biologically-inspired computing lecture 5 Informatics luis rocha 2015 biologically Inspired computing INDIANA UNIVERSITY

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Sections I485/H400 course outlook Assignments: 35% Students will complete 4/5 assignments based on algorithms presented in class Lab meets in I1 (West) 109 on Lab Wednesdays Lab 0 :

2 Sections I485/H400 course outlook Assignments: 35% Students will complete 4/5 assignments based on algorithms presented in class Lab meets in I1 (West) 109 on Lab Wednesdays Lab 0 : January 14 th (completed) Introduction to Python (No Assignment) Lab 1 : January 28 th Measuring Information (Assignment 1) Due February 11 th Lab 2 : February 11 th L-Systems (Assignment 2)

3 Readings until now Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 8 - Artificial Life Chapter 7, sections 7.1, 7.2 and 7.4 Fractals and L-Systems Appendix B.3.1 Production Grammars Lecture notes Chapter 1: What is Life? Chapter 2: The logical Mechanisms of Life Chapter 3: Formalizing and Modeling the World posted Papers and other materials Life and Information Kanehisa, M. [2000]. Post-genome Informatics. Oxford University Press. Chapter 1. Logical mechanisms of life (H400, Optional for I485) Langton, C. [1989]. Artificial Life In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp Optional Flake s [1998], The Computational Beauty of Life. MIT Press. Chapter 1 Introduction Chapters 5, 6 (7-9) Self-similarity, fractals, L-Systems

4 systemhood artificial life as (complex) systems science A system possesses systemhood and thinghood properties Thinghood refers to the specific material that makes up the system Systemhood are the abstracted properties E.g. a clock can be made of different things, but there are implementation-independent properties of clockness Systems science deals with the implementation-independent aspects of systems Robert Rosen, George Klir

5 example of general principle of organization Barabasi-Albert Model: leads to power-law node degree distributions in networks Amaral et al: Most real networks have a cut-off distribution for high degree nodes which can be computationally modeled with vertex aging. complex networks

operations research, evolutionary systems, artificial life, artificial intelligence Works orthogonally, but tightly with

6 study of systemhood separated from thinghood (complex) systems science Study of systemhood properties Search of general principles of organization approach Examples of subdisciplines machine learning, network science, dynamical systems theory, operations research, evolutionary systems, artificial life, artificial intelligence Works orthogonally, but tightly with classical science Interdisciplinary Artificial Life, Systems biology, computational biology, computational social science, etc. From Klir [2001]

7 success of artificial life Not so much on uncovering general principles of molecular life Embodied cognition Evolutionary robotics, morphodynamics Is artificial life just another way of doing artificial intelligence? Arguably not about logical forms What about general principles of life? why not more and surprising results about the living organization? even most successful research in artificial life rarely goes beyond showing that artificial organisms can observe the same behaviors as their real counterparts what can one do with artificial organisms that one cannot do with real bacteria?

(bio) complexity in the last few years Important issues for the study of general principles of life Post-genome informatics Minoro Kanehisa: biology is now moving onto synthesis from structural and

8 (bio) complexity in the last few years Important issues for the study of general principles of life Post-genome informatics Minoro Kanehisa: biology is now moving onto synthesis from structural and functional genomics Genome structure(design principle?) Computational and Systems Biology Non-reductionist, even emergent modeling of life from biochemical information Complex Systems Modeling Networks Modularity and hierarchies in evolution [Schlosser & Wagner, 2004 ] in networks [Newman, 2006; Guimerà et al 2007] Especially, biochemical regulation

) Computational and Systems Biology Non-reductionist, even emergent modeling of life from biochemical information Complex Systems

9 (bio) complexity in the last few years Important issues for the study of general principles of life Post-genome informatics Minoro Kanehisa: biology is now moving onto synthesis from structural and functional genomics Genome structure(design principle?) Computational and Systems Biology Non-reductionist, even emergent modeling of life from biochemical information Complex Systems Modeling Networks Modularity and hierarchies in evolution [Schlosser & Wagner, 2004 ] in networks [Newman, 2006; Guimerà et al 2007] Especially, biochemical regulation

10 information revolution in Biology post-genome informatics Reductionism in Biology (analysis) search and characterization of the function of building blocks (genes and molecules) Post-genome informatics or systems Biology Synthesis of biological knowledge from genomic information The genome contains information about building blocks but it is naive to assume that it also contains the information on how the building blocks relate, develop, and evolve. Towards an understanding of basic principles of life via the search and characterization of networks of building blocks (genes and molecules) Interdisciplinary Systems biology embraces the view that most interesting human organism traits such as immunity, development and even diseases such as cancer arise from the operation of complex biological systems or networks. Grand (Modeling) Challenge Given a complete genome sequence, reconstruct in a computer the functioning of a biological organism

