# Learning tensors for evolutionary or developmental biology

I'm looking for book recommendations on tensor algebra for use in biology.

Tensors are being used increasingly in evolutionary biology and developmental biology, it seems. For example, here is an example of an empirical study that makes use of tensors in its analysis of data: http://rspb.royalsocietypublishing.org/content/282/1819/20151119

There are lots of books on the mathematics of tensors, and many introductions online. So far, what I have found is that either (a) the presentation is heavily oriented toward physics applications, or (b) the presentation begins from an abstract algebra (or category theory?) point of view.

I'm willing to work through physics-style or abstract algebra presentations of tensor concepts and methods, but I am wondering whether there are any textbooks or other introductions that someone would recommend for biological applications.

(I have read the brief appendix on tensors in Sean Rice's book Evolutionary Theory. Although I've found his other mathematical appendices and most of the mathematics in the book accessible, the tensor material in the book is too brief for me--I need more.)

The field of tensor decompositons has been discovered in many fields simultaneously, each with their own notation. Many modern notation in fields like CS or STAT have originated from the work of Tamara Kolda http://www.kolda.net/publication/koba09/ However, this is restricted to the CP and Tucker decomposition, and we have many others now. See https://arxiv.org/pdf/1609.00893.pdf and https://arxiv.org/abs/1708.09165

## Evolutionary algorithm

In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, [1] a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators.

Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of microevolutionary processes and planning models based upon cellular processes. In most real applications of EAs, computational complexity is a prohibiting factor. [2] In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems [ citation needed ] therefore, there may be no direct link between algorithm complexity and problem complexity.

Office: Bellini Life Sciences, 267 | Email: gary.brouhard [at] mcgill.ca

Cells can build an amazing variety of structures from proteins. We are interested in the biophysical mechanisms by which cells engineer these large-scale structures--in other words, the molecular basis of morphology. More specifically, we investigate the proteins that control the microtubule cytoskeleton, the backbone of cellular morphology. Our experiments combine cutting-edge microscopy, cryo-EM, stem cells, and computational models.

## Obesity with associated developmental delay and/or learning disability in patients exhibiting additional features: Report of novel pathogenic copy number variants † ‡

Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Rua do Matao, 277, room 204/209, 05508-090, Sao Paulo, SP, Brazil.Search for more papers by this author

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Neurogenetics Unit, Department of Medical Genetics, School of Medicine, University of Sao Paulo, Ribeirao Preto, Brazil

Department of Morphology, Medical Genetics Center, Federal University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Rua do Matao, 277, room 204/209, 05508-090, Sao Paulo, SP, Brazil.Search for more papers by this author

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Neurogenetics Unit, Department of Medical Genetics, School of Medicine, University of Sao Paulo, Ribeirao Preto, Brazil

Department of Morphology, Medical Genetics Center, Federal University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Conflicts of interest: None.

How to Cite this Article: D'Angelo CS, Kohl I, Varela MC, de Castro CIE, Kim CA, Bertola DR, Lourenço CM, Perez ABA, Koiffmann CP. 2013. Obesity with associated developmental delay and/or learning disability in patients exhibiting additional features: Report of novel pathogenic copy number variants. Am J Med Genet Part A 161A: 479–486.

## Obesity with associated developmental delay and/or learning disability in patients exhibiting additional features: Report of novel pathogenic copy number variants † ‡

Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Rua do Matao, 277, room 204/209, 05508-090, Sao Paulo, SP, Brazil.Search for more papers by this author

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Neurogenetics Unit, Department of Medical Genetics, School of Medicine, University of Sao Paulo, Ribeirao Preto, Brazil

Department of Morphology, Medical Genetics Center, Federal University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Rua do Matao, 277, room 204/209, 05508-090, Sao Paulo, SP, Brazil.Search for more papers by this author

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Neurogenetics Unit, Department of Medical Genetics, School of Medicine, University of Sao Paulo, Ribeirao Preto, Brazil

Department of Morphology, Medical Genetics Center, Federal University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Conflicts of interest: None.

