Nabil HADDAD

Biologically Inspired Computing

Scientific and Technical Communication

Biologically Inspired Computing By: Nabil Haddad

Biologically Inspired Computing By: Nabil Haddad

Table of Contents Purpose Statement .................................................................................1 Introduction ...........................................................................................1 I. Biologically inspired computing, an overview ................................2 The evolution of the science ................................................................................... 2 The bi-directional relationships between computer science and biology ................. 2

II.

Applications and Techniques of biologically inspired computing 3

Artificial neural networks....................................................................................... 3 DNA Computing..................................................................................................... 3 Artificial immune systems....................................................................................... 4

III.

Challenges and Obstacles...........................................................4

Communication...................................................................................................... 4 Deployment............................................................................................................ 4 Technical ............................................................................................................... 4

Conclusion ..............................................................................................5 Bibliography...........................................................................................6

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Nabil HADDAD

Biologically Inspired Computing

Thesis Statement Biologically Inspired Computing

Purpose Statement The paper describes the evolution and the potential of the biologically inspired computing field or for short biocomputing science, its role in designing future computing machines. It also discusses the challenges facing the development of this field and highlights the major development activities and their future implication in human life.

Introduction Computing power has led to great advances in the filed of biological research. Will biology be able to return the favour? The development and progress of computer science, engineering, and technology at large has greatly contributed to each of the study fields, particularly to biological systems. Now scientists are looking at the things from the opposite direction, and are investigating nature to create a new generation of computer devises. Over the past few years there has been a growing interest in the use of biological models to help for solving computational problems. This area of research is referred to as Biologically Inspired Computing. The motivation of this field is to extract useful ways from natural biological systems, in order to create effective computational machines.

Nabil HADDAD

Biologically Inspired Computing

I. Biologically inspired computing, an overview The evolution of the science Human beings have been calculating since before 2000 BC. Driven by the need, chance, or investigations, we have proceed from using our fingers to writing on boards, to the mechanical adding machines, and finally, to the electronic computers. The modern computer has had an incredible impact on our ability to compute, letting us create complex algorithms to solve very complicate problems in a matter of milliseconds. Nevertheless, today’s computers have their limitations. With the silicon technology shrinking the microelectronics devices size by half roughly every 18 to 24 months. Scientists predict that, with this rapid development in silicon technologies, chip size and density will cause some physical problems in the next 10 to 20 years. Computing machines inspired by biological models are one possible alternative currently being investigated. The field of biologically inspired computing has a two different definitions, first the use of biological processes as model, inspiring, or helping researchers in developing new computing technologies, second the use of information science concepts and tools to explore biology from a different angles. The field is highly multidisciplinary, attracting a host of computer scientists, biologists, geneticists, mathematicians, physicists, and others.

The bi-directional relationships between computer science and biology The development and progress of computer science has greatly contributed almost to each field of study, particularly the biological systems and sciences. Recently, computing and engineering tools have been used to help gain a better understanding of biological processes and functions through the modeling and simulation of natural systems. The opposite is also true; computer science has been enriched with the ideas taken from biological phenomena to help developing new computational systems. This can be explained by the development of artificial neural networks, DNA computing, artificial immune systems.

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Nabil HADDAD

Biologically Inspired Computing

II. Applications and Techniques of biologically inspired computing Artificial neural networks An Artificial Neural Network (ANN) is an information-processing concept that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this idea is the structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Since neural networks are good at identifying patterns, they are well suited for prediction or forecasting needs including: ! ! ! ! ! !

Sales forecasting Industrial process control Customer research Data validation Risk management Target marketing Human brine

DNA Computing DNA computing is the use of DNA (Deoxyribose Nucleic Acid) molecules, the molecules that encode genetic information, in designing computing machines. This is accomplished by using the DNA computing power. DNA computing is currently one of the fastest growing fields in both computer science and Biology. DNA computing is useful because it has a capacity lacked by all current electronics computers and it is parallel nature. What does this mean, you may ask? Well, essentially while DNA can only carry out computations slowly, DNA computers can perform an amazing number of calculations simultaneously. To give you an idea of the difference in time, a calculation that would take

22

10

modern computers working in parallel to

complete in human's lifetime would take one DNA computer only 1 year to finish it off!

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DNA Computing Model

Nabil HADDAD

Biologically Inspired Computing

Artificial immune systems The Artificial Immune Systems (AIS) like other biologically inspired techniques, it tries to extract ideas from a natural system, in particular the immune system, in order to develop computational tools for solving engineering problems. The immune system contains many useful information-processing abilities, including pattern recognition, learning, memory and inherent distributed parallel processing. For these reasons the immune system has received a significant amount of interest to use as an idea within computing. This emerging field of research is known as Artificial Immune Systems (AIS). Applications of AIS include such areas as machine learning, fault diagnosis, computer security, scheduling, virus detection, and optimization.

III. Challenges and Obstacles Communication There is a lack of effective communication between biologists and computer scientists due to the absence of common code. A word or phrase means something different in each community (e.g. model). There is a mismatch in expectations between biologists and computer scientists. Hence, there is a major challenge to resolve the thought processes within both computer science and biology.

Deployment There is also a perceived difficulty in getting industrial partners to understand new techniques and use them, especially because some new methods have been tested against traditional ones and the traditional one has been found better.

Technical The major technical concerns are the scalability problem, testing and integration of the new computing techniques in modern environment. For example the neural network programs sometimes become unstable when applied to larger problems, which make the defence, nuclear, and space industries concerned about employing neural networks. Also the mathematical theories that used to guarantee the performance of these new computing techniques are still under development 4

Nabil HADDAD

Biologically Inspired Computing

Conclusion Only time will tell where all this will lead. Many scientists don’t feel that many of these research directions will impact directly information technology, however, much of it will define entirely new directions for information technology. Most experts think biologically computation models will complement -but not replace - today's computers, and instead it will be specialize in large computational problems. Whether biology will be able to pay back the favour to computer science is still unclear! The fact remain, studying biological systems can increase knowledge from both biological and computational perspective.

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Nabil HADDAD

Biologically Inspired Computing

Bibliography 1. Anne E. Condon -- Department of Computer Science, University of British Columbia, Vancouver, B.C., V6T 1Z4 Canada 2. Current news from multimedia services 3. L. Adleman et al., “On Applying Molecular Computation to the Data Encryption Standard,” DNA-Based Computers II, Vol. 44, L. Landweber and E. Baum, eds., Am. Math. Soc., Providence, R.I., 1999, pp. 31–44. 4. Q. Liu et al., “DNA Computing on Surfaces,” Nature, Vol. 403, No. 6,766, Jan. 2000, pp. 175–179; http://corninfo.chem.wisc.edu/writings/DNAcomputing.html#overview (current Oct. 2000). 5. T.H. LaBean et al., “The Construction, Analysis, Ligation, and Self-Assembly of DNA Triple Crossover Complexes,” J. Am. Chemistry Soc., Vol. 122, No. 9, 2000, pp. 1848–1860; www.cs.duke.edu/~reif

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