Perspectives on Principles of Cellular Behavior from the Biophysics of Protists

Synopsis

Cells are the fundamental unit of biological organization. Although it may be easy to think of them as little more than the simple building blocks of complex organisms such as animals, single cells are capable of behaviors of remarkable apparent sophistication. This is abundantly clear when considering the diversity of form and function among the microbial eukaryotes, the protists. How might we navigate this diversity in the search for general principles of cellular behavior? Here, we review cases in which the intensive study of protists from the perspective of cellular biophysics has driven insight into broad biological questions of morphogenesis, navigation and motility, and decision making. We argue that applying such approaches to questions of evolutionary cell biology presents rich, emerging opportunities. Integrating and expanding biophysical studies across protist diversity, exploiting the unique characteristics of each organism, will enrich our understanding of general underlying principles.

Introduction

Life is fundamentally cellular. Proper biological function emerges from the regulated interplay between subcellular processes, cellular behaviors, and physical constraints. The search for principles dictating this interplay therefore represents a major transdisciplinary challenge that requires integrative approaches. All cellular behavior takes place in the context of physical constraints such as those imposed by geometry, mechanics, and diffusive transport. By imposing limits, constraints can serve not only as barriers, but can also yield robustness of function (Ashby 1956). In this way, constraints can be mechanistically important, and physical processes can constitute mechanisms in their own rights. Combining perspectives from physics and biology has been instrumental to addressing many mechanistic questions in biology. In the context of cell biology, these include, for example, the regulation of cytokinesis (Burton and Taylor 1997; Pollard 2010; Dorn et al. 2016; Mogilner and Manhart 2016) and cell crawling (Lee et al. 1994; Mogilner and Oster 1996, 2003; Pollard and Cooper 2009). However, much of this work has been confined to a relatively small number of well-studied systems representing a small subset of eukaryotic diversity. The variety of plants and animals apparent from everyday experience belies that fact that protists, a paraphyletic group defined by the absence of the complex multicellularity found in plants, animals, and fungi, constitute the vast majority of eukaryotic diversity. The diversity, though, is reflected in sundry protistan forms, functions, and behaviors (Fig. 1).

Fig. 1.

Protists display a great diversity of cellular form and function. Various protists from historical literature (drawings) and from environmental samples illustrate this diversity. (A–E) Various flagellates. (A) Trichomonas intestinalis, an intestinal parasite of humans (Haeckel 1904). (B) Trichomitopsis sp. collected from the hindgut of a termite, a gut endosymbiont that participates in cellulose digestion. (C) Anthophysa vegetans, often found in rivers and noted for its tendency to accumulate iron and manganese in the stalks of its colonies (Haeckel 1904). (D) Rosette colony of the choanoflagellate Barroeca monosierra originally isolated from a saline soda lake, which harbors a microbiome (Hake et al. 2021). (E) Ornithocercus magnificus, a marine dinoflagellate noted for its particularly complex morphology. Adapted from (Haeckel 1904). (F–J) Various ciliates. (F) Unidentified ciliate collected from a hypersaline splash pool with prominent cortical rows of cilia, a common feature of ciliates. (G) Zoothamnium arbuscula, a colonial ciliate known for its ability to rapidly contract its stalk in response to aversive stimuli and grow to macroscopically visible sizes. Adapted from (Haeckel 1904). (H) Fabrea salina, often a dominant protist in hypersaline environments with the ability to inhibit the growth of other halotolerant species, collected from a marine splash pool (Guermazi et al. 2008). (I) The suctorian Trichophrya salparum on the gill of a salp, adapted from (Calkins 1926). Suctorians begin life as swimming cells before settling on a substrate and undergoing metamorphosis into the depicted tentacled form. (J) Euplotes sp., known for their ability to walk using leg-like bundles of cilia, isolated from a stagnant puddle filled with dead leaves. (K–O) Various amoebae. (K) Unidentified rhizarian amoeba with a complex reticulopodial network collected from a tide pool. (L) Amoeba proteus, a large amoeba studied by Jennings for its complex hunting behavior. Image adapted from (Jennings 1906) after Leidy. (M) Unidentified radiolarian with its characteristic spiny, mineralized silica skeleton, collected from a near shore marine sample. (N) Hyalosphenia papilo, amoeba with an organic test (shell) that harbors endosymbiotic algae. Drawing adapted from (Leidy 1879). (O) Difflugia sp., isolated from a vernal farm pond, which has built its test from mineral particles collected by the cell from its environment. Scale bars are 25 μm.

Protists play important roles in nearly every ecosystem and also hold key phylogenetic positions for elucidating major evolutionary events, including the origin and evolution of various organelles (Gray 1989; Elde et al. 2005; Gould et al. 2008; McFadden 2014), the origin of eukaryotes (Baldauf et al. 1996; Cavalier-Smith 1997; Fritz-Laylin et al. 2010; Koonin 2010; Williams 2014), and the origin of animals (King et al. 2003; Ruiz-Trillo et al. 2008; Sebe-Pedros et al. 2011; Brunet and King 2017; Richter et al. 2018). Despite their ecological and evolutionary importance, the biology of most protists remains poorly understood (Keeling and Campo 2017; Collier and Rest 2019). In the investigation of general principles underlying the regulation and evolution of form and function in eukaryotes, protists represent a vast, largely untapped resource.

