor interactions: Implications at the endocrine-xenobiotic signalling interface
Kolodkin, Alexey UL; Phillips, Anna; Hood, Steve et al

in Toxicology (2011), 290(2-3), 131-131

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See detailNuclear receptors as controlling factors in chemical metabolism: Determination of regulatory signal network crucial for co-ordinating cellular response to chemicals
Kolodkin, Alexey UL; Phillips, Anna; Hood, Steve R. et al

in Drug Metabolism Reviews (2010), 42(1), 279-280

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See detailDesign principles of nuclear receptor signaling: how complex networking improves signal transduction.
Kolodkin, Alexey UL; Bruggeman, Frank J.; Plant, Nick et al

in Molecular Systems Biology (2010), 6

The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of 'design' aspects of the topology of these networks that ... [more ▼]

The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of 'design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic 'nuclear' receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands. [less ▲]

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See detailSystems biology towards life in silico: mathematics of the control of living cells.
Westerhoff, Hans V.; Kolodkin, Alexey UL; Conradie, Riaan et al

in Journal of Mathematical Biology (2009), 58(1-2), 7-34

Systems Biology is the science that aims to understand how biological function absent from macromolecules in isolation, arises when they are components of their system. Dedicated to the memory of Reinhart ... [more ▼]

Systems Biology is the science that aims to understand how biological function absent from macromolecules in isolation, arises when they are components of their system. Dedicated to the memory of Reinhart Heinrich, this paper discusses the origin and evolution of the new part of systems biology that relates to metabolic and signal-transduction pathways and extends mathematical biology so as to address postgenomic experimental reality. Various approaches to modeling the dynamics generated by metabolic and signal-transduction pathways are compared. The silicon cell approach aims to describe the intracellular network of interest precisely, by numerically integrating the precise rate equations that characterize the ways macromolecules' interact with each other. The non-equilibrium thermodynamic or 'lin-log' approach approximates the enzyme rate equations in terms of linear functions of the logarithms of the concentrations. Biochemical Systems Analysis approximates in terms of power laws. Importantly all these approaches link system behavior to molecular interaction properties. The latter two do this less precisely but enable analytical solutions. By limiting the questions asked, to optimal flux patterns, or to control of fluxes and concentrations around the (patho)physiological state, Flux Balance Analysis and Metabolic/Hierarchical Control Analysis again enable analytical solutions. Both the silicon cell approach and Metabolic/Hierarchical Control Analysis are able to highlight where and how system function derives from molecular interactions. The latter approach has also discovered a set of fundamental principles underlying the control of biological systems. The new law that relates concentration control to control by time is illustrated for an important signal transduction pathway, i.e. nuclear hormone receptor signaling such as relevant to bone formation. It is envisaged that there is much more Mathematical Biology to be discovered in the area between molecules and Life. [less ▲]

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