Life Sciences, Information Sciences E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Geisteswissenschaft
- Sprache: Englisch
Developed from presentations given at the Cerisy SVSI (Sciences de la vie, sciences de l'information) conference held in 2016, this book presents a broad overview of thought and research at the intersection of life sciences and information sciences. The contributors to this edited volume explore life and information on an equal footing, with each considered as crucial to the other. In the first part of the book, the relation of life and information in the functioning of genes, at both the phylogenetic and ontogenetic levels, is articulated and the common understanding of DNA as code is problematized from a range of perspectives. The second part of the book homes in on the algorithmic nature of information, questioning the fit between life and automaton and the accompanying division between individualization and invariance. Consisting of both philosophical speculation and ethological research, the explorations in this book are a timely intervention into prevailing understandings of the relation between information and life.
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Table of Contents
Cover
Title
Copyright
Preface
Cerisy Symposiums: Selection of Publications
Introduction
PART 1: From Gene to Species: Variability, Randomness and Stability
1 The Emergence of Life: Some Notes on the Origin of Biological Information
1.1. Acknowledgments
1.2. Bibliography
2 Fluctuating RNA
2.1. The ribosome [YON 09]
2.2. Ribosome dynamics [ZAC 16]
2.3. Primitive RNA, ribozymes and viroids [MAU 14]
2.4. The proto-ribosome [YON 09]
2.5. Bibliography
3 Artificial Darwinian Evolution of Nucleic Acids
3.1. Refresher on Darwin’s theory of evolution
3.2. The molecular mechanisms of evolution
3.3. Molecular evolution external to the being
3.4. Imagery of molecular evolution
3.5. Conclusion
3.6. Acknowledgments
3.7. Bibliography
4 Information and Epigenetics
4.1. Bibliography
5 Molecular Forces and Motion in the Transmission of Information in Biology
5.1. The dynamics–function hypothesis
5.2. From thermodynamics to molecular forces
5.3. Like the devil, biology is in the details
5.4. The guitar in the river: theoretical MD
5.5. Experimental MD
5.6. Measuring average MD in whole cells
5.7. Dynamics response to stress
5.8. Conclusion: evolution “is obliged” to select dynamics
5.9. Bibliography
6 Decline and Contingency, Bases of Biological Evolution
6.1. Introduction
6.2. Too many genes in the genomes
6.3. Parasitism and symbiosis
6.4. Asexual eukaryotes
6.5. Yeasts
6.6. Conclusion
6.7. Bibliography
7 Conservation, Co-evolution and Dynamics: From Sequence to Function
7.1. Introduction
7.2. Reverse engineering: from the protein described in a single dimension to its 3D properties
7.3. Before any modeling, the geometric and physical properties, the behavior and history of proteins are characterized
7.4. Chance and selection govern the generation of observed sequences
7.5. Conservation and interaction sites of proteins
7.6. Co-evolution: identification of contacts that can occur at different moments in the lifetime of a protein
7.7. Co-evolution used to reconstruct protein–protein interaction networks in viruses
7.8. Molecular modeling of several partners used to reconstruct protein–protein interaction networks for prokaryotic and eukaryotic organisms
