Direct reprogramming of somatic cells into induced pluripotent stem cells (iPSCs) can be achieved by overexpression of Oct4, Sox2, Klf4 and c-Myc transcription factors, but only a minority of donor somatic cells can be reprogrammed to pluripotency. Here we demonstrate that reprogramming is a continuous stochastic process where almost all donor cells eventually give rise to iPSCs upon continued growth and transcription factor expression. Additional inhibition the p53/p21 pathway or overexpression of Lin28 increased the cell division rate and resulted in an accelerated kinetics of iPSC formation that was directly proportional to the increase in cell proliferation. In contrast, Nanog overexpression accelerated reprogramming in a predominantly cell division rate independent manner. Quantitative analyses define distinct cell division rate dependent and independent modes for accelerating the stochastic course of reprogramming, and suggest that the number of cell divisions is a key parameter driving epigenetic reprogramming to pluripotency.

Organoids are self‐organizing 3D structures grown from stem cells that recapitulate essential aspects of organ structure and function. Here, we describe a method to establish long‐term‐expanding human airway organoids from broncho‐alveolar resections or lavage material. The pseudostratified airway organoids consist of basal cells, functional multi‐ciliated cells, mucus‐producing secretory cells, and CC10‐secreting club cells. Airway organoids derived from cystic fibrosis (CF) patients allow assessment of CFTR function in an organoid swelling assay. Organoids established from lung cancer resections and metastasis biopsies retain tumor histopathology as well as cancer gene mutations and are amenable to drug screening. Respiratory syncytial virus (RSV) infection recapitulates central disease features, dramatically increases organoid cell motility via the non‐structural viral NS2 protein, and preferentially recruits neutrophils upon co‐culturing. We conclude that human airway organoids represent versatile models for the in vitro study of hereditary, malignant, and infectious pulmonary disease.

We have developed a new numerical technique, called Green's-function reaction dynamics (GFRD), that makes it possible to simulate biochemical networks at the particle level and in both time and space. In this scheme, a maximum time step is chosen such that only single particles or pairs of particles have to be considered. For these particles, the Smoluchowski equation can be solved analytically using Green's functions. The main idea of GFRD is to exploit the exact solution of the Smoluchoswki equation to set up an event-driven algorithm, which combines in one step the propagation of the particles in space with the reactions between them. The event-driven nature allows GFRD to make large jumps in time and space when the particles are far apart from each other. Here, we apply the technique to a simple model of gene expression. The simulations reveal that spatial fluctuations can be a major source of noise in biochemical networks. The calculations also show that GFRD is highly efficient. Under biologically relevant conditions, GFRD is up to five orders of magnitude faster than conventional particle-based techniques for simulating biochemical networks in time and space. GFRD is not limited to biochemical networks. It can also be applied to a large number of other reaction-diffusion problems.

Bacterial plasmids encode partitioning (par) loci that confer stable plasmid inheritance. We showed previously that, in the presence of ParB and parC encoded by the par2 locus of plasmid pB171, ParA formed cytoskeletal-like structures that dynamically relocated over the nucleoid. Simultaneously, the par2 locus distributed plasmids regularly over the nucleoid. We show here that the dynamic ParA patterns are not simple oscillations. Rather, ParA nucleates and polymerizes in between plasmids. When a ParA assembly reaches a plasmid, the assembly reaction reverses into disassembly. Strikingly, plasmids consistently migrate behind disassembling ParA cytoskeletal structures, suggesting that ParA filaments pull plasmids by depolymerization. The perpetual cycles of ParA assembly and disassembly result in continuous relocation of plasmids, which, on time averaging, results in equidistribution of the plasmids. Mathematical modeling of ParA and plasmid dynamics support these interpretations. Mutational analysis supports a molecular mechanism in which the ParB/parC complex controls ParA filament depolymerization.cytoskeleton ͉ DNA segregation ͉ mathematical modeling ͉ ParA ParB ͉ pulling I n bacteria, it has been difficult to analyze how chromosomes are segregated. To gain insight into the problem, partitioning (par) loci encoded by plasmids have been used extensively as model systems. Type I par loci encode 3 components: a Walker Box ATPase (ParA), a DNA binding protein (ParB), and one or more cis-acting DNA regions where the proteins act (parC). The ParB proteins bind site-specifically to their cognate parC sites to form a ''partition complex.'' ParB also interacts with the cognate ParA protein and thereby functions as an adaptor between ParA and parC DNA. Thus, the parC region at which the segregation apparatus congregates is functionally equivalent of a eukaryotic centromere. Interestingly, ParA ATPases form helical structures that dynamically relocate over the nucleoid (1-6). ParA relocation but not the formation of filamentous structures depends on the presence of ParB bound to parC (1, 2, 4, 6). The presence of helical ParA structures in living cells is consistent with the ability of the proteins to polymerize in vitro (4, 6-13).Purified ParAs of Thermus thermophilus and plasmid pSM19035 both dimerize in the presence of ATP (6, 14), whereas ParA of P1 dimerizes also without nucleotide (13). The ParA-ATP dimers bind cooperatively and nonspecifically to DNA. Thus, the in vitro DNA binding activity of ParA proteins is consistent with the nucleoid association seen in vivo (1,8). In all cases investigated, ParB stimulates ParA ATPase activity, either on its own or in the presence of its cognate centromere site (6,9,11,15).We showed previously that the type I par2 locus of pB171, on average, distributes plasmids regularly over the bacterial nucleoid (7). Our observations raised the possibility that the dynamic ParA filaments generate the mechanical force that move and position plasmids within the cell.Here we analyze the re...

