We present the design and deployment of a physical computing platform developed for a crossdisciplinary introduction to biology and computer science. Using the accessible Arduino interface as its foundation, students instantiate increasingly nuanced physical interactions with the environment. Biological and computational ideas receive equal attention through three layered projects that span fro...

In this text we will discuss different forms of randomness in Natural Sciences and present some recent results relating them. In finite processes, randomness differs in various theoretical context, or, to put it otherwise, there is no unifying notion of finite time randomness. In particular, we will introduce, classical (dynamical), quantum and algorithmic randomness. In physics, differing prob...

This paper discusses the interrelations between physics and biology. Particularly, we analyse the approaches for reconstructing the emergent properties of physical or biological systems. We propose approaches to scale emergence according to the degree of state-dependency of the system's component properties. Since the component properties of biological systems are state-dependent to a high exte...

As the historical trend of speed and energy efficiency improvements diminishes [2], radical departures from conventional approaches are becoming critical to improving the performance and energy efficiency of general-purpose processors. Inspired by biological nervous systems, neuromorphic computing delivers high performance within a very low power envelope. In fact, human brains can use only 20 ...

Modelling and computational simulation are crucial for the large-scale engineering of biological circuits since they allow the system under design to be simulated prior to implementation in vivo. To support automated, model-driven design it is desirable that in silico models are modular, composable and use standard formats. The synthetic biology design process typically involves the composition...

We believe that undergraduate biology students must acquire a foundational background in computing including how to formulate a computational problem; develop an algorithmic solution; implement their solution in software and then test, document and use their code to explore biological phenomena. Moreover, by learning these skills in the first year, students acquire a powerful tool set that they...

Biological systems can often be viewed as spatial computers: space-filling collections of computational devices with strongly localized communication. Applying a continuous-space abstraction allows the behavior of such systems to be modeled or specified in terms of aggregate geometry and information flow. This can simplify both the engineering of biological systems and the application of biolog...

The compartmental models using differential equations are basic models in epidemiology. The temporal evolution of spatial models for epidemic spreading is suitable for parallelization and GRID services are solutions for speeding the algorithms used in these models. We investigate several computational aspects of parallel algorithms used in cellular automata model and small world networks model....

DNA computing is an area of natural computing based on the idea that molecular biology processes can be used to perform arithmetic and logic operations on information encoded as DNA strands. The aim of this review is two-fold. First, we introduce the fundamentals of DNA computing, including basics of DNA structure and bio-operations, and two historically important DNA computing experiments. Sec...

Synthetic biology is an emerging research field, in which engineering principles are applied to natural, living systems. A major goal of synthetic biology is to harness the inherent “biological nanotechnology” of living cells for the purposes of computation, production, or diagnosis. As the field evolves, it is gradually developing from a singlecell approach (akin to using standalone computers)...