ICTS

agoldbet@ulb.ac.be

1. Modeling circadian clocks : From molecular mechanism to physiological disorders

   Rhythmic phenomena occur at all levels of biological organization, with periods ranging from milliseconds to years. Cellular rhythms originate from the regulatory feedback loops that control the dynamics of biochemical processes and represent a phenomenon of temporal self-organization: they illustrate how an emergent property, autonomous oscillatory behavior, arises from molecular interactions in regulatory networks. This explains why oscillatory phenomena abound at the cellular level. Biochemical oscillations and cellular rhythms can best be addressed by combining an experimental with a modeling approach. After providing an overview of biological rhythms, I will focus on the circadian clock which represents a major example of rhythmic behavior at the cellular level.

   Circadian oscillations occur with a period close to 24 h in all eukaryotic organisms as a result of genetic and post-translational regulation. Computational models were first proposed for the molecular mechanism of circadian rhythms in Drosophila, based on the negative autoregulation of clock gene expression. A detailed model was subsequently proposed for the mammalian circadian clock. Based on the interlocked positive and negative regulatory feedback loops involving the Per, Cry, Bmal1, and Clock genes, the model predicts the occurrence of endogenous circadian oscillations in continuous darkness. When incorporating the induction of Per gene expression in the light phase, this model also accounts for entrainment of the circadian clock by light- dark cycles. Besides the insights that the model provides into the molecular mechanism of circadian oscillations, we may use it to examine the dynamical bases of circadian clock-related physiological disorders in humans, such as jet lag and the Familial advanced sleep-phase syndrome (FASPS). Stochastic versions of the models for circadian rhythms allow us to investigate the emergence of rhythmic behavior and the robustness of circadian clocks with respect to molecular noise.

2. Modeling the cell division cycle : From amphibian embryos to mammalian cells

   An oscillatory network, known as the mitotic oscillator, controls the cell division cycle. This oscillatory network takes a relatively simple form in amphibian embryonic cells. After presenting a three-variable model for the mitotic oscillator driving early cell divisions in amphibian embryos, I will turn to a detailed computational model for the network of cyclin-dependent kinases (Cdks) that controls the dynamics of the mammalian cell cycle. The model contains four Cdk modules controlled by multiple, closely intertwined regulations. Numerical simulations show that when exceeding a critical bifurcation value, growth factors can trigger the transition from a quiescent, stable steady state to self-sustained oscillations in the Cdk network. The modeling approach suggests that the cell cycle is a limit cycle.The model accounts for major properties of the mammalian cell cycle such as continuous cell cycling in the presence of suprathreshold amounts of growth factor, control of cell cycle progression by the balance between antagonistic effects of the tumor suppressor pRB and the transcription factor E2F, existence of a restriction point in G1, and endoreplication. The model for the mammalian cell cycle shows how the regulatory structure of the Cdk network results in its temporal self-organization, leading to the repetitive, sequential activation of the four Cdk modules that brings about the orderly progression through the cell cycle phases.

   The cell cycle and the circadian clock represent two major cellular rhythms. In the last decade it has become clear that these two rhythms are closely coupled, because several proteins involved in the biochemical mechanisms of the cell cycle are induced by components of the circadian clock. This raises the possibility of entrainment of the cell cycle by the circadian clock. Mathematical modeling shows that the coupling of the cell cycle to the circadian clock can lead to synchronization of these two major cellular rhythms.

    

Olivier.hamant@ens-lyon.fr

Mechanical signals contribute to development

In “On growth and forms” (1917), D’Arcy Thompson stresses the inevitable interactions between physics and biology. Thanks to ongoing developments in live imaging and modeling, this field of study has been rejuvenated: the relation between mechanics and shape changes can now be addressed more comprehensively, notably in plants in which morphogenesis is mainly determined by cell walls. For instance, we and others showed that shape- and growth-derived forces act as signals that orient plant microtubules^1,2,3. This response channels key biological features, such as cell shape⁴, cell division plane orientation^5,6 and final organ shape⁷. Beyond microtubules, such forces also contribute to cell polarity⁸ and to gene expression patterns^9,10. The implications of this work are numerous and include a role of mechanical conflicts emerging from growth heterogeneity in the reproducibility of shapes^11,12. Altogether, this provides a picture in which mechanical forces add robustness to plant morphogenesis, by channeling the dynamics of cell effectors and molecular pathways. This work entails the development the quantitative imaging pipelines¹³, bridging mechanical and optical cues, and integrating computational modeling approaches¹⁴, to predict the patterns of mechanical stresses in tissues and their effects. This work also provides interesting points of comparison with cell and tissue morphogenesis in animals, where mechanical signals exhibit similar roles, albeit in a different mechanical context^15,16,17,18.
 

