Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?

he potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting “molecular communication” (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, “bottom-up” SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT

A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?

The potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting "molecular communication" (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, "bottom-up" SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT

Evolution of Opposing Regulatory Interactions Underlies the Emergence of Eukaryotic Cell Cycle Checkpoints

In eukaryotes the entry into mitosis is initiated by activation of cyclin-dependent kinases (CDKs), which in turn activate a large number of protein kinases to induce all mitotic processes. The general view is that kinases are active in mitosis and phosphatases turn them off in interphase. Kinases activate each other by cross- and self-phosphorylation, while phosphatases remove these phosphate groups to inactivate kinases. Crucial exceptions to this general rule are the interphase kinase Wee1 and the mitotic phosphatase Cdc25. Together they directly control CDK in an opposite way of the general rule of mitotic phosphorylation and interphase dephosphorylation. Here we investigate why this opposite system emerged and got fixed in almost all eukaryotes. Our results show that this reversed action of a kinase-phosphatase pair, Wee1 and Cdc25, on CDK is particularly suited to establish a stable G2 phase and to add checkpoints to the cell cycle. We show that all these regulators appeared together in LECA (Last Eukaryote Common Ancestor) and co-evolved in eukaryotes, suggesting that this twist in kinase-phosphatase regulation was a crucial step happening at the emergence of eukaryotes

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others

Fluigi: an end-to-end software workflow for microfluidic design

One goal of synthetic biology is to design and build genetic circuits in living cells for a range of applications with implications in health, materials, and sensing. Computational design methodologies allow for increased performance and reliability of these circuits. Major challenges that remain include increasing the scalability and robustness of engineered biological systems and streamlining and automating the synthetic biology workflow of “specify-design-build-test.” I summarize the advances in microfluidic technology, particularly microfluidic large scale integration, that can be used to address the challenges facing each step of the synthetic biology workflow for genetic circuits. Microfluidic technologies allow precise control over the flow of biological content within microscale devices, and thus may provide more reliable and scalable construction of synthetic biological systems. However, adoption of microfluidics for synthetic biology has been slow due to the expert knowledge and equipment needed to fabricate and control devices. I present an end-to-end workflow for a computer-aided-design (CAD) tool, Fluigi, for designing microfluidic devices and for integrating biological Boolean genetic circuits with microfluidics. The workflow starts with a ``netlist" input describing the connectivity of microfluidic device to be designed, and proceeds through placement, routing, and design rule checking in a process analogous to electronic computer aided design (CAD). The output is an image of the device for printing as a mask for photolithography or for computer numerical control (CNC) machining. I also introduced a second workflow to allocate biological circuits to microfluidic devices and to generate the valve control scheme to enable biological computation on the device. I used the CAD workflow to generate 15 designs including gradient generators, rotary pumps, and devices for housing biological circuits. I fabricated two designs, a gradient generator with CNC machining and a device for computing a biological XOR function with multilayer soft lithography, and verified their functions with dye. My efforts here show a first end-to-end demonstration of an extensible and foundational microfluidic CAD tool from design concept to fabricated device. This work provides a platform that when completed will automatically synthesize high level functional and performance specifications into fully realized microfluidic hardware, control software, and synthetic biological wetware

Searching for Order in Body Clocks

Physiological rhythms are central to life. Mammalian behavior and metabolism are organized around the day and night by the regulated action of cell-autonomous clocks that exist throughout our bodies. At the core of this molecular clockwork are multiple coupled feedback loops that generate sustained circadian rhythms in gene expression to ultimately orchestrate mammalian physiology. In this work we provide evidence for the role of metabolism in regulating the core clock. We present genes involved in energetic and redox pathways which we identified to be essential for the robustness of cellular timekeepers to temperature fluctuations. We developed the first computational model for circadian redox oscillations that contributes to the understanding of how cellular redox balance might adjust circadian rates in response to perturbations and convey timing information to the core molecular oscillator. Moreover, we show that our mathematical model can be coupled with prior published models of the transcriptional clockwork resulting in 1:1 entrainment. This experimental-theoretical approach exemplifies the need of a dynamic analysis at the system level to understand complex biological processes and provides insights into how basic timekeeping mechanisms are integrated into cellular physiology. Such knowledge might highlight new ways by which functional consequences of circadian timekeeping can be explored in the context of human health and disease