Network-Based Analysis of Nutraceuticals in Human Hepatocellular Carcinomas Reveals Mechanisms of Chemopreventive Action.

Chronic inflammation is associated with the development of human hepatocellular carcinoma (HCC), an essentially incurable cancer. Anti-inflammatory nutraceuticals have emerged as promising candidates against HCC, yet the mechanisms through which they influence the cell signaling machinery to impose phenotypic changes remain unresolved. Herein we implemented a systems biology approach in HCC cells, based on the integration of cytokine release and phospoproteomic data from high-throughput xMAP Luminex assays to elucidate the action mode of prominent nutraceuticals in terms of topology alterations of HCC-specific signaling networks. An optimization algorithm based on SigNetTrainer, an Integer Linear Programming formulation, was applied to construct networks linking signal transduction to cytokine secretion by combining prior knowledge of protein connectivity with proteomic data. Our analysis identified the most probable target phosphoproteins of interrogated compounds and predicted translational control as a new mechanism underlying their anticytokine action. Induced alterations corroborated with inhibition of HCC-driven angiogenesis and metastasis.

A model integration approach linking signalling and gene-regulatory logic with kinetic metabolic models.

Systems biology has to increasingly cope with large- and multi-scale biological systems. Many successful in silico representations and simulations of various cellular modules proved mathematical modelling to be an important tool in gaining a solid understanding of biological phenomena. However, models spanning different functional layers (e.g. metabolism, signalling and gene regulation) are still scarce. Consequently, model integration methods capable of fusing different types of biological networks and various model formalisms become a key methodology to increase the scope of cellular processes covered by mathematical models. Here we propose a new integration approach to couple logical models of signalling or/and gene-regulatory networks with kinetic models of metabolic processes. The procedure ends up with an integrated dynamic model of both layers relying on differential equations. The feasibility of the approach is shown in an illustrative case study integrating a kinetic model of central metabolic pathways in hepatocytes with a Boolean logical network depicting the hormonally induced signal transduction and gene regulation events involved. In silico simulations demonstrate the integrated model to qualitatively describe the physiological switch-like behaviour of hepatocytes in response to nutritionally regulated changes in extracellular glucagon and insulin levels. A simulated failure mode scenario addressing insulin resistance furthermore illustrates the pharmacological potential of a model covering interactions between signalling, gene regulation and metabolism.

Evaluating differences of metabolic performances: statistical methods and their application to animal cell cultivations.

In cell culture process development, monitoring and analyzing metabolic key parameters is routinely applied to demonstrate specific advantages of one experimental setup over another. It is of great importance that the observed differences and expected improvements are practically relevant and statistically significant. However, a systematic assessment whether observed differences in metabolic rates are statistically significant or not is often missing. This can lead to time-consuming and costly changes of an established biotechnological process due to false positive results. In the present work we demonstrate how well-established statistical tools can be employed to analyze systematically different sources of variations in metabolic rate determinations and to assess, in an unbiased way, their implications on the significance of the observed differences. As a case study, we evaluate differing growth characteristics and metabolic rates of the avian designer cell line AGE1.CR.pIX cultivated in a stirred tank reactor and in a wave bioreactor. Although large differences in metabolic rates and cell growth were expected (due to different aeration, agitation, pH-control, etc.) and partially observed (up to 79%), our results show that the inter-experimental variance between experiments performed under identical conditions but with different pre-cultures is a major contributor to the overall variance of metabolic rates. The lower bounds of the overall relative standard deviations for specific metabolic rates were between 4% and 73%. The application of available statistical methods revealed that the observed differences were statistically not significant and consequently insufficient to confirm relevant differences between both cultivation systems. Our study provides a general guideline for statistical analyses in comparative cultivation studies and emphasizes the necessity to account for the inter-experimental variance (mainly caused by biological variation) to avoid false-positive results.

An effective framework for reconstructing gene regulatory networks from genetical genomics data.

MOTIVATION: Systems Genetics approaches, in particular those relying on genetical genomics data, put forward a new paradigm of large-scale genome and network analysis. These methods use naturally occurring multi-factorial perturbations (e.g. polymorphisms) in properly controlled and screened genetic crosses to elucidate causal relationships in biological networks. However, although genetical genomics data contain rich information, a clear dissection of causes and effects as required for reconstructing gene regulatory networks is not easily possible. RESULTS: We present a framework for reconstructing gene regulatory networks from genetical genomics data where genotype and phenotype correlation measures are used to derive an initial graph which is subsequently reduced by pruning strategies to minimize false positive predictions. Applied to realistic simulated genetic data from a recent DREAM challenge, we demonstrate that our approach is simple yet effective and outperforms more complex methods (including the best performer) with respect to (i) reconstruction quality (especially for small sample sizes) and (ii) applicability to large data sets due to relatively low computational costs. We also present reconstruction results from real genetical genomics data of yeast. AVAILABILITY: A MATLAB implementation (script) of the reconstruction framework is available at www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html CONTACT: klamt@mpi-magdeburg.mpg.de.

Multistability of signal transduction motifs.

Protein domains are the basic units of signalling processes. The mechanisms they are involved in usually follow recurring patterns, such as phosphorylation/dephosphorylation cycles. A set of common motifs was defined and their dynamic models were analysed with respect to number and stability of steady states. In a first step, Feinberg's chemical reaction network theory was used to determine whether a motif can show multistationarity or not. The analysis revealed that, apart from double-step activation motifs including a distributive mechanism, only those motifs involving an autocatalytic reaction can show multistationarity. To further characterise these motifs, a large number of randomly chosen parameter sets leading to bistability was generated, followed by a bifurcation analysis of each parameter set and a statistical evaluation of the results. The statistical results can be used to explore robustness against noise, pointing to the observation that multistationarity at the single-motif level may not be a robust property; the range of protein concentrations compatible with multistationarity is fairly narrow. Furthermore, experimental evidence suggests that protein concentrations vary substantially between cells. Considering a motif designed to be a bistable switch, this implies that fluctuation of protein concentrations between cells would prevent a significant proportion of motifs from acting as a switch. The authors consider this to be a first step towards a catalogue of fully characterised signalling modules.

Algorithmic approaches for computing elementary modes in large biochemical reaction networks.

The concept of elementary (flux) modes provides a rigorous description of pathways in metabolic networks and proved to be valuable in a number of applications. However, the computation of elementary modes is a hard computational task that gave rise to several variants of algorithms during the last years. This work brings substantial progresses to this issue. The authors start with a brief review of results obtained from previous work regarding (a) a unified framework for elementary-mode computation, (b) network compression and redundancy removal and (c) the binary approach by which elementary modes are determined as binary patterns reducing the memory demand drastically without loss of speed. Then the authors will address herein further issues. First, a new way to perform the elementarity tests required during the computation of elementary modes which empirically improves significantly the computation time in large networks is proposed. Second, a method to compute only those elementary modes where certain reactions are involved is derived. Relying on this method, a promising approach for computing EMs in a completely distributed manner by decomposing the full problem in arbitrarity many sub-tasks is presented. The new methods have been implemented in the freely available software tools FluxAnalyzer and Metatool and benchmark tests in realistic networks emphasise the potential of our proposed algorithms.