What does a computational system mean?

The computation systems can be defined as the systems that are capable of solving a problem that includes calculations either mathematical or logical, and are able to produce the result as an output. For example, a simple calculator that is given a set of numbers and operators as input and produces the result as an output can be defined as a computational system. The above example is a simple illustration only, the computational systems range from simple devices to complex devices such as the systems that control the SpaceX companies, Starlink satellites which are being quite recently seen in the skies at many places globally.

Components of the Computational system

There are various types of computational systems present, for example, the ones used for medical purposes like an x-ray machine or for educational purposes like a scientific calculator or for economic purposes like a banking system. However, each computational system has five major components which are described as follows.

1. The Input Unit : In each of the computational systems the input unit is the component that is used to receive the data from the outside world like the user in case of a calculator. The data can be in textual format, image format or in any other format. The job of this unit however is always to take the input from the outside world and feed it to the computational system for further processing.
2. The Processing Unit : The processing unit is the component in the system which does the actual processing of the input. It is the unit which is used to execute the algorithm on the input to solve a problem and produce results.
3. The Storage Unit : This component is used for storage of the data that is being used to solve the problem or is being used by the computational system. This data includes the input, the result and any of the intermediate data that is being used by the algorithm.
4. The Output Unit : The component of the computational system that is responsible for displaying or producing the results and making them available to the outside world is called the computational system.
5. The Communication Unit : The component that is used in the computational systems for communication or passing of data or information between the various components like the input unit and processing unit, is called the communication unit.

Characteristics of the Computational Systems

The efficient working of a computational system is measured by the features that are generally the same for describing the efficient working of a system. The features of a computational system are as follows.

Speed: A computational system is used for solving problems at a faster rate as compared to the time the same problem will take to be solved by a person. The processing speed of a computational system is such that it can process millions of bits of data per second and produce efficient results.

Accuracy: The computational systems are not prone to errors, they can process input data and perform logical as well as mathematical calculations at high accuracies. Errors can only occur when the programming of the system is faulty or the input is wrong.

Diligence: A computational system lacks the concept of fatigue or tiredness due to long periods of work. They can work tirelessly without any break.

Versatility: The computational systems can solve all types of problems in their domain whether the input is of any size but is in their domain.

Reliability: A computational system is reliable and solves the same problem having the same set of inputs and produces the correct and same result each time.

Automation: A computational system can perform tasks without any human intervention.

Memory: A computational system has the capability of storing data that is used as input or output, or is used in an intermediate step.

Computational system biology

One of the emerging and important examples of computational systems is computational system biology. The computational systems can also be found in use for the creation of efficient algorithms, information structures, visualization, and broadcasting equipment necessary for the computer modeling of biological systems like x-rays and CT scans. It involves the use of computer simulations of biological systems, including cellular subsystems (metabolism, signal transmission pathways, and metabolism and enzymes with genetic regulatory networks) that analyze and visualize the advanced connections of each cellular process.

Systems biology refers to a collection of concepts and approaches from life sciences, physical sciences, technology, and engineering. Systems modeling designs are well established in the engineering fields, but new incase of systems biology. Microarray data analysis is one of the central research subjects in computational systems biology; however, there is a slight variation in emphasis.

In computational systems biology, the main concern is the development of the dynamic predicated model of biological processes (mostly genetic and biochemical). The primary stage of this method is the identification of interacting partners (used in a loose sense). One approach that recognizes genes is gene interaction, whose aim is to use determined correlations at gene microarray data/information to infer network communication.

The system's biology aim is to create a quantitative and dynamic model for the biological process of interest. One of the approaches to these problems is extending the top-down network model, which will provide certain quantitative data based on dynamics. Even so, this method has certain shortcomings as the elements of the model will not have any direct link to the physical parameter of interest. Thus, there is a serious interest in the various approaches, depending on the information to parameterize the bottom-up model in the biological processes. There are possibilities for non- Bayesian methods, but they are restricted for provided data.

The context of the deterministic models in the biological networks are based on the ordinary differential equations (ODEs), and there are considerable utilities for using the Bayesian approach which properly addresses noise modeling issues. It improves the parameter estimation with proper prior modeling of parameter uncertainty. There are possibilities in network interfaces to have various approaches, but generally used techniques in nature are fundamentally bayesian. This is most emphasizing confusion between separate Bayesian networks and general methods in bayesian.

Bayes net is the term that is used in communication, however, it is non-statistical and refers to the separate probabilistic graphical models, regardless of whether the method used to evaluate them is bayesian. Apart from the contrary suggestion in literature, there is no need for separating the continuous data to gain knowledge about the Bayesian network. The Bayesian methods are not needed to estimate the graphical method when the observations are small compared to the variables. System dynamic information is obtained from the data based on time-course expression and the dynamic model will provide a starting point for all top-down systems biology modeling.

The characterizing loss of cell cycle synchrony (CLOCCS) model for synchrony loss at yeast population is the best application of the Bayesian model. This model’s simple application is the alignment of the data set collection with various conditions. This model is the combination of population-level data (like gene expression array data), which recovers the data related to the single-cell dynamics from the averaged data. The powerful technique of this context is the complete model of the interesting process and its relationship with experimental information/data.

Context and Applications

This topic is important for postgraduate and undergraduate courses, particularly for,

• Bachelor's in Computer Science Engineering
• Associate of Science in Computer Science.

Practice Problems

Question 1: The aim of __________ is used to create and use efficient algorithms.

1. Computational system
2. System biology
3. Bioinformatics
4. Computational genetics

Answer: Option a is correct.

Explanation: Computational systems aim to create and use efficient algorithms, information structures, visualization, and broadcasting equipment necessary for the computer modeling of biological systems. It involves the use of computer simulation (simulation is the imitation of operation ) of biological systems, including cellular subsystems (metabolism, signal transmission pathways, and metabolism and enzymes with genetic regulatory networks) that analyze and visualize advanced connections of each cellular process.

Question 2: System biology refers to ________.

1. Collection of concepts
2. Collection of hardware
3. Collection of software
4. None of the above

Answer: Option a is correct.

Explanation: Systems biology refers to a collection of concepts and approaches from life sciences, physical sciences, technology, and engineering.

Question 3: Which of the following is not a component of Computational Systems?

1. Input Unit
2. Output Unit
3. Central Unit
4. Communication Unit

Answer: Option c is correct.

Explanation: The Computational Systems consists of five components, input, output, storage, processing and communication.

Question 4: Which of the following are characteristics of Computational Systems?

1. Reliability, speed, diligence
2. Speed, accuracy, power
3. Power, memory, accuracy
4. None of the above

Answer: Option a is correct.

Explanation: Computational Systems consist of the following features, speed, accuracy, diligence, versatility, reliability, automation, and memory.

Question 5: ___________ and ____________ are the multidisciplinary field that develops practical techniques for analyzing massive collections of biological information.

1. Computational system and bioinformatics
2. Computational biology and bioinformatics
3. Computational biology and computational system
4. Bioinformatics and computational genetics

Answer: Option b is correct.

Explanation: Computational biology and bioinformatics are multidisciplinary fields that develop practical techniques for analyzing massive collections of biological information, such as genetic sequences, cells, or supermolecular models which are used to make new predictions or to discover new biology.