How to Cite this Article: D'Angelo CS, Kohl I, Varela MC, de Castro CIE, Kim CA, Bertola DR, Lourenço CM, Perez ABA, Koiffmann CP. 2013. Obesity with associated developmental delay and/or learning disability in patients exhibiting additional features: Report of novel pathogenic copy number variants. Am J Med Genet Part A 161A: 479–486.

## DISCUSSION

The results of the final field test indicate the EvoDevoCI is a valid and reliable measure of student understanding of evo-devo, with test items that reference plausible biological scenarios validated by evo-devo experts. Cronbach's alpha is low for undergraduate students having taken few biology courses, and the tool has increased reliability among students who have taken five or more biology courses.

The difficulty range for items is high (0.22–0.55 lower numbers indicate more difficult items), and the overall difficulty of the CI is not significantly different for students having taken less than five biology courses, as compared with students having taken five or more biology courses (Figure 3). This latter result is disappointing, but expected, based on our previous work indicating that both novice and advanced students lack the foundational content knowledge needed to answer evo-devo questions correctly (Hiatt et al., 2013). As evo-devo instruction gains a larger foothold in mainstream biology courses, we expect student performance on the EvoDevoCI to improve. We are currently undertaking additional research to examine learning gains after specific instruction, asking whether evo-devo understanding improves when a student possesses developmental biology or other foundational knowledge.

### Student Reasoning and Item Context

Nehm and Ha (2011) have identified a number of contexts that affect how students reason about evolutionary situations: plants versus animals, familiar versus unfamiliar species, gain versus loss of traits, and evolution within versus among species. These dichotomies are notable, because, while they do not usually affect how experts interpret questions, students often view the opposing contexts as fundamentally different. In the EvoDevoCI, all of the scenarios referenced animals familiar to biology majors. With regard to gains versus losses, most items in the CI reference character-state changes that are neither straightforward gains nor straightforward losses. With regard to within- versus between-species differences, the CI contains a mix of both. In a case in which a shared target concept allowed for comparison, we found the pattern observed by Nehm and Ha (2011), namely, that items referencing between-species differences are more difficult, particularly for novices. By either controlling for contexts that provoke differences in students’ reasoning (i.e., for animals vs. plants and gains vs. losses) or including a mix of both sides of the dichotomy (i.e., for within- versus between-species differences), we have attempted to minimize unwanted variation in student reasoning, while examining a diversity of potential contexts when possible.

### Prevalence of Evo-Devo Conceptual Difficulties

Our previous research revealed that students often fail to master evo-devo concepts because they lack foundational concepts from developmental biology, genetics, and molecular biology (Hiatt et al., 2013). Because distracters for any particular item in the CI were based on conceptual difficulties empirically associated with the concept targeted by the item (Hiatt et al., 2013), more broadly associated conceptual difficulties have greater representation among distracters. The conceptual difficulty with the greatest representation, associated with four concepts, is “Lack of development” (DV1), followed by “Gene expression evolves only when genes appear or disappear” (ED2), which is associated with three concepts (Figure 2). In contrast, three of the conceptual difficulties associated with the concept “Mutations that are less pleiotropic are more likely to contribute to evolution” (CC3) are exclusively associated with that concept and thus are represented less among distracters.

Although advanced students did not perform significantly better on the EvoDevoCI than novice students, generally speaking, advanced students did choose specific distracters/conceptual difficulties at lower frequencies than did novice students (Table 4), in some cases, much lower (8.55% lower for DV1 in an item targeting CC2 11.4% lower for EV2 in a CC1 item and 8.95% lower for ED2 in a CC4 item). This trend is expected if indeed students overcome the conceptual difficulties associated with evo-devo concepts as they progress from novice to advanced. Exceptions to this trend identify conceptual difficulties for which current modes of instruction have either no effect or a reverse effect. For example, the percentage of students choosing the distracter “Lack of development” (DV1) in an item targeting CC5 did not change much. In the cases of “Changes in gene expression result only from mutations in said gene” (ED1) in a CC4 item and “Inheritance of acquired traits” (EV2) in a CC5 item, advanced students chose the distracters 6.1% and 4.4% more frequently than novice students, respectively.

