The origin of novel traits is among the most intriguing and enduring problems in evolutionary biology. It is intriguing because it lies at the heart of what motivates much of evolutionary biology: to understand the origins of exquisite adaptations and the evolutionary transitions and ecological radiations that they enabled. It is enduring because it embodies a fundamental paradox. On the one hand, Darwin's theory of evolution is based on descent with modification wherein everything new, ultimately, must come from the old. On the other hand, biologists are captivated by complex novel traits precisely because they lack obvious homology to preÂexisting traits. How, then, does novelty arise from within the confines of ancestral variation?
Combining approaches from evolutionary developmental genetics, behavioral ecology, and microbiology my research explores the genetic, developmental, and behavioral mechanisms, and the interactions among them, that promote innovation and diversification in the natural world. Most of the work in my research group focuses on the inordinately diverse and bizarre horns of scarab beetles, while side projects have explored the origins of light-Âproducing organs in fireflies as well as the exuberant helmets of treehoppers. In my talk I will first present recent results on the role of developmental repurposing in the evolution of novel morphological traits and developmental functions. In the second half I will discuss the significance of host microbiome interactions and environmentÂ-engineering in the origins of novelty, when collectives innovate, adapt and problem-Âsolve in ways single species cannot. Throughout my talk I use our findings to highlight where I believe they expand and revise our current understanding of the genesis of novelty in evolution.
We say that an input-output map xo(I) has infinitesimal homeostasis at I0 if xâ€²o(I0) = 0. A consequence of infinitesimal homeostasis is that xo(I) is approximately constant on a neighborhood of I0.
An input-output network is a network that has a designated input node , a designated output node o, and a set of regulatory nodes = (i, . . . , n). We assume that the system of network differential equations Ë™X = F(X, I) has a stable equilibrium at X0. The implicit function theorem implies that there exists a family of equilibria X(I) = (x(I), x(I), xo(I)), where xo(I) is the network input-output map.
We use the network architecture of input-output networks to classify infinitesimal homeostasis into three types: structural homeostasis, Haldane homeostasis, and appendage homeostasis. The first two types generalize feedforward excitation and substrate inhibition. The third type appears to be a new form of homeostasis.
This research is a joint project with Yangyang Wang, Ian Stewart, Joe Huang, and Fernando Antoneli.
Two universal Rules of Life are that all organisms are subject to variable environments, and all are also subject to continuous mutations in genes that are important for normal function and survival. Organisms have evolved a variety of mechanisms that buffer form and function against deleterious environmental and genetic variables. These are collectively called homeostatic and robustness mechanisms, which stabilize the phenotype, so that the same phenotype is produced in spite of genetic and environmental variation. Insofar as natural selection acts only on phenotypes, but heritable change comes from genotypes, it has been thought that robustness mechanisms produce a constraint on evolution by decoupling phenotype form genotype. An apparently contradictory fact is that many organisms have a variable phenotype that depends on environmental conditions. This is called plasticity, and produces different phenotypes from the same genotype. Plasticity, therefore, also seems to uncouple phenotype and genotype. Plasticity, like robustness, can be an adaptation to a variable environment. Using conceptual and mathematical models, I will discuss a diversity of mechanisms that produce robustness and plasticity and show they are closely related. I will also discuss why such mechanisms, rather than constraining evolution, actually enable rapid evolution.
Hopfield's classic paper on kinetic proofreading conceals an important observation. If a biochemical system implementing a given information processing task is operating at thermodynamic equilibrium, there is an upper limit to how well it can perform that task; the only way to exceed this limit is to maintain the system away from equilibrium by expending energy. We call the limit the Hopfield barrier for the task in question. We will discuss some examples and suggest that identifying Hopfield barriers for the various tasks which biological cells undertake offers a systematic way to rise above molecular complexity and discern the underlying Rules of Life.
