TY - JOUR
AB - 3D amoeboid cell migration is central to many developmental and disease-related processes such as cancer metastasis. Here, we identify a unique prototypic amoeboid cell migration mode in early zebrafish embryos, termed stable-bleb migration. Stable-bleb cells display an invariant polarized balloon-like shape with exceptional migration speed and persistence. Progenitor cells can be reversibly transformed into stable-bleb cells irrespective of their primary fate and motile characteristics by increasing myosin II activity through biochemical or mechanical stimuli. Using a combination of theory and experiments, we show that, in stable-bleb cells, cortical contractility fluctuations trigger a stochastic switch into amoeboid motility, and a positive feedback between cortical flows and gradients in contractility maintains stable-bleb cell polarization. We further show that rearward cortical flows drive stable-bleb cell migration in various adhesive and non-adhesive environments, unraveling a highly versatile amoeboid migration phenotype.
AU - Ruprecht, Verena
AU - Wieser, Stefan
AU - Callan Jones, Andrew
AU - Smutny, Michael
AU - Morita, Hitoshi
AU - Sako, Keisuke
AU - Barone, Vanessa
AU - Ritsch Marte, Monika
AU - Sixt, Michael K
AU - Voituriez, Raphaël
AU - Heisenberg, Carl-Philipp J
ID - 1537
IS - 4
JF - Cell
TI - Cortical contractility triggers a stochastic switch to fast amoeboid cell motility
VL - 160
ER -
TY - JOUR
AB - Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time.
AU - Ruess, Jakob
AU - Parise, Francesca
AU - Milias Argeitis, Andreas
AU - Khammash, Mustafa
AU - Lygeros, John
ID - 1538
IS - 26
JF - PNAS
TI - Iterative experiment design guides the characterization of a light-inducible gene expression circuit
VL - 112
ER -
TY - JOUR
AB - Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.
AU - Ruess, Jakob
ID - 1539
IS - 24
JF - Journal of Chemical Physics
TI - Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space
VL - 143
ER -
TY - JOUR
AB - Plant sexual reproduction involves highly structured and specialized organs: stamens (male) and gynoecia (female, containing ovules). These organs synchronously develop within protective flower buds, until anthesis, via tightly coordinated mechanisms that are essential for effective fertilization and production of viable seeds. The phytohormone auxin is one of the key endogenous signalling molecules controlling initiation and development of these, and other, plant organs. In particular, its uneven distribution, resulting from tightly controlled production, metabolism and directional transport, is an important morphogenic factor. In this review we discuss how developmentally controlled and localized auxin biosynthesis and transport contribute to the coordinated development of plants' reproductive organs, and their fertilized derivatives (embryos) via the regulation of auxin levels and distribution within and around them. Current understanding of the links between de novo local auxin biosynthesis, auxin transport and/or signalling is presented to highlight the importance of the non-cell autonomous action of auxin production on development and morphogenesis of reproductive organs and embryos. An overview of transcription factor families, which spatiotemporally define local auxin production by controlling key auxin biosynthetic enzymes, is also presented.
AU - Robert, Hélène
AU - Crhák Khaitová, Lucie
AU - Mroue, Souad
AU - Benková, Eva
ID - 1540
IS - 16
JF - Journal of Experimental Botany
TI - The importance of localized auxin production for morphogenesis of reproductive organs and embryos in Arabidopsis
VL - 66
ER -
TY - CONF
AB - We present XSpeed a parallel state-space exploration algorithm for continuous systems with linear dynamics and nondeterministic inputs. The motivation of having parallel algorithms is to exploit the computational power of multi-core processors to speed-up performance. The parallelization is achieved on two fronts. First, we propose a parallel implementation of the support function algorithm by sampling functions in parallel. Second, we propose a parallel state-space exploration by slicing the time horizon and computing the reachable states in the time slices in parallel. The second method can be however applied only to a class of linear systems with invertible dynamics and fixed input. A GP-GPU implementation is also presented following a lazy evaluation strategy on support functions. The parallel algorithms are implemented in the tool XSpeed. We evaluated the performance on two benchmarks including an 28 dimension Helicopter model. Comparison with the sequential counterpart shows a maximum speed-up of almost 7× on a 6 core, 12 thread Intel Xeon CPU E5-2420 processor. Our GP-GPU implementation shows a maximum speed-up of 12× over the sequential implementation and 53× over SpaceEx (LGG scenario), the state of the art tool for reachability analysis of linear hybrid systems. Experiments illustrate that our parallel algorithm with time slicing not only speeds-up performance but also improves precision.
AU - Ray, Rajarshi
AU - Gurung, Amit
AU - Das, Binayak
AU - Bartocci, Ezio
AU - Bogomolov, Sergiy
AU - Grosu, Radu
ID - 1541
TI - XSpeed: Accelerating reachability analysis on multi-core processors
VL - 9434
ER -