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Algorithmic Systems Biology


biological form computerized

Robert Hodgin

The convergence of computer science and biology will serve both disciplines, providing each with greater power and relevance.

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CACM Administrator

The following letter was published in the Letters to the Editor in the January 2010 CACM (http://cacm.acm.org/magazines/2010/1/55747).
--CACM Administrator

Congratulations to Corrado Priami for sharing his insight into biological simulations in his article "Algorithmic Systems Biology" (May 2009). My own interest in emulating biological systems has yielded similar conclusions. In addition to computerized algorithmic representations in software, I've designed analog component circuits and linear coprocessors using operational amplifiers, including integrate-and-fire artificial neurons based on Hodgkin's and Huxley's research, synthetic emotion-processing neurons using sum-and-difference operational amplifiers, and artificial neural networks for machine vision. These components add instantaneous analog parallelism to the digital computer's software concurrency, as Priami said.

For the past 10 years I've been developing a fairly elaborate nervous-system emulator that embodies many of Priami's concepts. Designed originally as a control system for robotics written in a multitasking version of Forth, I've extended the project into a modular, extensible, open-systems design embodied in a multiprocessor network that emulates the major functions of the human nervous system. Included are interchangeable hardware/software components, a socketed software bus with plug-and-play capability, and self-diagnostics. The computer hardware is based on IEEE P996.1 bus cards; the operating system uses IEEE 1275-1994 standard software. The overall system features object-oriented design techniques and programming. I've also created a machine-independent high-level byte-coded script command language to manage it all.

Emulated neural-anatomical structures include cortex, brain stem, cerebellum, spinal cord, and autonomic and peripheral nervous systems, along with motor, sensory, auto-regulatory, and higher-cognitive AI behavior and synthetic emotions. Emulated body functions range from hormones and drugs acting on cell membranes to high-level responses.

As part of the IEEE 1275 standard, Forth helped me create a source-code library of individually compilable nervous-system components, per Priami. The library includes human childhood development milestones, epinephrine and oxytocin hormone functions, a pain mechanism and narcotic effects, the fear mechanism, and retrograde neuronal signaling via the endocannabinoid system. Recent enhancements include a form of autism based on a defective oxytocin receptor, the fibromyalgia syndrome (with simulated viral activity, immune-system responses, and antiviral antibiotic effects), and Bayesian probabilistic functions.

The system reflects intentional software and hardware flexibility. Using tiny six-pin eight-bit PIC10Fxx series microcontrollers, I've designed 35 different digital McCulloch-Pitts and analog Hebb artificial neurons. I also added eight-core 32-bit Parallax processors for coordinating brain stem sensorimotor, cerebellar, and low-level cortical activities. Moreover, the system can extend its original Forth-based, byte-coded AI scripting language via genetic algorithms to provide a form of machine learning and execution. It is also capable of examining its own internal variables, short- and long-term memory, knowledge base, and preferences profile to provide a limited form of self-awareness and personality expression.

I look forward to more such intelligent machines created through the kind of algorithmic systems biology explored by Priami.

Paul Frenger MD
Houston, TX


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