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Biological systems are tremendously complex in their functioning and regulation. Studying the multifaceted behaviour and describing the performance of such complexity has challenged the scientific community for years. The reduction of real-world intricacy into simple descriptive models has therefore convinced many researchers of the usefulness of introducing mathematics into biological sciences. Predictive modelling takes such an approach another step further in that it takes advantage of existing knowledge to project the performance of a system in alternating scenarios. The ever growing amounts of available data generated by assessing biological systems at increasingly higher detail provide unique opportunities for future modelling and experiment design. Here we aim to provide an overview of the progress made in modelling over time and the currently prevalent approaches for iterative modelling cycles in modern biology. We will further argue for the importance of versatility in modelling approaches, including parameter estimation, model reduction and network reconstruction. Finally, we will discuss the difficulties in overcoming the mathematical interpretation of in vivo complexity and address some of the future challenges lying ahead

We consider the problem of inferring a biological network from given experimental data. In earlier work on metabolic pathway inference, we studied a situation where the molecular interactions were fairly well known, the kinetic parameters were completely unknown and the network topology was almost known. Starting from a similar setting, using simulations, we have investigated the inference of missing links in a network. We use two approaches, a deterministic one using ordinary differential equations and one employing a statistical approach. We find that the deterministic fit-based approach yields quite positive results in linear as well as in a non-linear context.

Brassinosteroid (BR) signaling is essential for plant growth and development. In Arabidopsis (Arabidopsis thaliana), BRs are perceived by the BRASSINOSTEROID INSENSITIVE1 (BRI1) receptor. Root growth and hypocotyl elongation are convenient downstream physiological outputs of BR signaling. A computational approach was employed to predict root growth solely on the basis of BRI1 receptor activity. The developed mathematical model predicts that during normal root growth, few receptors are occupied with ligand. The model faithfully predicts root growth, as observed in bri1 loss-of-function mutants. For roots, it incorporates one stimulatory and two inhibitory modules, while for hypocotyls, a single inhibitory module is sufficient. Root growth as observed when BRI1 is overexpressed can only be predicted assuming that a decrease occurred in the BRI1 half-maximum response values. Root growth appears highly sensitive to variation in BR concentration and much less to reduction in BRI1 receptor level, suggesting that regulation occurs primarily by ligand availability and biochemical activity.

Spatial organ arrangement plays an important role in flower development. The position and the identity of floral organs is influenced by various processes, in particular the expression of MADS-box transcription factors for identity and dynamics of the plant hormone auxin for positioning. We are currently integrating patterning processes of MADS and auxin into our computational models, based on interactions that are known from experiments, in order to get insight in how these define the floral body plan. The resulting computational model will help to explore hypothetical interactions between the MADS and auxin regulation networks in floral organ patterning.

De systeembiologie, de wetenschapstak die computermodellen combineert met biologische waarnemingen, groeit wereldwijd onstuimig. Wageningen liep achter, maar speelt nu mee, zegt wiskundige Jaap Molenaar van Wageningen University, onderdeel van Wageningen UR.

Met klimaatregeling kun je de bloei van planten beïnvloeden. Eigenlijk ben je dan bezig om processen te sturen op het niveau van genen, celprocessen, plantorganen, hele plant en omgeving. Systeembiologie maakt het mogelijk het inzicht in de samenhang daartussen op een hoger plan te brengen. Daar heeft de veredelaar baat bij en de teler op termijn ook.

