Abstracts

Opening Keynote Lecture: Network Medicine: From Cellular Networks to the Human Diseasome

Albert-László Barabási

Center of Complex Networks Research, Northeastern University and Department of Medicine, Harvard University, Boston, MA, USA.

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction are essential to the modeling of cellular systems, to identify new disease genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases.

The Gene Regulatory Logic of Sonic Hedgehog Morphogen Signaling

James Briscoe

NIMR, London, UK.

Secreted signals known as morphogens provide the positional information that organizes gene expression and cellular differentiation in many developing tissues. In the embryonic neural tube, Sonic Hedgehog (Shh) acts as a morphogen to control the pattern of neuronal subtype specification. A temporally and spatially changing gradient of Shh signaling determines the pattern of gene expression in neural progenitors. This dynamic signaling gradient is interpreted by the regulatory logic of a downstream transcriptional network that links three transcription factors to Shh signaling. Strikingly, the design of the network, not differential sensitivity of target genes to Shh signaling, is responsible for positioning gene expression boundaries. In addition, the network renders cells insensitive to fluctuations in signaling and confers hysteresis - memory of the signal. These data suggest that morphogen interpretation is an emergent property of the architecture of a transcriptional network that provides robustness and reliability to tissue patterning.

A methodological framework for multiscale and multiscience modeling

Bastien Chopard

Computer Science Department, University of Geneva, Switwerland.

Multiscale and multiscience problems are a challenge for computational science. Real systems are composed of several processes that interact across a huge range of scales, making direct numerical simulations intractable. This is for instance the case when considering biomedical systems in which mechanical processes are coupled with biological ones, spanning spatial scales from microns to centimeters and time scales from tenth of seconds to months. Although there is a large body of work dealing with multiscale applications, very few papers consider the methodological aspects. We propose a theoretical framework to design, run and analyze multiscale applications. We illustrate the approach with several examples.

Engineering Synthetic Mammalian Gene Networks – From Tools to Therapies

Martin Fussenegger

ETH Zurich, Department of Biosystems Science and Engineering (D-BSSE), Basel, Switzerland.

Capitalizing on our latest advances in the design of heterologous mammalian transgene control systems we have designed the first prosthetic networks that sense, monitor and score (disease-) relevant metabolites, process off-level concentrations and coordinate adjusted diagnostic, preventive or therapeutic responses in a seamless, automatic and self-sufficient manner. We believe that the design of synthetic gene networks, which process molecular signals with near digital precision, may provide novel therapeutic opportunities. Highlights will include our proof-of-concept studies on prosthetic networks improving artificial insemination and enabling the treatment of gout.

Genetic clocks from engineered oscillators

Jeff Hasty

Departments of Molecular Biology and Bioengineering BioCircuits Institute University of California, San Diego, CA, USA.

One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to design specs generated from computational modeling. This has resulted in the construction of several fundamental gene circuits, such as toggle switches and oscillators, which have been applied in novel contexts such as triggered biolm development and cellular population control. In this talk, I will rst describe an engineered genetic oscillator in Escherichia coli that is fast, robust, and persistent, with tunable oscillatory periods as fast as 13 minutes. This oscillator was designed using a previously modeled network architecture comprising linked positive and negative feedback loops. Experiments show remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude uorescence oscillations throughout observation runs. The period of oscillation can be tuned by altering inducer levels. Computational modeling reveals that the key design principle for constructing a robust oscillator is a small time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. I will then describe an engineered network with global intercellular coupling that is capable of generating synchronized oscillations in a growing population of cells. The network is based on the interaction of two quorum sensing genes; luxI, which produces an intercellular transcriptional activator (AHL, acyl-homoserine lactone), and aiiA, which degrades AHL intracellularly. Microfluidic devices tailored for cellular populations at differing length scales are used to demonstrate collective synchronization properties along with spatiotemporal waves occurring on millimeter scales. The period of the bulk oscillations ranges from 55-90 minutes, depending on the eective degradation rate of the AHL coupling molecule. In large monolayer colonies of cells, the time scale for the diffusive coupling of AHL is characterized by wavefront velocities that range from 8-30 microns/min.

Computational Science versus Computer Science.

Thomas A. Henzinger

IST, Vienna, Austria.