7.9. Dynamics and function
7.10. Conclusions
7.11. Acknowledgments
7.12. Bibliography
8 Localization of the Morphodynamic Information in Amniote Formation
8.1. Introduction
8.2. Schematic view of an amniote
8.3. Mechanism of amniote formation
8.4. Additional features
8.5. Discussion and conclusion
8.6. Bibliography
9 From the Century of the Gene to that of the Organism: Introduction to New Theoretical Perspectives
9.1. Introduction
9.2. Philosophical positions
9.3. From the inert to the living
9.4. Cell theory: a starting point toward a theory of organisms
9.5. The founding principles: from entanglement to integration?
9.6. Conclusion
9.7. Acknowledgments
9.8. Bibliography
10 The Game of Survival, Chance and Complexity
10.1. Introduction
10.2. Complex systems
10.3. Chance and robustness in living organisms
10.4. Evolution and chance
10.5. Conclusion: the logic of the living
10.6. Bibliography
11 Life from the Origins to Homo sapiens
11.1. Setting the scene
11.2. The conquest of solid earth by the vertebrates
11.3. A few insights on evolution
11.4. Primates and humans
12 Plankton Chronicles and the Tara Expeditions
12.1. Plankton
12.2. Plankton and climate
12.3. The Tara Oceans expedition
12.4. Bibliography
13 The Living Species is Not a Natural Kind but an Intellectual Construction
13.1. Introduction
13.2. Two ways to study evolution: genealogy versus phylogeny
13.3. Three main families of concepts of species
13.4. Reconciling the different concepts: pragmatism or essentialism?
13.5. The species and the taxon name
13.6. The nature of species: a salutatory philosophical exercise
13.7. Bibliography
14 The Boxes and their Content: What to Do with Invariants in Biology?
14.1. Natural history
14.2. Natural history and evolution
14.3. The species
14.4. The grade
14.5. Genetic information
14.6. The body plan
14.7. On the misuse of convergences
14.8. Conclusion
14.9. Bibliography
15 Probability, Sense and Evolution (Promenade)
15.1. Introduction
15.2. Difficult dialogue
15.3. Knowledge and big data
15.4. The probabilities
15.5. A few striking examples
15.6. The MCMC method
15.7. Neural networks
15.8. A few questions
15.9. Bibliography
PART 2: Program and Life: Individuation and Interaction
16 Towards an Algorithmic Approach to Life Sciences
16.1. Prologue
16.2. Matter, energy, waves and information
16.3. Medical imaging
16.4. The simulation of the living
16.5. Computer modeling and its levels of abstraction
16.6. The role of embedded computing
16.7. Other subjects
16.8. But is all this without danger?
16.9. The importance of training
17 Where Does the Notion of Function Come From?
18 The Contribution of Artificial Life to Theoretical Biology
18.1. Introduction
18.2. Support to pedagogy
18.3. Food for thought: a philosophy in software form
18.4. Conclusions: royal life, falsifiable modeling
18.5. Bibliography
19 Biochemical Programs and Analog-Digital Mixed Algorithms in the Cell
19.1. Introduction
19.2. Biochemical programs
19.3. Behavioral logical specifications
19.4. Analog specifications
19.5. Biochemical compilation of sequentiality and cell cycle
19.6. Discussion
19.7. Bibliography
20 From Computational Physics to the Origins of Life
20.1. Prebiotic emergence of the basic bricks of life
20.2. Computational approaches and simulations in chemistry
20.3. Computational approaches and simulations in prebiotic chemistry
20.4. New challenges in modeling: reaction networks
20.5. At the frontiers of modeling in prebiotic chemistry: topological approaches
20.6. Conclusion and perspectives
20.7. Bibliography
21 Computing and the Temptation of Babel
21.1. Introduction
21.2. The role of information technologies
21.3. On conflicts of rationality and more specifically on rationality in biology
21.4. Information and its role in biology
21.5. Conclusion
21.6. Acknowledgments
21.7. Bibliography
22 Big Data, Knowledge and Biology
22.1. Introduction
22.2. Big databases, prediction and chance
22.3. Bibliography
23 Natural Language, Formal Language and the Description of the Living World
23.1. Introduction
23.2. Describing the living world
23.3. Formal language
23.4. Conclusion
23.5. Bibliography
24 Vital Individuation and Morphogenetic Information
24.1. Introduction
24.2. The theory of vital individuation
24.3. Lamarck’s ghost
24.4. DNA and its transductions
24.5. Schrödinger’s flower
25 How to Account for Interspecies Socio-cultural Phenomena? An Evolutionist and Interactionist Model
25.1. The difficult dialogue between social sciences and life sciences
25.2. The empire of the principle of identity in theories of society and culture
25.3. A field of neglected social and cultural phenomena
25.4. Linking social sciences and life sciences
25.5. Bibliography
26 Life: A Simplex Whirlwind between Matter, Energy and Information
26.1. Introduction
26.2. The Craig–Lorenz principle, traditional base of animal and human behavior
26.3. The formulations incompatible with modern systemic biology
26.4. Lorenz’s principle reformulated based on current biological data
26.5. Ethosociological interpretation of the reformulated principle
26.6. Regulating societies through economy: ethoeconomy
26.7. The bioethological stages of a social evolution
26.8. Conclusion
26.9. Bibliography
27 Nutritional Interactions through the Living: from Individuals to Societies and Beyond
27.1. The living: a complex nutritional system
27.2. Nutrition at the individual level
27.3. Nutrition at the collective level
27.4. Toward a multilevel theory of nutrition?
27.5. Bibliography
28 Epigenetic Regulation of Protein Biosynthesis by Scale Resonance: Study of the Reduction of ESCA Effects on Vines in Field Applications – Summary 2016
28.1. Introduction
28.2. Materials and methods
28.3. 2003–2011 results
28.4. Results 2012
28.5. Results 2013
28.6. Results 2014
28.7. Results 2015
28.8. Results 2016
28.9. Conclusions
29 Quantum and Multiverse Inflation
29.1. Copernican and anti-Copernican revolutions
29.2. Selection criteria for the number of dimensions of space and time
29.3. Why is time monodimensional?
29.4. The bones of the void
29.5. The buzz effect of inflation
29.6. The eye hears and recognizes the fundamental and harmonic
30 Reontologization of the World and of Life
30.1. Philosophy of information
30.2. Method and levels of abstraction
30.3. “Inforgs” and infosphere
30.4. Originality of the infosphere
30.5. Reontologization
30.6. Ethics of information
30.7. Bibliography
31 Redesigning Life, a Serious and Credible Research Agenda?
31.1. Introduction
31.2. Favorite metaphors
31.3. Inappropriate metaphors
31.4. Ethical challenges and metaphysics
31.5. Bibliography
32 Transhumanism and the Future of Negation
List of Authors
Index
End User License Agreement
List of Illustrations
3 Artificial Darwinian Evolution of Nucleic Acids
Figure 3.1. Dendrogram published in 1859 by Darwin to illustrate the evolution of animal species through time
7 Conservation, Co-evolution and Dynamics: From Sequence to Function
Figure 7.1. Identification of non-conserved residues in the NS5A protein of the HCV genome. a) The sequences of three genotypes, 1b-MD, 2b and 4, from the HCV genome show a restricted number of non-conserved residues (corresponding to the yellow balls in the structures). b) The non-conserved pairs of the 2b genotype are shown in yellow. c) The non-conserved pairs for the three genotypes are shown in yellow (2b), red (1b-MD) and blue (4). For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
8 Localization of the Morphodynamic Information in Amniote Formation
Figure 8.1. Folding of the neural tube, and closure
Figure 8.2. Initial ring shape of the hind gut (colon) fold, as viewed ventrally. To the left, we see the tail bud
Figure 8.3. Initial ring shape of the body fold, in the anterior area. To the extreme right we see the ring of the presumptive amnion
Figure 8.4. Ventral view of the embryo by day 2.5. The posterior area shows the ring of the hindgut already forming a tube, next, the ring of the body, and next the ring of the amnion and the chorion
Figure 8.5. Initial ring shape of the amniotic fold in the anterior area, over the head area. In the background we see the brain folds and the eye cup. To the extreme right we see the ring of the yolk sac
Figure 8.6. Initial ring shape of the amniotic fold in the posterior area, over the tail bud. In the background we see the vertebrae precursors
Figure 8.7. (a) Image of the blastula at day 1 on its yolk. (b) Magnified view showing a structure of concentric rings
