Multicellular Organism

A hallmark of multicellular organisms is the differentiation of cells into functionally distinct cell types, such as a resting nerve cell or a proliferating skin cell.

From: Computational Systems Biology , 2006

Identifying CpG Islands: Sliding Window and Hidden Markov Model Approaches

Raina Robeva , ... Robin Davies , in Mathematical Concepts and Methods in Modern Biology, 2013

9.1.1 Biochemistry Background

In higher multicellular organisms the genetic composition of an individual is determined by the fusion of sperm and egg nuclei following fertilization. With a few exceptions 1 all cells of a multicellular organism have the same DNA sequence. However, the cells of the multicellular organism have very different patterns of gene expression and thus make very different groups of proteins. During the process of development, cells become differentiated and take on their mature pattern of gene expression. The following question is thus important: Once a tissue is produced during development, how is the tissue-specific pattern of gene expression maintained? Part of the answer lies with the production of specific proteins involved in the transcription of specific genes, part involves the histones which package the DNA, and another part of the answer lies in the chemical alteration of the DNA itself.

In complex organisms a fraction of the cytosine DNA bases may be methylated, with the degree of cytosine methylation varying considerably among fungi, plants, invertebrate and vertebrate animals [1]. Methylation of cytosine occurs on the #5 position (see Figure 9.1) and the resulting entity is called 5-methyl cytosine. If we were to compare the DNA of a pair of differentiated cells (e.g., liver cells or skin cells), we would observe that they had different patterns of methylation. Patterns of methylation are correlated with patterns of gene expression in an inverse relationship, in which silent (non-expressed) genes are methylated. In a particular differentiated cell type, the pattern of methylation is maintained through successive mitoses by the action of enzymes called maintenance methylases.

Figure 9.1. Comparison of unmethylated (left panel) and methylated (right panel) cytosines. The arrow in the left panel marks the #5 position. (The carbons and nitrogens are numbered, in this case, counter-clockwise beginning with the nitrogen on the bottom.)

In vertebrate animals, methylated cytosines occur in the dinucleotide sequence CpG . This dinucleotide is interesting in that its complement on the other strand of DNA is also CpG , and if the C on one strand is methylated, the C on the other strand is too. This state of affairs enables the pattern of DNA methylation to be perpetuated through successive rounds of replication. When a DNA sequence containing methylated C in a CpG dinucleotide is replicated, the two daughter strands will each have the C on the template strand methylated and the C on the new strand unmethylated. DNA in this state of half-methylation is the substrate for the maintenance methylase, which will methylate the unmethylated C on the new strand and thus restore the methylation pattern of the parent DNA strand. The methylation pattern, and the pattern of gene expression, will be inherited through subsequent mitoses.

Methylation of CpG dinucleotides is required for normal embryonic development and patterns of CpG methylation must be established following a generalized demethylation that occurs early in embryonic development [2]. The new methylation patterns are established by de novo methylases and appear to contribute to lineage restriction during development [3,4]. In other words, when pluripotent stem cells give rise to tissue-specific stem cells with more limited differentiation potential, the promoters of a subset of genes which were formerly active in the pluripotent stem cells become methylated and transcriptionally silent [5].

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Biological Foundations of Signal Transduction and the Systems Biology Perspective

Ursula Klingmüller , in Computational Systems Biology, 2006

B Signal transmission from the cell surface to the nucleus

In multicellular organisms, cells do not live in isolation but rely on specific mechanisms to communicate ( Figure 8.2). In close proximity, direct cell-to-cell contact is used, whereas soluble ligands also permit communication over distances. However, integral membrane proteins (receptors) in the cell membrane are essential because cells are surrounded by a lipid membrane that cannot be penetrated by hydrophilic ligands such as hormones and growth factors. They bind the ligand in the extracellular space and mediate signal transmission into the cell interior by activating specific signaling cascades.

Figure 8.2. Signals used for communication of cells. Secreted soluble ligands (light blue) bind to cell surface receptors (blue). In the extracellular space, hydrophobic ligands (purple) are bound to carrier proteins (rose). In proximity to cells, they dissociate from the carrier, migrate through the cell surface membrane, and bind to receptors (light purple) present in the cytoplasm or nucleus. Alternatively, signals are transmitted by direct cell-to-cell contact mediated by cell surface proteins (green).