References:

1. Green, P. & King, A. A mechanism for the origin of specifically oriented textures in development with special reference to Nitella wall texture. Aust J Biol Sci 421–437 (1966).Williamson, R. Alignment of cortical microtubules by anisotropic wall stresses. Aust J Plant Physiol 601–613 (1990).
2. Hamant, O. et al. Developmental patterning by mechanical signals in Arabidopsis. Science 322, 1650–1655 (2008).
3. Sampathkumar, A. et al. Subcellular and supracellular mechanical stress prescribes cytoskeleton behavior in Arabidopsis cotyledon pavement cells. eLife 3, (2014).
4. Louveaux, M., Julien, J.-D., Mirabet, V., Boudaoud, A. & Hamant, O. Cell division plane orientation based on tensile stress in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U. S. A. 113, E4294-4303 (2016).
5. Lintilhac, P. M. & Vesecky, T. B. Stress-induced alignment of division plane in plant tissues grown in vitro. Nature 307, 363–364 (1984).
6. Hervieux, N. et al. A Mechanical Feedback Restricts Sepal Growth and Shape in Arabidopsis. Curr. Biol. 26, 1019–1028 (2016).
7. Heisler, M. G. et al. Alignment between PIN1 polarity and microtubule orientation in the shoot apical meristem reveals a tight coupling between morphogenesis and auxin transport. PLoS Biol. 8, (2010).
8. Coutand, C. et al. Strain mechanosensing quantitatively controls diameter growth and PtaZFP2 gene expression in poplar. Plant Physiol. 151, 223–232 (2009).
9. Landrein, B. et al. Mechanical stress contributes to the expression of the STM homeobox gene in Arabidopsis shoot meristems. eLife 4, e07811 (2015).
10. Hervieux, N. et al. Mechanical Shielding of Rapidly Growing Cells Buffers Growth Heterogeneity and Contributes to Organ Shape Reproducibility. Curr. Biol. CB 27, 3468–3479.e4 (2017).
11. Uyttewaal, M. et al. Mechanical stress acts via katanin to amplify differences in growth rate between adjacent cells in Arabidopsis. Cell 149, 439–451 (2012).
12. Oates, A. C., Gorfinkiel, N., Gonzalez-Gaitan, M. & Heisenberg, C.-P. Quantitative approaches in developmental biology. Nat. Rev. Genet. 10, 517–530 (2009).
13. Coen, E., Rolland-Lagan, A.-G., Matthews, M., Bangham, J. A. & Prusinkiewicz, P. The genetics of geometry. Proc. Natl. Acad. Sci. U. S. A. 101, 4728–4735 (2004).
14. Lecuit, T., Lenne, P.-F. & Munro, E. Force generation, transmission, and integration during cell and tissue morphogenesis. Annu. Rev. Cell Dev. Biol. 27, 157–184 (2011).
15. Farge, E. Mechanical induction of Twist in the Drosophila foregut/stomodeal primordium. Curr. Biol. CB 13, 1365–1377 (2003).
16. Théry, M., Jiménez-Dalmaroni, A., Racine, V., Bornens, M. & Jülicher, F. Experimental and theoretical study of mitotic spindle orientation. Nature 447, 493–496 (2007).
17. Houk, A. R. et al. Membrane tension maintains cell polarity by confining signals to the leading edge during neutrophil migration. Cell 148, 175–188 (2012).
    
     

sandeep@ncbs.res.in

Bistability: a building block for cellular decisions

Over the two days, I will discuss bistability - the coexistence of two stable states - and examine how this mathematical notion from dynamical systems theory can be used to study certain kinds of `decisions' made by living cells. On the first day, I will discuss the decision a temperate bacteriophage makes between killing its host (lysis) or laying dormant (lysogeny); arguably the simplest case of a bistable `developmental decision'. We'll explore mathematically how feedback loops in the genetic regulatory networks of phage can generate this bistability and how additional functional needs constrain these feedback loops. On the second day, I will then partner with Sunil Laxman to explore some interesting consequences of two stable cell states - quiescent and proliferating - on the population level dynamics of yeast cells growing in a chemostat.
 