In a recent series of ground-breaking experiments, Nakajima et al.[Nakajima M, Imai K, Ito H, Nishiwaki T, Murayama Y, Iwasaki H, Oyama T, Kondo T (2005) Science 308:414 -415] showed that the three cyanobacterial clock proteins KaiA, KaiB, and KaiC are sufficient in vitro to generate circadian phosphorylation of KaiC. Here, we present a mathematical model of the Kai system. At its heart is the assumption that KaiC can exist in two conformational states, one favoring phosphorylation and the other dephosphorylation. Each individual KaiC hexamer then has a propensity to be phosphorylated in a cyclic manner. To generate macroscopic oscillations, however, the phosphorylation cycles of the different hexamers must be synchronized. We propose a novel synchronization mechanism based on differential affinity: KaiA stimulates KaiC phosphorylation, but the limited supply of KaiA dimers binds preferentially to those KaiC hexamers that are falling behind in the oscillation. KaiB sequesters KaiA and stabilizes the dephosphorylating KaiC state. We show that our model can reproduce a wide range of published data, including the observed insensitivity of the oscillation period to variations in temperature, and that it makes nontrivial predictions about the effects of varying the concentrations of the Kai proteins.

We present a technique, called Green's function reaction dynamics (GFRD), for particle-based simulations of reaction-diffusion systems. GFRD uses a maximum time step such that only single particles or pairs of particles have to be considered. For these particles, the Smoluchowski equations are solved analytically using Green's functions, which are used to set up an event-driven algorithm. We apply the technique to a model of gene expression. Under biologically relevant conditions, GFRD is up to 5 orders of magnitude faster than conventional particle-based schemes.

We study by Green's Function Reaction Dynamics the effect of the diffusive motion of repressor molecules on the noise in mRNA and protein levels for a gene that is under the control of a repressor. We find that spatial fluctuations due to diffusion can drastically enhance the noise in gene expression. After dissociation from the operator, a repressor can rapidly rebind to the DNA. Our results show that the rebinding trajectories are so short that, on this timescale, the RNA polymerase (RNAP) cannot effectively compete with the repressor for binding to the promoter. As a result, a dissociated repressor molecule will on average rebind many times, before it eventually diffuses away. These rebindings thus lower the effective dissociation rate, and this increases the noise in gene expression. Another consequence of the timescale separation between repressor rebinding and RNAP association is that the effect of spatial fluctuations can be described by a well-stirred, zero-dimensional, model by renormalizing the reaction rates for repressor-DNA (un) binding. Our results thus support the use of well-stirred, zero-dimensional models for describing noise in gene expression. We also show that for a fixed repressor strength, the noise due to diffusion can be minimized by increasing the number of repressors or by decreasing the rate of the open complex formation. Lastly, our results emphasize that power spectra are a highly useful tool for studying the propagation of noise through the different stages of gene expression.

Biological systems may perform reproducibly to generate invariant outcomes, despite external or internal noise. One example is the C. elegans vulva, in which the final cell fate pattern is remarkably robust. Although this system has been extensively studied and the molecular network underlying cell fate specification is well understood, very little is known in quantitative terms. Here, through pathway dosage modulation and single molecule fluorescence in situ hybridization, we show that the system can tolerate a 4-fold variation in genetic dose of the upstream signaling molecule LIN-3/epidermal growth factor (EGF) without phenotypic change in cell fate pattern. Furthermore, through tissue-specific dosage perturbations of the EGF and Notch pathways, we determine the first-appearing patterning errors. Finally, by combining different doses of both pathways, we explore how quantitative pathway interactions influence system behavior. Our results highlight the feasibility and significance of launching experimental studies of robustness and quantitative network analysis in genetically tractable, multicellular eukaryotes.