Most protists are free-living, so the natural behaviors of cells are amenable to direct observation and targeted manipulation in controlled environments. Indeed, in contrast to a modern focus on relatively few model organisms and cell lines, a substantial amount of the history of cell biology from the development of microscopy to the rise of molecular biology has been dominated by careful observation of protist behavior and structure, including some of the most extensive, detailed early observations of cells (protists described as wondrous “wee animalcules” by Antonie van Leeuwenhoek; Leeuwenhoek 1677; Leeuwenhoek and Dobell 1932; Dröscher 2014; Lane 2015; Richardson et al. 2015). Recently, coinciding with the increasing interest in nontraditional model systems, descriptive and mechanistic characterizations of protists are becoming more prevalent (Collier and Rest 2019; Faktorová et al. 2020). Although nontraditional, the development and study of these systems benefits from sometimes extensive historical documentation of cell structure, physiology, behavior, and life history. Both historical and recent work on protists illustrates how investigating function at the intersection of biology and physics can provide mechanistic insight into the emergence of organismal function. Furthermore, as the following sections of this manuscript illustrate, this perspective reveals general principles transcending the fascinating yet idiosyncratic biology of any particular protist.

Here, we review representative cases in which the intensive study of protists from the perspective of cellular biophysics has driven insight into the general, interrelated biological problems of morphogenesis, navigation and motility, and decision making. Sections concerning these problems focus primarily on some of the best-developed respective protist systems: the colonial green alga Volvox, which undergoes multicellular morphogenesis from an embryo stage and the social amoeba Dictyostelium, which can undergo an elaborate multicellular morphogenetic progression as part of its life cycle; the unicellular alga Chlamydomonas and multicellular Volvox, which both exhibit phototactic behavior; and the multinucleate slime mold Physarum, noted for its complex foraging behavior (Fig. 2). We argue that applying such biophysical perspectives to questions of evolutionary cell biology presents a wealth of emerging opportunities. Expanding biophysical studies and integrating them with complementary approaches across protist diversity, exploiting characteristics of each organism that uniquely highlight general biological questions, will enrich our understanding of underlying principles.

Fig. 2.

Biophysical approaches to understanding form and behavior in protist systems have led to mechanistic insight into organismal function. (A–C) Embryo inversion in the green alga Volvox serves as a model for morphogenesis based on the bending of cellular sheets. (A) Volvox forms large, spherical colonies composed of biflagellate cells connected by cytoplasmic bridges and embedded in an extracellular matrix (ECM). Colonies have differentiated germ and somatic cells, and embryos form and develop inside the colonies. (B) Developing embryos turn themselves inside out during development in a process called inversion. Inversion is driven by coordinated cell shape changes from spindle to flask to columnar cells. Cytoplasmic bridges (br) are also displaced and bridge tension ultimately leads to the bending of the sheet of cells. (C) Overview of shape change during embryo inversion. Different species of Volvox undergo different types of inversion processes. The most extensively studied one depicted here is known as “B” type. Gray shading represents the surface of the embryo with flagella pointing outward. Illustrations based in part on figures from (Haas and Goldstein 2018). (D) Dictyostelium morphogenesis under starvation conditions is controlled by reactiondiffusion pattern formation stemming from cAMP activity. Arrows indicate the progression of the starvation response from aggregation of amoeboid cells (i) leading to mound formation (ii) to slug (iii) and/or stalk formation (iv). In each stage, waves of cAMP activity, indicated in gray, orchestrate cellular behaviors including directed migration, ECM secretion, adhesion, and differentiation in order to robustly and reproducibly form the multicellular structure. Based in part on figures and information from (Dormann et al. 2002; Weijer 2004; Sriskanthadevan et al. 2011). (E) The green alga Chlamydomonas serves as a model for cellular motility and navigation in the context of fluid mechanics. The top images depict flagellar wave forms of the biflagellate Chlamydomonas cell. Bottom images indicate synchronous and asynchronous flagellar dynamics, which play a role in phototaxis. Light is sensed by an eyespot (red spot). Differential flagellar response in terms of flagellar beat amplitude (asymmetric) due to light sensing allows the cell to turn towards light when undergoing phototaxis (Bennett and Golestanian 2015; Leptos et al. 2023). Flagellar waveforms based in part on (Geyer et al. 2013). (F) The slime mold Physarum polycephalum is a large, multinucleate cell, up to centimeters in size, organized as a network of contractile tubes with dynamic structure through which cytoplasm is pumped. Due to its ability to select optimal paths connecting resources in complex environments, Physarum has long served as a model for cellular decision-making.

Protist behavior from a biophysical perspective

Morphogenesis

Cellular behavior in the context of morphogenesis has perhaps been most extensively studied from the perspective of biophysics. D'Arcy Thompson, in his pioneering “On Growth and Form,” pointed out that problems of growth and morphology are essentially mathematical and physical problems (Thompson 1917). This work is often identified as the first to bring ideas from mathematics and physics to bear on problems of biological morphogenesis. Although most are unicellular, various protists form multicellular colonies according to diverse morphogenetic processes. The relative simplicity of multicellular morphogenesis in protists can serve to clarify the roles of cellular behaviors and physical mechanisms.

The green alga Volvox, with its large, swimming colonies produced by a developmental process has long captivated microscopists (Leeuwenhoek 1700; Fig. 2A). Like embryogenesis in many animals, Volvox colony development begins with an early embryogenesis phase of synchronous cleavage divisions, during which cells divide without growth (Matt and Umen 2016). This early phase is followed by a growth and expansion phase mediated by ECM secretion (Ueki and Nishii 2009; Matt and Umen 2016). After the growth phase, nascent colonies undergo an inversion process, in which the entire colony flips inside out (Viamontes and Kirk 1977; Matt and Umen 2016) (Fig. 2B and C). This inversion process is reminiscent of gastrulation in many animals in that such processes involve the bending of sheets of cells through coordinated cellular deformations. In Volvox, inversion allows cells to reorient their flagella from pointing toward the inside of the spherical colony to the outside in order for the colony to swim.

