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6th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE 2016), Magdeburg, Germany, 9-12 October 2016

We developed a multiscale model to bridge neuropeptide receptor-activated signaling pathway activity with membrane electrophysiology. Typically, the neuromodulation of biochemical signaling and biophysics have been investigated separately in modeling studies. We studied the effects of Angiotensin II (AngII) on neuronal excitability changes mediated by signaling dynamics and downstream phosphorylation of ion channels. Experiments have shown that AngII binding to the AngII receptor type-1 elicits baseline-dependent regulation of cytosolic Ca2+ signaling. Our model simulations revealed a baseline Ca2+-dependent response to AngII receptor type-1 activation by AngII. Consistent with experimental observations, AngII evoked a rise in Ca2+ when starting at a low baseline Ca2+ level, and a decrease in Ca2+ when starting at a higher baseline. Our analysis predicted that the kinetics of Ca2+ transport into the endoplasmic reticulum play a critical role in shaping the Ca2+ response. The Ca2+ baseline also influenced the AngII-induced excitability changes such that lower Ca2+ levels were associated with a larger firing rate increase. We examined the relative contributions of signaling kinases protein kinase C and Ca2+/Calmodulin-dependent protein kinase II to AngII-mediated excitability changes by simulating activity blockade individually and in combination. We found that protein kinase C selectively controlled firing rate adaptation whereas Ca2+/Calmodulin-dependent protein kinase II induced a delayed effect on the firing rate increase. We tested whether signaling kinetics were necessary for the dynamic effects of AngII on excitability by simulating three scenarios of AngII-mediated KDR channel phosphorylation: (1), an increased steady state; (2), a step-change increase; and (3), dynamic modulation. Our results revealed that the kinetics emerging from neuromodulatory activation of the signaling network were required to account for the dynamical changes in excitability. In summary, our integrated multiscale model provides, to our knowledge, a new approach for quantitative investigation of neuromodulatory effects on signaling and electrophysiology. National Institute of General Medical Sciences National Heart, Lung, and Blood Institute

Introduction Breast cancer is the most common cancer among women affecting about 1 in 8 women during their lifetime. In most cases, the treatment is surgery combined with chemotherapy such as anthracyclines, including Doxorubicin. Unfortunately, the chemotherapy is only working for 25% to 50% of the patients showing a need to predict the patient’s response to the treatment. Chemotherapeutic drugs are known to activate apoptosis via the activation of JNK, p38 and p53 pathway. However, little is known about the interaction between these pathways and how the drugs activate them. My hypothesis is that dynamic behaviour and network interactions between JNK//p38 and p53 confer drug (in-)sensitivity and resistance. To address this problem, my project merges molecular and computational approaches to answer these two questions: • What are the activation dynamics and underlying network interactions? • Can a mathematical model of this network predict drug-responses? Material and methods To study the mechanism of action of Doxorubicin, I compared MCF10A cells, a non-cancerous cells used as a control, with five different breast cancer cell lines. The level of cell death was measured via flow cytometry after 1 µM of Doxorubicin treatment. In parallel, the cells’ molecular response to the treatment was assessed by monitoring phosphorylation of JNK and p38, and the total levels of p53 via Western blots after 1 µM of Doxorubicin treatment. Results and discussions Comparing the above pathways in MCF10A and T47D identified differences on two levels: network connectivity and activation dynamics. Currently I am constructing a mathematical model using ordinary differential equations (ODE) to test whether the identified network structures can explain network activation dynamics and drug responses. This predictive model will be validated using mammospheres and breast cancer tumour samples. Conclusion Modelling pathway interactions has already revealed correlation between the experimental data (Western blots) and the simulated outcome of Doxorubicin treatment in MCF10A cells. The next step is to explain the differential pathway connexions and dynamics in the various cell lines with different mutation pattern by using my mathematical model. By doing so, I hope to predict treatment response of other breast cancer cell lines, and ultimately patients, to develop a personalised treatment strategy.