Hertzian modeling paradigm Modeling the World The most direct and in a sense the most important problem which our conscious knowledge of nature should enable us to solve is the anticipation of future

11 Hertzian modeling paradigm Modeling the World The most direct and in a sense the most important problem which our conscious knowledge of nature should enable us to solve is the anticipation of future events, so that we may arrange our present affairs in accordance with such anticipation. (Hertz, 1894) Model Symbols (Images) Initial Conditions Formal Rules (syntax) Logical Consequence of Model Predicted Result???? Observed Result (Pragmatics) Encoding (Semantics) Measure Measure World 1 Physical Laws World 2

The Antikythera Mechanism 2,000-year-old astronomical calculator bronze

shipwreck, was used by ancient Greeks to display astronomical cycles.

positions With imaging and high-resolution X-ray tomography to study how

complicated arrangement of at least 30 precision, hand-cut bronze gears

12 The Antikythera Mechanism 2,000-year-old astronomical calculator bronze mechanical analog computer discovered more than 100 years ago in a Roman shipwreck, was used by ancient Greeks to display astronomical cycles. built around the end of the second century BC to calculate astronomical positions With imaging and high-resolution X-ray tomography to study how it worked. complicated arrangement of at least 30 precision, hand-cut bronze gears housed inside a wooden case covered in inscriptions. technically more complex than any known device for at least a millennium afterwards.

13 What do you see? Plants typically branch out How can we model that? Observe the distinct parts Color them Assign symbols Build Model Initial State: b b -> a a -> ab Doesn t quite Work! Let s Observe Nature! a b a a b a b b b a a b a a b b b Psilophyta/Psilotum

: abaababa n=6 : abaababaabaab n=7 : abaababaabaababaababa The length of the string is the Fibonacci

14 Our First Model Fibonacci Numbers! Rewriting production rules Initial State: b b-> a a-> ab n=0 : b n=1 : a n=2 : ab n=3 : aba n=4 : abaab n=5 : abaababa n=6 : abaababaabaab n=7 : abaababaabaababaababa The length of the string is the Fibonacci Sequence Fibonacci numbers in Nature Romanesco:

15 Mathematics Is The Language Of Nature

biomathematics Equations to describe static patterns of living

16 design principles artificial growth D Arcy Wentworth Thompson ( ) On Growth and Form (1917), laid the foundations of biomathematics Equations to describe static patterns of living organisms Shells, cauliflower head, etc. Transformations of form changing a few parameters

18 D Arcy Thompson transformations of form

exploring similarities across nature Natural design principles self-similar structures Trees, plants, clouds, mountains morphogenesis Mechanism Iteration, recursion, feedback Dynamical Systems and

19 exploring similarities across nature Natural design principles self-similar structures Trees, plants, clouds, mountains morphogenesis Mechanism Iteration, recursion, feedback Dynamical Systems and Unpredictability From limited knowledge or inherent in nature? Mechanism Chaos, measurement Collective behavior, emergence, and self-organization Complex behavior from collectives of many simple units or agents cellular automata, ant colonies, development, morphogenesis, brains, immune systems, economic markets Mechanism Parallelism, multiplicity, multi-solutions, redundancy Adaptation Evolution, learning, social evolution Mechanism Reproduction, transmission, variation, selection, Turing s tape Network causality (complexity) Behavior derived from many inseparable sources Environment, embodiment, epigenetics, culture Mechanism Modularity, connectivity, stigmergy

readings Next lectures Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 7, sections 7.1, 7.2 and 7.

20 readings Next lectures Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 7, sections 7.1, 7.2 and 7.4 Fractals and L-Systems Appendix B.3.1 Production Grammars Lecture notes Chapter 1: What is Life? Chapter 2: The logical Mechanisms of Life Chapter 3: Formalizing and Modeling the World posted Papers and other materials Logical mechanisms of life (H400, Optional for I485) Langton, C. [1989]. Artificial Life In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp Optional Flake s [1998], The Computational Beauty of Life. MIT Press. Chapter 1 Introduction Chapters 5, 6 (7-9) Self-similarity, fractals, L-Systems

biologically-inspired computing lecture 6 Informatics luis rocha 2015 INDIANA UNIVERSITY biologically Inspired computing

biologically-inspired computing lecture 6 Informatics luis rocha 2015 INDIANA UNIVERSITY biologically Inspired computing lecture 6 -inspired Sections I485/H400 course outlook Assignments: 35% Students will complete 4/5 assignments based on algorithms presented in class Lab meets in I1 (West) 109 on Lab Wednesdays Lab 0 :