Flavonoids are plant secondary metabolites and as such an integral part of the human diet. There is increasing evidence that these polyphenols may explain the beneficial effects of a diet rich in fruits and vegetables on the prevention of several chronic and age-related diseases. To control the amount that these micronutrients are present in our daily food, we need to understand the molecular pathways that lead to the accumulation of these compounds. In our earlier work on flavonoid pathway inference [1-3], we have found ourselves in what seems to be a typical situation in systems biology, where the molecular interactions are fairly well known, the kinetic parameters are completely unknown and where the network topology is almost known [4]. This means that we have only a few missing links in our network structure. There is an abundant literature that describe methods for finding such missing links accompanied with results, when these methods are a pplied to real data. Some of them are top-down and very abstract [5], others are more context-dependent relying on the existing knowledge on the features of the specific system dynamics [6]. Here we want to turn the problem setting upside down and ask how dramatic can the effect of a missing link be to the inference results. When the network is robust against structural changes in topology, the effect of such missing links on the network performance can be limited [7]. We focus here on dynamic networks and consider three different ordinary differential equation-(ODE) based models: a linear model, a non-linear Michaelis-Menten-type model and a complex non-linear model for gene regulatory networks as in the DREAM2012 challenge [8]. We investigated the discrepancy between the true and inferred network as a function of number of edges removed and their average connectivity. To measure the degree of failure, we look at the percentage of lost/red undant edges as well as the errors in the inferred parameters. We believe that our findings can be instructive and useful for the biological network inference practitioners.

The goal of this internship is threefold. First there is an assignment to compare two vehicle dynamics simulation software packages, a Simulink two track model provided by the TU/e and a commercial multibody full vehicle model from dSPACE. For this comparison use is made of a chassis tuning project, which is also part of this internship. The TU/e two track model does not contain kinematics and elasto-kinematics, so the preference is given to the dSPACE multibody model. Another nice feature in this package is the possibility for animation of the simulation results. The second project is a hybrid sportscar project. Here the question is two determine which configuration, rear wheel driven or all wheel driven, is the best choice for converting a current sportscar model to a hybrid variant. The decision is based on a test were the vehicle enters a corner at 50 km/h and starts accelerating full throttle at the beginning of that corner. From the results at the exit of the corner can be concluded that the all wheel driven variant is the faster vehicle. Although the traction control system development was a difficult process and a lot of improvements are still possible, the final settings enable the all wheel driven variant to have an exit speed which is 6 km/h higher than the rear wheel driven variant. Here the recommendation is made to optimize the chassis setup for all wheel drive and preferably to use better front tyres. These measures will even further increase the difference and in the simulations the speed difference at the corner exit increases to 15 km/h. The third and final part of this internship was to simulate the influence of an additional battery pack on the dynamic behaviour of a hybrid vehicle. This hybrid vehicle has to be compared with the original vehicle. Measures are taken to decrease this influence and to make the dynamic behaviour of the hybrid vehicle similar to the that of the original vehicle. The influence of adding the additional battery pack is that the unmodified hybrid vehicle is oversteered more and also has some time delay in the lateral acceleration. The measures taken to reduce these effects are adding 0.5 degrees negative camber on the rear wheels, increasing the front roll stiffness with 20% and finally decreasing the rear roll stiffness with 30%. These measures appear to make the hybrid vehicle behave similar to the original vehicle, but real life tests should be performed to check if these measures suffice and if they are feasible with respect to other characteristics (eg. ride comfort).

Flavonoids are plant secondary metabolites that are extensively studied for their proposed positive effects on human health. They are the end products of a cascade of enzymatic reactions that convert initially toxic substances to glycosylated forms. To determine which enzymes are precisely responsible for which conversions is by far not trivial, since hundreds of candidate genes are in principle capable of performing the transformation of interest. In this paper we propose a method to solve this problem for the glycosylation of flavonoids by coupling gene expression data to the metabolic pathway underlying glycosylation. The core of the method is to estimate time dependent coefficients in a highly efficient way. To show how this approach performs, we apply this method to study the flavonoid glycosylation pathway in tomato (Solanum lycopersicum) seedlings.

Flavonoids are plant secondary metabolites that are extensively studied for their proposed positive effects on human health. They are the end products of a cascade of enzymatic reactions that convert initially toxic substances to glycosylated forms. To determine which enzymes are precisely responsible for which conversions is by far not trivial, since hundreds of candidate genes are in principle capable of performing the transformation of interest. In this paper we propose a method to solve this problem for the glycosylation of flavonoids by coupling gene expression data to the metabolic pathway underlying glycosylation. The core of the method is to estimate time dependent coefficients in a highly efficient way. To show how this approach performs, we apply this method to study the flavonoid glycosylation pathway in tomato (Solanum lycopersicum) seedlings.

