ICMS

Oct 15, 2010

Organisers

Name	Institution
Tindall, Marcus	University of Reading

The application of mathematical modelling to biomedical areas has increased substantially over the past two decades. Such application has the long term goal of helping biomedical researchers and clinicians to understand complex biological problems which will ultimately improve the healthcare of patients.

This satellite workshop of the International Conference on Systems Biology (ICSB) 2010 will focus on the application of mathematical modelling to understanding biomedical problems. Examples include, but are not exclusive to, the modelling of tissue, tumour modelling, improving the function of medical implants and devices and developing new methods for drug delivery. Work clearly demonstrating the insight that mathematical modelling brings to problems in the biomedical sciences has been particularly encouraged.

The workshop is open to established academics, industry and young researchers and we have especially welcomed abstracts from PhD students.

The final programme is below and abstracts will be added next week.

This workshop is now full - after the event the presentations will be available to view from this webpage.

 

Arrangements

Location: Room Carrick 2&3, Edinburgh International Conference Centre Ltd, The Exchange, 150 Morrison Street, Edinburgh, EH3 8EE

Link of how to get to the EICC: http;//ww.eicc.co.uk/attending_an_event/how_to_get_here/

On arrival please proceed to the Carrick room for registration

 

Programme

8.30-9.00  Registration

9.00-9.05  Welcome and Introduction

9.05-9.25  Mathematical models of some signalling pathways regulating cell survival and death
  (Tongli Zhang, University of Oxford)

9.25-9.45  Interferon gamma stimulated STAT1 signalling in pancreatic stellate cells:  From experimental data to mathematical models
  (Katja Rateitschak, University of Rostock)

9.45-10.05 Modelling mosaic tissues, with applications to Blaschko lines
  (Jenny Bloomfield, Heriot-Watt University) 

10.05-10.25 A systems biology approach to explaining the logic of dorsal-ventral patterning of progenitors in the vertebrate neural tube 
  (Jasmina Panovska-Griffiths, University College London) download presentation

10.25-11.00 Coffee

11.00-11.20  Determining conditions for rebound in target mediated drug disposition
  (Philip Aston, University of Surrey) 

11.20-11.40  Modelling early initiation processes in smoking-induced lung adenocarcinomas
  (Gregory Vuillaume, Philip Morris International Research and Development

11.40-12.00  Mathematical modelling of fat metabolism
  (Marcus Tindall, University of Reading) download presentation 

12.00-12.20  Compact energy metabolism model: brain controlled energy supply
  (Britta Goebel, University of Luebeck) download presentation

12.20-12.30  Summary and thanks

 

 

Presentations:

