V. Life's Evolutionary Development Organizes Itself: A 2020s Genesis Synthesis
B. Systems Biology Integrates: Genomes, Networks, Symbiosis, Deep Homology
Neher, Richard and Boris Shraiman. Statistical Genetics and Evolution of Quantitative Traits. Reviews of Modern Physics. 83/4, 2011. Early in the 2010s, UC Santa Barbara, Kavli Institute for Theoretical Physics scope out approaches to technically achieve a vital, necessary unity of a material basis and physiological replication.
The distribution and heritability of many traits depends on numerous loci in the genome. In general, the astronomical number of possible genotypes makes the system with large numbers of loci difficult to describe. Multilocus evolution, however, greatly simplifies in the limit of weak selection and frequent recombination. In this limit, populations rapidly reach quasilinkage equilibrium in which the dynamics of the full genotype distribution, including correlations between alleles at different loci, can be parametrized by the allele frequencies. This review provides a simplified exposition of the concept and mathematics of QLE which is central to the statistical description of genotypes in sexual populations. (Abstract)
Newman, Stuart. Animal Egg as Evolutionary Innovation: A Solution to the “Embryonic Hourglass” Puzzle. Journal of Experimental Zoology: Molecular and Developmental Evolution. 316/467, 2011. By referring to original self-organizing “physical” processes, the New York Medical College biologist can go on to elucidate nature’s innate propensity for the formation of ovular cells. After citing a commonality across oviparous fauna of initial eggs, the “embryonic hourglass” in biology is defined as “a morphologically conserved intermediate state of development in vertebrates before they go on to assume their class-specific character,” say fish, bird or mammal. An explanatory resolve of this dilemma, and of life’s whole gestation, is achieved by the introduction of “dynamic pattern modules.” Not yet fully defined, these molecular assemblies appear to play an intermediary role, as if a translator, between self-organizing forces and generative cell to cell interactions and signaling functions. But reading the engaging paper, one gets a sense of an intrinsically fertile, egg producing, embryonic cosmos.
The evolutionary origin of the egg stage of animal development presents several difficulties for conventional developmental and evolutionary narratives. If the egg's internal organization represents a template for key features of the developed organism, why can taxa within a given phylum exhibit very different egg types, pass through a common intermediate morphology (the so-called “phylotypic stage”), only to diverge again, thus exemplifying the embryonic “hourglass”? Moreover, if different egg types typically represent adaptations to different environmental conditions, why do birds and mammals, for example, have such vastly different eggs with respect to size, shape, and postfertilization dynamics, whereas all these features are more similar for ascidians and mammals? Here, I consider the possibility that different body plans had their origin in self-organizing physical processes in ancient clusters of cells, and suggest that eggs represented a set of independent evolutionary innovations subsequently inserted into the developmental trajectories of such aggregates. (Abstract, 467)
Nielsen, Jens. Systems Biology of Metabolism. Annual Review of Biochemistry. 86/11.1, 2017. A Chalmers University of Technology, Sweden, biologist provides a good entry to this holistic approach which blends top down syntheses with bottom up molecular and cellular components and functions.
Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed. (Abstract)
Noble, Denis. A Biological Relativity View of the Relationships between Genomes and Phenotypes. Progress in Biophysics and Molecular Biology. 111/2-3, 2013. For an issue on “Conceptual Foundations of Systems Biology,” the Oxford University physiologist and innovative leader in this movement continues his advocacy of the equally present dynamic interconnections between all the nucleotide and molecular components. In accord with James Shapiro, Eva Jablonka, and others, by this view a whole vista of expanded “epi-omics” (my term) is being opened. “Biological relativity,” from Nottale and Auffray (search), denotes that organisms have multilevel, creative dynamics which are not ruled by any one scalar stage. See also “A Theory of Biological Relativity: No Privileged Level of Causation” in Interface Focus (6/2, 2011), and “Biophysics and Systems Biology” (2010) noted above.
This article explores the relativistic principle that there is no privileged scale of causality in biology to clarify the relationships between genomes and phenotypes. The idea that genetic causes are primary views the genome as a program. Initially, that view was vindicated by the discovery of mutations and knockouts that have large and specific effects on the phenotype. But we now know that these form the minority of cases. Many changes at the genome level are buffered by robust networks of interactions in cells, tissues and organs. The ‘differential’ view of genetics therefore fails because it is too restrictive. An ‘integral’ view, using reverse engineering from systems biological models to quantify contributions to function, can solve this problem. The article concludes by showing that far from breaking the supervenience principle, downward causation requires that it should be obeyed. (Abstract)
Noble, Denis. Biophysics and Systems Biology. Philosophical Transactions of the Royal Society A. 368/1125, 2010. After a state of the art survey, the emeritus Oxford University Chair of Cardiovascular Physiology offers his well honed view of organisms as upwardly and downward multifactorial and multilevel entities. Drawing on physical “scale relativity” theories of Laurent Nottale and Charles Auffray (search), it is advised that a certain “privileged level of causation” is not supported. As a result, novel, enhanced appreciations of “genetic programs” and a systems evolution can accrue. See also in this journal his earlier “Genes and Causation” (366/3001, 2008) and 2012 papers elsewhere on this site.