Computational science is an analytic science, which uses computation to make sense of given artifacts, such as data produced by experiments and equations produced by theories.  Computer science is a synthetic science, which builds artifacts that compute, including abstract artifacts such as algorithms and models of computation, and concrete artifacts such as hardware and software.  Among the main organizing principles of computer science are execution, composition, and abstraction. They address the questions of "What is an atomic unit of computation?" (the switching of a transistor? the execution of a Java instruction? the query of a database?) and how complex systems can be built from such atomic units. We argue that fundamental ideas from computer science - including execution, composition, and abstraction - can contribute also to the understanding of biological systems.

A Multiscale Model for In-Stent Restenosis

Alfons G. Hoekstra

University of Amsterdam, The Netherlands.

In-Stent Restenosis (ISR) is a complication that can arise after treating stenosed coronary arteries by balloon angioplasty and stenting. It is due to a Neo-Intimal Hyperplasia (NIH) following stenting. Following the Complex Automata methodology, an extended multiscale model for ISR will be described, with some emphasis on the most relevant single scale processes. Details of an agent-based model for the behavior of vascular Smooth Muscle Cells and endothelial cells will be discussed, as well as an alternative based on the Cellular Potts Model. Bloodflow is modelled using the Lattice Boltzmann Method. Results based on a two-dimensional implementation of the multiscale ISR model will be presented, and compared with histological porcine data. The current model reproduces experimental data in a qualitative way. The lecture will be concluded with a discussion on possible future directions, where input of the audience is highly appreciated.

Multiscale algorithms for deterministic and stochastic simulations of biological systems

Petros Koumoutsakos

ETHZ, Zürich, Switzerland.

A number of biological and bioengineered systems exhibit multiple spatial and temporal scales. In this talk, I will present work in adaptive mesh refinement and multiresolution algorithms for the simulation of spatially developing systems, accelerated simulations of stochastic systems and the coupling of atomistic and continuum simulations. Applications will include model biological and bioengineered systems.

Multiscale modeling of blood cells in health and disease

Igor V. Pivkin

University of Lugano, Switzerland.

The membrane properties of red blood cells (RBCs) control the cell's ability to deform as they flow through capillaries of squeeze through the narrow interendothelial slits of the venous sinus wall in the spleen. The importance of understanding the mechanical properties of RBCs has motivated a number of experimental, theoretical and numerical studies. We developed a general multiscale framework for the three-dimensional computational modeling of RBCs using Dissipative Particle Dynamics (DPD). Comparison of simulation results with quantitative experiments from different sources will be presented.

Multiscale cardiac contraction modeling: Think locally and act globally

John Jeremy Rice

IBM T.J. Watson Research Center, NY, USA.

The contraction of cardiac cells depends on the fundament molecular events at the subcellular level. The subcellular structures including molecular motors, sacromeres and myofilaments produce behaviors that manifest at the whole organ level. As with other problems in biology, the mapping from the molecular level to organ level requires some methods of abstraction and approximation to produce tractable models for simulations. Said another way, one can not simulate from molecules to organ level using first principles, so multiscale techniques are needed that are more computationally efficient. The talk will cover methods to bridge from subcellular to cellular to organ levels in mechanical cardiac models. Some examples will demonstrate how the subcellular properties can reflect up to organ-level phenomena.

Application of multi-scale models in drug development

Birgit Schoeberl

Merrimack Pharmaceuticals, Boston, MA, USA.

We will discuss the application of multi-scale computational models to drug development. We will show examples of how modeling can be used to identify patients who are likely to respond or who may experience adverse effects. Our first example highlights the importance of understanding pathway signaling in clinical practice. We explain the reported paradoxical activation of phospho-ERK by specific B-RAF kinase inhibitors by implementing the proposed mechanism into a computational model of the RAF/MEK/ERK pathway: wild-type RAF kinase activity can be activated by RAF dimerization and dimerization can be induced by B-RAF specific inhibitors and or other priming events like ligand activation or KRAS mutation. Furthermore, we demonstrate how these mechanistic biochemical models can be used to identify pathway intrinsic drug resistance mechanisms. In our second example, we will show how coupled mechanistic biochemical models with pharmacokinetic models can be used to design and engineer novel therapies.

In-silico Organogenesis: Multiscale modeling of limb development

James Sharpe

EMBL-CRG, Barcelona, Spain.