The fact that some conceptual difficulties in understanding evolution are encountered only or more commonly among advanced students has been reported (Andrews et al., 2012). In these cases, it could be that some conceptual difficulties actually require more knowledge and are not encountered until students have some exposure to developmental biology or evo-devo. An expert is able to apply a subset of his or her knowledge to particular problems with less effort than a student (Bransford et al., 2000). In our study, however, while advanced students likely hold a larger repertoire of evo-devo content knowledge than novice students, they still seem to lack the ability to apply this knowledge to particular problems and instead may incorrectly associate more sophisticated concepts or supply factually correct but unlikely solutions. A caveat here is that our categories of “novice” and “advanced” are based merely on the number of biology courses taken and likely include students that have had an array of different course experiences. More precise data on prior concept exposure would be useful for any future studies of students’ conceptual difficulties with evo-devo.

### Limitations

An instrument such as the EvoDevoCI has intrinsic limitations. For one, our goal of a short instrument that takes little class time required that our assessment be based on relatively few multiple-choice questions targeting only the most essential core concepts. This necessarily limited the breadth of the instrument, precluding the inclusion of more sophisticated evo-devo concepts that are nonetheless arguably of great evolutionary importance. These included canalization, genetic assimilation and accommodation, gene–environment interactions, epigenetic modification of DNA, gene duplication and genome evolution, serial homology, modularity, facilitated evolution, and the evolution of multicellularity. Supplementing the EvoDevoCI with 1) questions on additional topics, 2) reasoning contexts, and 3) two-tiered ( Treagust and Haslam, 1986) or open-ended questions (Nehm and Schonfeld, 2008) ought to increase breadth when assessing student understanding of evo-devo.

The utility of this particular tool lies in its ability to assess understanding of a range of evo-devo concepts, all considered vital for undergraduate biology majors, rather than exhaustively assessing a single knowledge construct. While, in theory, a maximally reliable CI would examine only a single knowledge construct, the EvoDevoCI includes items examining five distinct evo-devo concepts, all of which are interdisciplinary in nature. This predictably results in a lower Cronbach's alpha value, which is typical of similar CIs, such as the Genetics Concept Assessment (Smith et al., 2008). The construction of a CI requires balancing the reliability of the instrument to capture student understanding on the one hand with practicality and usability on the other (Adams and Wieman, 2010).

Finally, in constructing the EvoDevoCI, we have no desire to canonize any part of evolutionary developmental biology. Instead, we recognize that, as our scientific understanding of evo-devo improves, our inventory of evo-devo concepts and attendant conceptual difficulties, along with the tool we designed to assess them, must also change. Our hope is that future tools designed to assess student knowledge of evo-devo will benefit from and build upon the EvoDevoCI.

### Uses for the EvoDevoCI

The EvoDevoCI is a diagnostic test designed to assess conceptual understanding of a set of core concepts in evo-devo among undergraduate biology majors. Given that the CI has been validated with a geographically and institutionally diverse student population, ranging from freshmen to seniors, the tool has different potential applications.

At RIS, faculty members currently use the EvoDevoCI pre- and postinstruction to assess the knowledge students gain from an evo-devo unit taught in upper-level courses in evolution and embryology and lower-level courses in animal biology. In these applications, the CI is taken online with a 2- or 3-wk interval between pre- and postadministrations. Similarly, faculty members at MCU have administered the CI during the first and last weeks of courses in organismal biology to assess newly implemented evo-devo instruction in these courses.

Our hope is that the EvoDevoCI can be used to complement the growing number of diagnostic instruments, allowing instructors to capture a more complete snapshot of student understanding of evolution. As per the recommendations of Vision and Change ( Bauerle et al., 2011), the EvoDevoCI marries disciplines and focuses on assessing concepts. Because of the exclusive focus on concepts, however, we advise using the EvoDevoCI in conjunction with assessments designed to assess competencies. In this way, student knowledge of both evo-devo concepts and the practices used to arrive at them can be fully assessed.

## In development, it's all about the timing

Closely related organisms share most of their genes, but these similarities belie major differences in behavior, intelligence, and physical appearance. For example, we share nearly 99% of our genes with chimps, our closest relatives on the great "tree of life." Still, the differences between the two species are unmistakable. If not just genes, what else accounts for the disparities? Scientists are beginning to appreciate that the timing of the events that happen during development plays a decisive role in defining an organism, which may help to explain how species evolve without the creation of new genes.

