SESSIONSSESSIONS

Oral session

  • Invited Speakers



Makoto Asashima, (AIST)

Complex system and regulation in early animal development





Avigdor Eldar, (California Institute of Technology)

Partial penetrance facilitates developmental evolution in bacteria

Development normally occurs similarly in all individuals within an isogenic population, but mutations often affect the fates of individual organisms differently. This phenomenon, known as partial penetrance, has been observed in diverse developmental systems. However, it remains unclear how the underlying genetic network specifies the set of possible alternative fates and how the relative frequencies of these fates evolve. We examined this question in the context of a simple developmental network; Bacillus subtilis sporulation. Mutations in an intercompartmental signalling process generate a set of discrete alternative fates not observed in wild-type cells, including rare formation of two viable 'twin' spores, rather than one within a single cell. Using genetic perturbations and time-lapse microscopy we identify the genetic control over twin penetrance and suggest a mechanistic model for twin formation. The results suggest a potential pathway for developmental evolution between mono-sporulation and twin-sporulation through states of intermediate twin penetrance. Furthermore, time-lapse microscopy of twin sporulation in wild-type Clostridium oceanicum shows a strong resemblance to twin sporulation in these B. subtilis mutants. Together the results suggest that noise can facilitate developmental evolution by enabling the initial expression of discrete morphological traits at low penetrance, and allowing their stabilization by gradual adjustment of genetic parameters. A simple mathematical model capture some of the essential requirements for such an evolution to occur.

Koichi Fujimoto, (Department of Biological Sciences, Osaka University/ ERATO Complex Systems Biology, JST)

Network Evolution of Morphogenesis.

One of the major goals in evolutionary developmental biology is to understand the relationship between gene regulatory networks and the diverse morphologies and their functionalities. Segmentation in arthropod embryogenesis represents a well-known example of body plan diversity. Striped patterns of gene expression that lead to the future body segments appear simultaneously or sequentially in long and short germ-band development, respectively. Moreover, a combination of both is found in intermediate germ-band development. Regulatory genes relevant for stripe formation are evolutionarily conserved among arthropods, therefore the differences in the observed traits are thought to have originated from how the genes are wired.
To reveal the basic differences in the network structure, we have numerically evolved hundreds of gene regulatory networks that produce striped patterns of gene expression. By analyzing the topologies of the generated networks, we show that the characteristics of stripe formation in long and short germ-band development are determined by Feed-Forward Loops (FFLs) and negative Feed-Back Loops (FBLs) respectively, and those of intermediate germ-band development are determined by the interconnections between FFL and negative FBL. Network architectures, gene expression patterns and knockout responses exhibited by the artificially evolved networks agree with those reported in the fly Drosophila melanogaster and the beetle Tribolium castaneum. For other arthropod species, principal network architectures that remain largely unknown are predicted. Our results suggest that the emergence of the three modes of body segmentation in arthropods is an inherent property of the evolving networks.



Takema Fukatsu, (Institute for Biological Resources and Functions, National Institute of Advanced Industrial Science and Technology (AIST), Japan)

Biodiversity, Endosymbiosis and Evolution

In nature, organisms are living in close association with surrounding physical environment as well as other organisms. Thus, each of the organisms is regarded as a component of the ecosystem. Considering the diverse microbial community found inside the organisms in general, each of the organisms can also be regarded as constituting an ecosystem.
Many animals and other organisms constantly harbor microorganisms inside their body, which has been referred to as "endosymbiosis". Due to the close spatial proximity, extremely intimate biological interactions and inter-dependency are commonly found between the partners called host and symbiont. Novel biological properties are often generated through such associations. In many cases, host and symbiont are integrated into an almost inseparable entity.
Our main research targets are diverse endosymbiotic interactions found in insects. We are also interested in sophisticated biological interactions associated with such phenomena as parasitism, reproductive manipulation, morphological manipulation, animal sociality, etc. Mechanisms underlying these biological interactions are investigated by using multi-disciplinary approaches including molecular biology, genetics, physiology, ecology and evolutionary biology.
Here we present an overview of our research topics on the endosymbiosis and evolution in insects, which underpin the enormous biodiversity of the organismal group.

References:
*Kondo N., Nikoh N., Ijichi N., Shimada M., Fukatsu T. (2002) PNAS 99: 14280-14285.
*Tsuchida T., Koga R., Fukatsu T. (2004) Science 303: 1989-1989.
*Kutsukake M., Shibao H., Nikoh N., Morioka M., Tamura T., Hoshino T., Ohgiya S., Fukatsu T. (2004) PNAS 101: 11338-11343.
*Nakabachi A., Shigenobu S., Sakazume N., Shiraki T., Hayashizaki Y., Carninci P., Ishikawa H., Kudo T., Fukatsu T. (2005) PNAS 102: 5477-5482.
*Hosokawa T., Kikuchi Y., Nikoh, N., Shimada, M., Fukatsu T. (2006) PLoS Biol 4: e337.
*Hosokawa T., Kikuchi Y., Shimada M., Fukatsu T. (2007) Proc R Soc B 274: 1979-1984.
*Nikoh N., Tanaka K., Shibata F., Kondo N., Hizume M., Shimada M., Fukatsu T. (2008) Genome Res 18: 272-280.