Finally, the signal is transported across the nuclear membrane and gene expression is modulated. Alternatively, hydrophobic ligands such as steroid hormones or thyroxine are transported by carrier proteins and diffuse after dissociation from the carrier into the cytosol or nucleus, where they bind to specific receptors that regulate transcription of target genes. The principal modes used for intracellular communication are phosphorylation, second messengers, degradation, and complex formation.

Phosphorylation: To convey an intracellular signal, modifications introduced have to be transient. The most general regulatory device adopted by eukaryotic cells is protein phosphorylation because it is simple and reversible, and because ATP is readily available as a phosphoryl donor. The key enzymes for protein phosphorylation in target proteins are protein kinases (which transfer a phosphoryl group from ATP to the hydroxyl group of tyrosine), serine, or threonine residues, whereas protein phosphatases counter-balance the reaction by removing phosphate groups from proteins. Reversible phosphorylation of proteins regulates nearly every aspect of cell life by increasing or decreasing the biological activity of enzymes, stabilizing or marking proteins for destruction, facilitating or inhibiting movements between subcellular compartments, and initiating or disrupting protein-to-protein interaction. Abnormal phosphorylation is the cause or the consequence of many human diseases.

Protein kinases possess a highly conserved overall structure (Huse 2002) and operate as molecular switches. The "on" state (which represents maximal activity) is highly similar in different protein kinases, whereas in the "off" state kinases have minimal activity and adopt a conformation that is distinct for different protein kinase classes. The transition between the two states is highly regulated by phosphorylation, interaction with additional domains, and/or binding of regulatory proteins.

This tight regulatory mechanism was first identified in cytoplasmic tyrosine protein kinases of the src-family (Harrison 2003), which in addition to the protein kinase domain possess an src-homology (SH)2 domain facilitating binding to specific phosphotyrosine residues localized within certain binding motifs and an SH3 domain mediating binding to proline-rich motifs. In addition to cytoplasmic tyrosine kinases, several cell surface receptors possess a tyrosine kinase domain in their cytoplasmic part. Receptor tyrosine kinases—such as the epidermal growth factor receptor (EGF-R) (Schlessinger 2002) and the platelet-derived growth factor receptor (PDGF-R) (Heldin 1992)—are characterized by specific domains within the extracellular portion that interacts with the ligand, by a single transmembrane domain, and by a tyrosine kinase domain in part exposed to the cell interior.

The tyrosine kinase activity is tightly regulated by multiple autoinhibitory mechanisms, including an inhibitory conformation of the extracellular domain, the transmembrane domain, the juxtamembrane domain, and the activation loop. Ligand binding to the extracellular domain causes a conformational switch that leads to the activation of the tyrosine kinase domain. Other cell surface receptors (such as the hematopoietic cytokine receptors, including the interleukin receptors) lack enzymatic activity (D'Andrea 1989) but couple with cytoplasmic tyrosine kinases of the Janus kinase family. Ligand binding to the cytokine receptors causes activation of the receptor-associated Janus kinase and results in tyrosine phosphorylation of the receptor on multiple tyrosine residues.

Phosphorylation on serine or threonine residues occurs much more frequently than tyrosine phosphorylation but is less inducible. The overall structure of serine/threonine protein kinases is very similar to tyrosine protein kinases but the regulation is mediated by additional subunits that bind second messengers or vary in their expression level (Johnson 1996). Another mode of regulation is achieved by phosphorylation or dephosphorylation on multiple residues. For example, cell cycle control is performed by protein serine/threonine kinases of the cyclin-dependent kinase family that are inactive as monomers but activated by cyclin binding.

Regulation of the cell cycle is achieved by synthesis and destruction of cyclines, phosphorylation of the activation loop and the ATP binding loop in the cyclin-dependent kinases, and binding of an inhibitor. Counterintuitive is the regulation of the protein serine/threonine kinase glycogen synthase kinase 3 (GSK-3), which lies at the crossroads of metabolism and signal transduction (Dajani 2001). GSK-3 is active as kinase in the absence of signal and processively phosphorylates substrates at multiple residues that are already prephosphorylated at a C-terminal residue. Upon growth factor binding to cell surface receptors, GSK-3 is phosphorylated at the N-terminus, which turns the N-terminus into a pseudosubstrate and thereby blocks the catalytic cleft of the kinase.