While qualitative descriptions of the dynamics of inversion have existed for decades (Viamontes and Kirk 1977), recent advances in microscopy have facilitated detailed, quantitative analysis (Höhn et al. 2015; Haas et al. 2018). Dynamics alone, however, only provide the starting point for an explanation. As in any multicellular morphogenetic process, correlations between local cellular behavior and global, organism scale, deformations can be observed (Fig. 2A and B), but determining precisely how those cellular behaviors are related to the global shape changes requires further investigation. For example, whether cellular deformations are the cause or the result of tissue bending cannot necessarily be determined purely by observation. Understanding the mechanical aspects of shape changes can help answer such questions. Mathematical modeling together with quantitative analysis of Volvox inversion has shown how coordinated cell shape changes (Fig. 2B) along with geometrical and mechanical constraints inherent to elastic bending drive the inversion process (Höhn et al. 2015; Haas et al. 2018). Cells in Volvox colonies are connected by cytoplasmic bridges that constitute a stable, structural framework that physically couples all cells (Viamontes and Kirk 1977; Matt and Umen 2016; Fig. 2B). The details of the different phases of inversion, which differ between species, have been described in depth (Viamontes and Kirk 1977; Cole and Reedy 2003; Matt and Umen 2016; Haas et al. 2018). Briefly, inversion involves the formation of cells that are constricted on the side facing into the colony, called flask cells (Fig. 2B). Flask cells are analogous in form and function to bottle cells of animal epithelia, which also exhibit polarized constriction and are early drivers of tissue bending during gastrulation (Viamontes and Kirk 1977; Matt and Umen 2016). In a coordinated wave, cells beginning from a spindle morphology transition into flask cell morphology, followed by resolution to columnar morphology (Fig. 2B). As the flask cells constrict, bridges are displaced toward the narrow distal end of cells (Viamontes and Kirk 1977; Matt and Umen 2016) (Fig. 2B). A kinesin family molecular motor drives bridge displacement, which ultimately produces the force to drive inversion to completion (Nishii et al. 2003). Stresses in the connected sheet of cells induced by the cell shape changes lead to colony-wide deformation (Cole and Reedy 2003; Höhn et al. 2015). An early hypothesis that the cellular deformations eventually lead to a mechanical snap-through that passively carries the colony through the final stages of inversion (Viamontes and Kirk 1977) has been supported and refined by recent work involving a more detailed mechanical treatment (Höhn et al. 2015; Haas et al. 2018). These studies complement molecular studies that uncovered the role of a kinesin family molecular motor in displacing bridges, which ultimately provides the force to drive inversion to completion as well as that of a myosin motor in mediating by actomyosin contractility some cell shape changes required for inversion (Nishii and Ogihara 1999; Nishii et al. 2003).

Unlike multicellular sheet bending in many animals, which may involve many cellular behaviors such as growth, division, migration, and intercalation, the relative simplicity of Volvox inversion facilitates mathematical analysis (Höhn et al. 2015; Haas et al. 2018). As such, studies of the mechanics of Volvox inversion stand to clarify geometrical and mechanical aspects of cell sheet bending, which is ubiquitous in morphogenesis and movement in multicellular systems. In some cases of sheet bending, such as gastrulation (Holtfreter 1944; Odell et al. 1981; Forgács and Newman 2005; von Dassow and Davidson 2007; Rauzi et al. 2015), neurulation (Odell et al. 1981; Martin and Goldstein 2014; Vijayraghavan and Davidson 2017), placode formation (Huang et al. 2011), and primitive streak formation (Cherdantseva and Cherdantsev 2006) in various animals, polarized cell shape changes analogous to those in Volvox also drive sheet bending. Therefore, mechanistic studies of the physics of Volvox embryo inversion stand to illuminate morphogenetic processes involving cell sheet bending more generally.

Another protist system that has yielded insights into morphogenetic processes is the social amoeba Dictyostelium. Under starvation conditions, the normally solitary amoebae aggregate by chemotaxis to form a multicellular structure called a slug (Hagan and Cohen 1981; Weijer 2004; Fig. 2Di–iii). Slugs contain up to hundreds of thousands of individual cells and undergo coordinated, light- and temperature-directed motility to search for a suitable soil surface where the slug transforms into a stalked fruiting body that ultimately produces and releases spores (Weijer 2004; Abedin and King 2010; Fig. 2Div). Fruiting body morphogenesis arises from cellular behaviors such as directed motility, shape change, and intercalation in conjunction with physical mechanisms (Weijer 2004). The entire morphogenetic cycle, from aggregation to fruiting body, unfolds in a self-organized manner in which cells secrete and take in diffusible signals and subsequently modulate their movement and shape as well as adhesion to one another in order to undergo coordinated movement and morphogenesis (Kessler and Levine 1993; Höfer et al. 1995b; Weijer 2004).