Chikara Furusawa, (Graduate School of Information Science and Technology, Osaka University Complex Systems Biology Project, ERATO, JST )

A Dynamic Model for Irreversible Differentiation in a Stem Cell System: Chaos Hypothesis

During normal development, cells undergo a unidirectional course of differentiation that progressively decreases the number of cell types they can potentially become. Pluripotent cells can differentiate into any of the cell types, but terminally differentiated cells cannot differentiate any more. A fundamental problem in stem cell biology is how to characterize the difference in cellular states between undifferentiated stem cells and terminally differentiated cells. In this study, to answer the raised question, we have carried out a dynamical systems modeling of cells accounting for the loss of multipotency resulting from stem cell differentiation triggered by the cell-cell interactions. We used an abstract cell model in which intra-cellular states are controlled by gene regulatory network and cell-cell interaction. Using Genetic Algorithm (GA) simulations, we screened regulatory networks that enable to generate a cell population with heterogeneous cell types by the cell-cell interactions. After screening regulatory networks generating cell type heterogeneity, we found that intra-cellular gene expression dynamics of cells that have potential to differentiate (i.e. stem cells) generally show irregular (chaotic) oscillation, whereas such complex oscillation is lost for determined cell types. Also, such stem cells express larger number of genes in comparison with differentiated cells in which lineage-specific genes strongly expressed. Based on the simulation results we proposed following hypothesis: i) Expression dynamics in stem cells show irregular chaotic oscillation and such oscillation disappear in differentiated cells. ii) The oscillatory dynamics in stem cells involves changes of many genes, whose number (effective degrees of freedom in the sense of dynamical systems) decreases as the differentiation progresses. iii) Robustness in the population ratio of several cell types is maintained by regulated differentiation of stem cells with irregular oscillatory dynamics. The universal features in regulatory networks generating cell type heterogeneity and experimental results supporting this hypothesis will also be discussed.



Daniel Hartl, (Department of Organismic & Evolutionary Biology Harvard University, Cambridge MA 02138)

Surfaces of Selective Value Revisited, Again

Imagine a protein undergoing evolution driven by positive selection through the sequential replacement of individual amino acids, in a haploid population sufficiently large that random genetic drift can be ignored. After a single amino acid substitution at each of n distinct sites, the protein will have passed through an evolutionary pathway consisting of n successive alleles, each of which increased the fitness of the organism by some increment.
Given the sequences of only the original and derived protein, let us try to reconstruct the evolutionary pathway from first principles. The n amino acid replacements define 2n possible alleles of the gene. An organism with a genotype carrying any of the 2n alleles has an associated fitness, and these fitnesses specify a fitness landscape in n + 1 dimensions in which the evolutionary process has unfolded. Each of n of the dimensions corresponds to one of the amino acid replacements, and dimension n + 1 corresponds to organismal fitness.
Sojourns through a fitness landscape of this sort, based originally on the alleles of different genes rather than amino acid replacements at different sites in a single protein, were conceived independently by J. B. S. Haldane (1931) and Sewall Wright (1932) as a metaphor for the evolutionary process. Wright's (1932) attempt to use three-dimensional projections of such landscapes as pictorial representations of elementary evolutionary processes has been reproduced frequently in the evolutionary literature and widely referenced. In his biography of Wright, however, Provine (1989) emphasizes some shortcomings of this metaphor as applied to polymorphic multiple alleles of different genes in diploid organisms. But in the context of single amino acid replacements at multiple sites in a protein, none of these objections is relevant.
For the fitness landscape defined by n alternative amino acid replacements in a protein, there are n! possible pathways from the ancestral protein sequence to the derived protein sequence. Permissible paths are those in which fitness increases with each successive amino acid replacement. Fitness landscapes of this type are complex networks that are fundamental to systems evolutionary biology, yet they have been poorly explored. Such protein-fitness landscapes raise many questions: How many pathways exist between the ancestral and derived form in which fitness increases at every step? What are the relative probabilities of any alternative pathways? Are there pathways that terminate at submaximal fitness peaks, from which any single-step change imposes a decrease in fitness? How many such submaximal peaks occur, and how frequently are they realized?
More other questions are raised by two fundamental biological principles that, from an evolutionary perspective, profoundly affect the relative likelihood of any evolutionary pathway. The first is pleiotropy, which results from ancillary effects of an amino acid replacement on other traits that affect fitness in the organism. The other is epistasis, which results from non-additive interactions between mutant amino acid sites, because the effect of any amino acid replacement is often dependent on its context in the molecule.
Still more questions are raised by comparing theoretical evolutionary landscapes with those investigated experimentally. The principal experimental exemplars thus far examined are those of antibiotic resistance, but as evolutionary systems biology continues its advance into complex interactions, we may hope for many more examples based on theoretical and computational methods.