The mitogen-activated protein (MAP) kinases form a signaling cascade consisting of an array of protein serine/threonine kinases (Raman 2003). These protein kinases are characterized by their ability to use protein kinases as substrate and phosphorylate them at two residues, which is required for full activation. Contrary to receptor tyrosine kinases, only one receptor serine/threonine kinase family is known (Shi 2003). The transforming growth factor (TGFβ) beta receptors type I and II possess serine/threonine kinase activity in their cytoplasmic domain, which is regulated by autophosphorylation and inhibitor binding.

The activation of signal transduction is counter-balanced by the activation of protein phosphatases (Tonks 1996), which remove the phosphoryl group from tyrosine, serine, or threonine residues by a cystein-catalyzed mechanism. Characteristic of protein tyrosine phosphatases is the multidomain substructure. Protein tyrosine phosphatases that are located at the cell membrane contain tandem protein phosphatase domains autoregulated by wedge-like structures. The cytoplasmic protein tyrosine phosphatases of the SHP1/SHP2 family harbor two N-terminal SH2 domains that block the protein tyrosine phosphatase domain in the inactive state.

Upon activation of signal transduction, the SH2 domains mediate recruitment to tyrosine-phosphorylated receptors and thereby open the phosphatase domain. Tyrosine phosphorylation within cells is rapidly induced by stimulation of cells, but declines soon after. Serine/threonine protein phophatases share a homologous catalytic domain and are regulated by multiple regulatory subunits controlling phosphatase activity and selection of substrate (Janssens 2001). The most prominent examples are protein phosphatase PPI and PPIIa.

In addition to phosphorylation on proteins, phosphorylation of phospholipids (in particular, phosphoinositides) is used for signal transduction. Phosphoinositides are characterized by an inositol head group that can be phosphorylated by phosphoinositide kinases on multiple hydroxyl groups and that serves as a lipid-derived second messenger (playing a role in vesicle trafficking and signal transduction). The central enzyme for signal transduction is the phosphoinositide 3 (PI3) kinase, which phosphorylates phosphoinositides at the D-3 position of the inositol ring structure (Cantley 2002).

Best studied is the class IA PI3 kinase, which is composed of a regulatory subunit (p85) and a catalytic subunit (p110). Growth factor stimulation results in a transient increase in phosphoinositide-3,4-bisphosphate (PtdIns-3,4-P2) or phosphoinositide-3,4,5-trisphosphate (PtdIns-3,4,5-P3), which is rapidly counteracted by phospho-inositde phosphatases—such as the SH2-domain, containing inositol 5-phosphatase SHIP, and the phosphatase and tensin homolog deleted on chromosom 10 (PTEN)—which removes specific phosphate groups of phosphoinositides.

Another mode used for intracellular communication is protein-bound guanosine triphosphate (GTP). GTP-binding proteins such as Ras belong to the GTPase super-family and are molecular switches that alternate between the GTP-bound activated state and a GDP-bound off state (Downward 1997). The activation is accelerated by a guanine nucleotide-exchange factor (GEF) that promotes dissociation of GDP from Ras and thus the formation of a Ras-GTP complex. Binding of a GTPase-activating protein (GAP) to the Ras-GTP complex results in GTP hydrolysis and GAP dissociation and thus the formation of the inactive Ras-GDP complex.

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Multistability and Multicellularity: Cell Fates as High-Dimensional Attractors of Gene Regulatory Networks

Sui Huang , in Computational Systems Biology, 2006

ABSTRACT

Cells in multicellular organisms exhibit discrete mutually exclusive phenotypic states, such as proliferation, apoptosis, or differentiation into various cell types. Each of these "cell fates" is associated with a particular stable genome-wide gene expression profile defined by 25,000 genes. To explain the collapse of the hyperastronomical number of combinatorially possible expression configurations into those characteristic of observable cell fates, the latter have been proposed to be high-dimensional attractors in gene activity state space.

Here we review the biology of cell fate regulation from a "systems" perspective and discuss two gene network models (small systems of differential equations and high-dimensional Boolean networks) to illustrate how molecular interactions produce multistability and attractors. Implications for cell fate regulation, stem cell multipotency, stochastic fate decisions, and cancer are discussed. This chapter also illustrates the necessity for embracing both pathway details as well as simplifying abstraction in computational systems biology.

