Jorrit J. Hornberg

Control of MAPK signalling: from complexity to what really matters.

Oncogenesis results from changes in kinetics or in abundance of proteins in signal transduction networks. Recently, it was shown that control of signalling cannot reside in a single gene product, and might well be dispersed over many components. Which of the reactions in these complex networks are most important, and how can the existing molecular information be used to understand why particular genes are oncogenes whereas others are not? We implement a new method to help address such questions. We apply control analysis to a detailed kinetic model of the epidermal growth factor-induced mitogen-activated protein kinase network. We determine the control of each reaction with respect to three biologically relevant characteristics of the output of this network: the amplitude, duration and integrated output of the transient phosphorylation of extracellular signal-regulated kinase (ERK). We confirm that control is distributed, but far from randomly: a small proportion of reactions substantially control signalling. In particular, the activity of Raf is in control of all characteristics of the transient profile of ERK phosphorylation, which may clarify why Raf is an oncogene. Most reactions that really matter for one signalling characteristic are also important for the other characteristics. Our analysis also predicts the effects of mutations and changes in gene expression.

Principles behind the multifarious control of signal transduction. ERK phosphorylation and kinase/phosphatase control.

General and simple principles are identified that govern signal transduction. The effects of kinase and phosphatase inhibition on a MAP kinase pathway are first examined in silico. Quantitative measures for the control of signal amplitude, duration and integral strength are introduced. We then identify and prove new principles, such that total control on signal amplitude and on final signal strength must amount to zero, and total control on signal duration and on integral signal intensity must equal −1. Collectively, kinases control amplitudes more than duration, whereas phosphatases tend to control both. We illustrate and validate these principles experimentally: (a) a kinase inhibitor affects the amplitude of EGF‐induced ERK phosphorylation much more than its duration and (b) a phosphatase inhibitor influences both signal duration and signal amplitude, in particular long after EGF administration. Implications for the cellular decision between growth and differentiation are discussed.

Metabolic control analysis to identify optimal drug targets.

This chapter describes the basic principles of Metabolic Control Analysis (MCA) which is a quantitative methodology to evaluate the importance and relative contribution of individual metabolic steps in the overall functioning of a particular system. The control on the flux through a metabolic pathway or subsystem can be quantified by the control coefficients of the individual enzymes or components which reflects the extent to which the component is rate-limiting. The perturbation of an individual step is measured by its elasticity coefficient. The effect of perturbation of a single step on the entire pathway or subsystem is, in turn, measured by the response coefficient. Differential control analysis can be used to compare flux through a single metabolic pathway in a pathogen with the same pathway in its host to identify uniquely vulnerable steps with the greatest potential for specifically inhibiting flux through the pathogen metabolic pathway. The utility of this methodology is illustrated with the glycolysis in Trypanosomes and with oncogenic signaling.

The NosX and NirX Proteins of Paracoccus denitrificans Are Functional Homologues: Their Role in Maturation of Nitrous Oxide Reductase

The nos (nitrous oxide reductase) operon of Paracoccus denitrificans contains a nosX gene homologous to those found in the nos operons of other denitrifiers. NosX is also homologous to NirX, which is so far unique to P. denitrificans. Single mutations of these genes did not result in any apparent phenotype, but a double nosX nirX mutant was unable to reduce nitrous oxide. Promoter-lacZ assays and immunoblotting against nitrous oxide reductase showed that the defect was not due to failure of expression of nosZ, the structural gene for nitrous oxide reductase. Electron paramagnetic resonance spectroscopy showed that nitrous oxide reductase in cells of the double mutant lacked the Cu(A) center. A twin-arginine motif in both NosX and NirX suggests that the NosX proteins are exported to the periplasm via the TAT translocon.

Introduction to systems biology

The developments in the molecular biosciences have made possible a shift to combined molecular and system-level approaches to biological research under the name of Systems Biology. It integrates many types of molecular knowledge, which can best be achieved by the synergistic use of models and experimental data. Many different types of modeling approaches are useful depending on the amount and quality of the molecular data available and the purpose of the model. Analysis of such models and the structure of molecular networks have led to the discovery of principles of cell functioning overarching single species. Two main approaches of systems biology can be distinguished. Top-down systems biology is a method to characterize cells using system-wide data originating from the Omics in combination with modeling. Those models are often phenomenological but serve to discover new insights into the molecular network under study. Bottom-up systems biology does not start with data but with a detailed model of a molecular network on the basis of its molecular properties. In this approach, molecular networks can be quantitatively studied leading to predictive models that can be applied in drug design and optimization of product formation in bioengineering. In this chapter we introduce analysis of molecular network by use of models, the two approaches to systems biology, and we shall discuss a number of examples of recent successes in systems biology.

Oncogenes are to lose control on signaling following mutation. Should we aim off target.

Rationalized cancer therapy aims at blocking overactive signaling pathways in cancer cells using kinase inhibitors. Essential for its success is the identification of suitable drug targets. Several recent reports have shown that by using control analysis, one can determine which component of a pathway is in control of its output. However, it has not been analyzed how a mutation in an oncogene affects the extent to which the various components are important. Are the same proteins still important after an oncogene has been activated? In the present study, we show that, upon mutation, oncogenes such as mutant kinases tend to lose part of their control on signaling. On the other hand, some of the nonmutated genes may become more important, when compared to the situation before the mutation. This may imply that, perhaps paradoxically, signaling proteins encoded by nonmutated genes should make better drug targets against cancer.

Introduction to Computational Models of Biochemical Reaction Networks

ABSTRACT Cellular and molecular biology have led to the understanding that cells resemble highly-organized spatiotemporal biochemical reaction networks composed of interacting gene networks, metabolic networks, and signaling networks. Within such networks many types of molecular processes take place on a wide range of time scales. These include diffusive and mediated (active and passive) transport, complex formation/dissociation, and chemical conversions. Systems biology aims at understanding the functioning of cells that stems from the interactions between their constituent (macro) molecules. Its research typically combines experiment, theory, and computation to analyze cellular behavior. This chapter deals with the computational and theoretical components of systems biology research. It gives an overview of the methods available to (1) analyze structural, regulatory, and kinetic models of the networks, (2) simulate the behavior of the networks in kinetic models, and (3) perform metabolic control analysis of these kinetic models.

Epidermal Growth Factor Receptor-Induced Activator Protein 1 Activity Controls Density-Dependent Growth Inhibition in Normal Rat Kidney Fibroblasts

Density-dependent growth inhibition secures tissue homeostasis. Dysfunction of the mechanisms, which regulate this type of growth control is a major cause of neoplasia. In confluent normal rat kidney (NRK) fibroblasts, epidermal growth factor (EGF) receptor levels decline, ultimately rendering these cells irresponsive to EGF. Using an activator protein (AP)-1 sensitive reporter construct, we show that AP-1 activity is strongly decreased in density-arrested NRK cells, but is restored after relaxation of density-dependent growth inhibition by removing neighboring cells. EGF could not induce AP-1 activity or S-phase entry in density-arrested cells, but could do so after pretreatment with retinoic acid, which enhances EGF receptor expression. Our results support a model in which the EGF receptor regulates density-dependent growth control in NRK fibroblasts, which is reflected by EGF-induced mitogenic signaling and consequent AP-1 activity.