Today, a team of scientists at Cold Spring Harbor Laboratory (CSHL) has identified a key regulator of developmental timing. Led by CSHL Assistant Professor Christopher Hammell, the researchers describe how LIN-42, a gene that is found in animals across the evolutionary tree, governs a broad range of events throughout development.

"A great deal of science is focused on understanding how a single gene functions in the cell," says Hammell. "But we are learning that when a gene is active is just as important as what it does."

An organism develops in well-defined stages: nerves and muscles mature before reproductive tissues, for example. The stages unfold sequentially, like movements in a symphony. Played all at once, they would produce a terrible developmental cacophony, but with proper timing, a natural harmony can emerge.

Developmental stages are marked by the activation or repression of a specific and unique complement of genes, like individual notes within movements of a song. The order and duration of when these key developmental genes (or notes) are active (or played) within a given cell is controlled by a class of molecules called microRNAs (miRNAs). A single miRNA gene can control hundreds of other genes at once. If a miRNA turns off these specific genes too early or too late, the organism will suffer severe developmental defects. But little is known about how the activities of these miRNAs are regulated.

In work published in PLoS Genetics, Hammell and his team describe the genetic approach they used to search for genes that control developmental timing through miRNAs. The team uses a tiny roundworm, called C. elegans, as a simple model for the events that occur during development, even in higher organisms. These worms have a fixed number of cells and each cell division is precisely timed. "It is the perfect model for our work," says Hammell. "It enables us to understand exactly how a mutation affects development, whether maturation is precocious or delayed, by directly observing defects in the timing of gene expression."

The team's search uncovered the gene LIN-42 as a crucial regulator of developmental timing via its pervasive role in controlling miRNAs. "LIN-42 shares a significant amount of similarity to the genes that control circadian rhythms in organisms such as mice and humans," says Roberto Perales, PhD, one of the lead authors of the study. "These are genes that control the timing of cellular processes on a daily basis for you and me. In the worm, these same genes and mechanisms control development, growth, and behavior and this system will provide us with leverage to understand how all of these things are coordinated."

Hammell and his team found that LIN-42 controls the repression of numerous genes in addition to miRNAs. They also discovered that levels of the protein encoded by LIN-42 tend to oscillate over the course of development and form a part of a developmental clock. "LIN-42 provides the organism with a kind of cadence or temporal memory, so that it can remember that it has completed one developmental step before it moves on to the next," says Hammell. "This way, LIN-42 coordinates optimal levels of the genes required throughout development."

## EDITORIAL article

The computational theory of mind, which views the brain as an information processor that operates on cognitive representations, is central to modern cognitive psychology and is the dominant perspective from which brain function is conceptualized and studied. Evolutionary Psychology (EP) is the application of evolutionary theory to understanding human behavior and cognition. Unlike other core Psychology topic areas (such as Personality, Learning, or Developmental Psychology), however, EP is not defined by the subset of psychological phenomena it seeks to describe and understand. It is instead defined by a specific meta-theoretical perspective, from which it seeks to (potentially) explain all psychological phenomena. The central question posed by this volume is whether this over-arching nature provides an opportunity for evolutionary approaches to offer an alternative meta-theoretical perspective to the information processing/representational view of brain function and behavior.

Readers of this volume will notice a sharp demarcation between descriptions of traditional Evolutionary Psychology, which several authors (Barret et al. Stotz Stulp et al.) have presented as indistinguishable from the information processing approach, and newer conceptualizations of EP. Indeed one of the major themes running through several of the contributions (Burke Barret et al. Stephen Stotz Stulp et al.) concerns the appropriate conceptualization of EP itself, with the Santa Barbara school of massive modularity (made famous by John Tooby and Leda Cosmides) receiving the most scrutiny. As Barret et al. and Stotz describe, early conceptualizations of EP embraced the notion of massive modularity of mind. Individual modules were presumed to act as evolved computers, sensitive to domain specific information and processing it in adaptive ways. Framed in this manner, EP fits well within even a very strict definition of a computational theory of mind and could hardly be seen as the source of an alternative meta-theoretical approach to understanding brain and behavior.