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Cell Death (Apoptosis)

Masato Enari , in Encyclopedia of Physical Science and Technology (Third Edition), 2003

I Overview

Homeostasis in multicellular organisms is based on a balance between life and death of cells. Apoptosis was recognized as a phenomenon distinct from necrosis by Wyllie and Kerr in 1972. In the necrotic process, swelling of cells precedes their explosion and results in the release of intracellular components that may be toxic to other cells. In apoptosis, the dying cells exhibit nuclear and cytoplasmic condensation, fragmentation of cell bodies, chromosomal DNA fragmentation into nucleosomal units, loss of mitochondrial function, and alterations of cell membrane composition ( Fig. 1). Subsequently, apoptotic cells are engulfed by phagocytes and neighboring cells, and are recycled. Most cells suffering physiological cell death undergo the apoptotic process and the superfluous or harmful cells generated during the developmental process are removed by apoptosis. For example, apoptosis occurs in tail resorption, neuronal network formation, clonal deletion of immature and autoreactive T cells, Wolffian and Mullerian duct regression during sexual development, tumor regression, and in elimination of virus-infected cells. Furthermore, it has been suggested that apoptosis occurs in many diseases such as cancer, fulminant hepatitis, acquired immune deficiency syndrome (AIDS), diabetes mellitus, and neurodegenerative disorders such as Alzheimer's disease and prion disease.

FIGURE 1. (a) Fas-induced apoptosis in lymphoid cells (WR19L cells overexpressing Fas). The cells were incubated with 0.5 μg/ml of an agonistic anti-Fas antibody at 37   °C for 120   min and their ultrastructure was examined under a transmission electron microscope. The electron micrograph of untreated cells is shown in the upper panel. Bars,   1   μm. (b) Chromosomal DNA of growing cells (lane 1) or dying cells (lane 2) was run through a 1.5% agarose gel. M indicates molecular weight markers.

Growth and differentiation of cells are strictly regulated by factors such as cytokines and low-molecular-weight compounds such as steroid hormones. These factors are generally bound to the corresponding receptors in order to transduce the appropriate cell signals, to promote growth and differentiation. On the other hand, apoptotic cell death is aggressively controlled by a number of polypeptides, so-called death factors exposed at the cell surface or circulating in the body as soluble factors in some situations. Growth and differentiation factors act via transcriptional regulation through the activation of a series of protein kinases. On the other hand, death factors execute apoptosis through the activation of caspases, and many proteins essential for cell survival are degraded by these activated caspases. It is currently believed that apoptotic death is due to the degradation of many functional proteins by caspases. Cell-free systems for the study of apoptosis have been established and facilitate our understanding of the underlying molecular mechanisms. Several factors involved in apoptotic pathways have been identified and part of these pathways has been revealed by biochemical approaches based on cell-free apoptosis system. Regulatory mechanisms for apoptosis include: the CED-3/caspase family proteases and CED-4/Apaf-1 family which act as executors of apoptosis; CED-9/Bcl-2 family (including anti-apoptotic and pro-apoptotic factors) which act as regulators of apoptosis, and many factors which contribute to apoptotic morphological changes have been identified. Here, I shall focus on the currently proposed molecular mechanisms of death receptor activity and caspase activation. Moreover, I will also discuss the DNase responsible for apoptotic DNA fragmentation, both in vitro and in vivo, and finally the mechanism by which apoptotic cells are cleared.

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Volume 2

David T. Harris , in Encyclopedia of Tissue Engineering and Regenerative Medicine, 2019

Introduction

Stem cells are found in all multicellular organisms and are defined as cells that can differentiate into specialized mature cells as well as divide to produce more stem cells. Stem cells can be divided into embryonic/fetal stem cells and adult stem cells, based on their origin. Stem cells can be further classified as totipotential, capable of giving rise to all tissues in the organism including the organism itself; as pluripotential able to give rise to multiple lineages of tissues and cells from different germ lineages; as multipotential which can give rise to different cell types generally within the same germ lineage; or as progenitor cells which only give rise to more lineage-restricted cells and tissues from a single germ layer origin. This appreciation for the numbers, types and potentials of stem cells has given rise to the fields of regenerative medicine and tissue engineering which encompasses a variety of cellular therapies. The most likely use of stem cells in regenerative medicine and tissue engineering are in applications for orthopedics (joints, ligaments, tendons), neurology (stroke, brain injury [TBI], Parkinson's) and cardiology (myocardial infarction [MI], heart failure). While the odds of using stem cells for a typical cancer transplant (i.e., bone marrow transplant) is only 1 in 2000, the odds of needing stem cells for a regenerative medicine or tissue engineering applications (based on indications listed above) is estimated at 1 in 5 to 1 in 10 ( http://www.dhhs.gov/reference/newfuture.shtml) (West, 2011). Fortunately, there are many different sources of stem cells available, with the most studied source being hematopoietic stem cells (HSC) and mesenchymal stem/stromal cells (MSC), both of which can be found in perinatal tissues.