Figure 8.8. Imaging of the cells in the rings reveals a stepwise variation of cell sizes from the center toward the periphery
Figure 8.9. Folds tend to be locked at domain boundaries. As shown here, when a stiffer area exists (a white sticker) the rubber folds are deformed and follow the edge of the stiffer area
Figure 8.10. The amniotic ring is triggered by the contact of the tail bud
10 The Game of Survival, Chance and Complexity
Figure 10.1. A partial network of yeast proteins. For a color version of this figure, see www.iste.eo.uk/gaudin/lifesciences.zip
13 The Living Species is Not a Natural Kind but an Intellectual Construction
Figure 13.1. Genealogical level of study with network of reproduction relations in a group of ancestors and descendants; each black dot is an individual (adapted from [SAM 06])
Figure 13.2. Phylogenetic tree of individuals (E, A, B, C) representing their relative and nested kinship relations. From top to bottom: samples, character matrices, nonrooted then rooted on the E individual tree and finally the two tree-solutions showing different changes in the state of characters. B and C are sister-groups, then (B, C) is a sister-group of A. (B, C) or (A, B, C) form a group known as “monophyletic”
18 The Contribution of Artificial Life to Theoretical Biology
Figure 18.1. Two of Dawkins’ biomorphs. These figures are produced by recursive algorithms operating from a specific genetic code capable of evolution
Figure 18.2. In the cellular automata of the “game of life”, a small pattern called a “glider” moves as a result of the synchronous operation of the three basic rules that define them
Figure 18.3. Simulation of a cellular automata in two dimensions in which the rules are such that each cell positively influences its neighbors and negatively influences the cells that are further from it. The produced pattern is made up of alternating black (active cells) and white (inactive cells) bands
Figure 18.4. Software simulation in 2D of membrane closure and cell reproduction, a double layer of amphiphilic molecules forms surrounded by molecules of water. The pink molecules inside are the precursors of amphiphilic molecules. For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
Figure 18.5. Simulation by cell automaton of a minimal autopoietic model. For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
Figure 18.6. The chemotron model depicts the formalization of minimal life and couples three autocatalytic chemical systems: the internal metabolism, the membrane and the information matrix
Figure 18.7. How a swarm of robots are able to cross the ditch
20 From Computational Physics to the Origins of Life
Figure 20.1. Artist’s image of the primordial synthesis of molecules of the living by the action of a lightning bolt, reproduced in the Miller experiment (copyright B. Masella Saitta). For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
Figure 20.2. Scheme illustrating the chemical network of a given chemical system “A”, potentially evolving to “B”, “C”, “D”, “E” or “F”
Figure 20.3. Scheme of dephosphorylation of ATP into ADP
Figure 20.4 Qualitative description of an “A ? B” chemical reaction according to a generalized reaction coordinate and the corresponding thermodynamic landscape. The transformation can occur directly or via the intermediate states “C” or “D”. The free energy in the reaction is shown by ?G and the barrier by ?G‡. For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
Figure 20.5. Scheme defining the coordinates of reactant and product atoms in a given reaction of the decomposition of formamide into CO + NH
3
. The collective coordinate of the reaction is defined in [1] as the (multiple) pathways connecting the two matrices [PIE 15], while z allows for deviation from path [2], and thus for the free exploration of the chemical space of the reaction. R
1
and R
2
are the reference configurations of the reactants and products, while R(t) is the instant configuration throughout the simulation. D(R
i
, R
j
) is the topological (metric) distance between any two configurations (matrices) of the system, and is obtained [3] from the number of instant coordination numbers C
15
(t) between atom I of element S’ (rows) and all the atoms J of element S (columns), that is between the important elements of these matrices, and those that change through the reaction. For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
Figure 20.6. Free energy landscapes in the decomposition of formamide into carbon monoxide and ammonia, in a gaseous state (left) and in a water solution (right), according to reaction coordinates (s, z) defined in the text. The values of the contours are shown on the right. The decomposition in aqueous solution revealed another stability basin, corresponding to the decomposition into ammonia and formic acid. For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
22 Big Data, Knowledge and Biology
Figure 22.1 People who drowned after falling out of a fishing boat correlates with marriage rate in Kentucky (extracted from “Spurious correlations” http://www.tylervigen.com/spurious-correlations, November, 2015. Data sources: Centers for Disease Control & Prevention and Internet Movie Database). For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
23 Natural Language, Formal Language and the Description of the Living World
Figure 23.1. Extract from an illustrated botanical glossary [DOU 07]
Figure 23.2. Text and illustration extracted from the recent description of a new orchid species [KOL 16]
Figure 23.3. Different levels of expression of knowledge from a text in natural language to a structured computerized representation (extracted from [GRA 14])
Figure 23.4. Evolution of the average number of characteristics in the descriptions of new phlebotomine species (Diptera, Psychodidae) published between 1907 and 1988 [VIG 02]
26 Life: A Simplex Whirlwind between Matter, Energy and Information
Figure 26.1. Craig–Lorenz principle in the three parts of the brain
Figure 26.2. Motivating inversion – motivated in economics
27 Nutritional Interactions through the Living: from Individuals to Societies and Beyond
Figure 27.1. Example models of nutritional geometry for a hypothetical organism. The nutritional space is defined by two nutrients (X, Y) and four foods (rails) characterized by different X:Y ratios. The individual is represented by its current and targeted nutritional state. (A) The individual has a choice of three foods. Ingesting food 2 (balanced) allows it to reach its target state fastest, unlike foods 1 and 3 (unbalanced). (B) The individual has a choice between two unbalanced but complementary foods. The arrow sequence illustrates two possibilities of alternating food intake to reach the target state. (C) The individual is in the presence of a single unbalanced food. It can either satisfy its need in Y and suffer a deficit in X (1), satisfy its need in X and suffer from an excess in Y (2) or make a compromise between the two (3). For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
Figure 27.2. Examples of nutritional geometry approaches for studying populations. (A) Example of species-state model (see details in Figure 27.1). The population is composed of two subpopulations of individuals, each with a different target state. The arrows represent interaction networks between individuals (different colors correspond to different types of interactions). (B) Examples of numerical simulation of an individual-based model integrating the principles of nutritional geometry. The individuals exploit a food rich in Y (green) and a food rich in X (red). The trajectory of each individual is shown in white. (C) Experimental group composed of two subpopulations of drosophila (1 and 2) aggregated and interacting on an artificial diet (photo: ML). For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
28 Epigenetic Regulation of Protein Biosynthesis by Scale Resonance: Study of the Reduction of ESCA Effects on Vines in Field Applications – Summary 2016
Figure 28.1.
Figure 28.2.
Figure 28.3. Genodics data and wood disease Observatory data distribution: spread of evolution ESCA rates from year to year, in the regions of Alsace, Gironde, Bourgogne and Val-de-Loire (light gray) and on 30 plots with genodics in the same regions (dark gray). Abscissa: ESCA evolution rates from year to year (final rate/initial rate). Ordinates: Number of data per annual ESCA rate class of evolution.
