Overview of Cellular Computing-Basic Principles and Applications

Overview of Cellular Computing-Basic Principles and Applications

Amit Das (University of Kalyani, India), Rakhi Dasgupta (University of Kalyani, India) and Angshuman Bagchi (University of Kalyani, India)
DOI: 10.4018/978-1-5225-0058-2.ch026
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Computers, due to their raw speed and massive computing power, have been highly used by biologists to expedite life science research whereas several computational algorithms like artificial neural network, genetic algorithm and many similar ones have been inspired by the behaviors of several biological or cellular entities. However till date both these disciplines i.e. life sciences and computer sciences have mostly progressed separately while recent studies are increasingly highlighting the impact of each discipline on the other. The chapter describes several features of biological systems which could be used for further optimizations of computer programs or could be engineered to harness necessary computational capabilities in lieu of traditional silico chip systems. We also highlight underlying challenges and avenues of implementations of cellular computing.
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Computers, especially the modern day desktops and workstations, owing to their massive processing power and speed, have been traditionally used for deeper understanding of several biological phenomena. Techniques essential for high through put processing of genetic (gene sequencing), transcriptomic (micro-array), proteomic (mass spectroscopy) and imaging (microscopy and diagnostic techniques like CT-Scan, PET, MRI etc.) data, heavily rely on traditional silicon chip based computing systems (Kuznetsov et al., 2013). However at the heart of every computer, lies a processor which ultimately understands Boolean logic and rely on direct or modified form of Von Neumann architecture. This basic simplistic core of a computer also resembles the behavior of an individual cell which responds to certain input parameters (signals) through the generation of an output based on certain sets of logic. This common feature between a computer and a cell gives rise to a relatively new field of study, termed as ‘Cellular Computing’ (CC) which focuses on harnessing the power and characteristics of biological entities like cells for the purpose of computation rather than using traditional in-silico network analogy based methods for better understanding of complex systems (Sipper, 1999; C Teuscher, 2012). In case of CC, the word ‘computation’ does not necessarily restrict its meaning to ‘adding numbers’ only, rather it is used to describe achievable outcomes through direct or engineered (or synthetic) exploitation of bio-molecular / cellular logic sets which have been evolutionarily selected.

The two following example will provide better understanding of the analogy between computer and cellular logic processing system and a couple of the advantages of CC:

  • 1.

    The bacterial growth scenario:

Logics like IF/ELSE or AND/OR are a common part of almost every kind of algorithm and rarely requires any introduction. Now it is remarkable to highlight that even the miniature scale cellular biological entity i.e. a bacteria is always responding to its surrounding environment based on such logics and that too is related to its primary aim which is bacterial growth and division (Figure 1). For example IF nutrition is present in the surrounding medium, then continue growth ELSE suspend growth and prepare for tough situation. So it is clear that, without going into any underlying biological pathway details, it is possible to find to similarity between a cell (or bacteria) and a computer. To make a little complex scenario lets add one more factor in the bacterial growth scenario i.e. the temperature. IF nutrition is present in surroundings AND temperature is favorable, continue growth ELSE suspend growth (Figure 1).

Figure 1.

Flow diagram of the series of logical behaviors which a bacterial cell undertakes to make a decision on the suitability of its division

Like any computer program which assess the input values based on predefined conditions and logics to generate the output, bacterial cells also undergoes similar logical steps to make a decision on their division. First the bacterial sensing machinery senses the environmental conditions (mainly temperature and food) and based on their sensing outcome a decision is made on whether division favorable conditions exist or not. While in reality, several parameters are assessed, a two factor (temperature and food) conditions determination system shows that four different possible outcome of the sensing machinery, only one of which is favorable to continue with bacterial division.

The underlying molecular details of this bacterial sensing and growth regulation are also an outcome of series of events of such logic and thereby can be specifically categorized as ‘Molecular Computing’ which although related but for the better understanding of the readers are discussed in details in a separate chapter and thereby carefully kept outside the scope of this chapter.

  • 2.

    The traveling salesman problem:

Key Terms in this Chapter

Modeling of Biological Systems: A part of systems biology that deals with the computational modeling of biological systems or pathways like signaling cascade, metabolic network, disease network etc. The main purpose of such modeling is simulated replications of biological phenomena for ease of understanding in the event of deviations of certain parameters from natural values and prediction of their possible impact.

Genetic Engineering: A field of molecular biology that deals with purposeful manipulation of an organisms (or cells) native genetic content for the gain or deletion of certain traits through addition of foreign DNA material or deletion of certain portion of native DNA.

Parallel Computing: A type of computation which facilitates simultaneous execution of different tasks which are primarily sub-tasks of a larger task. The main aim of parallel computing is improvement of total execution time by distributing parts of a task to separate computing nodes or cores from a single such computing unit.

Cell: A cell is an independently sustainable and self-replicating unit of any organism. For multi-cellular organisms, a cell also serves as the structural and functional unit. Cells bear the capability of responding to surrounding environment through logical outputs of the received external or internal signals.

Bio-Inspired Algorithms: Computational algorithms which has been designed and optimized with inputs from the behavior of biological systems like an ant or bee colony or biological entities like a cell or neuron. Example: artificial neural network, genetic algorithm, membrane computing etc.

Natural Computing: Natural computing is an interdisciplinary arena that primarily encompasses computer science and biology along with indirect association with all other possible scientific fields. Natural computing primarily encompasses three major areas – A) Development and optimizations of computational algorithms with inspirations from nature, mainly biological systems, B) Computation modeling of biological systems or pathways and C) Use of biological entities like cells for the purpose of computation.

Molecular Computation: The use of biological macro-molecules like DNA or proteins (like enzymes) for the purpose of computation like calculations, storing, analyses or processing of data is known as molecular computation or bio-molecular computation.

Central Dogma: The flow of genetic information, coded in DNA, to proteins via RNA is termed as the central dogma of molecular biology. It is mainly composed of two processes i.e. transcription which synthesizes RNA from DNA and translation which converts the RNA encoded message into a string of amino acids that subsequently folds in to a protein.

Systems Biology: A biology centered interdisciplinary field of study that focuses on systemic study (holistic approach) of complex interactions of biological systems. With insights gained from experimental biology, systems biology incorporates computational and mathematical (with some statistical inputs) modeling and simulation techniques and tools for a comprehensive and quantitative analysis of biological interactions.

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