Sui Huang, (Systems Biology,Calgary),

Multipotency and Cell fate decision on the Epigenetic landscape: From Metaphor to Molecules and Mathematical Model

Each cell type in the metazoan body is characterized by its specific genome-wide profile of gene expression - which now can be approximated by measuring the transcriptome. How do cells, given the unfathomable combinatorial possibilities in the expression of ten thousands of genes, establish the correct transcriptome that specifies their fate with such stunning reliability and generates the discrete cell types? Obviously, the answer lies in the gene regulatory network that orchestrates the improbable yet inevitable establishment of each of the cell type-specific gene expression configurations. Conrad Waddington, not knowing about gene networks, proposed in the 1940s a now famous metaphor that captures the dynamics of cell differentiation into discrete and robust entities: The 'Epigenetic Landscape'. But what is the molecular and mathematical basis of this metaphor? In this talk, I will explain some intuitive theoretical concepts that may offer a formal basis for Waddington's landscape, and then, present experimental evidence in support of the ideas (i) that cell fates are "attractors" of the network (= Waddington's "valleys") and (ii) that cell fate decisions correspond to bifurcation-driven destabilization of the stem cell attractors (Waddington's "watersheds"). Finally I will show how transcriptome-wide gene expression fluctuations are at the core of multi-potency, allowing cells to vacillate in the state of indeterminacy before making fate decisions in response to external signals.



Norikazu Ichihashi, (Graduate School of Information Science and Technology, Osaka University)

Constructing self-replication system in liposome

To date, most knowledge regarding biological systems has been obtained by analyzing existing biological systems, such as cells or viruses. However, "analysis" is only a partial approach to understanding a system, which can be compensated by the constructive approach; the constructive approach is a method to construct a biological system from defined components. During the construction process, we can improve our understanding of the biological systems. We are focusing on a self-replication system of genetic information, which is a fundamental characteristic of biological systems.
Based on previous data, we constructed a self-replication system of genetic information from RNA (the genetic information) encoding RNA replicase (Qβ replicase) and a cell-free translation system (PURE system) in lipid vesicles (liposomes). During the reaction, RNA replicase was translated from the RNA, and then bound to the original RNA and catalyzed its replication1. These successive reactions are designated here as self-replication of genetic information. However the reaction efficiency was markedly lower than expected from the activity of the replicase and the translation system. This poor efficiency suggests that there are as yet unknown conditions required for efficient self-replication.
To clarify the problems, we analyzed the self-replication system by mathematical modeling, which indicated three limiting factors: 1) competition between translation and replication for RNA2; 2) parasitic RNA amplification; and 3) inactive double-stranded RNA formation. Overcoming these problems will be necessary for realization of an in vitro self-replication system. Recent progress in solving these problems is also discussed.

References:
1, Kita, H. Matsuura, T. Sunami, T. Hosoda, K. Ichihashi, N. Tsukada, K. Urabe, I. Yomo, T. (2008). "Replication of genetic information with self-encoded replicase in liposomes." Chembiochem 9(15): 2403-10.
2, Ichihashi, N. Matsuura, T. Kita, H. Hosoda, K. Sunami, T. Tsukada, K. Yomo, T. (2008). "Importance of translation-replication balance for efficient replication by the self-encoded replicase." Chembiochem 9(18): 3023-8.



Mark Isalan, (Centre for Genomic Regulation (CRG))

Shuffled gene networks and liar paradoxes

Synthetic biology aims to understand biological systems through reconstruction. We tested the limits of reconstruction by "rewiring" a large-scale network - the transcription system in Escherichia coli. By constructing 598 recombinations of promoters (including regulatory regions) with different transcription or sigma-factor genes, new network connections were added over the wild-type genetic background. This revealed both the robustness of the underlying network and its propensity to evolve new properties. In the process of shuffling bacterial transcription networks, many new network motifs were created, involving both positive and negative feedback loops. Commonly, such networks are illustrated with topology diagrams - where pointed or blunt arrows indicate network interactions. Because the same topology can have drastically different behaviours in different dimensions, it is important to emphasise that arrow diagrams are not sufficient to understand biological systems. Unless the dimensions of time and space are considered explicitly, these 'snapshot' diagrams contain inherent contradictions and resemble the classical Greek philosophy problems known as liar paradoxes.



