Phloem transport in Ricinus: Concentration gradients between source and sink

Planta ◽  
1974 ◽  
Vol 117 (4) ◽  
pp. 303-319 ◽  
Author(s):  
John A. Milburn
Keyword(s):  
Phloem Transport ◽  
Concentration Gradients ◽  
Source And Sink

The position of regenerating cambia: auxin/sucrose ratio and the gradient induction hypothesis

1978 ◽  
Vol 203 (1151) ◽  
pp. 153-176 ◽  
Keyword(s):  
Control System ◽  
Induction Hypothesis ◽  
Sucrose Concentration ◽  
Vascular Differentiation ◽  
Concentration Gradients ◽  
Published Evidence ◽  
Different Types ◽  
Source And Sink ◽  
Positional Control

To account for the positions in which vascular cambia regenerate in wound callus, a gradient induction hypothesis was proposed in 1961 in terms of gradients in ‘some factor as yet unknown’. It now seems likely that the gradient is based on morphogen diffusion between source and sink on opposite sides of existing cambia, with morphogen diffusing into the adjoining wound callus. It is specifically proposed that there are two morphogens, auxin diffusing centrifugally and sucrose diffusing centripetally. The cambium then regenerates along a path where the ratio of auxin to sucrose concentration is similar to that at the original cambium, and its orientation (as regards xylem and phloem formation) is determined by the direction of the gradient in this ratio. These proposals are supported by published evidence on auxin and sucrose concentration gradients across the cambium, and on their sources, movements, and known effects on vascular differentiation. Simulations of the proposed positional control system predict patterns of cambial regeneration and orientation corresponding to those observed in four different types of would and graft.

scholarly journals Testing the Münch hypothesis of long distance phloem transport in plants

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Michael Knoblauch ◽  
Jan Knoblauch ◽  
Daniel L Mullendore ◽  
Jessica A Savage ◽  
Benjamin A Babst ◽  
...  
Keyword(s):  
Strong Support ◽  
Sieve Tube ◽  
Phloem Transport ◽  
Plant Tissues ◽  
Long Distance ◽  
Pressure Flow ◽  
Long Distance Transport ◽  
Sieve Tubes ◽  
Distance Transport ◽  
Source And Sink

Long distance transport in plants occurs in sieve tubes of the phloem. The pressure flow hypothesis introduced by Ernst Münch in 1930 describes a mechanism of osmotically generated pressure differentials that are supposed to drive the movement of sugars and other solutes in the phloem, but this hypothesis has long faced major challenges. The key issue is whether the conductance of sieve tubes, including sieve plate pores, is sufficient to allow pressure flow. We show that with increasing distance between source and sink, sieve tube conductivity and turgor increases dramatically in Ipomoea nil. Our results provide strong support for the Münch hypothesis, while providing new tools for the investigation of one of the least understood plant tissues.

Consequences of phloem pathway unloading/reloading on equilibrium flows between source and sink: a modelling approach

2017 ◽  
Vol 44 (5) ◽  
pp. 507 ◽  
Author(s):  
Peter E. H. Minchin ◽  
André Lacointe
Keyword(s):  
Large Fraction ◽  
Phloem Transport ◽  
Solute Concentration ◽  
Sources And Sinks ◽  
Pressure Profiles ◽  
Flow Mechanisms ◽  
Source Sink ◽  
Pressure Driven Flow ◽  
Source And Sink ◽  
Modelling Approach

It is now accepted that the transport phloem, linking major sources and sinks, is leaky, and this leakage can be considerable. Hence for phloem transport to function over the long distances observed, a large fraction of this unloaded photosynthate must be reloaded. A fraction of this unloaded solute is used to maintain tissues surrounding the phloem, as well as being stored. Also, pathway unloading/reloading acts as a short-term buffer to source and sink changes. In this work we present the first attempt to include both pathway unloading and reloading of carbohydrate in the modelling of pressure driven flow to determine if this has any significant effect upon source–sink dynamics. Our results indicated that the flow does not follow Poiseuille dynamics, and that pathway unloading alters the solute concentration and hydrostatic pressure profiles. Hence, measurement of either of these without considerable other detail tells us very little about the flow mechanisms. With adequate reloading along the pathway, the effects of pathway unloading can completely compensate for, making the entire system look like one with no pathway unloading.

Modelling phloem transport within a pruned dwarf bean: a 2-source-3-sink system

2011 ◽  
Vol 38 (2) ◽  
pp. 127 ◽  
Author(s):  
Michael R. Thorpe ◽  
André Lacointe ◽  
Peter E. H. Minchin
Keyword(s):  
Mechanistic Model ◽  
Phloem Transport ◽  
Phaseolus Vulgaris L ◽  
Model Predictions ◽  
Complex Architectures ◽  
Treatment Responses ◽  
Model Platform ◽  
Bidirectional Flow ◽  
Source And Sink ◽  
Dwarf Bean

A mechanistic model of carbon partitioning, based on the Münch hypothesis of phloem transport and implemented with PIAF-Münch modelling platform (Lacointe and Minchin 2008), was tested for an architecture more complex than any tested previously. Using 11C to label photosynthate, responses in transport of photosynthate within a heavily pruned dwarf bean plant (Phaseolus vulgaris L.) to changes in source and sink activities were compared with model predictions. The observed treatment responses were successfully predicted. However, the observations could not be completely explained if the modelled stem contained only one phloem pathway: tracer from a labelled leaf was always detected in both shoot apex and root, whichever of the two leaves was labelled. This shows that bidirectional flow occurred within the stem, with solute moving simultaneously in both directions. Nevertheless, a model architecture with very little more complexity could incorporate such bidirectional flow. We concluded that the model could explain the observations, and that the PIAF-Münch model platform can be expected to describe partitioning in even more complex architectures.

On the structural organization of biological pumps and channels

1984 ◽  
Vol 42 ◽  
pp. 138-141
Author(s):  
G. Zampighi ◽  
M. Kreman
Keyword(s):  
Active Transport ◽  
Transport System ◽  
Plasma Membranes ◽  
Transport Proteins ◽  
Molecular Organization ◽  
Passive Transport ◽  
Concentration Gradients ◽  
Cell Functions ◽  
Natural Concentration ◽  
Active Transport System

The plasma membranes of most animal cells contain transport proteins which function to provide passageways for the transported species across essentially impermeable lipid bilayers. The channel is a passive transport system which allows the movement of ions and low molecular weight molecules along their concentration gradients. The pump is an active transport system and can translocate cations against their natural concentration gradients. The actions and interplay of these two kinds of transport proteins control crucial cell functions such as active transport, excitability and cell communication. In this paper, we will describe and compare several features of the molecular organization of pumps and channels. As an example of an active transport system, we will discuss the structure of the sodium and potassium ion-activated triphosphatase [(Na+ +K+)-ATPase] and as an example of a passive transport system, the communicating channel of gap junctions and lens junctions.

Maxwell’s Equations versus Newton’s Third Law

2018 ◽  
Author(s):  
Glyn Kennell ◽  
Richard Evitts
Keyword(s):  
Electric Fields ◽  
Maxwell’S Equations ◽  
Maxwell's Equations ◽  
Simulated Data ◽  
Numerical Data ◽  
Transport Equations ◽  
Concentration Gradients ◽  
Third Law

The presented simulated data compares concentration gradients and electric fields with experimental and numerical data of others. This data is simulated for cases involving liquid junctions and electrolytic transport. The objective of presenting this data is to support a model and theory. This theory demonstrates the incompatibility between conventional electrostatics inherent in Maxwell's equations with conventional transport equations. <br>

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