SWARTHMORE PHYS 120 - Biological information processing by slime molds

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Frances TaschukPhysics 120Spring 2008Biological information processing by slime moldsIntroduction: Slime MoldsTrue slime molds are members of the phylumMyxomycota within the kingdom Protista. Their lifecycle consists of a haploid stage in which the cellsexist as flagellated amoebas and a diploidmultinucleated plasmodium stage that is the result ofamoeboid cells merging and their nuclei replicatingwithout cell division. The plasmodium structureconsists of continuous tubes through which thecytosol is able to stream.Although there are many types of slimemolds, the species Physarum polycephalum is oftenused in research. Although individuals of this speciesform multinucleated masses many centimeters across,they are still essentially single cells because theircytoplasm is continuous. This morphology allowsstudy of intracellular signaling and informationprocessing in response to environmental stimuli. Theproblem solving and processing abilities of Physarum are often described as a form of primitive behavioral intelligence.Several studies have focused on the ability of slime molds to find the best path to connect multiple food sources, whether they be at opposite ends of a maze, involve travelthrough “risky” areas, or are simply on opposite sides of an agar plate. The mechanisms leading to these behaviors can be described in terms of biological oscillators and flux through tubes in the slime mold’s structure.Studies of efficient pathsNakagaki et al. demonstrated in 2004that the plasmodium phase of Physarum could approximate the Steiner’s minimum tree route to connect several food sources presented to it on opposite sides. Steiner’s minimum tree is the mathematically shortestroute that connects an entire network. Most cases of the problem are NP-complete, meaning a solution can be verified in polynomial time, but cannot be generated algorithmically in polynomial time. Thus, the ability of slime molds to solve such a problem is impressive.Figure 1. Life cycle (above) and plasmodial stage (right) of PhysarumFigure 2. Time series as a Physarum plasmodium network evolves in response to the presentation of food sources. The final picture in the series was taken after 33 hours.The experiment consisted of growing asolid layer of Physarum’s plasmodium stageon agar. Food blocks were then added atcertain positions. After about 30 hours, thefinal shapes of the networks could beobserved. Network shapes were typicallyanalogous to the either the SMT solution, aslightly less efficient (although more robust todisruption of a tube) cyclical solution, or acombination of the two (Figure 3). Althoughthe slime mold kept the total length of tubesshort when connecting the various foodsources, it did not find the Steiner pointprecisely. Most junctions of SMT-likesolutions were less than 5% longer than theideal solution, but the junctions were notevenly distributed around the Steiner point.The physical principles at work here can be described in terms of efficient transport within the cell. The selection of short, thick tubes for slime mold networks corresponds to the most efficient flux of cellular contents through the tubes. These tubes are self-organized from actin fibers, which become oriented in the same direction as the stretching force that results from cytoplasmic streaming in response to rhythmic contraction of the organism.While not fundamentally different from the efficient path finding described above,this ability of slime molds allows them to solve mazes. Maze-solving by slime molds provides a stunning illustration of the power of cellular information processing. Computer modeling of cytoplasmic flux in Physarum plasmodia in mazes can correspondwell to their observed behavior (Tero, 2007).The challenges of slime mold plasmodia seeking efficient paths can be further complicated by exposingthem to light in one partof the path. Since theextension of plasmodia isslowed when theorganism is exposed tolight, the path ofFigure 3. SMT and cyclical network patterns. SMT-like patterns were observed 26% of the time, while cyclical solutions were observed 20% of the time. (i) Structure analogous to SMT. (ii) Top half analogous to SMT; bottom nodes form a cycle. (iii) The ideal SMT solution.Figure 5. Comparison of observed path (dots) with predicted path (dotted line). The part of the figure above the horizontal line is the lighted area. Notice how much less of the path is exposed to light than would be for a direct diagonal path. (Nakagaki, 2007)Figure 4. Maze solution found by Physarummaximum efficiency is no longer a straight line between the two food sources. Nevertheless, slime molds can find this path in a way consistent with predictions of the path that would provide minimum risk to the slime mold, given knowledge about how much growth is slowed under different light conditions (Nakagaki, 2007)Cellular “memory”Another set of experiments have demonstrated that Physarum plasmodia are able to anticipate periodic stimuli. The speed of linear growth of a plasmodium is decreased under cooler and drier conditions. Repeated stimulations of plasmodia with cool, dry air at consistent intervals of 60 minutes caused the organism to decrease its growth again after the next period, even without stimulation. This response dies out after a few more periods, but can be brought back by providing a single additional stimulation. The mechanism for such behavior is unknown, but it may be the result of natural biochemical oscillations within the organism that interact over a distance by diffusion with the result of synchronizing their phase (Tero, 2008).ConclusionsThe ability of cells to respond to the environments they encounter is the result of a complicated network of biochemical interactions and physical constraints. As we have seen many times this semester, sophisticated and interesting behaviors of biological systems can often be approached with models derived from underlying physical principles of the system. In the case of slime molds, the phenomenon of cytoplasmic streaming plays a key role not only in distribution of materials throughout the cell, but, by doing so in ways that maximize efficiency, in explaining the morphology of the entire plasmodium.The examples given above demonstrate that single cells have enough processing power to solve interesting problems as part of their natural response to their environment.Such observations lead to biologically-inspired applications. In


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