Kunihiko Kaneko, (University of Tokyo, Komaba and ERATO Complex Systems Biology, JST)

Macroscopic relationship among plasticity, robustness, evolvablity, and phenotypic fluctuations: Waddinton's legacy revisited under the spirit of Einstein

Characterization of biological plasticity, robustness, and evolvability in terms of dynamical systems and statistical analysis is an important issue in Complex Systems Biology.
First, proportionality among evolution speed, phenotypic plasticity, and isogenic phenotypic fluctuation is derived as an extension of fluctuation-response relationship in physics. Following an evolutionary stability hypothesis we then derive a general proportionality relationship between the phenotypic fluctuations of epigenetic and genetic origin; The former is the variance of phenotype due to noise in developmental process, and the latter due to genetic mutation. The relationship suggests a link between robustness to noise and to mutation, as robustness can be defined by the sharpness of the distribution of phenotype. Next, the proportionality between the variances is demonstrated to hold also over different phenotypic traits, with which a measure for phenotypic plasticity is proposed. The obtained relationships are confirmed in models of gene expression dynamics, as well as in laboratory experiments.
Based on the results, we revisit Waddington's canalization and genetic assimilation, and discuss how consistency between evolutionary and developmental scales constrains robust developmental process and leads to universal laws on phenotypic fluctuations.

References: K.K. Life: An Introduction to Complex Systems Biology, Springer (2006);K. K., PLoS One(2007) 2 e434



Akiko Kashiwagi, (Hirosaki University)

Adaptive Response of a Gene Network to Environmental Changes by Fitness-induced Attractor Selection

Cells switch between various stable genetic programs (attractors) to accommodate environmental conditions. Signal transduction machineries efficiently convey environmental changes to the gene regulation apparatus in order to express the appropriate genetic program. However, since the number of environmental conditions is much larger than that of available genetic programs so that the cell may utilize the same genetic program for a large set of conditions, it may not have evolved a signaling pathway for every environmental condition, notably, those that are rarely encountered. Here we show that in the absence of signal transduction, switching to the appropriate attractor state expressing the genes that afford adaptation to the external condition can occur. In a synthetic bistable gene switch in Escherichia coli in which mutually inhibitory operons govern the expression of two genes required in two alternative nutritional environments, cells reliably selected the "adaptive attractor" driven by gene expression noise. A mathematical model suggests that the "non-adaptive attractor" is avoided because in unfavorable conditions, cellular activity is lower which suppresses mRNA metabolism, leading to larger fluctuations in gene expression. This in turn renders the non-adaptive state less stable. Although attractor selection is not as efficient as signal transduction via a dedicated cascade, it is simple and robust and may represent a primordial mechanism for adaptive responses that preceded the evolution of signaling cascades for the frequently encountered environmental changes.

References:
Akiko Kashiwagi, Itaru Urabe, Kunihiko Kaneko and Tetsuya Yomo,
PloS ONE, 2006, 1(1), e49



Stuart Kauffman,

Are Cells Dynamically Critical?





Roy Kishony, (Harvard),

Resistance in multi-drug environments

The application of antibiotics is hindered by a well-known "catch-22": The use of a drug promotes the emergence and spread of drug-resistant mutants that ultimately render it ineffective. While certain combination therapies are known to be more effective than single drugs, the impact of such treatments on the evolution of drug resistance is unclear. In particular, very little is known about how the evolution of resistance is affected by the nature of the interactions - synergy or antagonism - between the drugs. I will describe a combined theoretical-experimental approach to understand drug interactions and their effect on the evolution of resistance in bacteria. Our results indicate that synergistic drug pairs, typically preferred in clinical settings, can accelerate bacterial adaptation. Drug antagonism, on the other hand, can generate selection against resistant bacteria, thereby slowing down the evolution of resistance. These results suggest a tradeoff in drug-combination therapy between immediate inhibition of growth and long-term inhibition of resistance.



Tomoaki Matsuura, (Department of Bioinformatics Engineering, Osaka University)

Exploring the fitness landscape of the protein translation system

The protein translation reaction, one of the most important regulators of cell behavior, involves the interactions of a large number of components, and has been studied extensively because of its importance in the cell. By reconstruction of an Escherichia coli-based in vitro translation system with protein components highly purified on an individual basis, it has been demonstrated that 36 enzymes and ribosomes are sufficient to carry out protein translation [1]. Thus, the minimum number of components required for the reaction has been identified. Systems in which the components interact with each other are nonlinear, which makes it difficult to link the component concentrations and the synthesis activity, and therefore the relationship between the two remains to be elucidated. In a system composed of n components, the activity of the system (activity) is written as a function of the component concentrations (c1, c2, ..., cn):
activity=f(c1, c2, ..., cn)
where ci is the concentration of component i. When we denote the activity as the fitness of the system, the fitness landscape appears in the n-dimensional concentration space. Due to the vast size of the concentration space, obtaining information regarding the entire fitness landscape is not feasible. We used a cell-free protein synthesis system (PURE system [1]) composed of 69 defined components to elucidate the coarse-grained picture of the fitness landscape of the protein translation system. We quantified the ruggedness of the fitness landscape [2] as well as the expandability in the dimension of the fitness landscape [3]. The results of the present study not only implied high adaptability and evolvability of the protein translation system but were also found to be useful in optimizing the protein synthesis activity of the cell-free protein synthesis system. The similarities between the fitness landscape of the protein translation system and proteins [4,5] are also discussed.