In terms of physical mechanisms, reaction-diffusion based pattern formation is key to directing Dictyostelium aggregation and morphogenesis. Turing pattern formation, arising from reaction-diffusion systems, represents one of the most influential theories of physical mechanism in biology (Turing 1952; Howard et al. 2011). Pattern formation in reaction-diffusion systems stems from the dynamic interaction between local excitation, in which chemical reactions transform substances into one another, and lateral inhibition, due to diffusive transport. This competition between local activation (positive feedback) and long-range inhibition (negative feedback) is the essential feature of reaction-diffusion pattern formation (Meinhardt and Gierer 2000). The resulting patterns of chemical concentrations can take a variety of forms, from spots and stripes to spirals and traveling waves. In Dictyostelium, periodic cyclic adenosine monophosphate (cAMP) signals resulting from the cellular production, secretion, sensing, and degradation of cAMP drive the cellular behavior underlying aggregation as well as spatial patterning and morphogenesis at later steps of the starvation response (Shaffer 1975; Weijer 2004) (Fig. 2D). These physico-chemical patterns (Fig. 2D) orchestrate the directed crawling of cells (Fig. 2Di, ii). Specifically, cAMP reaction-diffusion patterns generally take the form of traveling linear and spiral waves (Durston 1973; Shaffer 1975; Nagano 1998; Weijer 2004). These patterns are robust to differences in cell numbers and variability in the spatial distribution of cells (Weijer 2004). They play an important role in organizing the timing and spatial organization of crawling, adhesion, and cell shape change that constitute slug formation and motility (Fig. 2Diii) and the differentiation, adhesion, secretion of ECM, cell shape change, and migration that constitute fruiting body formation (Kessler and Levine 1993; Höfer et al. 1995a; Marée and Hogeweg 2001; Weijer 2004).

Outside of Dictyostelium, reaction-diffusion systems have long been proposed to underlie morphogenetic processes, particularly Turing pattern formation in animal development such as in vertebrate skin patterning and limb development (Turing 1952; Oster et al. 1983). Only recently, however, has integration of experiment and theory clarified the role of cellular behaviors (such as motility and contraction) in the context of the mechanics of cells and tissues. Turing-like patterns in vertebrate skin are perhaps the best-established instances. Work in various fishes has shown that horizontal stripes of different colors stemming from different cell populations form according to a reaction-diffusion mechanism (Kondo and Asai 1995; Yamaguchi et al. 2007). Other examples of such pattern formation include hair follicle patterning in mice (Sick et al. 2006) and feather bud formation in chickens (Jung et al. 1998; Shyer et al. 2017). Studies of tooth development have also demonstrated a similar convergence of biological and physical mechanisms. In this case, morphogenesis stems from Turing-like mechanisms as well as differential tissue growth, an additional physical mechanism which drives the bending of tissues (Salazar-Ciudad and Jernvall 2002, 2010).

The existence of a similar physical mechanisms in disparate developmental contexts speaks to the generality of reaction-diffusion-based pattern formation. Dictyostelium represents a system of intermediate complexity between non-living reaction-diffusion systems such as the Belousov-Zhabotinsky reaction (Zhabotinsky 1964; Tyson 1979; Hudson and Mankin 1981; Petrov et al. 1993) and animals (Newman et al. 2006). The relative simplicity and tractability of Dictyostelium, in addition to shared physical mechanisms, presents the opportunity for insight into reaction-diffusion based coordination of cellular behavior in morphogenesis.

Motility and navigation

Due to their small sizes and fluid environments, nearly all cells are subject to the effects of a low Reynolds number local environment, where viscous forces dominate over inertial forces (Purcell 1977). This highly viscous environment imposes constraints on motility and navigation. Unlike when we humans swim, where inertia allows us to glide for rather long distances, due to its extremely viscous environment, a cell's movement will stop within a distance equivalent to about the width of an atom if it ceases swimming. The highly viscous fluid environment of the cell also means that at the scale of a cell, there is little mixing due to turbulence. Instead, diffusion often dominates in terms of passive dispersal However, molecular transport by flow can be more important relative to diffusion for large protists (Goldstein 2015), and turbulence and other large scale flows can have marked effects on the population level spatial distribution of swimming microbes (Guasto et al. 2012).

Many protists execute directed swimming, and while the underlying mechanistic details vary, all such modalities of directed swimming are robust to the influence of diffusion, which acts to spread out chemical signals and divert microswimmers from otherwise linear trajectories. As far as protists go, the biflagellate green alga Chlamydomonas, a member of the order Volvocales like Volvox, has been extensively studied in terms of swimming behavior (Fig. 2E). Interestingly, Chlamydomonas can swim with two phases analogous to those of the model bacterium Escherichia coli: one where flagella are synchronized leading to linear trajectories and the other where flagella beat asynchronously (Fig. 2E), leading to an abrupt change in direction (Polin et al. 2009). This behavior allows Chlamydomonas cells to diffuse rapidly in the dark. Differential flagellar response also underlies phototaxis. Photosensing by an eyespot composed of photoreceptor proteins and pigment granules, differential flagellar beat amplitude, and asymmetric yet synchronous flagellar beating together enable directed swimming toward light (Polin et al. 2009; Bennett and Golestanian 2015; Leptos et al. 2023). Proper phototaxis relies on differential responses between the trans- and cis-flagellum (the flagellum distal and proximal to the eyespot respectively; Smyth and Berg 1982; Isogai et al. 2000). Changes in flagellar beat patterns are driven by activity of dynein molecular motors, which can be brought about by changes in cytoplasmic Ca²⁺ levels (Kamiya et al. 1984; King and Dutcher 1997; Sineshchekov and Govorunova 1999). Chlamydomonas rotates as it swims, and by modulating the relative beating amplitudes of its flagella, it is able to execute turns while radially scanning its environment to maintain a constant frequency of eyespot stimulation (Bennett and Golestanian 2015; Cortese and Wan 2021; Leptos et al. 2023). Navigation by organisms that rotate as they swim, ultimately following helical trajectories due to processive motion (helical klinotaxis), was first proposed as a general strategy to deterministically navigate low Reynolds environments based primarily on theoretical considerations (Crenshaw and Edelstein-Keshet 1993; Crenshaw 1993a, 1993b; Fenchel and Blackburn 1999). Subsequent work established it as an efficient and widespread means for microorganisms to locate favorable environments (Blackburn and Fenchel 1999; Fenchel and Blackburn 1999).