It may not be appropriate, however, to view either the computational theory of mind or the field of EP so narrowly. As Klasios argues, many evolutionary psychologists adopt a more generic notion of computation, one that commits more to the abstract representation and manipulation of information, rather than to digital computation in its literal sense (although see also Bryant). EP too, is no longer wed to notions of massive modularity (Stephen), with the majority of research in the field motivated by consideration of first principles of evolutionary theory and is neither constrained nor informed by assumptions of massive modularity or domain specific mechanisms (Burke). With these considerations in mind, Klasios and Bryant both argue that computation is still the most profitable account of the mind and is able to accommodate both evolutionary and e-cognition (extended, embodied approaches which place emphasis on the role played by the whole organism and its environment in the decision-making process, rather than simply the brain) perspectives, that favor notions of neural adaptations that are 𠇌omplex, widely distributed, and highly diffuse” (Klasios) over the more strictly isolated mental modules supposed by massive modularity.

Burke further argues that commitments to massive modularity, or to either a computational, direct, or e-cognition view of the brain, are unnecessary for evolutionary theory to become the foundational theory of psychological science. Presenting a series of six reasons for the current failure of evolutionary theory to inform most research within psychological science, Burke (with supporting arguments given by Jonason and Dane, and Stephen), suggests that a mixture of misunderstandings about the field of EP coupled with motivated opposition and misguided skepticism are to blame.

If Burke’s assessment is accurate, such barriers may only be overcome by a concerted effort to unite EP with Behavioral Ecology and Evolutionary Biology. Stotz proposes one such unity with her Extended Evolutionary Psychology. Combining evolutionary theories concerning genetic, epigenetic, behavioral, and cultural systems of inheritance, developmental plasticity and niche construction, with e-cognition, Stotz outlines a truly integrative EP. Stotz’ Extended Evolutionary Psychology draws on complex mechanisms of inheritance to help understand the evolution of psychological traits. But it also sees investigations of e-cognition informing theories of niche construction and transgenerational developmental plasticity. Thus, the integration of evolutionary theory with psychology provides reciprocated benefits to both fields.

Barrett et al. Barrett et al. and Stulp et al. argue for an Extended Mind Hypothesis. The Extended Mind Hypothesis sits within an evolutionarily informed framework, but places much emphasis on the sociocultural nature of human psychology and the external resources (cultural and technological artifacts) that form part of the modern human cognitive system. The Extended Mind Hypothesis offers the various forms of e-cognition, rather than EP, as the appropriate meta-theoretical perspective to succeed the computational theory of mind. In arguments that mirror those presented by Burke, however, Stephen et al. argue that while e-cognition represents an interesting alternative to more traditional proximal explanations of behavior (such as computational theory of mind), behavior must still be examined through a sophisticated evolutionary lens if an ultimate understanding is to be reached.

Newer conceptualizations of EP are uncommitted to notions of massive modularity, look beyond the Pleistocene for the selection pressures that have shaped psychological mechanisms and incorporate developmental and cultural impacts into theories concerning the evolved functions of psychological mechanisms. It is clear however, that the massive modularity roots of modern EP still influence how many, including both advocates and critics, view the field. One message that is clear from the works presented in this volume, is that EP must mature and free itself of many of its early assumptions and assertions (as seems to be currently happening empirically, if not yet theoretically, Burke). Only if this occurs, will EP be placed to properly integrate with Evolutionary Biology and be in a position to cement evolutionary theory as a unifying meta-theory for Psychological Science. Whether such a New Evolutionary Psychology should incorporate computational theories of mind or reject these in favor of the newer e-cognition perspectives is an empirical question and not one whose answer needs to be decided before the weight of evidence has settled in either court (Stephen).

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## Biologists Call For Better Choice Of Model Organisms In 'Evo-devo'

Research in evolutionary developmental biology, known as &lsquoevo-devo&rsquo, is being held back because the dominant model organisms used by scientists are unable to illustrate key questions about evolution, argue biologists in the latest issue of Nature Reviews Genetics.