Introduction Neuroblastoma (NB) is the most common solid tumour in children under the age of five and it is responsible for 15% of paediatric cancer deaths. MYCN amplification represents the most frequent genetic alterations in high-risk NB and it is associated with poor prognosis. Our aim was to identify vulnerable, therapeutically targetable nodes that function as critical regulators or effectors of MYCN in neuroblastoma by using an integrative ‘-omics’ approach. Material and methods We used a panel of NB cell lines (SY5Y: non-amplified; NBLS: MYCN overexpression from a single gene copy; KCN, KCNR, Be2C: MYCN-amplified). Interaction proteomics was performed by immunoprecipitating MYCN protein complexes (Co-IP) followed by quantitative label-free mass spectrometry (Q-exactive and MaxQuant). Transcriptomic analysis of total RNA was performed by real-time PCR on an ABI Prism 7700 System (Applied Biosistems). Results and discussions MYCN interaction proteomics revealed that MYCN is physically interacting with TFAM, a mitochondrial transcription factor. Therefore, we demonstrated that MYCN is present in the mitochondria of NB cells by Western blot and confocal microscopy (MYCN-Alexa488 and MitoTracker-Red-CMXRos staining), and that MYCN may repress or activate mitochondrial genes in cells rendered MYCN deficient by siRNA. Moreover, we found that certain mitochondrial genes are downregulated in patients with MYCN-amplified neuroblastoma, and they correlate with poor patient survival (Kaplan-Meier analysis of 709 patients). In addition, we performed a high throughput drug screening (~4000 compounds) and we found that diphenyleneiodonium chloride (DPI) inhibits the viability of MYCN-expressing NB cells. Interestingly, DPI significantly downregulated MYCN expression in the mitochondria of NB cells, and in turn, modulated mitochondrial gene expression. Moreover, DPI treatment resulted in a mitochondrial superoxide-mediated apoptosis in MYCN-amplified cells. In addition, soft agar colony formation assay demonstrated the tumour suppressive effects of DPI, and NB zebrafish models showed that treatment with DPI significantly reduced the neuroblastoma tumour size in vivo. Conclusion In summary, we found a vulnerable node in the MYCN interactome and we demonstrated that DPI was able to reduce the MYCN level in the mitochondria of NB cells, to induce a ROS-mediated apoptosis in MYCN-amplified, and to reduce the size of the tumour in vivo. Therefore, DPI might serve as a potential novel drug to treat MYCN-amplified NB.

Estimation of kinetic parameters is a key step in modelling, as direct measurements are often expensive, time-consuming or even infeasible. The class of dynamic models in polynomial form is particularly relevant in systems biology and biochemical engineering, as those models naturally arise from modelling biochemical reactions using for instance mass action, Michaelis-Menten or Hill kinetics. Often the parameters are not uniquely identifiable for a given model structure and measurement set. Thus the question of which parameters are consistent or inconsistent with the data arises naturally. Here we present a method capable of proving inconsistency of entire parameter regions with the data. Based on the polynomial representation of the system, we formulate a feasibility problem that can be solved efficiently by semi-definite programming. The feasibility problem allows us to check consistency of entire parameter regions by using upper and lower bounds on the parameters. This drastically limits the search space for subsequent parameter estimation methods. In contrast to similar approaches in the literature, the here presented approach does not require a steady state assumption. Measurements at discrete time points are used, but neither regular sampling intervals, nor a time discretisation of the system is required. Measurement uncertainties are dealt with using upper and lower bounds on the measured states.

Estimation of kinetic parameters is a key step in modelling biochemical reaction networks as, often, their direct estimation is expensive, time-consuming or even infeasible. This article proposes a parameter estimation procedure, which explicitly takes into account the model structure of the biological systems. The convergence is guaranteed using a dissipativity argument and a coordinate transformation yielding a parameter-free system description. The application to a basic enzyme kinetic model illustrates the proposed methodology.

An essential part of mathematical modelling is the accurate and reliable estimation of model parameters. In biology, the required parameters are particularly difficult to measure due to either shortcomings of the measurement technology or a lack of direct measurements. In both cases, parameters must be estimated from indirect measurements, usually in the form of time-series data. Here, we present a novel approach for parameter estimation that is particularly tailored to biological models consisting of nonlinear ordinary differential equations. By assuming specific types of nonlinearities common in biology, resulting from generalised mass action, Hill kinetics and products thereof, we can take a three step approach: (1) transform the identification into an observer problem using a suitable model extension that decouples the estimation of non-measured states from the parameters; (2) reconstruct all extended states using suitable nonlinear observers; (3) estimate the parameters using the reconstructed states. The actual estimation of the parameters is based on the intrinsic dependencies of the extended states arising from the definitions of the extended variables. An important advantage of the proposed method is that it allows to identify suitable measurements and/or model structures for which the parameters can be estimated. Furthermore, the proposed identification approach is generally applicable to models of metabolic networks, signal transduction and gene regulation.