Regulation of Spatiotemporal Patterns by Biological Variability: General Principles and Applications to Dictyostelium discoideum

Spatiotemporal patterns often emerge from local interactions in a self-organizing fashion. In biology, the resulting patterns are also subject to the influence of the systematic differences between the system’s constituents (biological variability). This regulation of spatiotemporal patterns by biological variability is the topic of our review. We discuss several examples of correlations between cell properties and the self-organized spatiotemporal patterns, together with their relevance for biology. Our guiding, illustrative example will be spiral waves of cAMP in a colony of Dictyostelium discoideum cells. Analogous processes take place in diverse situations (such as cardiac tissue, where spiral waves occur in potentially fatal ventricular fibrillation) so a deeper understanding of this additional layer of self-organized pattern formation would be beneficial to a wide range of applications. One of the most striking differences between pattern-forming systems in physics or chemistry and those in biology is the potential importance of variability. In the former, system components are essentially identical with random fluctuations determining the details of the self-organization process and the resulting patterns. In biology, due to variability, the properties of potentially very few cells can have a driving influence on the resulting asymptotic collective state of the colony. Variability is one means of implementing a few-element control on the collective mode. Regulatory architectures, parameters of signaling cascades, and properties of structure formation processes can be "reverse-engineered" from observed spatiotemporal patterns, as different types of regulation and forms of interactions between the constituents can lead to markedly different correlations. The power of this biology-inspired view of pattern formation lies in building a bridge between two scales: the patterns as a collective state of a very large number of cells on the one hand, and the internal parameters of the single cells on the other.     

Achievements and challenges of sialic acid research

Sialic acids are one of the most important molecules of life, since they occupy the terminal position on macromolecules and cell membranes and are involved in many biological and pathological phenomena. The structures of sialic acids, comprising a family of over 40 neuraminic acid derivatives, have been elucidated. However, many aspects of the regulation of their metabolism at the enzyme and gene levels, as well as of their functions remain mysterious. Sialic acids play a dual role, not only are they indispensable for the protection to and adaptation of life, but are also utilised by life-threatening infectious microorganisms. In this article the present state of knowledge in sialobiology, with an emphasis on my personal experience in this research area, is outlined including a discussion of necessary future work in this fascinating field of cell biology.     

BIO-LGCA: A cellular automaton modelling class for analysing collective cell migration

Collective dynamics in multicellular systems such as biological organs and tissues plays a key role in biological development, regeneration, and pathological conditions. Collective tissue dynamics—understood as population behaviour arising from the interplay of the constituting discrete cells—can be studied with on- and off-lattice agent-based models. However, classical on-lattice agent-based models, also known as cellular automata, fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. To overcome drawbacks of classical on-lattice models, we introduce an on-lattice, agent-based modelling class for collective cell migration, which we call biological lattice-gas cellular automaton (BIO-LGCA). The BIO-LGCA is characterised by synchronous time updates, and the explicit consideration of individual cell velocities. While rules in classical cellular automata are typically chosen ad hoc, rules for cell-cell and cell-environment interactions in the BIO-LGCA can also be derived from experimental cell migration data or biophysical laws for individual cell migration. We introduce elementary BIO-LGCA models of fundamental cell interactions, which may be combined in a modular fashion to model complex multicellular phenomena. We exemplify the mathematical mean-field analysis of specific BIO-LGCA models, which allows to explain collective behaviour. The first example predicts the formation of clusters in adhesively interacting cells. The second example is based on a novel BIO-LGCA combining adhesive interactions and alignment. For this model, our analysis clarifies the nature of the recently discovered invasion plasticity of breast cancer cells in heterogeneous environments.     

Studies of folding and misfolding using simplified models

Computer simulations are as vital to our studies of biological systems as experiments. They bridge and rationalize experimental observations, extend the experimental "field of view", which is often limited to a specific time or length scale, and, most importantly, provide novel insights into biological systems, offering hypotheses about yet-to-be uncovered phenomena. These hypotheses spur further experimental discoveries. Simplified molecular models have a special place in the field of computational biology. Branded as less accurate than all-atom protein models, they have offered what all-atom molecular dynamics simulations could not--the resolution of the length and time scales of biological phenomena. Not only have simplified models proven to be accurate in explaining or reproducing several biological phenomena, they have also offered a novel multiscale computational strategy for accessing a broad range of time and length scales upon integration with traditional all-atom simulations. Recent computer simulations of simplified models have shaken or advanced the established understanding of biological phenomena. It was demonstrated that simplified models can be as accurate as traditional molecular dynamics approaches in identifying native conformations of proteins. Their application to protein structure prediction yielded phenomenal accuracy in recapitulating native protein conformations. New studies that utilize the synergy of simplified protein models with all-atom models and experiments yielded novel insights into complex biological processes, such as protein folding, aggregation and the formation of large protein complexes.     