Many adults who are overweight were already overweight as children.Â What accounts for who gains excess weight early in life and who, as a result, is at increased risk for becoming an overweight or obese adult?Â In my talk I will present our recent work that shows that the brain accounts for a lifetime peak of 66% of the bodyâ€™s resting metabolic expenditure at 4-5 years of age, and that there is a strong inverse relationship between developmental changes in brain energetics and the rate of body weight gain between infancy and puberty. The peak in brain developmental energetics traces to synaptic and other energetically costly processes related to neuronal plasticity and learning, and requires compensatory reductions in other expenditures like body growth.Â In the second half of my talk, I will review evidence linking brain energetics with overweight and obesity during childhood, and argue that variation in the timing and intensity of the brain energetics peak could help explain the well-documented finding of an inverse relationship between the BMI and cognitive function.Â This framework could also help explain emerging evidence for genetically-mediated trade-offs (pleiotropy) between cognitive development and body fat gain.Â In closing, I argue that educational interventions that harness plasticity in these traits, and increase the peak or duration of brain developmental energetics, could lower obesity risk by increasing the brainâ€™s energy needs and the strength of energetic trade-offs with fat deposition.
Organisms as diverse as bacteria, fungi, plants, and animals manifest a property called â€œpolarity.â€? The literature shows that polarity emerges as a consequence of different mechanisms in different lineages. However, across all unicellular and multicellular organisms, polarity is evident when cells, organs, or organisms manifest one or more of the following: orientation, axiation, and asymmetry. I will review the relationships among these three features in the context of cell division and the evolution of multicellular polarity primarily in plants (defined here to include the algae). Data from unicellular and unbranched filamentous organisms (e.g., Chlamydomonas and Ulothrix) show that cell orientation and axiation are marked by cytoplasmic asymmetries. Branched filamentous organisms (e.g., Cladophora and moss protonema) require an orthogonal reorientation of axiation, or a localized cell asymmetry (e.g., â€œtipâ€? growth in pollen tubes and fungal hyphae). The evolution of complex multicellular meristematic polarity required a third reorientation of axiation. These transitions show that polarity and the orientation of the future plane(s) of cell division are dyadic dynamical patterning modules that were critical for multicellular eukaryotic organisms.
The mathematical field of dynamical systems plays a crucial role in describing the behavior of a cellular or genetic regulatory network over time. Traditional dynamical systems studies concentrate on trajectories and invariant sets as the primary approaches to network analysis. We present a new angle on dynamical systems that instead focuses on a robust, scalable and computable description of dynamics in terms of graphs and partially ordered sets (posets). A poset represents a â€œdynamic signatureâ€? of the network that is constant over a large region of parameter space. The number of such parameter regions is finite, leading to a global description of the dynamics across high dimensional parameter space. Our software tool Dynamic Signatures Generated by Regulatory Networks (DSGRN) ingests a regulatory network and produces the posets representing network dynamics over all of parameter space. The dynamic signatures generated by DSGRN can be used to answer questions about regulatory network performance in the context of network discovery, as well as other goals such as network design in synthetic biology and diagnosis of misbehavior. I will briefly overview the graphical approach of DSGRN and then discuss the role of DSGRN in a pipeline for network discovery using a case study of time series data measured in vitro from the malaria parasite P. falciparum.
One of the basic characteristics of living organisms is their ability to process information about their external environment and internal state and to implement adaptive responses to the challenges they face. At the cellular level, these information processing tasks are carried out by complex networks of interacting genes and proteins; quite differently than the information processing done by digital computers or (analog) central nervous systems. Despite the triumphs of molecular biologists over the past 40 years in identifying and characterizing the components of these networks, their information-processing capabilities are still largely mysterious. Is there a basic theory of information-processing by molecular reaction networks that is biochemically realistic, reasonably accurate and comprehensive, and of predictive value? I will make the case that bifurcation theory of dynamical systems provides a framework for thinking about this problem. Briefly put, a one-parameter bifurcation diagram (dynamical variable as a function of control parameter) is the mathematical analog of the physiologistâ€™s â€œsignal-responseâ€? curve; and a two-parameter bifurcation diagram (e.g., physiological control parameter versus level of gene expression) can provide insight into the translation from genotype to phenotype. I will illustrate these principles with a number of classic examples from the field of network dynamics and cell physiology, and I will relate this particular problem to broader considerations of the â€œRules of Lifeâ€?.