Presentation Details
Aston, Philip
Determining Conditions for Rebound in Target Mediated Drug Disposition
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We consider TMDD models in which single (or multiple) IV bolus dosing of a drug is used to reduce the levels of a target receptor. Once dosing ceases, rebound occurs if receptor levels exceed pre-dose baseline levels. A simple model consisting of a drug combining reversibly with a target receptor to form a drug-receptor complex is considered. The baseline level of the receptor is the steady state of the model differential equations and the rebound problem is studied by considering the approach of trajectories to this steady state. A variety of different mathematical methods are used to show that the region of parameter space in which rebound occurs can be described in terms of two inequalities involving only the elimination rates of the drug, the receptor and the complex. These results are then generalised to a higher dimensional model which describes the effect of efalizumab on patients with psoriasis in which rebound has been observed experimentally and it is shown that the model parameters satisfy the predicted conditions for rebound to occur. With: Gianne Derks, Adewale Raji, Balaji M. Agoram and Piet H. Van Der Graaf.
Bloomfield, Jenny
Modelling mosaic tissues, with applications to Blaschko lines
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Mosaic tissues are composed of two or more genetically distinct cell types. They are useful experimentally for exploring tissue growth and maintenance; by marking the different cell types, one can study the patterns formed by proliferation, renewal and migration. In this talk I will present a continuous integro-partial differentiation model which shows that small changes in the type of interaction that cells have with their local cellular environment can lead to very different outcomes in the composition of mosaics. The model has applications to the understanding of pathological conditions which present in the skin along so-called Blaschko lines; these are thought to demarcate the boundaries of different proliferating cell clones. I will end the talk by discussing this biomedical application. With: Kevin Painter and Jonathan Sherratt.
Goebel, Britta
Compact energy metabolism model: brain controlled energy supply
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The regulation of human energy metabolism is crucial to ensure the functionality of the entire organism. The decisive role of the brain as an active controller and heavy consumer in the complex whole body energy metabolism is subject to recent research activities. The latest studies suggest that brain energy supply takes priority in the competition for energy within the organism. We have developed and investigated a model, which describes the energy metabolism in a novel and compact dynamical system. The model takes into account the two central roles of the brain in the energy metabolism, as consumer and superior regulatory instance. As one novel characteristic, insulin is regarded as a central feedback signal of the brain. Consequently, our model contains the competition for energy between the brain and the body periphery. The model realistically reproduces the qualitative and quantitative behavior of the energy metabolism. Short time observations demonstrate the physiological periodic food intake. Integration over the daily cycle yields a stable long-term model in accordance with the homeostatic regulation of the energy metabolism on a long time scale. The presented model is a step towards a systemic understanding of the energy metabolism and thus sheds light onto deregulations causing metabolic diseases as diabetes mellitus.
Panovska-Griffiths, Jasmina
A systems biology approach to explaining the logic of dorsal-ventral patterning
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A major challenge in developmental biology is to understand the mechanisms of pattern formation. Secreted signals, known as morphogens, provide the positional information that organizes gene expression and cellular differentiation in many developing tissues. The current belief is that these morphogens induce cell differentiations in a concentration-dependent manner. Previous results by our group have suggested that signal duration as well as concentration are important for this cell patterning. Furthermore, the transcriptional network regulated by the morphogen has also been implicated in determining cellular responses. In this work we study how the morphogen, Sonic Hedgehog (Shh), controls pattern formation in the vertebrate central nervous system. We use mathematical modelling and in vivo experimental resuls to provide evidence that morphogen signalling is dynamic concept interpreted by a transcriptional regulatory circuit that links Shh signalling to three transcription factors. Our results show that the design of this circuit unifies the temporal and graded response to Shh signalling. In addition, we show that the cells confer hysteresis i.e. memory of the signal. Together, these data indicate that the morphogen response of neural cells to Shh is an emergent property of the architecture of the transcriptional circuit. Our results also infer that the conceptual simplicity of the circuit could represent a general strategy for processing positional information encoded by morphogen gradients. With: Karen Page and James Briscoe.
Rateitschak, Katja
Interferon gamma stimulated STAT1 signalling in pancreatic stellate cells: From experimental data to mathematical models
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Pancreatic fibrosis plays an active role in pancreatic cancer progression and is linked to the activation of pancreatic stellate cells (PSC). Previous studies have shown that interferons exert inhibitory effects on PSC activation [1]. We have established a mathematical model describing the dynamics of the interferon-gamma stimulated STAT1 signalling pathway on the basis of quantitative temporal data from pancreatic stellate cell lines [2]. The parameters of our differential equation model were estimated on the basis of quantitative time series for Stat1 expression, phosphorylation and for Socs1 mRNA expression. We have performed an identifiability analysis of the model parameters according to the method described in [3]. We present our results for the initial profile likelihood and alterations of the profile likelihood after simplifying the model and taking into account additional data. Next we discuss rate limiting steps and show first results from a sensitivity analysis. Finally we compare our model predictions for different restimulation scenarios with respective experimental time series. Ultimate goal of our studies is the optimisation of interferon-gamma treatment in pancreatic fibrosis. [1] Baumert, J.T. et al. (2006) World J. Gastroenterol. 12:896. [2] Rateitschak, K. et al. (2010) Cellular Signalling 22:97. [3] Raue, A. et al. (2009) Bioinformatics 25:1923.
Tindall, Marcus
Mathematical modelling of fat metabolism
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This talk will focus on the development and application of mathematical models in lipoprotein metabolism. Mathematical modelling is a useful tool for understanding complex biological systems. It can aid in directing experimental work and lead to new insight into such systems, not obvious from conventional approaches. In the past few years we have begun to develop ordinary and partial differential equation models of both lipoprotein endocytosis in hepatocytes and the VLDL to LDL delipidation pathway, including the role of CETP. Recent work has focused on incorporating models of the genetic regulation of receptor and cholesterol production by hepatocytes to produce a more informed model of lipoprotein endocytosis. These models have been parameterised with data from the literature along with experimental work undertaken by our experimental colleagues. This talk will provide an overview of our work and discuss future directions for it.
Vuillaume, Grégory
Modeling Early Initiation Processes in Smoking-Induced Lung Adenocarcinomas
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Smoking induces a variety of poorly understood complex molecular and physiological changes in the lungs of smokers, with adenocarcinomas sometimes being a disease outcome. A better understanding of these complex interactions is clearly needed. Mathematical modeling is a powerful approach to bringing together diverse information including high-throughput systems biology data and targeted endpoints and biomarkers measured in in vitro, in vivo, and clinical studies. The objective is to develop an integrative, central mechanistic hypothesis of the early initiation processes of the development of adenocarcinomas in the lung and to build a plausible, calibrated model that includes most of the major phenomenology. There are three major steps in the development of this computational model. First, the key biological behaviors are identified which distinguish the earliest transition from normal cells (prior to smoke exposure) to neoplastic cells. From these considerations, it is possible to build a model that is neither too complex nor too simplistic, but that can accurately explain the effects of smoking. In a second step, the biological diagrams are translated to corresponding ordinary differential equations. A third step consists of the acquisition and analysis of quantitative biological data to calculate the rate equations. The data are evaluated under a rigorous set of criteria to determine whether or not they are within the scope of the modeling biology and pass the stringent quality criteria. A method has been developed for the translation of animal data (doses and ages) to the corresponding human situation. Finally, an aggressive optimization strategy is used to obtain a single set of parameters so that the model predictions simultaneously provide a good fit to all the data and accurately reproduce the key biological phenomena. The model is currently being built. The biology of the development of lung adenocarcinomas has been investigated and a phenomenological representation suitable for the mathematical challenges developed. Experience has been gained in determining what data are suitable for the surrogate strategy and in overcoming some of the poor data collection practices, e.g., using smoking pack-years instead of actually recording the amount of smoking and the duration of exposure. We will discuss the model building process, the value of a formal mathematical language for the expression of complex biological knowledge, assumptions, and hypotheses. There are also important lessons to be learned with respect to systems biology data requirements and collection practices.
Zhang, Tongli
Mathematical Models of Some Signalling Pathways Regulating Cell Survival and Death
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In a multi-cellular organism, cells constantly receive signals on their internal condition and surrounding environment. In response to various signals, cells proliferate, move around or even undergo suicide. The signal-response is controlled by complex molecular machinery, understanding of which is an important goal of basic molecular biological research. Such understanding is also valuable for clinical application, since lethal diseases like cancer show maladaptive responses to growth-regulating signals. Because the multiple feedbacks in the molecular regulatory machinery obscure cause-effect relations, it is hard to understand these control systems by intuition alone. Here we translate the molecular interactions into differential equations and recapture the cellular physiological properties with the help of numerical simulations and non-linear dynamical tools. The models address the physiological features of programmed cell death, the cell fate decision by p53 and the dynamics of the NF-?B control system. These models identify key molecular interactions responsible for the observed physiological properties, and they generate experimentally testable predictions to validate the assumptions made in the models.