Formation,collective motion,and merging of macroscopic bacterial aggregates

Chemotactic bacteria form emergent spatial patterns of variable cell density within cultures that are initially spatially uniform. These patterns are the result of chemical gradients that are created from the directed movement and metabolic activity of billions of cells. A recent study on pattern formation in wild bacterial isolates has revealed unique collective behaviors of the bacteria Enterobacter cloacae. As in other bacterial species, Enterobacter cloacae form macroscopic aggregates. Once formed, these bacterial clusters can migrate several millimeters, sometimes resulting in the merging of two or more clusters. To better understand these phenomena, we examine the formation and dynamics of thousands of bacterial clusters that form within a 22 cm square culture dish filled with soft agar over two days. At the macroscale, the aggregates display spatial order at short length scales, and the migration of cell clusters is superdiffusive, with a merging acceleration that is correlated with aggregate size. At the microscale, aggregates are composed of immotile cells surrounded by low density regions of motile cells. The collective movement of the aggregates is the result of an asymmetric flux of bacteria at the boundary. An agent-based model is developed to examine how these phenomena are the result of both chemotactic movement and a change in motility at high cell density. These results identify and characterize a new mechanism for collective bacterial motility driven by a transient, density-dependent change in motility.     

Synthetic biology: a new approach to study biological pattern formation

The principles and molecular mechanisms underlying biological pattern formation are difficult to elucidate in most cases due to the overwhelming physiologic complexity associated with the natural context. The understanding of a particular mechanism, not to speak of underlying universal principles, is difficult due to the diversity and uncertainty of the biological systems. Although current genetic and biochemical approaches have greatly advanced our understanding of pattern formation, the progress mainly relies on experimental phenotypes obtained from time-consuming studies of gain or loss of function mutants. It is prevailingly considered that synthetic biology will come to the application age, but more importantly synthetic biology can be used to understand the life. Using periodic stripe pattern formation as a paradigm, we discuss how to apply synthetic biology in understanding biological pattern formation and hereafter foster the applications like tissue engineering.     

Shining light on Drosophila oogenesis: live imaging of egg development

Drosophila oogenesis is a powerful model for the study of numerous questions in cell and developmental biology. In addition to its longstanding value as a genetically tractable model of organogenesis, recently it has emerged as an excellent system in which to combine genetics and live imaging. Rapidly improving ex vivo culture conditions, new fluorescent biosensors and photo-manipulation tools, and advances in microscopy have allowed direct observation in real time of processes such as stem cell self-renewal, collective cell migration, and polarized mRNA and protein transport. In addition, entirely new phenomena have been discovered, including revolution of the follicle within the basement membrane and oscillating assembly and disassembly of myosin on a polarized actin network, both of which contribute to elongating this tissue. This review focuses on recent advances in live-cell imaging techniques and the biological insights gleaned from live imaging of egg chamber development.     

Seeing the forest for the trees: the limitations of phylogenies in comparative biology. (American Society of Naturalists Address)

The past 30 years have seen a revolution in comparative biology. Before that time, systematics was not at the forefront of the biological sciences, and few scientists considered phylogenetic relationships when investigating evolutionary questions. By contrast, systematic biology is now one of the most vigorous disciplines in biology, and the use of phylogenies not only is requisite in macroevolutionary studies but also has been applied to a wide range of topics and fields that no one could possibly have envisioned 30 years ago. My message is simple: phylogenies are fundamental to comparative biology, but they are not the be-all and end-all. Phylogenies are powerful tools for understanding the past, but like any tool, they have their limitations. In addition, phylogenies are much more informative about pattern than they are about process. The best way to fully understand the past-both pattern and process-is to integrate phylogenies with other types of historical data as well as with direct studies of evolutionary process.     