[References]
1. Shimizu, Y. et al. Cell-free translation reconstituted with purified components. Nat. Biotechnol. 19, 751-5 (2001).
2. Matsuura, T. et al. Quantifying contributions of inter-component interactions on the activity of protein translation system (Submitted)
3. Kazuta, Y. et al. Comprehensive analysis of the effects of Escherichia coli ORFs on protein translation reaction. Mol. Cell. Proteomics. 7, 1530-1540 (2008),
4. Matsuura, T. et al. Evolutionary molecular engineering by random elongation mutagenesis. Nat. Biotechnol. 17(1): 58-61(1999).
5. Matsuura, T. et al. Nonadditivity of mutational effects on the properties of catalase I and its application to efficient directed evolution. Protein Eng. 11(9): 789-795 (1998).



Stuart Newman, (New York Medical College, Valhalla, New York USA)

A "pattern language" for evolution and development of animal form

Ancient animals arose more than half a billion years ago from unicellular organisms with sophisticated genetic repertoires. Unexpectedly from the neo-Darwinian perspective, most of the genes of the metazoan "developmental-genetic toolkit," the products of which coordinate multicellular development, were present in these single-celled forms. The rapidity with which animal developmental mechanisms emerged during the Precambrian-Cambrian transition suggests that more than just incremental adaptive evolution was involved. Here I will consider the role played by a core set of "dynamical patterning modules" (DPMs) in the origination, development and evolution of multicellular animals. DPMs are functional modules that consist of the products of certain toolkit genes in association with physical processes and effects characteristic of chemically and mechanically excitable systems of the "mesoscale" (i.e., linear dimension ~0.1-10 mm). Once cellular life achieved this spatial scale by the most basic DPM, cell-cell adhesion, a variety of physical forces and effects came into play, including cohesion, viscoelasticity, diffusion, spatiotemporal heterogeneity based on lateral inhibition, and global synchronization of oscillatory dynamics. I will show how toolkit gene products and pathways that pre-existed the metazoans acquired novel morphogenetic functions simply by virtue of the change in scale and context inherent to multicellularity. DPMs, acting singly and in combination with each other, will be seen to constitute a "pattern language" capable of generating all metazoan body plans and organ forms. The stable developmental trajectories ("developmental programs") and morphological phenotypes of modern organisms, in this view, arose by stabilizing selection that routinized the generation of morphological motifs which were originally manifestations of the physical properties of multicellular aggregates. With respect to the origination of multicellular form, this perspective diverges from the tenets of the Modern Synthesis by relinquishing uniformitarianism, incrementalism, and a central role for adaptive directional selection.



Satoshi Sawai, (Graduate school of Arts and Sciences, University of Tokyo / ERATO Complex Systems Biology, JST)

A cell-density dependent transition to collective behavior in Dictyostelium

Coherent dynamics in populations of microorganisms such as collective migration and synchronized oscillations often emerge as a result of communication among individual cells via cell-cell signaling. Recent studies in chemical oscillators suggest a possibility that a dynamical density-sensing maybe at work whereby the coupling between the excitable cells gives rise to synchronized oscillations whose frequency encodes cell-density. Although the scheme provides a robust means to encode cell-density information as a frequency in the periodic intracellular signaling, whether such mechanism is at work remain elusive. The chemoattractant signaling in social amoebae Dictyostelium discoideum is one of the best known examples of synchronized oscillations. Chemoattractant cAMP is synthesized and secreted periodically and serves as a cue that directs chemotaxis of individual cells. Our recent live-cell FRET imaging revealed the very onset of oscillations and sequential changes in the frequency at the single cell level resolution. Together with a mathematical modeling, we demonstrate that the cells prefer inputs of a certain resonant frequency and that this frequency is determined by cell-intrinsic feedback. In the talk, I will focus on how such intracellular dynamics gives rise to the transition between decoupled and collective states of the periodic cAMP signaling. We show from a series of perfusion experiments that the transition is abrupt and the frequency of the oscillations depends strictly on cell density. Similar quorum-sensing schemes are also thought to be at work in glycolytic oscillations in yeast and maybe prevalent in other organisms.



Tadashi Sugawara, (Research Center of Life Science as Complex Systems, Dept. of Basic Science, The University of Tokyo, Japan)

A Road to Artificial Cell

INTRODUCTION
In order to answer such profound questions as "From where has a life come?" and "Where is a boundary between animate and inanimate objects?" a constructive approach is effective. We have been challenging to construct an artificial using well-defined organic molecules and bio-polymers and have studied nonlinear dynamics exhibited by self-reproduction of giant vesicles and self-replication of informational molecules. Coupling between these two dynamics is expected to lead to a primitive artificial cell.