Understanding the regulatory mechanisms in molecular interaction networks is an important goal in systems biology. This talk will focus on processes at the molecular level that determine the state of an individual cell, involving signaling or cell regulation. The mathematical framework to be used is that of Boolean networks and their multi-state generalization. These models represent the interactions of different molecular species as logical rules that describe how these species combine to regulate others. Regulatory rules that appear in published models tend to have special features such as the property of being nested canalizing, a concept inspired by the concept of canalization in evolutionary biology. This talk will survey a set of results about nested canalizing rules and how these constrain network dynamics. It has been shown that networks comprised of nested canalizing functions have dynamic properties that make them suitable for modeling gene regulatory networks, namely small number of attractors and short limit cycles. In this talk, I will discuss a normal form as polynomial function that applies to any Boolean or multi-state function. This description provides a partition of the inputs of any Boolean function or multi-state function into canalizing and non-canalizing variables and, within the canalizing ones, we can categorize the input variables into layers of canalization. I will also describe the structure of the non-canalizing variables. Applications for how to use this normal form and some other properties of these functions will be given at the end of the talk.
The first wave of systems and synthetic biology has provided general network design principles that lead to robust dynamic behavior seen in nature such as adaptation, oscillations, and bi-stability.Â The next frontier in this respect is finding general design principles for collective behavior of systems such as single-strain colonies with cell-to-cell communication or even multi-cellular systems. Mapping single-cell dynamics to collective behavior is not a trivial task. Some of the most well-known work includes theory developed by A. Turing for architectures leading to pattern formation. Here, I discuss a different mechanism, unique from Turing, that leads to similar patterning based on the well-known toggle switch architecture and associated properties.
Natural selection shapes living systems to such remarkable efficacy and robustness that disease vulnerability is usually and correctly attributed to the limits of natural selection. Mutation, migration, genetic drift, path dependence and the slow pace of evolution are important explanations for disease vulnerability. Some systems are, however, intrinsically vulnerable to failure for other reasons. The role of tradeoffs is well-recognized, but it may have wider applications than is often appreciated. For instance, antagonistic pleiotropy provides benefits early in life that maximize reproduction at the cost of a shorter life span. Systems that regulate defenses such as immune responses and anxiety are expected to generate false alarms because the costs of not responding are far higher than the costs of a false alarm. Some such systems become more responsive after repeated arousal, making them inherently vulnerable to runaway positive feedback, as may be illustrated by panic disorder and cytokine storm. Traits with cliff-edged fitness functions are especially vulnerable to failure. Strong selection on a trait vulnerable to catastrophic failure, such as racehorse bones, is an example. Even in the absence of strong recent selection, such traits are likely to be vulnerable because natural selection shapes them to a mean value that maximizes multigenerational gene transmission despite the associated increase in the proportion of population with low fitness. Pathogen pressure is likely to also shape fitness functions with steep slopes. Higher telomerase activity and numbers of stem cells provide advantageous tissue repair but increases the risk of cancer. Uric acid concentrations give increasing antioxidant benefits until crystals form and cause gout. The high heritability of many diseases is turning out to arise from the tiny effects of many alleles spread across the entire genome. Some are deleterious mutations subject to mutation selection balance, but some may be maintained because they influence the level of a trait with a cliff-edged fitness function. Disorders that can be considered in this light include epilepsy, atrial fibrillation, migraine headaches, and schizophrenia.