Contextualizing biomolecular circuit models for synthetic biology

Design automation in synthetic biology is hampered by insufficient accuracy of computational models of synthetic circuits. More specifically, traditional models ignore the molecular context of the host cell that interacts with a synthetic circuit in numerous ways. For instance, transcription factors of the circuit may get tethered away by unspecific binding sites on the host DNA. Hence, in order to finally realize a predictive in-silico design framework, as done in electronic design automation, computational models need to account for all context effects encountered by the circuits.
In this talk, I will provide an overview of different context effects and our formal attempts to accommodate some of them in computational models. In particular, we derive a stand-alone equivalent model of a circuit that behave as if the circuit is still embedded in the host cell. Such contextualized models also lead to new calibration algorithms on which I will briefly touch upon.


Heinz got his MSc in physics and his PhD in electrical engineering from Graz University of Technology, Austria. After postdoc positions at UC Berkeley and EPFL, he was appointed at ETH Zurich as an assistant professorship in 2010. In 2013 he additionally led the systems biology efforts at IBM Research Zurich. Since 2014, Heinz is full professor at the department of electrical engineering and at the department of biology of Technische Universitat Darmstadt, Germany. He received the Schrodinger fellowship (2005), the SNF professorship (2009), the IBM faculty award (2014) and an ERC consolidator grant (2017).
He is interested in probabilistic models for biomolecular circuits and their inference using single-cell measurements.

Heinz Koeppl

Department of Electrical Engineering and Information Technology
TU Darmstadt, Darmstadt, Germany

Designing Energy-Efficient and Robust Bio-inspired Wireless Sensor Networks

Many properties of biological and engineered networks are conserved due to the fundamental nature of communication. The interactions between genes constitute the gene regulatory networks (GRN) that contribute to the robustness and adaptability of biological systems. GRNs can be modeled as graphs, where the nodes represent the genes and the edges denote the biological processes of transcription and translation between gene pairs. GRN topologies, optimized through millions of years of evolution, possess certain graph properties that contribute to their intrinsic robustness despite environmental adversities. However, like most scale-free networks, GRNs are also vulnerable to failures.
Leveraging on the principles of robustness of genetic interactions, this keynote talk will aim to design energy-efficient and robust wireless sensor networks (WSN) based on a novel mapping that transfers key structural properties, such as motif abundance, small-world property, low graph density and resilience of GRNs to the mapped WSNs. We will also develop an edge rewiring mechanism in the GRN graph to remedy its vulnerability to failures, and show that the rewired GRNs preserve optimal number of feed forward loop motifs that contribute to the topological robustness of GRNs. Our analysis and experiments will demonstrate that (dynamically) rewired E. coli and Yeast GRNs are robust against random and targeted failures, and yield better packet delivery and network latency in the mapped WSNs. The talk will be concluded with directions for future research.


Dr. Sajal K. Das, an IEEE Fellow, is the Daniel St. Clair Endowed Chair of Computer Science at Missouri University of Science and Technology, Rolla where he was the Chair of Computer Science Department during 2013-2017. He served the NSF as a Program Director in CISE/CNS division during 2008-2011. Prior to 2013, he was a University Distinguished Scholar Professor of Computer Science and Engineering and founding director of the Center for Research in Wireless Mobility and Networking (CReWMaN) at the University of Texas at Arlington.
His current research interests include security and privacy, wireless sensor networks, mobile and pervasive computing, cyber-physical systems, smart environments (including smart grid and smart healthcare), distributed and cloud computing, systems biology, and social networks.

Sajal K. Das

Computer Science department
Missouri University of Science and Technology, Rolla, Missouri, US

How do (stem) cells compute?

Cellular decision-making – whether it’s the decision to divide, die, migrate or differentiate - arises as the result of molecular level computation. Uncovering this information-processing is the central challenge in each domain of biology, and part of the challenge is to determine what levels of abstraction lead to explanatory and predictive models of the dynamic processes governing cellular behaviour.
In this talk, an approach to understand biological information-processing in stem cell biology will be discussed. The framework of logical models and the power of automated formal reasoning is exploited to generate a predictive understanding of the biological programs that govern stem cell decision-making at the earliest stages of development. Furthermore, it will be illustrated how this approach has generated insight into how fate-restricted cells can be ‘reprogrammed’ to the embryonic stem-like state.


Sara-Jane Dunn is a Scientist at Microsoft Research, Cambridge. She studied Mathematics at the University of Oxford, graduating with a MMath in 2007. She remained in Oxford for her doctoral research, as part of the Computational Biology group at the Department of Computer Science. In 2012, she joined Microsoft Research as a postdoctoral researcher, before transitioning to a permanent Scientist role in 2014. In 2016, she was invited to become an Affiliate Researcher of the Wellcome Trust-Medical Research Council Stem Cell Institute, University of Cambridge. Her research focuses on uncovering the fundamental principles of biological information-processing, particularly investigating decision-making in Development.

Sara-Jane Dunn

Biological Computation group
Microsoft Research Lab, Cambridge, UK


Laboratory construction of cell-like systems based on the incorporation of biomolecules inside lipid vesicles

A central topic in bottom-up synthetic biology

About 20 years ago scientists interested in origins of life [1] started an exciting new research program specifically designed for designing and constructing a sort of “synthetic cells” based on the incorporation (encapsulation) of biological macromolecules inside lipid vesicles.
The pioneer work was mainly developed to answer the question “what is life?” and inspired by lack of knowledge about the early self-organization steps that led to the emergence of cells on Earth.

