Info biologically-inspired computing · 2015. 12. 14. · Reductionism in Biology (analysis) ......

[email protected]://informatics.indiana.edu/rocha/i-bic

biologicallyInspired

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Informatics luis rocha 2015

biologically-inspired computinglecture 5


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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 14th (completed)

Introduction to Python (No Assignment) Lab 1 : January 28th

Measuring Information (Assignment 1) Due February 11th

Lab 2 : February 11th

L-Systems (Assignment 2)

Sections I485/H400


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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 online @ http://informatics.indiana.edu/rocha/i-bic 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. 1-47.

Optional Flake’s [1998], The Computational Beauty of Life. MIT Press.

Chapter 1 – Introduction Chapters 5, 6 (7-9) – Self-similarity, fractals, L-Systems


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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…

systemhood



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complex networksexample of general principle of organizationBarabasi-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.


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(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.

study of “systemhood” separated from “thinghood”

From Klir [2001]


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success of artificial 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?

Not so much on uncovering “general principles” of molecular life


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(bio) complexity in the last few years

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

Important issues for the study of general principles of life


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(bio) complexity in the last few years

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

Important issues for the study of general principles of life


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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

information revolution in Biology


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Modeling the World

World1

Measure

Symbols(Images)

Initial Conditions

Measure

Logical Consequence of Model

ModelFormal Rules

(syntax)

World2Physical Laws

Observed Result

Predicted Result????

Enco

ding

(Sem

antic

s)

(Pragmatics)

“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)

Hertzian modeling paradigm


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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.


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Let’s Observe Nature!

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! Psilophyta/Psilotum

bab

bb

bb

bb b

aa

aa

a aa


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Fibonacci Numbers!

Our First Model 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 1 1 2 3 5 8 13 21 34 55 89 ...

Fibonacci numbers in Nature http://ccins.camosun.bc.ca/~jbritton/fibslide/jbfibslide.htm Romanesco:

http://alt.venus.co.uk/weed/fractals/romanesco.htm


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http://pithemovie.com

Mathematics Is The

Language Of Nature


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artificial growth

D’Arcy Wentworth Thompson (1860 - 1948) On Growth and Form (1917),

laid the foundations of bio-mathematics Equations to describe static

patterns of living organisms Shells, cauliflower head, etc.

Transformations of form changing a few parameters

design principles


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transformations of formD’Arcy Thompson


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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

exploring similarities across nature


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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 online @ http://informatics.indiana.edu/rocha/i-bic 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. 1-47. Optional

Flake’s [1998], The Computational Beauty of Life. MIT Press. Chapter 1 – Introduction Chapters 5, 6 (7-9) – Self-similarity, fractals, L-Systems

readings

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