The subject of evo-devo, which became established almost a decade ago, is particularly dependent on the six main model organisms that have been inherited from developmental biology (fruit fly, nematode worm, frog, zebrafish, chick and mouse).

To help understand how developmental change underpins evolution, evo-devo researchers have, over recent years, selected dozens of new model organisms, ranging from sea anemones to dung beetles, to study.

One of the selection criteria deemed most crucial is the phylogenetic position of prospective model organisms, which reflects their evolutionary relationships.

Phylogenetic position is employed in two common, but problematic, ways, either as a guide to plug holes in unexplored regions of the phylogenetic tree, or as a pointer to species with presumed primitive (ancestral) characteristics.

Drs Ronald Jenner and Matthew Wills from the Department of Biology & Biochemistry at the University of Bath (UK), call for a more judicious approach to selecting organisms, based on the evo-devo themes that the organism can shed light on.

&ldquoIt is fair to say that, since its inception, some workers feel that evo-devo hasn&rsquot quite lived up to its early expectations,&rdquo said Dr Jenner.

&ldquoPartly this is because too much was expected too soon, but we suspect that in terms of its future promise the current choice of new model organisms has not yet been optimised.

Dr Wills said: &ldquoMany models to date, in particular the big six, have been chosen because they are easy to keep in the laboratory, select and breed.

&ldquoWhilst this is generally fine in the context of development research, the benefits to evo-devo as a subject are limited.

&ldquoThere are upwards of 35 phyla of animals, and four of our six best models come from just one phylum.

&ldquoHowever, that doesn&rsquot mean that simply choosing new models to plug holes in the phylogenetic tree is the best option for further progress in evo-devo.&rdquo

Dr Jenner added: &ldquoThe popular advice of choosing new model organisms to maximise phylogenetic spread is nice to show diversity, but it doesn&rsquot necessarily lead to new general insights about evolution.

&ldquoChoosing new models in this way leaves it entirely a matter of chance whether a new model will illuminate a particular evo-devo theme.

&ldquoInstead, we urge workers to select new models specifically to illuminate hitherto neglected general themes within evo-devo.&rdquo.

In other cases, new model organisms are chosen on the basis of how well they are thought to represent a particular ancestral organism. In connection to this practice, the researchers point to &lsquobasal bias&rsquo as another way that scientists may get it wrong when choosing new model organisms.

This occurs when scientists choose an organism because it was the first to branch off from its ancestor, rather than because it has known genetic or developmental similarities to it.

&ldquoWe caution against this widely used rule of thumb, and advise the use of additional criteria, such as molecular branch lengths, to choose species as best representatives of ancestral body plans,&rdquo said Dr Jenner.

&ldquoJust because an organism has sprung from the base of the evolutionary tree does not make it more primitive and representative.

&ldquoEqually, those that became separate species further down the evolutionary line are not necessarily increasingly different from that common ancestor.

&ldquoAmong living species that descended from a particular common ancestor, those designated as &lsquobasal&rsquo are those that are separated from this ancestor by the smallest number of speciation events.

&ldquoSometimes evolution speeds up in association with speciation &ndash an organism can change a lot in this time.

&ldquoHowever, substantial evolutionary change may also occur in the absence of speciation, so basal species are not necessarily, or even likely a more conserved model of the ancestor.

&ldquoWe need to make better use of the techniques that allow us to calculate how much an organism&rsquos genome has changed over time, when making assessments about how much an animal resembles its ancestor, because this information can be helpful in estimating how much an organism&rsquos phenotype has changed.&rdquo

Dr Wills added: &ldquoEstablishing criteria for choosing model organisms is important in this field, especially given the pressure on available funding sources.

&ldquoWe encourage evo-devo workers to communicate with funding agents so that the limited resources available will not be disproportionately channelled to the &lsquobig six&rsquo, which, while important, cannot illuminate all evo-devo&rsquos central themes.

&ldquoIf we want to understand how insects evolved wings or how legs developed from fins, we need to judiciously choose several models from specific parts of the phylogenetic tree.

&ldquoThere is little point in blindly increasing the diversity of model systems, without some specific goals in mind.

&ldquoOur toolkit is too narrow, so as a community we need to clarify our objectives and set the agenda for future studies.&rdquo

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