The mechanisms underlying phototactic swimming behavior of Volvox and more recently another member of the Volvocales, Gonium, have also been studied (Ueki et al. 2010; de Maleprade et al. 2019) but in less detail than Chlamydomonas. While both Gonium and Volvox are colonial, the principles by which they undergo directed swimming display interesting similarities to Chlamydomonas. At the cellular level, photosensory modulation of ciliary activity shows some similarities between colonial and unicellular organisms (Ueki et al. 2010; Ueki and Wakabayashi 2018). Additionally, all three organisms use rotational scanning of the environment with eyespots in conjunction with flagellar responses that are tuned to the frequency of rotation (Drescher et al. 2010; Ueki et al. 2010; Bennett and Golestanian 2015; de Maleprade et al. 2019). This synchrony allows for phototactic behavior that is robust to perturbations that would otherwise push the swimming organism off course and also to spurious changes in light due to transient environmental conditions (de Maleprade et al. 2019).

The mechanics and hydrodynamics of swimming protists have also yielded mechanistic insight. Hydrodynamic effects have also been implicated in the synchronization of flagella in various situations including motility of the green algae Chlamydomonas and Volvox (Gueron et al. 1997; Goldstein et al. 2009; Brumley et al. 2012; Friedrich and Jülicher 2012; Geyer et al. 2013; Craddock et al. 2015; Wan and Goldstein 2016). Flagellar synchrony is required for proper motility, and hydrodynamic coupling has been shown to be sufficient to produce synchrony (Gueron et al. 1997; Goldstein et al. 2009; Brumley et al. 2012; Wan and Goldstein 2016). Outside of protists, flagellar synchrony mediated by hydrodynamic interactions has been observed in spermatozoa (Machin 1963; Riedel et al. 2005; Woolley et al. 2009). In contrast to Volvox where hydrodynamics effects alone appear to be sufficient for coordination during swimming, flagellar synchrony in swimming Chlamydomonas seems to be driven by direct mechanical coupling of flagella through basal bodies as well as hydrodynamic effects and cell body movement (Geyer et al. 2013; Quaranta et al. 2015; Wan and Goldstein 2016), although the relative contributions of all of these mechanisms remains to be determined. Swimming of many ciliates is also mediated by synchronous ciliary activity (note that when referring to eukaryotes, we use the terms cilia and flagella interchangeably as they are homologous and structurally identical, and any morphological or functional differences are not relevant to our purposes here). Presumably, this may be coordinated in part through hydrodynamic effects and basal body coupling, although mechanisms of ciliary coordination in ciliates has received less attention (Párducz 1967; Machemer 1972). Although conflicting historical accounts exist, recent work in the ciliate Euplotes, noted for its complex locomotor behavior, has implicated mechanical cytoskeletal coupling in coordinating complex ciliary movements (Taylor 1921; Okajima and Kinosita 1966; Larson et al. 2022). Cytoskeletal elements associated with the model ciliate Tetrahymena have also been shown to mechanically regulate proper swimming behavior (Galati et al. 2014; Soh et al. 2020, 2022). Further, coordination and proper orientation among cilia in ciliated epithelia of animals can play an important role in human health and disease by directing the flow of mucus and other biological fluids (Chilvers et al. 2003; Mitchell et al. 2007; Crest et al. 2017; Chioccioli et al. 2019; Ramirez-San Juan et al. 2020). Again, the relative simplicity of protists presents an opportunity for detailed investigation of physical mechanisms. Such fundamental understanding with respect to physical mechanisms stands to illuminate more complex cases, for example, instances of ciliary coordination in animal epithelia, where neural control and planar cell polarity are known to be important and add additional layers of complexity (Jorissen et al. 1989; Mitchell et al. 2007; Verasztó et al. 2017; Wan 2018).

Investigating diverse forms of sensorimotor activity in protists stands to clarify general principles of cellular decision making, navigation, and motility. Combinations of a few basic sensorimotor behaviors including kinetic responses, where cells change swimming speed or direction, temporal gradient sensing, where cells sense and respond to environmental changes in scalar quantities, and helical klinotaxis (described above) have been proposed as characterizing the general strategies by which swimming protists, including diverse flagellates and ciliates, locate favorable environments (Fenchel and Blackburn 1999). Even restricted to flagellar-based swimming, the modalities detailed in the preceding paragraphs represent only a few of the many diverse modes of protist locomotion. For example, the rosette colonies (rosettes) Salpingoeca rosetta, a species of choanoflagellate, the closest living relatives of animals, swim along helical trajectories and undergo directed motility in response to oxygen gradients (Kirkegaard et al. 2016). Unlike directed motility in the previously described systems, in rosettes, there is no flagellar coordination. Instead, directed motility, which can be described by an aggregate random walker model, arises from the joint independent behavior of cells that modulate their flagellar beating based on changes in oxygen levels, leading to changes in colony swimming direction with a frequency dependent on oxygen concentration (Kirkegaard et al. 2016). Crawling cells can also exhibit directed motility, with Dictyostelium being one of the best studied systems among the amoeboid protists, which often navigate by sensing gradients over their bodies (King and Insall 2009; Wan and Jékely 2021). Work investigating Dictyostelium navigation in spatially complex environments using a combination of theory and experiments has demonstrated the importance of self-generated gradients, which may also play an important role in animal cell navigation in health and disease (Tweedy et al. 2016, 2020; Tweedy and Insall 2020). In nature, chemotaxis and cell motility more generally can play an important role in nutrient cycling at the ecosystem and even global scale (Guasto et al. 2012). At a population level, the interaction between active swimming and shear due to natural turbulence in aquatic environments can lead to up to a 10-fold increase in local phytoplankton density (Durham et al. 2013). Therefore, understanding these mechanisms and dynamics in their environmental context will be important for understanding nutrient cycling in myriad ecosystems where protists play important roles.