Figure 28.4.
Figure 28.5. Analysis of the correlations between the average mortality rates due to ESCA over the three to four years before broadcast and the decrease after broadcast
Figure 28.6.
Figure 28.7.
Figure 28.8.
Figure 28.9
29 Quantum and Multiverse Inflation
Figure 29.1. Dimensional weave
Figure 29.2. Evolution of the density of matter and dark energy
Figure 29.3. Historic snows (Planck). For a color version of this figure, see www.iste.co.uk/gaudin/lifescienes.zip
Figure 29.4. Minimum values possible of the density of energy of the vacuum according to string theorists: each valley designates a possible universe. If we are in the lowest of photons and neutrinos, definitive products of the disintegration of matter, we would possess eternity. If we are in a perched valley (false vacuum), our universe would not be eternal since all that is high in altitude or energy must fall
Figure 29.5. Anthropic coincidences: it would have taken very little for us not to exist. The least variation in the intensity of electromagnetic and strong interactions would have resulted in a catastrophe. This hypersensitivity appears to imply that if the universe is unique, it is miraculous
Figure 29.6. Spectrum of fluctuations in the density of matter. The cosmic structures are the result of the gigantic increase in quantum fluctuations due to inflation, taken up and amplified by gravity. The spectrum of density fluctuations is measured on scales varying from 10 to 100,000 million light-years and marvelously reproduced by the cosmological model based on dark energy and dark matter. Superimposing it with the observed spectrum built by piecing the data together (RCF + galaxies + gravitational lens + intergalactic clouds) completes the discussion. The accord is unhoped-for. Such is the best justification for the inflation paradigm; now it must be provided with a theoretical basis and the physical field(s) responsible must be identified. And if it is not the Higg’s field, it is then its brother. For a color version of this figure, see www.iste.co.uk/gaudin/lifesciences.zip
31 Redesigning Life, a Serious and Credible Research Agenda?
Figure 31.1. First logo of the BioBricks Foundation
Figure 31.2. Schematic showing a synthetic life domain
Guide
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Table of Contents
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Life Sciences, Information Sciences
Edited by
Thierry Gaudin
Dominique Lacroix
Marie-Christine Maurel
Jean-Charles Pomerol
First published 2018 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
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www.iste.co.uk
John Wiley & Sons, Inc.
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www.wiley.com
© ISTE Ltd 2018
The rights of Thierry Gaudin, Dominique Lacroix, Marie-Christine Maurel and Jean-Charles Pomerol to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2018930643
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-243-4
Cover photo: Western crowned pigeon (Goura cristata), ZooParc de Beauval, 9th August 2015, photo Dom Lacroix, CC BY-NC-SA.
Preface
The Centre Culturel International de Cerisy proposes, each year from the end of May to early October and within the welcoming context of a 17th-Century castle, an historic monument, meetings to bring together artists, researchers, teachers, students, social and economical actors, as well as the wider public interested in cultural and scientific exchanges.
A long cultural tradition
– Between 1910 and 1939, Paul Desjardins organized the famous “decades” in Pontigny abbey, to unite eminent personalities for debates on literary, social and political themes.
– In 1952, Anne Heurgon-Desjardins, while repairing the castle, created the
Centre Culturel
and continued, with her own personal touch, the work of her father.
– From 1977 to 2006, her daughters, Catherine Peyrou and Edith Heurgon, took the lead and brought a new dimension to the activities.
– Today, after the departure of Catherine and then of Jacques Peyrou, Cerisy continues under the management of Edith Heurgon and Dominique Peyrou, supported by Anne Peyrou-Bas and Christian Peyrou, also part of Cerisy castle’s Civil Society, as well as with the support of an efficient and dedicated team led by Philippe Kister.
A like-minded original project
– They receive, in a prestigious setting far removed from urban disturbances and for a relatively long time period, people who are animated by the same attraction for discussion, in order to, through communal contemplation, invent new ideas and weave lasting connections.
– The Civil Society graciously puts the premises at the disposal of the
Association des Amis de Pontigny-Cerisy
, with no lucrative purpose and recognized for its public interest, currently presided over by Jean-Baptiste de Foucauld, the inspector general of finances.
A regularly supported action
– The
Centre Culturel
, the main means of action of the Association, has organized nearly
750 symposiums
broaching, through completely independent routes, the most diverse of themes. These symposiums have given rise, through various editors, to the publication of approximately
550 books
.
– The
Centre National du Livre
ensures continuous support to the organization and publication of the symposiums. The
territorial collectivities
(Normandy Region, department Council of the Manche, Coutances Mer and Bocage) and the
regional directorate of cultural affairs
bring their support to the Center, which also organizes, alongside the
Universities of Caen
and
Rennes 2
, encounters on themes concerning Normandy and the Great West.
– A
Cercle des Partenaires
, circle of partners, formed of enterprises, local collectives and public bodies, supports and even initiates
prospective
encounters on the main
contemporary challenges
.
Since 2012, a new modern and accessible conference room has allowed for a new formula: the “Entretiens de la Laiterie”, days for exchanges and debates, a collaborative effort from the partners of the Association.
Information:
CCIC, Le Château, 50210 Cerisy-la-Salle, France
Tel.: 02 33 46 91 66
Fax: 02 33 46 11 39
Website: www.ccic-cerisy.asso.fr; email: [email protected]
Cerisy SymposiumsSelection of Publications
Les animaux : deux ou trois choses que nous savons d’eux, Hermann, 2014.
Déterminismes et complexités (autour d’Henri Atlan), La Découverte, 2008.
L’auto-organisation : de la physique au politique, Le Seuil, 1983.
Bachelard, UGE, 10-18, réédition, Hermann, 2011.
Gaston Bachelard : science et poétique, une nouvelle éthique, Hermann, 2013.
Yves Bonnefoy : poésie, recherche et savoirs, Hermann, 2007.
Civilisations mondialisées ? De l’éthologie à la prospective, L’Aube, 2004.
Les nouveaux régimes de la conception, réedition, Hermann, 2014.