Cord blood (CB) was rediscovered by Boyse (Broxmeyer et al., 1989) in the early 1980s, and early work done by Broxmeyer and others (Broxmeyer et al., 1989; Harris, 1996) in the late 1980s demonstrated it to be a rich source of HSC. The first transplant using cord blood (CBTx) was performed in 1989 by Gluckman et al. (1989) and the first CB biobanks were started by Harris (1998) and Rubinstein et al. (1993)both in 1992. Since that time more than 750,000 CB have been stored in public banks and more than 4   million CB are estimated to be stored in private banks (Ballen, 2017). However, most samples have never been and probably never will be used for transplant (luckily) due to the infrequency of these malignant and genetic blood disorders (www.lls.org). With the discovery of embryonic stem (ES) cells by investigators at the University of Wisconsin (Thomson et al., 1998) and the subsequent "creation" of the fields of regenerative medicine and tissue engineering there now is new hope for use of these banked stem cells. Early laboratory and animal work by many investigators (He et al., 2005) has demonstrated that CB and more recently cord tissue (CT) stem cells lay somewhere between fetal and adult stem cells in terms of plasticity and potential utility. These stem cells can only be obtained from a live birth and thus avoid the ethical issues associated with ES cells, the costs of making iPS cells, and the limitations of adult stem cells.

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Complexity in Organismal Evolution

Stuart A. Newman , in Philosophy of Complex Systems, 2011

4 Variation and Innovation in the Evolution of Morphological Complexity

With respect to shape and form in multicellular organisms, two types of evolutionary change can be distinguished: those that incrementally remold an existing structure and those that lead to structures not previously present, i.e., "novelties" [ Müller and Newman, 2005]. Since nothing comes from nothing, there will sometimes be disagreements over the attribution of novelty to a biological structure [Moczek, 2008]. At the extremes, however, the cases are usually clear: the reshaping of birds' beaks occurs gradually, over time, with both modern and paleontological intermediates in evidence [Weiner, 1994]; the beak itself, however, appeared fairly suddenly in the fossil record.

Darwin's mechanism of evolution by selection for increased fitness provides a plausible account of the wide array of bird beak morphologies, and indeed the Galapagos finches, with beaks variously adapted to particular food resources, were a paradigmatic example for this investigator. The molecular mechanisms underlying the shaping and reshaping of the bird beak provide a straightforward causal basis for the incremental operation of natural selection. The epithelia (embryonic skin) covering the two growing masses of tissue of the developing bird face that form the beak secrete a protein known as BMP4. This protein acts as a "mor-phogen," (i.e., a molecule produced by certain cells that affects the developmental fate of cells at a distance from the source), which regulates the rate of growth of the underlying mesenchyme (embryonic connective tissue) that in turn forms the species-characteristic upper and lower jaws. The relative amounts and timing [Merrill et al., 2008] of BMP4 production by the epithelium, and then by the mesenchyme, can explain differences in beak shapes not only among Darwin's finches [Abzhanov et al., 2004] but also between chickens and ducks [Wu et al., 2004].

Because developmentally efficacious genes such as that specifying BMP4 can be regulated in a tissue-specific fashion [Shentu et al., 2003], it is a reasonable assumption that finch populations will exhibit genetic variability in the embryonic expression of BMP4 in the developing beak tissues. When confronted with new seeds or insects, as a result, perhaps, of climatological changes, genetically different subpopulations of finches would exhibit different fitnesses, and the population means of various parameters of beak size and shape would thereby change by classic Darwinian natural selection.