Decision making

Spanning elements of morphogenesis as well as navigation, the slime mold P. polycephalum has become a model for studying how cells coordinate complex behaviors in terms of computation and decision-making (Nakagaki et al. 2000; Tero et al. 2010; Beekman and Latty 2015; Reid et al. 2016). Physarum is a large (easily visible to the naked eye), plasmodial, or multinucleated, cell that grows as a dynamic network of interconnected, contractile tubes through which cytoplasm is rhythmically pumped back and forth ( Matsumoto et al. 2008). In nature, Physarum crawls across forest environments in search of food and favorable environments, which are generally dark and damp (Fig. 2F). Upon finding food, the cell alters its structure to increase cytoplasmic flow and growth toward the food, which it then digests extracellularly or engulfs by phagocytosis depending on the food source (Goodman 1980; Bailey 1995; Matsumoto et al. 2008). Foraging proceeds in two phases: an exploratory expansion phase in which the cell grows outward, growing its network, and a contraction phase in which the cell prunes back regions that did not encounter nutrients while expanding tube diameters in regions involved in nutrient transport (Nakagaki et al. 2001). Extracellular secretions may serve as a self-produced cue for Physarum to avoid exploring regions it has already visited (Reid et al. 2012, 2013). In the lab, Physarum has been shown to be able to solve mazes (Nakagaki et al. 2000), to find the shortest distance between points of interest (Tero et al. 2010), a notoriously difficult computational problem, and to organize its structure to optimize its diet (Dussutour et al. 2010). Physarum also displays strategic decision making when confronted with conflicting environmental stimuli. When presented with a choice between high and low quality food in different light conditions (with light generally evoking an aversive response), cells tend to choose the high-quality food regardless of light condition once the difference in quality between the two food sources is high enough (Latty and Beekman 2009). The modulation of foraging behavior based on risk (light) exposure shows that Physarum can make multi-objective foraging decisions. Together, these examples illustrate Physarum’s capacity for making the kinds of complex decisions often associated with nervous systems.

In order to accomplish this kind of sophisticated behavior, the undifferentiated cell needs to coordinate morphogenesis across centimeters. Diffusion alone would be prohibitively slow, taking even ions days to travel such distances assuming a cell size of 2 cm and diffusion on the order of 10⁻⁵ m²/s. However, cytoplasmic flow driven by rhythmic actomyosin mediated contractions of the gel-like tubular cell structure can transport signals much more rapidly (Matsumoto et al. 2008; Alim et al. 2013). Flow increases Taylor dispersion, the effective dispersion of molecular substances beyond pure diffusivity, and work has shown that Physarum can reorganize its structure to optimize Taylor dispersion (Marbach et al. 2016; Alim et al. 2017). Additionally, behavioral coordination arises in a self-organizing manner as the cell lacks any sort of centralized organizational structure (Tsuda et al. 2004; Tero et al. 2010; Alim et al. 2013, 2017; Beekman and Latty 2015; Reid et al. 2016). Quantitative analysis of Physarum dynamics has shown how advected cues, likely intracellular Ca²⁺ (Alim et al. 2017), along with changes in network structure and tube diameter affected by those signals, are sufficient to account for the observed foraging behaviors (Matsumoto et al. 1986; Tero et al. 2010; Alim et al. 2013, 2017). In this way, the cell is an active reaction diffusion system (Nakagaki et al. 1999; Yamada et al. 1999; Adamatzky 2007).

In Physaurm, analysis of biophysical processes has provided mechanistic insight into the coordination of complex behaviors. It has also allowed for the testing of competing hypotheses as to the underlying molecular mechanisms of signal propagation (i.e., elastic waves vs. electrical impulses vs. advected molecular signals) (Alim et al. 2017). Insights from this work may inform our understanding of systems beyond Physarum including hyphal networks by which fungi grow and feed, which share many physical similarities. More generally, biophysical approaches to analyzing decision making can also inform our understanding of how cells and other “simple” systems are able to coordinate and control complex behavior and of how living systems perform computational tasks (Beekman and Latty 2015; Reid et al. 2016; Alim et al. 2017; Oettmeier et al. 2017; Coyle et al. 2019; Larson et al. 2022).

After a time of considerable interest in the early to mid 20th century, ciliates have re-emerged as a system for studying decision making and even learning in single cells (Jennings 1906; Bennet and Francis 1972; Coyle et al. 2019; Dexter et al. 2019; Marshall 2019; Trinh et al. 2019; Gershman et al. 2021; Larson et al. 2022; Rajan et al. 2023). Ciliates are attractive systems for studying cellular decision making owing in part to their obvious, rapid, stereotyped behaviors, along with their polarized and highly stereotyped cell morphologies that together make them amenable to rigorous analyses. Cytoskeletal mechanics have been implicated in coordinating complex movement patterns of Lacrymaria, a predator noted for its long, flexible neck used for hunting and of Euplotes, a cell that walks using leg-like bundles of cilia (Coyle et al. 2019; Larson et al. 2022). Experiments in Stentor, a cell perhaps best known for its impressive capacity for regeneration, grounded in quantitative analysis in concert with mathematical modeling have demonstrated sequential logic in avoidance reactions in response to aversive stimuli as well as discrete cell state changes involved in learning to ignore a benign mechanical stimulus (Dexter et al. 2019; Rajan et al. 2023). Similarly to Stentor, Physarum is capable of habituation-based learning, in which cells can learn to ignore aversive chemical stimuli (Boisseau et al. 2016). Fascinatingly, conditioned responses in Physaruym can be transferred by cell fusion (Vogel and Dussutour 2016; Boussard et al. 2018). Generally, the mechanistic underpinnings of these complex computational processes in cells remain to be clarified, although sodium may function as a substrate for encoding memories during Physarum learning (Boussard et al. 2018). Extending insights from decision making in ciliates to other eukaryotes stands to advance understanding of similar mechanisms and processes that underlie cellular function. Recently, work combining theory and experiments to model sensorimotor behavior in Paramecium, a longstanding model ciliate, has initiated such an approach, comparing the cell with its calcium-based action potential triggered movements to a swimming neuron (Brette 2021; Elices et al. 2023).