Connaissance, activité, organisation, La Découverte, 2005.
Le labyrinthe du continu, Springer-Verlag, 1992.
Jean-Pierre Dupuy : l’œil du cyclone, Carnets Nord, 2008.
L’économie de la connaissance et ses territoires, Hermann, 2010.
L’entreprise, point aveugle du savoir, Editions Sciences Humaines, 2014.
Gestes spéculatifs, Les presses du réel, 2015.
Lieux et figures de l’imaginaire, Hermann, 2017.
Imaginaire, industrie, innovation, Manucius, 2016.
L’industrie, notre avenir, Eyrolles, 2015.
Intelligence de la complexité, L’Aube, réédition Hermann, 2013.
One Hundred Years of Intuitionism (1907-2007), Birkhäuser Verlag AG, 2008.
Renouveau des jardins : clés pour un monde durable ?, Hermann, 2014.
Des possibles de la pensée (itinéraire philosophique de F. Jullien), Hermann, 2014.
Logique de l’espace, esprit des lieux, Belin, 2000.
Ouvrir la logique au monde, Hermann, 2009.
Nietzsche aujourd’hui ? 1. Intensités, 2. Passion, Hermann 2011.
Nourritures jardinières dans les sociétés urbanisées, Hermann, 2016.
Les sens du mouvement, Belin, 2004.
S.I.E.C.L.E., 100 ans de rencontres : Pontigny, Cerisy, IMEC, 2005.
De Pontigny à Cerisy : des lieux pour “penser avec ensemble”, Hermann, 2011.
Temps et devenir (autour d’Ilya Prigogine), Patiño, réédition, Hermann, 2012.
Du risque à la menace : penser la catastrophe, PUF, 2013.
La démocratie à l’œuvre : autour de Pierre Rosanvallon, Seuil, 2015.
Les nouvelles raisons du Savoir, L’Aube, 2002.
Introduction aux sciences cognitives, Gallimard/Folio, 1993, revised 2004.
La sérendipité. Le hasard heureux, Hermann, 2011.
Gilbert Simondon ou l’invention du futur, Klincksieck, 2016.
L’empreinte de la technique. Ethnotechnologie prospective, L’Harmattan, 2010.
Logos et théorie des catastrophes (autour de René Thom), Patiño, 1988.
L’âge de la transition, Les Petits Matins, 2016.
Transplanter : une approche transdisciplinaire, Hermann, 2014.
Villes, territoires, réversibilités, Hermann, 2013.
Le moment du Vivant, PUF, 2016.
Introduction
The organizers of the Cerisy SVSI (Sciences de la vie, sciences de l’information) week wagered that high-caliber researchers from different disciplines, ranging from life sciences on the one hand to information sciences on the other, would have subject material to discuss in Château de Cerisy - this exceptional location conducive to reflection – and would be happy to exchange their respective thoughts on the fundamental question of the link between life and information, an intangible element that is even more elusive than life. Several philosophers and ethologists have also brought us their vision. We hope that you will agree, by reading this book, that the wager paid off.
This book begins by questioning the link between the information contained within the genome and the resulting phenotype. This challenging question does not have a simple and unambiguous answer as was initially thought following the discovery of DNA’s double helix. Indeed, Antonio Lazcano reminded us that, shortly after its discovery, Crick said to his son: “The gene is a code”. This is based on the assumption that a cell follows the program written with the four letters of the genome. It is, however, not so simple, as we now know that the follow-up of the process of genome to individual is in no way a straightforward path. Since the 1960s, we have begun to understand that the environment affects gene expression and that in consequence, in a certain sense, the environment influences the phenotype. With the progresses made in epigenetics, it is now well established that methylation affects gene activation and that, in consequence, the same genome can lead to different genotypic traits.
Let us remind ourselves that genes1 contain sequences of nucleotides, assembled three by three in messenger RNA (transcribed from DNA), and are the “control signals” for any transfer RNA (tRNA), which in turn carry an amino acid. Transported by tRNA to the ribosome, amino acids bind to each other and form a protein. This correspondence system that we call the “genetic code” is “globally deterministic”, but at each stage remains subject to the physiochemical variations of molecules and to the fact that a tRNA can recognize several well-defined nucleic bases (wobble pairing).
We are far from the working principle of a von Neumann computer program, or more precisely a series of instructions (program) transformed by a compiler into machine language that controls elementary operations on data (input) and produces a result (output) or an action. To spell out this analogy, DNA is the program (the instructions); a compiler, tRNA, reads the instructions that are carried out in an assembly machine (the ribosome), which then puts the material (amino acids) together. The proteins represent the result of the program. Unlike von Neumann’s program, as we saw during this symposium, there are many variations that come between DNA and proteins. It is more like a software program, horresco referens, which randomly jumps or modifies some instructions, a compiler so poorly made that it is sensitive to external interventions, but so roundabout that it always leads to the production of machine instructions and therefore results!
To return to biology, the first thing that Bernard Dujon demonstrated was that the genome contains a lot of other “information” than the nucleic bases use for their immediate function. It contains ancient “relic” sequences, redundant parts, forgotten parts and bits of genes of undetermined origins. Bernard Dujon’s presentation gives an idea of the immense mish-mash that is the genome. If software programs were like this, not a single application would work!
Bernard Dujon also showed us that evolution mostly works through regression events, a far cry from notions of “progress” and from the linear phylums often cited from a determinist-finalist perspective and in graphic representations of evolution. Bernard Dujon gave us a simple number to show that the size of the genome does not indicate its place in evolution nor the organism’s complexity – Paramecium has 49,000 genes whereas Homo sapiens have 23,000.
Giuseppe Zaccai, Marie-Christine Maurel, Ada Yonath, András Páldi, Alessandra Carbone and Frédéric Ducongé’s presentations demonstrated the complexity of the operations from DNA to protein and to phenotype. The DNA → RNA → protein → phenotype pathway, although globally determinist, undergoes undeniable variations due to the external environment and internal chaos due to inevitable anomalies arising from such a complex process. As such, in each DNA to RNA, RNA to protein and protein to phenotype pathway, information from outside of the genome sequence has a role. The environment plays a part in the DNA to RNA process by acting on gene expression, the organism’s external environment and the cell environment. In particular, several papers highlighted the role of physical constraints, thermodynamics and molecular arrangement and pairing dynamics (Giuseppe Zaccai, Alessandra Carbone and Vincent Fleury’s papers). Antonio Lazcano, for his part, demonstrated the essential role of RNA in the origin of life. To finish, Jean Fourtaux paints a rapid picture of evolution whose richness is illustrated by the prodigious diversity of planktonic life, discovered by the “Tara” expedition presented by Christian Sardet.
