What time is it?
Professor Dr Ilka Maria Axmann, Heinrich-Heine-Universität Düsseldorf, Germany
Even without looking at a watch, we have an inner feeling for time. How do we measure time? Our body, in particular each of our cells has an inner clock enabling all our rhythmic biological activities like sleeping. Astonishingly, prokaryotic cyanobacteria, which can divide faster than ones a day, also use an inner timing system to foresee the accompanying daily changes of light and temperature and regulate their physiology and behavior in 24-hour cycles. Their underlying biochemical oscillator fulfills all criteria of a true circadian clock though it is made of solely three proteins. It ticks with a robust 24-hour period even under fluctuating or continuous conditions. Nevertheless, it can be entrained by light, temperature or nutrients. Reconstituted from the purified protein components, KaiC, KaiB, and KaiA, the cyanobacterial protein clock can tick autonomously in a test tube for weeks. This apparent simplicity has proven to be an ideal system for answering questions about information transfer and robustness of circadian clocks.
Computational modeling identified various aspects of this interesting system like nested feedback loops, period robustness against noise and seemingly conflicting synchronisation with the environment.
Dynamics, Feedback, and Noise in Natural and Synthetic Gene Regulatory Networks
Professor Mary Dunlop, Boston University, USA
Cells live in uncertain, dynamic environments and have many feedback mechanisms for sensing and responding to changes in their surroundings. This talk will discuss examples of both natural and engineered feedback circuits and how they can be used to alter dynamics of gene expression. Using a combination of time-lapse microscopy experiments and stochastic modeling, the talk will show how E. coli bacteria use feedback to generate dynamics and noise in expression of a key regulatory protein, providing transient antibiotic resistance at the single-cell level. In addition, it will highlight diverse examples of how feedback can be used to allow cells to responds to changing environments.
Computing with motile biological agents exploring networks
Professor Dan V. Nicolau, McGill University, Canada
Many mathematical problems, e.g., cryptography, network routing, require the exploration a large number of candidate solutions. Because the time required for solving these problems grows exponentially with their size, electronic computers, which operate sequentially, cannot solve them in a reasonable time. In contrast, biological organisms routinely process information in parallel for essential tasks, e.g., foraging, searching for space opens three possible biocomputing avenues.
Biomimetic algorithms translate biological procedures, e.g., space searching, chemotaxis, etc., into mathematical algorithms. This approach was used to derive fungi-inspired algorithms for searching space and bacterial chemotaxis-inspired algorithms for finding the edges of geometrical patterns.
Biosimulation uses the procedures of large numbers of motile biological agents, directly, without any translation to formal mathematical algorithms, thus by-passing computation-proper. The agents explore complex networks that mimic real situations, e.g., traffic. This approach focused almost entirely on traffic optimization, using an amoeboid organisms placed in confined geometries, with chemotactic ‘cues’, e.g., nutrients in node coordinates.
Computing with biological agents in networks uses very large number of agents exploring microfluidics networks, purposefully designed to encode hard mathematical problems. The foundations of a parallel-computation system in which a combinatorial problem (SUBSET SUM) is encoded into a graphical, modular network embedded in a nanofabricated planar device was reported. Exploring the network in a parallel fashion using a large number of independent agents, e.g., molecular motor-propelled cytoskeleton filaments, bacteria, algae, solves the mathematical problem. This device uses orders of magnitude less energy than conventional computers, additionally addressing issues related to parallel computing implementation.