Evolution at the intersection of biology and physics

While considerable work on the intersection of physical constraints and cellular behavior has identified mechanisms of organismal function, less work has focused on the role of this coaction in evolution, leaving mostly open a frontier at the interface of biology and physics. There is, however, precedent for such studies, although most work has focused on animals and plants at or above the tissue scale, without explicit treatment of cells, and has tended to emphasize phenotypic limitations and optimization of function, yielding a rather narrow view of the role of physical constraints and mechanism.

For example, work on the evolution of ammonite shell coiling grounded in a theoretical morphospace (Raup 1967) explained the lack of certain shell morphologies (Chamberlain and Westermann 1976) and predicted the existence of others, which were later discovered (Chamberlain 1981), based on hydrodynamic considerations of swimming efficiency (Raup 1967; Chamberlain and Westermann 1976). Another prominent example comes from the investigation of the evolution of plant morphologies. Niklas and Kerchner developed a simulation framework using a rule-based model of plant growth grounded in physical and physiological constraints was able to capture much of the evolutionary trajectory of land plants (Niklas and Kerchner 1984; Niklas 1988, 1997, 1999, 2013). This simulation framework was also able to generate specific predictions about how the intensity and diversity of selective pressures (the roughness of the fitness landscape) along with developmental constraints affect the evolution of morphological diversity and complexity (Niklas 1999). While the previous two examples do not explicitly treat the behavior of cells underlying morphogenesis, evolution at the intersection of cellular behavior and physical constraints has also been considered. The theory of Turing patterns involving behaviors such as migration and haptotaxis, in addition to mechanics (Newman and Frisch 1979; Oster et al. 1983, 1988; Murray and Oster 1984; Murray et al. 1988), has recently been tested and built upon in the context of vertebrate limb (Sheth et al. 2012; Cooper et al. 2014; Lopez-Rios et al. 2014; Cooper 2015; Newman et al. 2018) and tooth evolution and development (Salazar-Ciudad and Jernvall 2002; Salazar-Ciudad 2012). Results from these studies show how even in complex biological processes, the underlying control parameters may not be so numerous (Salazar-Ciudad 2012; Newman et al. 2018). While not focused on protists, research on morphogenesis in plants and animals highlights how considering physical mechanisms can sharpen focus on essential molecular or genetic components.

Investigation of physical mechanisms naturally fits into an evolutionary context because biophysical models can generate predictions about the relationships between changes in cellular properties or behaviors and resultant phenotypes. Studies combining biology and physics to understand protist evolution are less extensive compared to those described so far. These studies have also tended to focus primarily on adaptation and optimal performance. Work on the evolution of volvocine algae in the context of physical constraints has perhaps received the most attention (Goldstein 2015). Experiments and theory together suggest important physical constraints on colony size scaling. First, experiments showed that external fluid flow is important for Volvox colony metabolism. Specifically, germ cells of deflagellated colonies did not grow well, but growth could be rescued by increasing external flow by stirring (Solari et al. 2006). Experiments and theory then demonstrated that Volvox colonies were able to maintain nutrient flux through phenotypic plasticity (increasing cell spacing and flagellar length) even in the face of reduced nutrient growth media (Solari et al. 2011), while the unicellular relative Chlamydomonas did not show such compensatory phenotypic plasticity (Solari et al. 2011). Theory suggested that for large spherical colonies, transport of nutrients by flagellar driven flow should play an important role (Short et al. 2006; Goldstein 2015). This is because nutrient uptake scales proportionally with radius while metabolic needs of the colony will scale as the square of the radius (Short et al. 2006; Goldstein 2015), so metabolic needs outpace the increase in diffusive transport of nutrients with increasing colony size. Increasing swimming speed, however can circumvent this constraint by increasing nutrient transport (Short et al. 2006; Goldstein 2015). These results and arguments suggest that, independently of other selective pressures and constraints, large colonies should optimize flow around colonies via swimming to maximize growth rates. Interestingly, the predicted point of decreasing diffusive uptake with colony size sits near the middle of the size of Volvox colonies (Short et al. 2006; Goldstein 2015).

Theory and experiments have also considered optimality of protist morphology for swimming in Chlamydomonas (Tam and Hosoi 2011; Bauer et al. 2021) and feeding in choanoflagellates (Roper et al. 2013; Kirkegaard and Goldstein 2016; Asadzadeh et al. 2019). Studies of Chlamydomonas found that flagellar lengths on average sit near the optimal length for swimming and gliding speeds (Bauer et al. 2021) and that the modes of swimming employed by Chlamydomonas are themselves close to optimal for the size and shape of the organism (Kuchka and Jarvik 1987; Tam and Hosoi 2011; Khona et al. 2013). Work on choanoflagellates has argued that colony morphology may be optimized for cooperative feeding (Roper et al. 2013). However, this result is contentious, with theoretical work arguing that different environmental tradeoffs exist for different life history stages (Kirkegaard and Goldstein 2016). Computational fluid mechanics modeling was also used to test hypotheses about the function of lorica, silica cages produced by some choanoflagellates (Asadzadeh et al. 2019). Simulations, calibrated by data, showed that the lorica does not enhance feeding by increasing drag and preventing feeding current recirculation as previously hypothesized (Leadbeater 2015; Asadzadeh et al. 2019), but instead may increase prey capture efficiency by stabilizing cellular motion (Asadzadeh et al. 2019).