The second part of these presentations revolves around the notion of variability, randomness, probability and species. As András Páldi wrote: “Variation is an intrinsic property of the living […]. It is stability that must be explained”. This opinion is shared by Philippe Kourilsky who returns to the evolutionary consequences of the principle of variation, the central focus of Maël Montévil, Giuseppe Longo and Ana Soto’s presentations who also remind us that, in Philippe Kourilsky’s words, it is a matter of “freedom under constraint”. We often forget, as Guillaume Lecointre pointed out, that the preservation of species through individual variation is one of the concepts underlaid by Darwin’s famous book on the origin of species. It must be said that the role of randomness in biology is often poorly understood, so what can be said about probability? This is one of the subjects touched on by Cédric Villani.
Since variability is intrinsic to life, it is not surprising that the notion of a species becomes difficult to define, as shown by Philippe Grandcolas. Computer science teaches us that the class, like the species, is a category (Guillaume Lecointre) or a representation, according to Kavé Salamatian, whose term is useful for simplifying the work of programmers or scientists. We are in full nominalism, as reminded by Guillaume Lecointre. However, categories or species are just simplifications, with no tangible existence, and Kavé Salamatian warns us against (Babel’s) temptation of considering programming as a universal representation, which would be very limiting. To avoid intentional categorization, all the elements of a category should be enumerated, for example all elephants with an electronic chip. It is exactly this enumeration that we wish to avoid, despite its potential for certain species on their way to rarefaction! To return to the notion of a species, it is therefore not surprising that several papers returned to this problematic (Philippe Grandcolas, Régine Vignes Lebbe and Guillaume Lecointre). Although it seems very complicated, it is simply because we cannot say that there is an exact correspondence between the genome and the species. Bernard Dujon reminds us that the genomes of two representatives of the same species are not exactly identical, although they have approximately 95% of their base pairs in common.
From another viewpoint, computer scientists interpret any program as a function that combines an output with all inputs in a determinist manner, following a more or less complicated algorithm. For a given input, regardless of the machine and the environment, the output will always be the same. As pointed out by Gérard Berry, what characterizes this algorithm is its independence from the machine, just as the information is independent of the medium. The algorithm is non-substantial according to Heinz Wismann. He explains how the notion of function arose in the Middle Ages to allow the transmission of ecclesiastic rights at the death of the holder, such as with episcopal function. There is a dialectic between a nonsubstantiality and incorporation, which refers to the materialization of the functionprogram on any machine. Cédric Villani returns to this important notion of function by questioning the specific function expressed in a network of neurons, a function that is neither analytical nor definable by extension (contrary to the role of a bishop) because it contains several thousand parameters. What epistemological status should we give to such functions that explain nothing, but that are now widely used?
The property of non-dependence of information on a medium makes it such that, as illustrated by Gérard Berry in his report, algorithms are everywhere, including in life sciences (images, three-dimensional representations, models). Although gigantic, databases do not create knowledge or good representations without algorithms (Maël Montévil and Giuseppe Longo, Régine Vignes Lebbe, Cédric Villani). However, modeling in life sciences poses very specific problems, since life has but very distant links with the automatons of artificial life described by Hugues Bersini, despite some spectacular convergences of form. Life is not totally “determinist” in the traditional sense of the term. It is much more “analogical” than discrete, hence the great interest in research on analogical simulators such as those presented by François Fages and Guillaume Le Guludec, and from which the importance placed on computer reconstitutions of chemical reactions (A. Marco Saitta) that shed precious light on prebiotic reactions.
If we return to the cell, there is no doubt that it expresses its genes, but with sufficient variability that we cannot seriously support the analogy with a computer software program. It is a return to function, because even if the DNA molecule is perfectly defined at the chemical level, in the cell its function is subject to the variations in all the other molecules present in the cell, unlike computer programs that are also based on function, but are not subject to variation but to potential errors! It is precisely the conservation through variation that makes the cell different to the machine. The machine’s motto would more likely be: “Conservation through immutability”. Cédric Villani, from his standpoint, also insists on the differences between biology and mathematics in terms of sciences.
However, the process of reproduction as a whole is robust: the daughter yeast resembles the mother yeast and assumes the same functions, the nth generation drosophila resembles its distant ancestor as though they were two drops of water. This being said, although we can say that one drop of water resembles another, there are still many differences at the macro and nano levels. Life is stable in its variation (Guillaume Lecointre), that is to say variability is the very source of robustness. With all its more or less successful proteins, more or less folded, there will always be some, or often one, that binds to the right place, as demonstrated by Alessandra Carbone.
An overview of the presentations led to the idea that the emergence of life and its development are the result of constant trial and error, devoid of sense, in which what “works” best has the ability to supplant the other inhabitants of a niche and last there as long as there are nutritional resources within it. As we were told by Giuseppe Zaccai, “in physics there are laws, in biology there are only exceptions”.
We return to Darwinism at this stage, but at the heart of the organism at the cell level, this cellular Darwinism opens up therapeutic perspectives in the fight against cancer as indicated by Guillaume Lecointre. However, as Philippe Kourilsky says, this trial and error has its rules (as we have just mentioned) and its constraints (notably that of its environment). It is therefore necessary to complete Giuseppe Zaccai’s comment by saying that the exceptions do however obey laws and are subject to constraints.
In terms of evolution, as evoked by Bernard Dujon, there is a lot of destruction and creation of species through crises. The analogy that springs to mind is that of the creative destruction of Schumpeter. If we were to latch back onto programming, evolution is like an algorithm of simulated annealing, aptly named genetic algorithm. We optimize locally and, from time to time, we jostle the system randomly to prevent from finding ourselves trapped in a minimum local (let us call this an evolutionary impasse). On this topic, Cédric Villani spoke of the “Metropolis” algorithm that is a precursor of genetic algorithms. He did, however, also underline the limitations of simulations and Monte Carlo type algorithms: in the end, what can we understand without a model?