Biophysics at the systems level, as distinct from molecular biophysics, acquired its most famous paradigm in the work of Hodgkin and Huxley, who integrated their equations for the nerve impulse in 1952. The modern field of computational biology has expanded rapidly during the first decade of the twenty-first century and, through its contribution to what is now called systems biology, it is set to revise many of the fundamental principles of biology, including the relations between genotypes and phenotypes. Evolutionary theory, in particular, will require re-assessment. To succeed in this, computational and systems biology will need to develop the theoretical framework required to deal with multilevel interactions. While computational power is necessary, and is forthcoming, it is not sufficient. We will also require mathematical insight, perhaps of a nature we have not yet identified. (Abstract)
Noble, Denis. The Music of Life. Oxford: Oxford University Press, 2006. The renowned Professor of Cardiovascular Physiology at University College London offers lyrical insights into systems biology guided by musical metaphors - but seemingly without a composer since score and melody must co-evolve. An insightful chapter then finds a deep correspondence between genetic realms and human language. Similar to many Chinese characters based on a small number of prime cases, a genome uses a few regulatory modules which control how multitudes of DNA strands are expressed. Natural selection then acts upon a preferential result of such heretofore unknown “invisible general principles.” See a book review by Eric Werner in Science for August 10, 2007.
Systems biology is where we are moving to. Only, it requires a quite different mind-set. It is about putting together rather than taking apart, integration rather than reduction. (xi)
Nurse, Paul. Biology as an Organized System. www.guardian.co.uk/science/video/2010/nov/05/paul-nurse-life-information-networks. We note this 11 minute clip from November 12, 2010 by the Nobel Laureate biologist for several reasons. (Also access via keywords) This 21st century systems turn, it is said, now ought to put a greater emphasis to informational and self-organization qualities. In accord with its vernacular, DNA is a “digital storage device,” life is a “chemical machine,” cells are “logical computational machines” and so on, for mechanism rules. But then near its conclusion, Nurse suddenly avers that this is in fact a male, one particle at a time, myopia. Going forward, if to truly perceive and appreciate life’s viable networks, a “woman’s” integrative, systems, holistic vision is imperative. See also his 2008 article “Life, Logic and Information” in Nature (454/424).
O’Malley, Maureen and John Dupre. Fundamental Issues in Systems Biology. BioEssays. 27/12, 2005. University of Exeter philosophers consider a necessary conceptual basis for the transition from molecular reduction to an equal emphasis on global complexity and dynamic networks in genomes and cellular metabolism.
O’Malley, Maureen, et al. A Philosophical Perspective on Evolutionary Systems Biology. Biological Theory. 10/1, 2015. O,Malley, University of Sydney, Orkun Soyer, University of Warwick, and Mark Siegal, NYU, introduce a special issue on this expanded perspective. With a systems biology integration underway since circa 2000, a further phase would be to reconsider and embellish life’s long developmental course by a similar synthesis. See also Explanatory Integration Challenges in Evolutionary Systems Biology by Sara Green, et al, and The Comet Cometh: Evolving Developmental Systems by Jaeger, Johannes Jaeger, et al, search each name.
Evolutionary systems biology (ESB) is an emerging hybrid approach that integrates methods, models, and data from evolutionary and systems biology. Drawing on themes that arose at a cross-disciplinary meeting on ESB in 2013, we discuss in detail some of the explanatory friction that arises in the interaction between evolutionary and systems biology. These tensions appear because of different modeling approaches, diverse explanatory aims and strategies, and divergent views about the scope of the evolutionary synthesis. We locate these discussions in the context of long-running philosophical deliberations on explanation, modeling, and theoretical synthesis. We show how many of the issues central to ESB’s progress can be understood as general philosophical problems. The benefits of addressing these philosophical issues feed back into philosophy too, because ESB provides excellent examples of scientific practice for the development of philosophy of science and philosophy of biology. (Abstract)
Ogura, Takehiko and Wolfgang Busch. Genotypes, Networks, Phenotypes: Moving Toward Plant Systems Genetics. Annual Review of Cell and Developmental Biology. 32/103, 2016. Vienna Biocenter, Gregor Mendel Institute, Austrian Academy of Sciences researchers scope out ways to perceive botanical flora by way of the latest complex network dynamics.
One of the central goals in biology is to understand how and how much of the phenotype of an organism is encoded in its genome. Although many genes that are crucial for organismal processes have been identified, much less is known about the genetic bases underlying quantitative phenotypic differences in natural populations. We discuss the fundamental gap between the large body of knowledge generated over the past decades by experimental genetics in the laboratory and what is needed to understand the genotype-to-phenotype problem on a broader scale. We argue that systems genetics, a combination of systems biology and the study of natural variation using quantitative genetics, will help to address this problem. We present major advances in these two mostly disconnected areas that have increased our understanding of the developmental processes of flowering time control and root growth. (Abstract)
Padilla, Dianna and Brian Tsukimura. A New Organismal Systems Biology: How Animals Walk the Tight Rope between Stability and Change. Integrative & Comparative Biology. 54/2, 2014. SUNY Stony Brook, and California State University naturalists introduce a special section of presentations from the ICB Society 2013 annual meeting on this “grand challenge” to their field. Typical papers are A System Approach to Integrative Biology, and Developmental Change in the Function of Movement Systems.
Our ability to predict which features of complex integrated systems provide the capacity to be robust in changing environments is poorly developed. A predictive organismal biology is needed, but will require more quantitative approaches than are typical in biology, including complex systems-modeling approaches common to engineering. This new organismal systems biology will have reciprocal benefits for biologists, engineers, and mathematicians who address similar questions, including those working on control theory and dynamical systems biology, and will develop the tools we need to address the grand challenge questions of the 21st century. (Abstract)
Palsson, Bernhard. Systems Biology. Cambridge: Cambridge University Press, 2006. An introduction to the encompassing shift from a 20th century reduction to a 21st century integrative approach. This new paradigm involves four steps – identify components parts, prepare “wiring diagrams” of their mainly genetic interactions, mathematically describe such reconstructed networks, and use these models to analyze, interpret and predict experimental outcomes.