SELF-REPRODUCING SYSTEM COMPOSED OF GIANT VESICLES
We have observed following self-reproducing dynamics exhibited by giant vesicles (GVs). When a bolaamphiphilic membrane-precursor is added to giant vesicles containing a catalyst, the membrane precursor is hydrolyzed to give the same membrane molecule as the original GVs. Increase of the membrane molecules causes corpulence and self-division of GVs bearing the same composition. By means of a flow cytometric population analysis of self-reproducing GVs we confirmed that the self-reproduction continues over several generations, keeping the similar size-distribution of the original.

SELF-REPLICATION OF INFORMATIONAL MOLECULES INSIDE GIANT VESICLES
The replication of a template DNA, which produces a green fluorescent protein, in cell-sized GVs was conducted in the presence of a fluorescent probe for the duplicated DNA. We directly observed the replication of DNA in GVs by a two-step thermal cycling process and the efficiency of PCR in the GVs was as high as 16 % and the dependence of GV-size was elucidated by means of flow cytometry. The current DNA replication (PCR) condition could be encountered by prebiotic cells around a hydrothermal vent in the deep sea, this process maybe realistic model for the process by which life originated.



Masashi Tachikawa, (ERATO Complex Systems Biology, JST)

Mathematical structure of the evolution of cell differentiation in clonal cell groups

Multicellular organisms are composed of a number of cell types which are produced through cell differentiation processes from clonal cell populations or single fertilized cells. Since multicellularity has evolved several times independently in different lineages, universality in these evolutions should be taken into consideration. In particular, here we investigate the evolutionary establishment of cell differentiation in clonal cell groups. How the evolutionary processes are characterized mathematically? Are they classified into several types based on the characterization? In this study, we attempt to classify the evolution of cell differentiation by bifurcation theory. Suppose a dynamical system which (potentially) controls the cell differentiation, the evolutionary establishment and/or change of differentiation process can be characterized by the bifurcation of the dynamical system. In order to test the idea, we performed evolutionary simulations of differentiation for cell groups with gene regulatory network models which describe cell state dynamics. Clonal cell groups that develop into same cell types were subjected to selection pressure to produce two cell types whose population ratio is controlled to become about 1:1. We then found two clearly distinguishable evolutionary pathways; i) a function to produce two cell types was achieved first, and then a function to control the population ratio was achieved, ii) both functions were achieved simultaneously. The results indicate that the evolutionary relation of the two cellular functions noted above depends on the situation. To verify that these evolutionary pathways are two distinct processes, we identified the bifurcations (mathematical description for qualitative changes in the behavior of a dynamical system) involved in these pathways. The analysis reveals that the above two pathways correspond to two different bifurcation types of cellular systems; i.e. these pathways are characterized by qualitatively different mathematical structures. Our study suggests the flexibility of evolution of differentiation, and proposes the concept of applying the bifurcation analysis for characterization of flexible evolutionary pathways.



Andreas Wagner,

On the relationship between robustness and evolutionary innovation

Mutational robustness and evolvability, a system's ability to produce heritable phenotypic variation, harbour a paradoxical tension. On one hand, high robustness implies low production of heritable phenotypic variation. On the other hand, both experimental and computational analyses of neutral networks indicate that robustness enhances evolvability. I here resolve this tension using a molecular study system. To resolve the tension, one must distinguish between robustness of a genotype and a phenotype. I confirm that genotype (sequence) robustness and evolvability share an antagonistic relationship. In stark contrast, phenotype (structure) robustness promotes structure evolvability. A consequence is that finite populations of sequences with a robust phenotype can access large amounts of phenotypic variation while spreading through a neutral network. Population-level processes and phenotypes rather than individual sequences are key to understand the relationship between robustness and evolvability. My observations may apply to other genetic systems where many connected genotypes produce the same phenotypes.



Yuichi Wakamoto, (The University of Tokyo)

Bacterial persistence against antibiotics

Bacterial persistence is an epigenetic phenomenon in which a subset of cells in an isogenic population shows phenotypic tolerance to bactericidal drugs and is able to survive prolonged exposure to the drugs. We analyzed a persistence phenomenon in Mycobacterium smegmatis generated on exposure to the antibiotic, isoniazid (INH), by time-lapse single cell microscopy utilizing a microfluidic device. Our results demonstrate that persistence trait in M. smegmatis against INH is independent of the growth state of the bacteria prior to the drug exposure, which counteracts the widely accepted hypothesis for the INH persistence of mycobacteria that persisters are pre-existing non-growing cells selected on exposure to the drug. Furthermore, on INH exposure they continue to grow and divide at suppressed rates while the killing by the drug is ongoing; persistence phase, where a number of surviving cells is nearly unchanged at the population-level, is in fact quite dynamic at the single-cell level and emerges by balancing the division and killing rates in a population.