Between DNA and the individual, there are many variations, leading to the question of individuality from a Simondonian perspective. This reflection is introduced by Vincent Bontems. In the process of individualization, several presentations insisted on the role of interaction and instruction. Dominique Guillo introduced the notion of interspecies interaction and the transmission of associated knowledge. Mathieu Lihoreau, Jean-Claude Barrey and Pedro Ferrandiz, Michel Duhamel and Joël Sternheimer team put these exchange processes back into an ecological and ethological perspective.
Such rich exchanges between different disciplines lead to astronomical queries (Michel Cassé) and philosophizing on the roles of the living and the artificial (inforgs) that populate the infosphere (Jean-Gabriel Ganascia). The philosophers question themselves on the possibility of creating informational organisms (inforg) with the attributes of life: is it feasible? Desirable? The answers from Bernadette Bensaude Vincent and Jean-Michel Besnier are clearly and resolutely negative. As we are reminded by Jean-Michel Besnier, in a well established philosophical tradition, humanism is the power to say “no”. Coming from the symposium, there was a desire to add that individualization is, in this sense, the obligate passage of humanism and this power to say “no” begins in the cell with the possibility to not completely obey the genes. From this single action, the cell is most definitely not an automaton.
In a metaphorical sense, let us say that life appears to constitute tremendous trial and error, an intense handiwork fluctuating with the whims of Darwinian selection and varying interactions in a limited and unstable environment. We are very far from a program, but what appears clear is that it is just as important to have information as it is to have matter and that the information is not read the same way at each level. At the genome and cellular levels, there is information that resembles a code but is not one in terms of its execution, which is neither essential nor certain. It is the passage of DNA to phenotype and, finally, to that which characterizes a species, that is the object of the first part of this book: From Gene to Species.
The programming vision is formulated around the notion of an algorithm as explained by Gérard Berry. The algorithm does not vary (or according to another algorithm), which differentiates it radically from the living. Unvarying, it does not individualize itself and this leads to reflections on the notion of individualization and the individual uniqueness in life sciences. This draws a boundary between the automaton and the living, even if they are each informational organisms that interact in the infosphere. The interaction then becomes a central concept in the construction of knowledge. From program to life passing through individualization, interaction and philosophy, such is the main thread for the second part of this book.
Acknowledgments
In addition to the countless individual encouragements and support, the organizers would like to thank the following institutions without whose support this symposium could not have occurred: Électricité de France (Region of Paris), Centre National de la Recherche Scientifique (CNRS), Commissariat à l’énergie atomique (CEA), Institut National de Recherche en Informatique et en Automatique (INRIA), Association Reso, Délégation Générale à la Langue Française et aux Langues de France (DGLFLF), Centre des Monuments Nationaux (Administration de l’Abbaye du Mont Saint-Michel) and ISTE Group.
1
Here, we refer only of protein-coding genes, since there are other genes whose products remain in the form of RNA that are very important at the functional level in cells, but that are never translated into proteins.
PART 1From Gene to Species: Variability, Randomness and Stability
1The Emergence of Life: Some Notes on the Origin of Biological Information
We will never know how life first appeared on our planet. The geological record of the young Earth is scarce, and the few sediments that have been preserved from those times have been metamorphosed to a considerable extent, impeding our understanding of the planetary settings at the time of the origin of life. There are no molecular or physical remnants that provide information about the evolutionary processes that preceded the appearance of the first cellular organisms found in the early fossil record. Direct data are generally lacking not only on the composition of the terrestrial atmosphere during the period of the origin of life, but also on the general and local environmental conditions that may or may not have been important for the emergence of living systems.
Our understanding of the emergence of living systems is also hindered by the lack of an all-embracing, generally agreed definition of life. It is generally assumed that any explanation of the origin of the first organisms should attempt, at least implicitly, to propose the definition of a set of minimal criteria for what constitutes life. However, this has proven to be an elusive, frustrating intellectual endeavor. The absence of such definition sometimes gives the impression that what is meant by the origin of life is described in somewhat imprecise terms, and that several entirely different questions are often confused [LAZ 08]. As discussed here, one of the current debates centers on the possibility raised by some theoreticians that the nature of life can be understood as an emergent complex system, a much publicized alternative that displaces the evolutionary history of living systems into a secondary role, and this is by no means generally accepted among evolutionary biologists.
It is wrongly assumed that since classical antiquity, early philosophers and naturalists appealed to spontaneous generation to explain the appearance of life. This is not quite true. The phrase “origin of life” has several possible meanings, but from the perspective of contemporary evolutionary biology, it refers to the emergence of the first ancestors of all contemporary life forms. As a matter of fact, during two millennia, spontaneous generation was seen mostly as a non-sexual reproductive mechanism, and it was not until Erasmus Darwin, Georges Louis Leclerc de Buffon and Jean-Baptiste de Lamarck incorporated spontaneous generation within their transformist views that it was conceived as the mechanism that had led to the first appearance of life on our planet [FAR 77].
Like some of his scientific predecessors, Charles Darwin surmised that plants and animals arose naturally from some primordial non-living matter, but rejected the idea that the putrefaction of preexisting organic compounds could lead to the appearance of organisms. As shown by his letters, notebooks and few published statements, he assumed that living organisms were the historical outcome of a gradual transformation of lifeless matter, and his reluctance to address the origin of life in public was due in part to the recognition of the difficulties to develop fruitful experimental approaches to address the problem [PER 09]. Darwin’s attitude, most likely, was due to the fact that in contrast to other scientific fields, 19th Century chemistry never developed an evolutionary perspective [LAZ 16b].
Darwin’s reluctance to discuss the origin of life surprised many of his followers, including Ernst Haeckel, one of his most faithful advocates. As he stated in 1862, “the chief defect of the Darwinian theory is that it throws no light on the origin of the primitive organism – probably a simple cell – from which all the others have descended. When Darwin assumes a special creative act for this first species, he is not consistent, and, I think, not quite sincere …” [HAE 62].
