An introduction to Biomodel engineering, illustrated for signal transduction pathways

BioModel Engineering is the science of designing, constructing and analyzing computational models of biological systems. It is inspired by concepts from software engineering and computing science. This paper illustrates a major theme in BioModel Engineering, namely that identifying a quantitative model of a dynamic system means building the structure, finding an initial state, and parameter fitting. In our approach, the structure is obtained by piecewise construction of models from modular parts, the initial state is obtained by analysis of the structure and parameter fitting comprises determining the rate parameters of the kinetic equations. We illustrate this with an example in the area of intracellular signalling pathways

Coarse-Graining and Self-Dissimilarity of Complex Networks

Can complex engineered and biological networks be coarse-grained into smaller and more understandable versions in which each node represents an entire pattern in the original network? To address this, we define coarse-graining units (CGU) as connectivity patterns which can serve as the nodes of a coarse-grained network, and present algorithms to detect them. We use this approach to systematically reverse-engineer electronic circuits, forming understandable high-level maps from incomprehensible transistor wiring: first, a coarse-grained version in which each node is a gate made of several transistors is established. Then, the coarse-grained network is itself coarse-grained, resulting in a high-level blueprint in which each node is a circuit-module made of multiple gates. We apply our approach also to a mammalian protein-signaling network, to find a simplified coarse-grained network with three main signaling channels that correspond to cross-interacting MAP-kinase cascades. We find that both biological and electronic networks are 'self-dissimilar', with different network motifs found at each level. The present approach can be used to simplify a wide variety of directed and nondirected, natural and designed networks.Comment: 11 pages, 11 figure

Cycle time optimization by timing driven placement with simultaneous netlist transformations

We present new concepts to integrate logic synthesis and physical design. Our methodology uses general Boolean transformations as known from technology-independent synthesis, and a recursive bi-partitioning placement algorithm. In each partitioning step, the precision of the layout data increases. This allows effective guidance of the logic synthesis operations for cycle time optimization. An additional advantage of our approach is that no complicated layout corrections are needed when the netlist is changed

Synthetic Biology: A Bridge between Artificial and Natural Cells.

Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world