Tetsuya Yomo, (Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University/ ERATO, JST, Suita, Osaka, Japan)

Phenotypic fluctuation in experimental evolution

Recent studies on stochastic gene expression of individual cells have uncovered the large degree of phenotypic fluctuation, thus leading substantial phenotypic diversity even under identical environments. What does the phenotypic fluctuation affect survival in Darwinian evolution? We have conducted evolution experiments of random mutation on GFP gene in E.coli and selection based on expressed green fluorescence as phenotype in two different methods. In each cycle of the first method, the mutant gene with the highest "average" green fluorescence was selected in excluding the effect of cell-to-cell variation. As expected, the fitness (the average green fluorescence) of the mutant selected genotype increased while the cell-to-cell variation of each of the selected genotypes became smaller. But, interestingly, the linear correlation between the evolution rate and the cell-to-cell variation in the phenotype was found (Sato K, et al. (2003)). In contrast to the above selection based on the "average" phenotype, in the second selection method where 0.2% of individual cells were selected for higher green fluorescence, some of the selected genotypes showed a low average green fluorescence but larger cell-to-cell variations in their isogenic populations. Because the cells of each of the selected mutants, even with their low averages, occasionally could show higher green fluorescence by phenotypic fluctuation, some of the cells survived under the strong selection of the top 0.2% in the phenotype distribution. These mutant genotypes of larger fluctuation appeared independently on the different branches of the phylogenic tree. In addition to the average phenotype change by genetic mutation, the observed increase in phenotypic fluctuation acts as an evolutionary strategy to produce an extreme phenotype under severe selective environments (Ito Y, et al. (2009)).