vijaykumar@icts.res.in

Mechanochemical patterns

We will start by discussing generic aspects of pattern formation in biology, in the framework of reaction-diffusion systems. We will then consider how mechanical forces generated in cells and tissues, resulting typically from cytoskeletal activity, can couple to morphogens and lead to the emergence of mechanochemical patterns.
 

sunil@instem.res.in

A metabolic basis to study cellular heterogeneity, cooperation, and self-organization

Isogenic populations of cells, even when growing in a common environment, can exhibit different fates. For example, a subset of cells can commit to growth and cell division, some others can differentiate and so on. This is widely observed for example in systems as diverse as unicellular microbes exhibit collective behaviors at a population level, or in cells within a tumor. While these phenomena have been studied at different levels, I will focus on how metabolism, and key metabolic events can be causal in controlling such cellular behavior.

In my first talk, I will discuss well-studied, synchronized metabolic oscillation phenomena in a model organism, S. cerevisiae. During these oscillations, cells oscillate between quiescent and growth states. Here, I will explain the observed biological phenomenon, the underlying metabolic component, and an apparent bistability in the system. Together with Sandeep Krishna, we will build a novel bistability model to explain these oscillations, and point to how a metabolite can control such oscillations.

In my second talk, I will discuss phenotypic heterogeneity in simple microbial populations, some of the sources of this heterogeneity, and general rules to understand cooperation vs defectors within a population. I will end with how specific metabolic processes can lead to the emergence of specialized sub- populations, and division of labor.
 

mahi@iisc.ac.in

Evolution of new metabolic functions in bacteria

The extraordinary versatility and adaptability of bacteria are primarily responsible for their remarkable success in terms of occupying a broad spectrum of niches ranging from arctic glaciers to ocean floor hydrothermal vents. Faced with a variety of challenges in their natural habitats, bacteria have evolved several strategies to adapt and survive. In the laboratory, we study the evolutionary strategies adopted by bacteria such as E. coli and related organisms when confronted with a novel substrate as the sole carbon source. Often times, this involves the mutational modification of a pre-existing genetic systems specific for the catabolism of a related substrate. A large proportion of such genetic modifications involve regulatory mutations. Interestingly, in many instances when mutations in genes encoding enzymes are involved, the original enzymatic function is retained while gaining the new metabolic capability without a trade-off. Closely related microorganisms may resort to different paths for gaining additional metabolic capabilities depending on the diversity of transposable elements they carry in their genomes as these elements are necessary for the genome rearrangements involved in the evolutionary process. Though these observations have been made in relation to the genetic systems involved in the catabolism of beta-glucosides, these strategies are likely to be part of the normal arsenal that bacteria carry in their struggle for survival in the complex environments they live.
 

vidyan@alumni.iisc.ac.in

Unicellular organisms and the evolution of social behaviour

Many unicellular organisms go through a multicellular phase, which can also be thought of as a social phase. Cooperative social behaviour, in particular the extreme instance of it referred to as altruism, has long intrigued evolutionary biologists. Explanations vary; most rely on a group-level advantage of altruism at the level of groups of related individuals, popularly known as kin selection. However, it is possible that the origin of cooperative groups can have more than one cause, and similarly the maintenance of cooperation may depend on several factors. Self-organisation based on pre-adapted traits is among them. Even when natural selection via the differential reproductive success of individuals plays a significant role, it is important to keep in mind that what counts is success over the entire life cycle, measured in the relevant ecological context.

“Living Matter” workshop, ICTS, Bangalore, 16-26 April 2018
 

utpalnath@iisc.ac.in

Genetic control of two-dimensional growth and geometry in plants

How shape evolves during organ growth is an important question in developmental biology. Over past few years, there has been some progress in our understanding in the growth dynamics of planar organs such as wings of insects and leaves of higher plants because these organs grow primarily in two-dimensions and are therefore more amenable to study. Studies using model plants have shown that leaves grow more from the base and progressively less towards the tip. This polar growth has been investigated in diverse plant species by using the law of allometry and found that leaves of some species grow from the base, others from the tip, while yet another group grows with no apparent polarity. The plant-specific micro RNA miR396 regulates this growth diversity. It is still an open question as to how leaves, despite their anisometric growth dynamics, retain their surface flatness with ‘zero’ Gaussian curvature throughout the growth phase. Recent work over past few years have reported mutants with cup-shaped (positive Gaussian curvature) and saddle-shaped (negative Gaussian curvature) leaves, thus demonstrating genetic control of biological surface curvature. I will discuss some aspects of growth dynamics and shape parameters in two-dimension, and their genetic control, using leaf as a model organ.