In addition to explaining limitations on evolutionary outcomes, investigating the interplay between active processes, and physical constraints can also bring deeper or more general understanding of mechanism, both in terms of conserved and convergent processes. In particular, the generality of the physical context of many evolutionary processes can lead to the emergence of rules in evolutionary dynamics. Experimental evolution constitutes a powerful method by which to understand these rules. Investigations of bacterial evolution have tended to dominate work in experimental evolution. The reproducibly, convergently evolved “wrinkly spreader” phenotype in Pseudomonas fluorescens, characterized by a wrinkled surface to increase surface area for oxygen absorption, provides a salient example of phenotypic convergence in the face of physical constraints (Spiers et al. 2002; Spiers 2014; Lind et al. 2015, 2017). However, eukaryotic systems including protists stand to provide insights as well. Yeast and Sphaeroforma, a close relative of animals, have already illuminated the evolution of multicellularity along these lines, demonstrating the potential of such approaches (Libby et al. 2014; Jacobeen et al. 2018; Day et al. 2022; Dudin et al. 2022). Recent work has argued that constraints imposed by cell packing may be unavoidable in the evolution of simple multicellularity (Jacobeen et al. 2018; Day et al. 2022). In the experimental evolution of snowflake yeast, a class of lab evolved strains of multicellular Saccharomyces cerevisiae that form by incomplete cytokinesis, under selection for larger colony size, cells reproducibly evolve to become more elongated in order to reduce accumulated stress that would otherwise fragment colonies at larger colony sizes (Jacobeen et al. 2018). The authors argue that accumulated stress may in fact be a generic feature of developmental, multicellular systems (Jacobeen et al. 2018). In this case, accumulated residual stress acts as both a constraint (by limiting overall colony size) and a mechanism of multicellular reproduction by causing fragmentation of colonies. Another study of experimental evolution of snowflake yeast in conjunction with mathematical modeling argued that accounting for cell packing geometry was key to understanding the counterintuitive evolution of increasing rates of apoptosis (Libby et al. 2014). Recently, work showed that changes in cell morphology leading to dense entanglement of cells were key to overcoming colony fragmentation due to accumulated stress in the evolution of macroscopic snowflake yeast (Bozdag et al. 2023). In these studies, geometrical constraints proved to play a key role in explaining evolutionary processes. Similar constraints on cell packing have been implicated in the evolution of diverse colony morphologies among choanoflagellates (Larson et al. 2020).

These examples, though not all dealing with protists, illustrate how understanding of physical constraints can elucidate rules in evolution. Furthermore, in several cases, physical constraints played a key role in clarifying the functional or adaptive significance of cellular modification. The broad spectrum of cell biology represented by microbial eukaryotes along with the development of new model systems points to the potential for a great expansion of research in this direction. Experimental evolution using protists would greatly benefit from high quality genomes and defined culture conditions in addition to high throughput methods for quantifying diverse phenotypes.

Conclusion

Together, the work reviewed here illustrates the power in combining perspectives from physics and biology to understand principles of function and evolution in biological systems. Importantly, it shows how investigating biological function in terms of the regulated interplay between cellular behavior and physical constraints drives mechanistic insight. Additionally, the reviewed examples serve to illustrate the value of close collaboration between theory and experiment. As we have seen, physical constraints can act not only as limitations but can also underpin robust mechanisms and can highlight shared principles among diverse organisms spanning the tree of life.

In general, protists present a rich, comparatively unexplored region of cell biology ripe for investigation by quantitative methods. The examples presented here have barely scratched the surface of protist diversity. We are only beginning to understand the principles of cell behavior and how they play out beyond the most intensively studied systems. In addition to seeking out new biological insight in the lab or in the field, the historical cell biology literature represents an extensive resource for interesting observations and questions that can now be effectively addressed with modern methods. Advances in microscopy and computation along with molecular techniques stand to continue to drive deep mechanistic insight into fundamental processes across the stunning diversity of eukaryotic cells. Promising future directions include investigating how cellular behaviors are controlled and play out in natural or naturalistic environmental contexts, the continued development of phylogenetically diverse model systems including cultivation methods and natural history studies (Keeling 2019) as well as the discovery and isolation of new organisms from the field, using comparative approaches to study the evolution of cellular behavior, and capitalizing on recent progress in the physics of behavior to quantitatively characterize the behavioral repertoires of cells (Bialek 2022). At the intersection of these lines of research lie the following interrelated questions: How are behaviorally relevant geometrical features of cells such as the size, shape, number, position of appendages or other structures controlled, and how do these features afford behaviors? How do cells manage the flow of information across their bodies to properly execute behaviors in specific environmental contexts? Continued synthesis of complementary perspectives on diverse systems will clarify unifying themes and fundamental biological principles.

Notes

From the symposium “Large-scale biological phenomena arising from small-scale biophysical processes” presented at the virtual annual meeting of the Society for Integrative and Comparative Biology, January 16–March 31, 2023.

Funding

This work was supported in part by a National Science Foundation Graduate Research Fellowship (DGE 1106400) and by a Merck Fellowship of the Jane Coffin Childs Memorial Fund for Medical Research.

Conflict of interest statement

The author declares that there is no conflict of interest.