Understanding the dynamics of biological regulatory networks is a primary challenge for systems biology, and much focus is currently placed on modelling single-cell networks, such as gene regulatory networks (GRNs), signal transduction pathways and metabolic systems. However, understanding complex organisms requires addressing tissue-level phenomena, which will involve the extra complexity of multicellular organisation. Here I will describe our work to transform a classical model of developmental biology – the developing vertebrate limb bud – into a quantitative model system for systems biology and multiscale modeling. In particular, we have developed: (a) 3D and 4D quantitative data-capture tools [1], and (b) new multiscale simulation software. This integrated approach is allowing us to combine two of the primary questions of organogenesis: mechanical morphogenesis (the active cellular movements which lead to shaping of the tissue [2,3]), and molecular patterning (the spatial regulatory networks which control gene expression - [3] and unpublished work).

[1] Boot et al. (2008) Nature Methods 5(7):609-12
[2] Boehm et al. (2010) PLoS Biology 8(7):e1000420
[3] Marcon et al. (2011) PLoS Computational Biology 7(2):e1001071

Gene regulation by MAPK substrate competition

Stanislav Y. Shvartsman

Princeton University, NJ, USA.

Developing tissues are patterned by coordinated action of signaling systems, which can be integrated by the regulatory region of a gene that binds multiple transcription factors or by a transcription factor that is modified by multiple enzymes. Based on a combination of genetic and imaging experiments in the early Drosophila embryo, we describe a signal integration mechanism that cannot be reduced to a single gene regulatory element or a single transcription factor. This mechanism relies on an enzymatic network formed by Mitogen Activated Protein Kinase (MAPK) and its substrates. Specifically, anteriorly localized MAPK substrates, such as Bicoid, antagonize MAPK-dependent downregulation of Capicua, a transcriptional repressor involved in gene regulation along the dorsoventral axis of the embryo. MAPK substrate competition provides a basis for ternary interaction of the anterior, dorsoventral, and terminal patterning systems.

Designing Biological Systems

Pamela A. Silver

Department of Systems Biology, Harvard Medical School and the Wyss Institute of Biologically Inspired Engineering, Harvard University, Boston, MA.

Biology presents us with an array of design principles. From studies of both simple and more complex systems, we understand some of the fundamentals of how nature works. We are interested in using the foundations of biology to engineer cells in a logical and predictable way to perform certain functions. By necessity, the predictable engineering of biology requires knowledge of quantitative behavior of individual cells and communities and the ability to construct reliable models. By building and analyzing synthetic systems, we learn more about the fundamentals of biological design as well as engineer useful living devices with myriad applications. For example, we are interested in building cells that can perform specific tasks, such as counting mitotic divisions and remembering past events thus acting as a biological computer. Moreover, we design cells with predictable biological properties that serve as cell-based sensors, factories for generating useful commodities and improved centers for carbon fixation. In doing so, we have made new findings about how cells interact with and impact on their environment.

Thomas Smith

Swiss Tropical and Public Health Institute, University of Basel, Switzerland.

The malaria parasite, Plasmodium falciparum, has a complex life-cycle including both human and mosquito stages. Malaria persists in erythrocytic stages for long periods, using a strategy of antigenic switching to evade the complex immunological reactions that are stimulated. A number of vaccines against malaria are at different stages of pre-clinical and clinical development and there is a need for models that can predict the likely impact of these on both infection dynamics and averting disease. To fulfil this need we have developed multiple simulation models both of the course of individual infections in humans, and of population-level epidemiological interactions between infections.

Largely to avoid oversimplification of the effects of immunity, we have used microsimulation approaches for population modeling. To reduce computational demands, our initial models represented individual infections with a statistical description of observed parasite densities over time. This leads to deviations from mass action principles, contributing to uncertainty in predictions of the effects of vaccines.

We present several improvements to this approach: (i) The use of volunteer computing to access the computational resources required for mechanistic models. (ii) Intelligent simplification of the representation of immunity to reduce storage requirements. (iii) Use of model ensembles to evaluate the sensitivity of predictions of public health outcomes to model structure.

The presentation will consider the implications of these different approaches for predictions of vaccine effects and their relevance for infectious disease modeling in general.

Coping with stress in a synthetic world

Lingchong You

Duke University, NC, USA.

A major focus of synthetic biology is the engineering of gene circuits to perform user-defined functions. In addition to generating systems of practical applications, such efforts have led to the identification and evaluation of design strategies that enable robust control of dynamics in single cells and in cell populations. On the other hand, there is an increasing emphasis on using engineered systems programmed by simple circuits to explore fundamental biological questions of broad significance. In this talk, I will discuss our efforts along this line of research, whereby we have used engineered gene circuits to examine the evolutionary dynamics of two common bacterial survival strategies in stress response: programmed death and cell-cell communication.