References:

1. Das Gupta, M. and Nath U. (2015). Divergence in Patterns of Leaf Growth Polarity Is Associated with the Expression Divergence of miR396. Plant Cell 27: 2785-2799.
2. Donnelly, P.M., Bonetta, D., Tsukaya, H., Dengler, R.E., and Dengler, N.G. (1999). Cell cycling and cell enlargement in developing leaves of Arabidopsis. Dev. Biol. 215: 407–419.
3. Huxley, J.S. (1932). Problems of Relative Growth. (London: Methuen).
4. Kuchen, E.E., Fox, S., de Reuille, P.B., Kennaway, R., Bensmihen, S., Avondo, J., Calder, G.M., Southam, P., Robinson, S., Bangham, A., and Coen, E. (2012). Generation of leaf shape through early patterns of growth and tissue polarity. Science 335: 1092–1096.
5. Nath, U., Crawford, B.C., Carpenter, R., and Coen, E. (2003). Genetic control of surface curvature. Science 299: 1404–1407.
6. Ori, N., et al. (2007). Regulation of LANCEOLATE by miR319 is required for compound-leaf development in tomato. Nat. Genet. 39: 787–791.
7. Poethig, R.S., and Sussex, I.M. (1985). The developmental morphology and growth dynamics of the tobacco leaf. Planta 165: 158–169.
8. Stern, D.L., and Emlen, D.J. (1999). The developmental basis for allometry in insects. Development 126: 1091–1101.
   
    

clement.nizak@espci.fr

Large scale molecular evolution experiments

I will present how to combine screening techniques (phage display, cytometry, droplet microfluidics) and high-throughput DNA sequencing to analyze the sequence-function relation of proteins and their evolution. This approach is directly motivated by the statistical analysis of protein sequence databases that Anne-Florence and Olivier will present. In my first lecture I will present essentially tools and methods, while in the second lecture I will emphasise the importance of confronting large-scale datasets produced by this approach to statistical analysis and models.
 

Andras.Paldi@ephe.sorbonne.fr

Chance and determinism in cell differentiation

At the molecular level, biochemical reactions, such as gene expression, are stochastic. Yet, the living cells appear to function in an orderly way. How to reconcile stochasticity and determinism? Using examples taken from recent theoretical and experimental work I propose to explore the potential explanations and their practical and conceptual implications. The discussion will converge to a coherent explanatory frame of cell differentiation.
 

arahaman@niser.ac.in

Genesis and Remodeling of Membranes in Biological System

Biogenesis and remodeling of membrane is crucial for survival and multiplication of cellular life. The process is temporally and spatially regulated to serve appropriate function during cell cycle. While cell membranes are common to all the life forms, eukaryotes harbor additional organelles such as mitochondria, nucleus that are membrane enclosed. The cellular organelles are dynamic structures requiring continuous membrane remodeling. Membrane remodeling is a complex process that requires biosynthesis and incorporation of new lipids/proteins, and restructuring the already existing membrane. In this talk, I would discuss about the mechanism and regulation of membrane biogenesis and membrane remodeling. Specially I would emphasize role of various proteins involved in membrane remodeling and the cell cycle regulation of this process.
 

olivier.rivoire@college-de-france.fr

Physics meets Evolution: Understanding how proteins are designed

Proteins illustrate the limitations of purely physical approaches to understanding biological systems: the physics of proteins is well known, the composition and structure of many proteins are well known, and yet we do not know, neither practically nor theoretically, how to read the function of a protein from its sequence. 

I will introduce another complementary approach, based on evolutionary and statistical principles, which provides insights beyond physical principles.

In a first lecture, I will discuss results obtained from applying evolutionary and statistical principles to datasets of protein sequences. More results from inference methods applied to natural protein sequences will be presented by Anne-Florence, and complementary insights gained from experimental approaches to generate and characterize artificial proteins will be presented by Clément.

In a second lecture, I will discuss how we may define and study mathematical models to understand theoretically the interplay between physical constraints and evolutionary dynamics. This lecture will use some of the concepts introduced in Anne-Florence’s lecture on the mathematical principles of evolutionary dynamics.