On the other hand, such an approach was soon recognized as highly relevant for the nascent field of synthetic biology [2], because it can be applied to construct cell-like systems under the programmable-orthogonal-modular-bottom up paradigm that is typical of synthetic biology.

Today, several groups are involved in this research arena, which has advanced significantly, and it is based on the convergence of four main concepts: (a) liposome technology, (b) cell-free systems, (c) microfluidics, (d) numerical modelling.

In this tutorial we will present the exciting topic of synthetic cell construction from historical (30%), technological (60%), and epistemological (10%) viewpoints. The approaches of the main groups working on this topic will be presented and commented. Ideally, the tutorial will provide sufficient knowledge to appreciate the state-of-the-art and devise future applications, with some emphasis on the use of synthetic cells as bio-chem ITs tools [3].


[1] Luisi, P. L. Toward the Engineering of Minimal Living Cells. Anat. Rec. 2002, 268, 208–214.
[2] Luisi, P. L.; Ferri, F.; Stano, P. Approaches to Semi-Synthetic Minimal Cells: A Review. Naturwissenschaften 2006, 93, 1–13.
[3] Stano, P.; Rampioni, G.; Carrara, P.; Damiano, L.; Leoni, L.; Luisi, P. L. Semi-Synthetic Minimal Cells as a Tool for Biochemical ICT. BioSystems 2012, 109, 24–34.

Pasquale Stano

Organic Chemistry Laboratory
Department of Biological and Environmental Sciences and Technologies (DiSTeBA)
University of Salento, Lecce, Italy

Fluid Dynamic Aspects of Molecular Communications

In this tutorial, we want to show experimental setup and results for understanding molecular communications in realistic fluid dynamic environments, especially at the macroscopic scale where the effects of turbulence and dynamic densities come into play. We will show what experimental apparatus is required to observe these effects, how to extract data using particle image velocimetry (PIV), the important fluid dynamic scenarios and regimes relevant to industry, and how much it will cost to conduct experiments. We will aim to show how dynamic distributions in information molecules map to established fluid dynamic knowledge as well as the achievable information rate. This knowledge in general can help multi-disciplinary research, and reduce uncertainty when combining communication theory and fluid dynamics.


Dr. Weisi Guo is an Associate Professor at the University of Warwick and a Turing Fellow. He heads the Data Embedded Networks Lab and leads 12 researchers to make new discoveries in network science and nano-scale communications.

Weisi Guo

Data Embedded Networks (DEN) Lab
University of Warwick, Coventry, UK

Technical talks

Neurocommunications & calcium signaling

An information-theoretical framework for studying the specificity of calcium signaling

Teresa Vaz Martins and Richard Morris

Neurotherapy using Ultrasound Activated Nanodevices

Michael Donohoe, Brendan Jennings and Sasitharan Balasubramaniam

Towards the Brain Information Capacity

Mladen Veletic and Ilangko Balasingham

Receiver models

Modeling a Fully-absorbing Receiver for Magnetic Nanoparticles

Wayan Wicke, Arman Ahmadzadeh and Robert Schober

Impulse Response of 3-D Molecular Communication via Diffusion and Flow Channel with an Absorbing Receiver

Bayram Cevdet Akdeniz, H.Birkan Yilmaz, Ali Emre Pusane and Tuna Tugcu

Metric Combinations in Non-coherent Signal Detection for Molecular Communication

Shenghan Liu, Bin Li, Weisi Guo and Chenglin Zhao

Transport models

Modeling of Transport Processes in Bounded Domains

Maximilian Schäfer and Rudolf Rabenstein

Toward molecular communication between gated nanodevices

Antoni Llopis-Lorente, Beatriz de Luis, Elena Aznar, Félix Sancenón, Reynaldo Villalonga and Ramón Martínez-Máñez

Unimolecular FRET Sensors: Simple Linker Designs and Properties

Shourjya Sanyal, David F. Coker and Donal Mackernan

Experimentation oriented research

Towards Droplet on Demand for Microfluidic Networks

Medina Hamidovic, Werner Haselmayr, Andreas Grimmer and Robert Wille

The first Cell Phone in Molecular Communications: A Pilot Model of a Biological Transmitter

Laura Grebenstein, Jens Kirchner, Renata Stavracakis-Peixoto, Robert Schober and Andreas Burkovski

Modeling interactions with biological cells

Exploring molecular communication between synthetic and biological cells

Giordano Rampioni, Livia Leoni, Fabio Mavelli, Luisa Damiano and Pasquale Stano

Characterizing Glutamatergic Synapse of a Diffusion-based Molecular Communication Channel to Measure Aßeta Oriented to Alzheimer’s Disease

Simon Assaf, Josep Solé-Pareta and Eduard Alarcón

System-wide design & modeling

Bio-Receptor Clustering for Cooperative Molecular Communications

Zhuangkun Wei, Weisi Guo, Bin Li and Chenglin Zhao

A Design Process for Molecular Communication Systems Based on Biological Circuits in Cells

Colton Harper, Massimiliano Pierobon and Maurizio Magarini

Cognitive Molecular Communication

Malcom Egan, Trung Q. Duong, Marco Di Renzo, Jean-Marie Gorce, Ido Nevat and Valeria Loscri

Simulation models

Voxel-based Solver for Diffusion-based Molecular Communications

H. Birkan Yilmaz and Ilker Demirkol

Empirical Turbulent Diffusion Channel Model for Molecular Communications

Mahmoud Abbaszadeh, Peter Thomas and Weisi Guo

Fast Collision Detection for Nanosimulators

Pieter Stroobant, Luca Felicetti, Wouter Tavernier, Didier Colle, Mauro Femminella, Gianluca Reali and Mario Pickavet