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1070 results in Computational Biology and Bioinformatics

Page 5 of 54

Algorithmic Information Dynamics
  • A Computational Approach to Causality with Applications to Living Systems
  • Hector Zenil, Narsis A. Kiani, Jesper Tegnér
  • Published online:
    11 May 2023
    Print publication:
    25 May 2023
  • Biological systems are extensively studied as interactions forming complex networks. Reconstructing causal knowledge from, and principles of, these networks from noisy and incomplete data is a challenge in the field of systems biology. Based on an online course hosted by the Santa Fe Institute Complexity Explorer, this book introduces the field of Algorithmic Information Dynamics, a model-driven approach to the study and manipulation of dynamical systems . It draws tools from network and systems biology as well as information theory, complexity science and dynamical systems to study natural and artificial phenomena in software space. It consists of a theoretical and methodological framework to guide an exploration and generate computable candidate models able to explain complex phenomena in particular adaptable adaptive systems, making the book valuable for graduate students and researchers in a wide number of fields in science from physics to cell biology to cognitive sciences.

  • 1 - Crash Introduction to Python

  • from Part I: - Programming in Python
  • Benny Chor, Tel-Aviv University, Amir Rubinstein, Tel-Aviv University
  • Book:
    Computational Thinking for Life Scientists
    Published online:
    19 August 2022
    Print publication:
    08 September 2022, pp 7-41

  • Summary

    This chapter kicks off with an example for a simple program in Python. Through this example, we will delve right away into the syntax of this popular programming language. There will be quite a few technical details in this example, which are probably new to anyone with no prior Python experience. The rest of the chapter will then walk the reader briefly yet methodologically through the basic Pythonic syntax. By the end of this chapter, you will be able to write simple programs in Python to solve various problems of a computational nature.

  • Part III: - Graphs and Networks

  • Benny Chor, Tel-Aviv University, Amir Rubinstein, Tel-Aviv University
  • Book:
    Computational Thinking for Life Scientists
    Published online:
    19 August 2022
    Print publication:
    08 September 2022, pp 91-144

  • Summary

    A graph is defined by two sets. One set, the nodes, describes a collection of objects, for example biological species, such as human, whale, mountain lion, crow, pigeon, sea gull, Arabian surgeonfish, zebra fish, and Anemonefish. The second set, the edges, comprises pairs of nodes, such as {human, mountain lion}, {pigeon, sea gull}, {Arabian surgeonfish, Anemonefish}. Each pair represents a connection between objects, which has some meaning. For example, two species are related to one another if they belong to the same taxonomic family, or they both live on the same continent, or they are active in the same part of the day, and so on. Each such feature will create a different graph, because different properties create different sets of pairs (edges).

  • Part V: - Limitations of Computing

  • Benny Chor, Tel-Aviv University, Amir Rubinstein, Tel-Aviv University
  • Book:
    Computational Thinking for Life Scientists
    Published online:
    19 August 2022
    Print publication:
    08 September 2022, pp 187-207

  • Summary

    Computers and computer programs are harnessed these days to solve an extremely diverse range of problems. These problems include weather forecast, scheduling problems, designing, and controlling trajectories for satellites, recognizing individual human faces, playing chess, trading in the stock market, diagnosing cancer cells, assembling automobiles and airplanes, and driving autonomous cars (this is obviously a very partial list). In many cases, computers outperform human experts working on the same problem. In the future decades, we may expect computers to gradually replace medical doctors (completely or partially), express emotions, compose high quality music, etc. etc. Given these amazing fits, one wonders if computers are omnipotent: Can computers solve every problem, provided it is well defined?

Page 5 of 54