For completeness, however, it should also be noted that BMP4, like some other developmental factors, is regulated in a tissue-specific fashion by vitamin A and other nutrients and dietary regimes [Baleato et al., 2005; Villaneuve et al., 2006; Abdel-Hakeem et al., 2008; Battacharyya et al., 2008]. If a group of adventurous finches explored new sources of food rich in these development-perturbing factors there would be no necessary good-fit between induced beak shape changes and the ability to exploit the new resources in the birds' offspring. But where there happened to be such a correspondence, this lineage would tend to remain in its chosen niche [Odling-Smee et al., 2003], perhaps becoming genetically isolated from the rest of the founding population by drift, or by stabilizing selection.

This evolutionary scenario, termed the "Baldwin effect" [Crispo, 2007], can be reconciled with Darwinian mechanisms if selection closely tracks the environmentally induced phenotypic alteration. However, in cases where the new phenotype sets off in new ecological directions, as happens with "transgressive segregation" in plants, whereby hybrids with extreme or novel phenotypes establish themselves in new niches [Rieseberg et al., 1999], the challenge to the standard explanatory model would be substantially greater.

While it is thus clear that evolutionary changes in the shaping of a structure, such as a finch's beak, can arise from either Darwinian or non-Darwinian mechanisms, it is less apparent how to decide whether such changes (which follow relatively continuous trajectories and are often reversible; [Grant and Grant, 2002]), represent real increases in biological complexity. Conversely, novel structures certainly add complexity to an organism's phenotype, but it is difficult to see how they could arise by purely Darwinian means. Part of the problem is logical: Darwinian evolution is based on differential fitness of organisms that are variable in given characters, but until the character exists it cannot be variable [Müller and Newman, 2005].

A further source of confusion concerning natural selection and phenotypic novelty arises from misinterpretation of a tenet of population genetics established by R. A. Fisher, one of the founders of the Modern Synthesis. Using an abstract "geometrical model" to represent deviation, due to gene mutation, of a phenotype from some optimal value, Fisher showed that "genes (i.e., alleles) of large effect" are unlikely to remain (become "fixed") within the population [Fisher, 1930]. This engendered the widely-held view that theoretical population genetics confirms Darwin's notion (encapsulated in the quotation above concerning "numerous, successive, slight modifications") that evolution overwhelming proceeds by gradual steps, and that macroevolution can only result form microevolution over long times.

In fact, Fisher's argument has nothing to do with phenotypic innovation, since it only pertains to "quantitative traits," i.e., those like the size and shape of the finch's beak discussed above which are transformed in a continuous fashion in response to genetic variation. The effects of genes associated with the choice between discrete traits (i.e., the direction of coiling of a snail's shell; the number of body segments) or with the presence of morphological novelties (feathers; limbs; segmentation itself) are not addressed by Fisher's geometric model [Clarke and Arthur, 2000; Orr, 2001, 2005], although once an innovation is in place it may become a quantitative trait.

Genes of large effect, moreover, are in fact harbored by natural populations, some influencing adaptively useful characters that appear repeatedly in independently evolving lineages, such as the body armor spines on the stickleback fish [Colosimo et al., 2005], and others influencing features that are non-adaptive, such as the change in identity of body segments in the fruit fly Drosophila [Gibson and Hogness, 1996]. Significantly, however, it is not the alleles themselves that define the effect: in different biological systems or even in different sister species [Phinchongsakuldit et al., 2004], the same genetic change can lead to markedly different quantitative or qualitative outcomes.

To return to phenotypic novelties: while they are not likely to arise directly by a Darwinian process of gradual adaptation for the reasons given above, they can potentially emerge suddenly in a population, even simulataneously in multiple individuals, if the organisms are stressed during embryogenesis, environmentally [Badyaev, 2005; Badyaev et al., 2005; Goodman, 2008], mechanically [Müller and Streicher, 1989], or socially-endocrinologically [Trut et al., 2009]. Since development is mediated by complex cellular-biochemical systems that typically exhibit nonlinear dynamics, small mutation-induced changes in the rate or strength of component interactions can lead to abrupt ("saltational") changes in morphological outcome by the crossing of a developmental threshold. In such cases the genetic manifestation will be an allele of large effect. Novelties could also arise as "side effects" [Müller, 1990] of readjustments (possibly due to selection for an entirely different trait) in the set of cell interactions that generate developmental forms and patterns [Salazar-Ciudad et al., 2003], with no single allele uniquely associated with the modification. And, since the threshold-overcoming stress may originate in the ecological or social environment, a subpopulation of organisms could sustain the transformed phenotype with no genetic change at all, so long as the precipitating environmental effect persists.