Honey has been used successfully in wound healing for thousands of years. The peptide hormone human epidermal growth factor (hEGF) is also known to have a beneficial effect in various wound healing processes via mechanisms that differ from those for honey. In this study, we show that hEGF can be incorporated into honey via nectar. Plants of Nicotiana langsdorffii × N. sanderae were transformed with the gene for hEGF, equipped with a nectary-targeted promoter and a signal sequence for secretion to nectar. These plants accumulated hEGF in the nectar. The maximum hEGF concentration recorded with ELISA in these plants is 2.5 ng·ml-1. There is a significant linear relationship (P

We study non-stationary flow in open discharge channels. A model is derived from basic principles, conservation of mass and momentum, which is solved numerically for the cross sectional area and discharge as a function of time and position along the channel. The model describes the effect of external inflow from fields adjacent to the channel. Several scenarios are calculated, both for very slowly, and more rapidly flowing water courses.

Honey has been used successfully in wound healing for thousands of years. The peptide hormone human epidermal growth factor (hEGF) is also known to have a beneficial effect in various wound healing processes via mechanisms that differ from those for honey. In this study, we show that hEGF can be incorporated into honey via nectar. Plants of Nicotiana langsdorffii x N. sanderae were transformed with the gene for hEGF, equipped with a nectary-targeted promoter and a signal sequence for secretion to nectar. These plants accumulated hEGF in the nectar. The maximum hEGF concentration recorded with ELISA in these plants is 2.5 ng·ml⁻¹. There is a significant linear relationship (P

We study stationary flow in open discharge channels. A model is derived from basic principles, which is solved numerically for the water level and discharge as a function of position along the channel. The model describes the effect of external inflow from fields adjacent to the channel. Several scenarios are calculated, both for very slowly, and more rapidly flowing water courses.

We study a hybrid model of Sch¨obel-Zhu-Hull-White-type from a singular-perturbationanalysis perspective. The merit of the paper is twofold: On one hand, we find boundary conditions for the deterministic non-linear degenerate parabolic partial differential equation for the evolution of the stock price. On the other hand, we combine two-scales regular- and singular-perturbation techniques to find an approximate solution to the pricing PDE. The aim is to produce an expression that can be evaluated numerically very fast.

In this paper we develop two methods to calculate thermodynamic properties of mixtures. Starting point are the basic assumptions that also form the basis for the COSMO-RS model. In this approach, the individual molecules are represented by their geometrical shape with an electrical charge density on their surfaces. Next, the surface is split up into surface segments each with its own charge. In COSMO-RS a strong reduction is introduced by treating the segments as if they are completely independent. In the present study we take into account that the coupling between two patches is essentially dependent on the charge distribution on neighboring segments and on the local geometrical structure of the surface. Two approaches are followed. The first one points out how the model equations, which comprise the optimization of the entropy and conservation of internal energy, can efficiently be solved in general, thus also if the dependency between segments and the local geometry is included in the expression for the coupling energy between segments. In the second method the configuration with maximal entropy and prescribed energy is sought via simulation. Successive molecular configurations of the mixture are simulated and updated via a genetic algorithm to optimize the entropy. The second method is more time consuming but very general.

We present two models for the drying of waterborne paints, which consist of non-volatile latex particles suspended in water. One model considers the water and latex density in a layer as a function of time. Water evaporation at the surface represents the drying. This model results in a one-dimensional free boundary problem, which is solved numerically. Extensions to the model are given that describe the stiffening of the paint. A second model is a particle based dynamical simulation where latex particles form a network through which water particles move. A thin slab of the suspension in a threedimensional box is studied. Water particles escaping the slab at the surface represent the drying, progressing network formation the stiffening of the paint. Both models allow for validation with material properties as determined experimentally on real coatings.