For Haeckel, life had started with microorganisms which, as is well known, are conspicuously absent in Darwin’s work. Few years after the publication of On the Origin of Species, Haeckel raised microbes to a new taxonomic status, creating the Kingdom Protista and recognized them as ancestors of plants and animals. During his lifetime, Haeckel varied the limits of his newly created microbial realm, but he consistently considered bacteria as the most primitive life forms and grouped them in the Monera, a Protistan subgroup that lacked a cell nucleus, which he had speculated was the repository of heredity material [RIC 08].
As a committed monist, Haeckel was convinced that there was no essential difference between living and inert matter, and assumed a spontaneous origin of life was a consequence of the evolutionary continuity between the inorganic world and living entities. As summarized elsewhere, Haeckel concluded in The History of Creation that “[t]he differences which exist between the simplest organic individuals and inorganic crystals are determined by the solid state of aggregation of the latter, and by the semi-fluid state of the former. Beyond that the causes producing form are exactly the same in both. This conviction forces itself upon us most clearly, if we compare the exceedingly remarkable phenomena of growth, adaptation, and the ‘correlation of parts’ of developing crystals with the corresponding phenomena of the origin of the simplest organic individuals (Monera and cells). The analogy between the two is so great that, in reality, no accurate boundary can be drawn” (see [LAZ 16a]).
Haeckel’s ideas exerted an extraordinary influence in many 19th Century naturalists and philosophers, including Jacques Loeb, Jerome Alexander, Stephane Leduc, Alfonso L. Herrera and many others, who conceived cells as chemical machines and developed research programs on experimental abiogenesis that can be considered as a direct intellectual predecessor of current efforts on synthetic biology [PER 16]. In an attempt to understand the origin and nature of life from a strictly materialistic perspective, Loeb, Alexander, Leduc, Herrera and others devoted considerable efforts to the production of lifelike structures from various combinations of inorganic compounds, crystals and different fluids. Their work provided morphological analogs of cell-like features, and the mesmerizing reports of lifelike behavior in microscopic droplets of different compositions led many to the conclusion that these structures provided direct insights on the nature of cells and living processes.
Some of these results have their contemporary equivalents in the proposals of the advocates of complexity theories that attempt to explain the origin and nature of life on the basis of complexity theory and self-assembly phenomena. These approaches, however, do not represent a new, emergent school of thought. As Lewontin and Levin [LEW 96] wrote, “emergence and complexity theories assert the predictability of biological phenomena from simple generating principles […] this view has led to catastrophe theory, chaos theory and more recently, complexity theory”. Indeed, as underlined by Evelyn Fox Keller [KEL 02], these ideas fit smugly into the deeply rooted intellectual tradition that has led many physicists to search for all encompassing laws that can be part of grand theory encompassing many, if not all, complex systems.
Self-assembly is not unique to biology, and may indeed be found in a wide variety of systems, including cellular automata, the complex flow patterns of many different fluids such as tornadoes, cyclic chemical phenomena (such as the Belousov–Zhabotinsky reaction, for instance) and, quite significantly for our understanding of the origin of life, the auto-organization of lipidic molecules in bilayers, micelles and liposomes (see [FAR 05]). Of course, these systems show that self-organizing physical systems lead to highly ordered structures demonstrating that, in addition to natural selection, there are other mechanisms of ordered complexity that operate. There are indeed some common features among these systems, and it has been claimed that they follow general principles that are in fact equivalent to universal Laws of Nature [KAU 93]. Perhaps this is true, but what is available so far is a collection of facts and sets of elegant equations. Unfortunately, underlying all-encompassing principles, if they exist at all, have remained undiscovered, and as noted by Fenchel [FEN 02], in some cases invocations to spontaneous generation appear to be lurking behind appeals of undefined “emergent properties” or “self-organizing principles” that are used as the basis for what many life scientists see as grand, sweeping generalizations with little, if any, relationship to actual biological phenomena.
This has not stopped a number of researchers to attempt to explain the emergence of life as a continuously renewing complex interactive system that emerged as selforganizing metabolic cycles that at first did not require genetic polymers. Evidence for the spontaneous origin of catalytic systems and of metabolic replication would indeed be exciting [KAU 93] – if it could be established. What is quite evident is that complexity models have promised much but have delivered little. As argued forcefully by Szathmary [SZA 00], Anet [ANE 04], Pross [PRO 04] and others, hypothetical replicator networks such as those advocated by Dyson, Kauffman and others, even if they can renew themselves and maintain a given dynamic but stable regime, are in fact phenotypic replicators with limited heredity. Phenotypic replicators have the ability to pass on only some aspects of their phenotypes, not of their genotypic components (if any).
There is little doubt that self-organization phenomena played a role in the emergence of life as shown, for instance, by the remarkable spontaneous assembly of amphiphiles into micelles and bilayer membranes, as well as the dynamical selfassembly properties of nucleic acids. History, in biology, implies genealogy and, in the long term, phylogeny. This requires an intracellular genetic apparatus able to store, express and, upon reproduction, transmit to its progeny information capable of undergoing evolutionary change. Current biology indicates that the biosphere could not have evolved in the absence of a genetic replicating mechanism insuring the stability and diversification of its basic components. How did such replicating genetic polymers appear?
To understand the current emphasis of origin-of-life research in the abiotic appearance of replicative polymers and genetic information let us return briefly to Haeckel’s assumption of cell nuclei as the above of heredity, together with his characterization of the Monera as the oldest biological group, in which the gel-like protoplasm was the organ of both inheritance and nutrition [LAZ 16a, LAZ 16b]. As summarized by Cobb [COB 15], the rediscovery of Mendel’s work in 1900 was rapidly followed by Walter Sutton’s 1902 insight that chromosomes “may constitute the physical basis of the Mendelian law of heredity” and Wilhem Johannsen’s speculations on genes - entities whose very existence was held in doubt for several decades. During the same historical period, bacterial genetics was basically ignored, and for very simple reasons: they lack nuclei and chromosomes, bacterial inheritance was explained in terms of growth and division of the protoplasm and, moreover, they were considered by many as pathogens.
In contrast to an extended prejudice, long before Schrödinger’s 1944 book What is Life?