When a novelty first appears it will not typically have an immediate function and may even compromise the fitness of its carriers in the ecological setting in which it originated. But occasionally the novel character will enable a new mode of life, initiating the exploration and construction of a new niche [Odling-Smee et al., 2003], and selection for persistence (epigenetic or genetic assimilation; [West-Eberhard, 2003; Jablonka, Lamb, 2006]) will likely follow. In this case the novelty will have acquired an adaptive function, becoming (in retrospect) a preadaptation, or an "exaptation" [Gould and Vrba, 1982].

To summarize some implications of the preceding discussion that will prove useful later: while organisms can indeed evolve by incremental adaptation to new conditions based on existing phenotypic variabilty, increase in complexity of a lineage is often associated with phenotypic innovation, and this can occur abruptly, thus deviating from classic Darwinian gradualism. Moreover, while the "genotype determines phenotype" scenario (neo-Darwinism) is a biologically reasonable model for generating the raw material of both gradual and saltational evolutionary change, so is the "phenotype precedes genotype" scenario. All of these ideas flow directly from relinquishing strict genetic determinism, recognizing the existence of phenotypic and developmental plasticity, and appreciating the nonlinear and self-organizing dynamics of developmental mechanisms.

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ARTIFICIAL LIFE

Mark A. Bedau , in Philosophy of Biology, 2007

Autonomous agents

Much work in artificial life at the level of multicellular organisms has occurred in "hard" artificial life concerned with various forms of autonomous physical agents or robots. This is artificial life's most direct overlap with artificial intelligence. Hard artificial life tries to synthesize autonomous adaptive and intelligent behavior in the real world. It contrasts with traditional artificial intelligence and robotics by exploiting biological inspiration whenever possible, and also by aiming to synthesize behaviors characteristic of much simpler organisms than humans. One of the tricks is to let the physical environment be largely responsible for generating the behavior. Rather than relying on an elaborate and detailed internal representation of the external environment, the behavior of biologically-inspired robotics quite directly depends on the system's sensory input from its immediate environment. With the right sensory-motor connections, a system can quickly and intelligently navigate in complex and unpredictable environments. This so-called "behavior-based" robotics has been pioneered by Rodney Brooks [1989; 1990; 1991]. The initial successes involved insect-like robots and it has since been extended to humanoid robots [ Adams et al., 2000]. Another trick is to let the physical materials out of which the robot is embodied to automatically provide as much functionality as possible [Pfeifer and Scheier, 2001].

Even with behavior-based robots, design of intelligent autonomous agents is difficult because it involves creating the right interconnections among many complex components. The intelligent autonomous agents found in nature are all alive, and their design was achieved spontaneously through an evolutionary process. So artificial life uses evolution to design autonomous agents [Cliff et al., 1993]. To this end, genetic algorithms have been used to design many aspects of robots, including control systems and sensors [Nolfi and Floreano, 2000; 2002].

In natural autonomous agents, the control system is tightly coupled with morphology. Sims [1994] showed ten years ago how to recreate this interconnection when he simultaneously coevolved simulated creatures' controllers, sensors, and morphology, but he relied on special-purpose software running on extremely expensive supercomputers. More recent advances in hardware and software have enabled this line of research to be pursued with off-the-shelf software running on laptops [Taylor and Massey, 2001]. This work, like Sims's, involves simulations alone. Jordan Pollack and his students have taken the next step and used similar methods to develop actual physical robots. They have connected simulated co-evolution of controllers and morphology with off-the-shelf rapid prototyping technology, allowing their evolutionary design to be automatically implemented in the real world [Lipson and Pollack 2000; Pollack et al., 2001].