Stochastic Responses May Allow Genetically Diverse Cell Populations to Optimize Performance with Simpler Signaling Networks

Two theories have emerged for the role that stochasticity plays in biological responses: first, that it degrades biological responses, so the performance of biological signaling machinery could be improved by increasing molecular copy numbers of key proteins; second, that it enhances biological performance, by enabling diversification of population-level responses. Using T cell biology as an example, we demonstrate that these roles for stochastic responses are not sufficient to understand experimental observations of stochastic response in complex biological systems that utilize environmental and genetic diversity to make cooperative responses. We propose a new role for stochastic responses in biology: they enable populations to make complex responses with simpler biochemical signaling machinery than would be required in the absence of stochasticity. Thus, the evolution of stochastic responses may be linked to the evolvability of different signaling machineries.     

Computational modeling of chemo-bio-mechanical coupling: a systems-biology approach toward wound healing

Wound healing is a synchronized cascade of chemical, biological, and mechanical phenomena, which act in concert to restore the damaged tissue. An imbalance between these events can induce painful scarring. Despite intense efforts to decipher the mechanisms of wound healing, the role of mechanics remains poorly understood. Here, we establish a computational systems biology model to identify the chemical, biological, and mechanical mechanisms of scar formation. First, we introduce the generic problem of coupled chemo-bio-mechanics. Then, we introduce the model problem of wound healing in terms of a particular chemical signal, inflammation, a particular biological cell type, fibroblasts, and a particular mechanical model, isotropic hyperelasticity. We explore the cross-talk between chemical, biological, and mechanical signals and show that all three fields have a significant impact on scar formation. Our model is the first step toward rigorous multiscale, multifield modeling in wound healing. Our formulation has the potential to improve effective wound management and optimize treatment on an individualized patient-specific basis.     

Chemokine‐guided cell migration and motility in zebrafish development

Chemokines are vertebrate‐specific, structurally related proteins that function primarily in controlling cell movements by activating specific 7‐transmembrane receptors. Chemokines play critical roles in a large number of biological processes and are also involved in a range of pathological conditions. For these reasons, chemokines are at the focus of studies in developmental biology and of clinically oriented research aimed at controlling cancer, inflammation, and immunological diseases. The small size of the zebrafish embryos, their rapid external development, and optical properties as well as the large number of eggs and the fast expansion in genetic tools available make this model an extremely useful one for studying the function of chemokines and chemokine receptors in an in vivo setting. Here, we review the findings relevant to the role that chemokines play in the context of directed single‐cell migration, primarily in neutrophils and germ cells, and compare it to the collective cell migration of the zebrafish lateral line. We present the current knowledge concerning the formation of the chemokine gradient, its interpretation within the cell, and the molecular mechanisms underlying the cellular response to chemokine signals during directed migration.     

Self-organization phenomena resulting from cell-cell contact

Self-organization phenomena in an ensemble of cells in contact is considered. By a simple and general argument it is shown that in order to have pattern formation or spatio-temporal organization in these systems it is not necessary that chemicals move throughout the field. The influence of one cell on its neighbour can be transmitted by surface contact interactions in which molecules in the surface of one cell influence the rates of reactions in neighbouring cells without the actual passage of molecules from one cell to the other. It is shown that these systems present all the self-organizing properties seen in reaction diffusion systems. The possible biological applications and the role of first bifurcating solution out of the homogeneous state to provide “positional information” in morphogenesis is discussed.     

Engineering microbial systems to explore ecological and evolutionary dynamics

A major goal of biological research is to provide a mechanistic understanding of diverse biological processes. To this end, synthetic biology offers a powerful approach, whereby biological questions can be addressed in a well-defined framework. By constructing simple gene circuits, such studies have generated new insights into the design principles of gene regulatory networks. Recently, this strategy has been applied to analyze ecological and evolutionary questions, where population-level interactions are critical. Here, we highlight recent development of such systems and discuss how they were used to address problems in ecology and evolutionary biology. As illustrated by these examples, synthetic ecosystems provide a unique platform to study ecological and evolutionary phenomena that are challenging to study in their natural contexts.     

Travelling waves in a tissue interaction model for skin pattern formation

Tissue interaction plays a major role in many morphogenetic processes, particularly those associated with skin organ primordia. We examine travelling wave solutions in a tissue interaction model for skin pattern formation which is firmly based on the known biology. From a phase space analysis we conjecture the existence of travelling waves with specific wave speeds. Subsequently, analytical approximations to the wave profiles are derived using perturbation methods. We then show numerically that such travelling wave solutions do exist and that they are in good agreement with our analytical results. Finally, the biological implications of our analysis are discussed.     