Participants

Name	Institution
Aston, Philip	The University of Surrey
Bloomfield, Jenny	Heriot-Watt University
Bordage, Simon	University of Glasgow
Bridle, Helen	ICMS
Bystrykh, Leonid	UMCG
Cascante, Marta	University of Barcelona
Cisar, Petr	Institute of Physical Biology
De Meyts, Pierre	Novo Nordisk
Durzinsky, Markus	University of Magdeburg
Fages, Francois	Inria
Goebel, Britta	University of Luebeck, Institute of Mathematics
Gunawan, Rudiyanto	National University of Singapore
Hollmann, Susanne	The University of Potsdam
Hwang, Pei-Ing	Academia Sinica
Jabbari, Sara	The University of Nottingham
Jett, Marti	Walter Reed Army Institute of Research
Katzov-Eckert, Hagit	University of Sao Paulo
Kirouac, Daniel	MIT
Kuperstein, Inna	Institut Curie
Lampe, Paul-Henri	Bio-Modelling Systems
Lange, Falko	University of Rostock
Lau, Wei	University of California, Irvine
Lio, Pietro	University of Cambridge
Meruelo, Alejandro	University of California, Los Angeles
Mesrob, Lilia	Inserm
Messiha, Hanan	Manchester Centre for Integrative Systems Biology
Mina, Petros	University of Bristol
Mistry, Hitesh	AstraZeneca
Nair, Anil	Sanofi-Aventis
Nordling, Torbjorn	KTH
Ossareh, Hamid-Reza	University of Michigan
Panovska-Griffiths, Jasmina	UCL
Penalver Bernabe, Beatriz	Northwestern University
Perfahl, Holger	University of Stuttgart
Perkins, Theodore	Ottawa Hospital Research Institute
Pettersson, Sofia	Linkoping University
Proctor, Carole	Newcastle University
Rastergari, Ali Asghar	Islamic azad University, Falavarjan branch
Rateitschak, Katja	University of Rostock, Systems Biology and Bioinformatics
Reyes Palomares, Armando	University of Malaga
Rzhetsky, Andrey	University of Chicago
Santos, Cristina	University of North Carolina
Stanski, Donald	Novartis Pharma AG
Sung-Young, Shin	KAIST
Tindall, Marcus	University of Reading
Toma, Alina	University of Luebeck
Tondel, Kristin	Centre for Integrative Genetics
Vanlier, Joep	Eindhoven University of Technology
Vuillaume, Grégory	Philip Morris International R&D
Xu, Jun	PG
Yano, Kojiro	The University of Cambridge
Zartman, Jeremiah	University of Zurich
Zhang, Tongli	The University of Oxford