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Biological Crystallization

Jaime Gómez-Morales , ... Juan Manuel García-Ruiz , in Handbook of Crystal Growth (Second Edition), 2015

Abstract

At least since the explosion of diversity that life experienced about 540 million years ago, multicellular organisms have constantly been exploring different pathways to use minerals for building sophisticated structures with different purposes. While that search occurs by trial and error, the efficient hierarchical mineral architectures found by life are made by the strict control of the nucleation and growth steps during their formation, with different patterns depending on the scale. This chapter will cover the current knowledge on the nucleation, growth, and organization of mineral crystals within living organisms, either inorganic or organic crystalline structures. We review the biological control of these processes for a number of functional composite inorganic/organic biominerals such as mollusk shells, echinoderm spines, bones, teeth, otoliths, eggshells, magnetosomes, pearls, and stromatolites. We have also included organic structures such as the mammalian stratum corneum, reptilian molts, fish scales, and butterfly wings. Furthermore, some common techniques to study in vitro the role of biological molecules or templates on crystallization, including batch, vapor diffusion, or gel crystallization, or to induce crystallization in vivo, are described. Finally, current trends and future perspectives of biological crystallization linked to its potential applications in fields such as biomedicine, paleontology, pathological crystallization, and materials science—where it is a source of inspiration for the fabrication of composite materials—are briefly summarized.

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An introduction to computational development

Sanjeev Kumar , Peter J. Bentley , in On Growth, Form and Computers, 2003

1.2.5 Summary of development

This first section of the chapter has given a very brief overview of natural development, or how multicellular organisms are built. We have seen that cells undergo five important processes: cleavage divisions, pattern formation, morphogenesis, cellular differentiation and growth. We examined some of the complexity of cells: how they sense and react to their environments, how they divide and how they are controlled by proteins and use proteins to signal their companions. We briefly looked at proteins: how they act as catalysts, how their molecular structure forces them to fold into specific shapes which then affects their function and how they are used for everything from structural components of cells and tissues, to signals. Finally, we examined the genome: how genes within DNA are transcribed with the help of RNA to make proteins and how the transcription is regulated by the presence or absence of other proteins, thus forming complex gene regulatory networks.

Chapters 2 to 5 2 3 4 5 of this volume continue this exploration of development further. Chapters 6 to 9 6 7 8 9 begin to explain how some of our understandings of the biology can be formalized and explained mathematically and through computer modelling.

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Extreme Character of Evolution in Trophic Pyramid of Biological Systems and the Maximum Energy Dissipation/Least Action Principle

Adam Moroz , in The Common Extremalities in Biology and Physics (Second Edition), 2012

4.2.7 Organismic Level—From Acellular to Multicellular Biosystems

Therefore, as it follows from the previous two sections, the emergence of the next organizational level of free energy consumption— the multicellular organisms —can be linked to the cooperative association of unicellular organisms and their subsequent functional specialization and evolution into developed modern multicellular organisms, as it is illustrated in Figure 4.17. Further specialization of the cells inside the multicellular body, accumulation in the genome of the adoptive experience of millions of generations provide the modern organism with enormous functional abilities and adaptation. Within millions of years of natural selection, the individual organisms of every multicellular species demonstrated a complicated hierarchy of organs and tissues.

Figure 4.17. Emergence of multicellular and social phenomenon as a result of cooperation between individuals in a local population.

Having recognized the complexity of bioenergetic processes and the organizational complexity of multicellular organisms, we are not aiming here for discussion of the specific organization of multicellular metabolism. Consideration here is limited to an illustration of the fact that the multicellular organism is highly organized and with a robust dynamic structure, possessing multileveled regulation loops of a molecular and cellular nature. From a thermodynamic perspective, the multicellular organism is a dissipative machine, an autonomic unit/agent of particular complex dissipative processes, like a biospecies is also.

At the same time, the emergence and existence of multicellular organisms are, from a thermodynamic perspective, the emergence of new structural forms of free energy, which must be dissipated and utilized to comply with the second law of thermodynamics. The free energy of the biological form, contained in the biomass of multicellular species, creates an important potential for emergence of essentially new ways of utilization, which could create the next level of organization and be processed at the next level of organization.

Let us note that cooperation in the spectrum of unicellular organisms leads to the emergence of colonies and further integration of cell functions accompanied by a stockpile of individual changes at the level of the genome. When the first multicellular colonies emerged, they had acquired significant advantage in competition with an individual cellular organism living alone. In this kind of cooperativity, some cells perform one type of process, whereas other cells perform other types of processes, developing an overall process that gives advantages to all cells in a colony. After a long period of evolution, within the multicellular body, it is possible to observe complex changes in the energetical consumption, which characterize the effectiveness and adaptivity of individuals and species.

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