Cancer as an emergent phenomenon in systems radiation biology

Radiation-induced DNA damage elicits dramatic cell signaling transitions, some of which are directed towards deciding the fate of that particular cell, while others lead to signaling to other cells. Each irradiated cell type and tissue has a characteristic pattern of radiation-induced gene expression, distinct from that of the unirradiated tissue and different from that of other irradiated tissues. It is the sum of such events, highly modulated by genotype that sometimes leads to cancer. The challenge is to determine as to which of these phenomena have persistent effect that should be incorporated into models of how radiation increases the risk of developing cancer. The application of systems biology to radiation effects may help to identify which biological responses are significant players in radiation carcinogenesis. In contrast to the radiation biology paradigm that focuses on genomic changes, systems biology seeks to integrate responses at multiple scales (e.g. molecular, cellular, organ, and organism). A key property of a system is that some phenomenon emerges as a property of the system rather than of the parts. Here, the idea that cancer in an organism can be considered as an emergent phenomenon of a perturbed system is discussed. Given the current research goal to determine the consequences of high and low radiation exposures, broadening the scope of radiation studies to include systems biology concepts should benefit risk modeling of radiation carcinogenesis. Presented at the First International Workshop on Systems Radiation Biology, February 14–16 2007, GSF-Research Centre, Neuherberg, Germany.     

Eduard Strasburger

Eduard Strasburger Eduard Strasburger, director of the Institute of Botany and the Botanical Garden at the University of Bonn from 1881 to 1912, was one of the most admirable scientists in the field of plant biology, not just as the founder of modern plant cell biology but in addition as inspirator for sensory plant biology. He contributed to plant cell biology by discovering the discrete stages of karyokinesis and cytokinesis in algae and land plants, describing cytoplasmic streaming in different systems, and reporting on the growth of the pollen tube into the embryo sac and guidance of the tube by the synergides. Strasburger attracted attention to many biological phenomena which remain hot spots even in current plant cell biology research, e.g., mechanisms of cell plate formation, vesicle trafficking as a basis for most important developmental processes, structure and function of the plasmodesmata, and signaling related to fertilization. By his early industrial cooperation, internationality, integrating research profile and public teaching he fulfills even today's most admired and desirable qualities of a distinguished and highly honored academic scholar and frontier scientist.     

进化细胞生物学的提出及其任务

作者提出应创建一门源于进化生物学与细胞生物学两者的交叉学科一进化细胞生物学(细胞的进化生物学)。其根本任务在于用进化的观点考察真核细胞的一切方面,从它们的起源和演化来认识它们的现在。文中列举了其具体的研究内容,并分析了其研究方法上的特点,指出在这里需要把进化生物学的综合性分析与细胞生物学的实验研究最紧密地结合起来。文中还论述了真核细胞的细胞器的“不进化”现象,指出其根本原因在于进化焦点的转移。     

Microfluidics meet cell biology: bridging the gap by validation and application of microscale techniques for cell biological assays

Microscale techniques have been applied to biological assays for nearly two decades, but haven't been widely integrated as common tools in biological laboratories. The significant differences between several physical phenomena at the microscale versus the macroscale have been exploited to provide a variety of new types of assays (such as gradient production or spatial cell patterning). However, the use of these devices by biologists seems to be limited by issues regarding biological validation, ease of use, and the limited available readouts for assays done using microtechnology. Critical validation work has been done recently that highlights the current challenges for microfluidic methods and suggest ways in which future devices might be improved to better integrate with biological assays. With more validation and improved designs, microscale techniques hold immense promise as a platform to study aspects of cell biology that are not possible using current macroscale techniques.     

Aggregation pattern transitions by slightly varying the attractive/repulsive function

Among collective behaviors of biological swarms and flocks, the attractive/repulsive (A/R) functional links between particles play an important role. By slightly changing the cutoff distance of the A/R function, a drastic transition between two distinct aggregation patterns is observed. More precisely, a large cutoff distance yields a liquid-like aggregation pattern where the particle density decreases monotonously from the inside to the outwards within each aggregated cluster. Conversely, a small cutoff distance produces a crystal-like aggregation pattern where the distance between each pair of neighboring particles remains constant. Significantly, there is an obvious spinodal in the variance curve of the inter-particle distances along the increasing cutoff distances, implying a legible transition pattern between the liquid-like and crystal-like aggregations. This work bridges the aggregation phenomena of physical particles and swarming of organisms in nature upon revealing some common mechanism behind them by slightly varying their inter-individual attractive/repulsive functions, and may find its potential engineering applications, for example, in the formation design of multi-robot systems and unmanned aerial vehicles (UAVs).