  • Invited Speakers

Poster session

1. Shuji Ishihara (The University of Tokyo),
Analyzing mechanical processes of growing epithelium
2. Takeshi Sugawara (The University of Tokyo, Center for Complex Systems Biology),
Chromosome dynamics driven by chemical potential gradient
3. Akihiko Nakajima (The University of Tokyo),
Functional constraint and robustness in the genetic network for the temporal expression in Drosophila neurogenesis
4. Masayo Inoue (The University of Tokyo),
Various response processes against the environmental change by coupled adaptive elements model
5. Tomoki Kurikawa (The University of Tokyo),
Change in structure of phase space through learning with multiple timescales.
6. Daisuke Shimaoka (The University of Tokyo),
Transient Dynamics of EEG Activity during Necker Cube Perception; from clustering to Global Synchronization
7. Yohei Kondo (The University of Tokyo),
A metabolic network model of sustaining a non equilibrium state by growth
8. Taro Toyota (Chiba University, The University of Tokyo),
Centrifugation Effect on Calcium-Induced Fusion of Giant Vesicles
9. Kentaro Suzuki (The University of Tokyo),
Vesicular Fusion Induced by pH-dependent Association of Oppositely Charged Hybrid Vesicles with Different Phospholipids
10. Hiroshi Takahashi (The University of Tokyo),
Morphological Changes of Vesicles Induced by Autocatalytic Membrane-formation on Their Anionic Surfaces
11. Kensuke Kurihara (The University of Tokyo),
Self-division of Giant Vesicles including amplified DNA Induced by Addition of Precursors of Cationic Membrane Molecules
12. Noritaka Masaki (ERATO Complex Systems Biology Project, JST),
Spatio-temporal regulation of transcriptional pulses during self-organization in Dictyostelium
13. Nao Shimada (JSPS),
Analysis of the relationship between cytoskeleton and nuclear dynamics in ameboid cell migration
14. Keita Kamino (The University of Tokyo),
Single-cell level analysis of adaptation in chemoattractant signaling in Dictyostelium
15. Daisuke Taniguchi (The University of Tokyo),
Phase Response Analysis of Chemotactic Movement
16. Takehiko Oonuki (The University of Tokyo),
Dictyostelium growth dynamics
17. Isao Kubo (Osaka University),
Synthetic predator-prey mutualism of Escherichia coli and Dictyostelium discoideum toward reconstruction of endosymbiosis
18. Kazufumi Hosoda (Osaka University),
Adaptive phenotypic change in a synthetic ecosystem composed of two strains of Escherichia coli
19. Leo Iijima (Osaka University),
Genome-wide mutation analysis of the thermostable bacteria from experimental evolution
20. Saburo Tsuru (Osaka University),
Analysis of adaptive gene expression to nutrient depletion out of native regulatory mechanisms
21. Hayato Yanagida (Osaka University),
Compensatory Evolution of a WW Domain Variant Lacking the Strictly Conserved Trp Residue
22. Kazuya Nishimura (Osaka University),
Size control of unilamellar giant vesicle using microfluics
23. Hiroshi Kita (ERATO Complex Systems Biology Project, JST),
Replication of genetic information with self-encoded replicase in lipid vesicles
24. Norikazu Ichihashi (ERATO Complex Systems Biology Project, JST),
Constructing self-replication system in liposome
25. Yoshihiro Shimizu (Osaka University),
Adaptive transition between the bistable states in response to starvation
26. Naoaki Ono (Osaka University),
Development of a thermodynamic model to predict hybridization on high-density oligonucleotide microarray
27. Takaaki Horinouchi (Osaka University),
Genome-wide mutational and expression analysis of ethanol-tolerant Escherichia coli
28. Robert Sinclair (Okinawa Institute of Science and Technology),
Selection for Complexity can Induce Modularity
29. Ayaka Sakata (Graduate School of Arts and Science, University of Tokyo),
Funnel landscape and mutational robustness as a result of evolution under thermal noise
30. Yuval Hart (Weizmann Institute of Science),
Robust Control in the Nitrogen System of E.coli
31. Akinori Awazu (Department of Mathematical and Life Sciences, Hiroshima University),
Discreteness-induced transition and self-organized criticality in catalytic reaction networks
32. Tetsuya J. Kobayashi (Institute of Industrial Science, The University of Tokyo),
Statistically optimal response of cells to change in environment
33. Haruka Tsubota (Department of Applied Physics, Advanced School of Science and Engineering, Waseda University),
Vegetation pattern -a case of nonlocal diffusion-
34. Ryohei Mito (Department of Applied Physics, Advanced School of Science and Engineering, Waseda University),
Statistical properties of networks generated by multi-agent
35. Atsushi Kamimura (The University of Tokyo),
Formation of units in simple catalytic processes
36. Taeko Kobayashi (Institute for Virus Research, Kyoto University),
The cyclic gene Hes1 contributes to diverse differentiation responses of embryonic stem cells.
37. Daniel Madar (Weizmann Institute of Science),
Negative auto-regulation of a transcription factor can increase the input dynamic range of its target genes
38. Masatoshi Nishikawa (Hiroshima University),
Nonadaptive fluctuation in adaptive sensory system of bacterial chemotaxis
39. Masataka Kikuchi (Tokyo Medical and Dental University),
Modularity as an evolutionary constraint on the yeast protein interaction networks
40. Hiroaki Takagi (Nara Medical University),
How does spontaneous cell migration contribute to the efficiency of electrotaxis ?
41. Vincent Piras (Keio University, Institute for Advanced Biosciences),
Emergence of whole genome expression collective behaviors in lipopolysaccharide stimulated macrophages
42. Aitor Gonzalez (Kyoto University),
Automatic Reconstruction of the Regulatory Network of the Mouse Segmentation
43. Masaki Tsuda (Division of Ecology and Evolutionary Biology, Graduate School of Life Sciences, Tohoku University),
Evolutionary Origins of Structural and Mutational Properties in Gene Regulatory Networks
44. Shin I. Nishimura (Hiroshima University),
Strategies for chemotaxis of amoeboid cells
46. Satoru Morita (Shizuoka University),
Random Networks with Clustering
47. Hiroya Nakao (Dept. of Physics, Kyoto University),
Turing patterns in network-organized activator-inhibitor systems
48. Hiroshi Kori (Ochanomizu University),
Relation between fluctuations in isolated components and those in networked components
49. Shinji Nakaoka (Graduate School of Mathematical Sciences, The University of Tokyo),
Intra/inter-cellular dynamics of CD4 positive cells
50. Takao Suzuki (RIKEN CDB),
Quantitative analysis of moth wing pattern mimicking a 'dead leaf'
51. Yuka Shirokawa (Department of Biological Sciences, the University of Tokyo),
Phenotypic plasticity and cell to cell interaction of a centric diatom in response to environmental changes.
52. Yukuto Sato (Division of Population Genetics, National Institute of Genetics),
Combination of genome data and systems biology approach: Evolution of multiple phosphodiesterases in stickleback involved in olfactory transduction system
53. Kazuki Iida (Dept. of Human and Intelligent System Univ. of Fukui, Dept. of Infomation Science Nagoya Univ.),
Wave on Circle pattern exhibited by a normal form of chemotactic oscillators
54. Yoshihiro Morishita (Kyushu university),
Optimal design of positional information encoding
55. Shu-ichi Kinoshita (Meiji University),
Statistical properties of information conserving loop of Random Boolean dynamics in complex network
56. Watal M. Iwasaki (Graduate School of Life Sciences, Tohoku University),
Cryptic polymorphisms induced by environments in phenotypes produced by gene regulatory networks.
57. Ken Nagai, Hiroshi Kori (Ochanomizu University Division of Advanced Sciences),
Noise induced synchronization in Kuramoto model
58. Miki Yamamoto (The University of Tokyo),
The nonlinear phase equation to describe cell locomotion
59. Masatomo Iwasa (Department of Information Science, Nagoya University),
Clusters with Fractal-like Structure in One-Dimensional Swarm-Oscillators Model
60. Koichiro Uriu (Kyushu University),
Traveling wave formation in vertebrate segmentation
61. Kumar Selvarajoo (Institute for Advanced Biosciences, Keio University),
Simple governing rules in Toll-like receptor signaling
62. Masa Tsuchiya (Institute for Advanced Biosciences, Keio University),
The genome-wide synchronized switching on differentiation of na$B+A(Bve CD4 positive T cell
63. Mitsuo Takase (LINFOPS Inc.),
Formation of knowledge structure in genes by memory compression like neural networks and an effective evolution






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