Branching morphogenesis governs the formation of many organs such as lung, kidney, and the neurovascular system. Many studies have explored system-specific molecular and cellular regulatory mechanisms, as well as self-organizing rules underlying branching morphogenesis. However, in addition to local cues, branched tissue growth can also be influenced by global guidance. Here, we develop a theoretical framework for a stochastic self-organized branching process in the presence of external cues. Combining analytical theory with numerical simulations, we predict differential signatures of global vs. local regulatory mechanisms on the branching pattern, such as angle distributions, domain size, and space-filling efficiency. We find that branch alignment follows a generic scaling law determined by the strength of global guidance, while local interactions influence the tissue density but not its overall territory. Finally, using zebrafish innervation as a model system, we test these key features of the model experimentally. Our work thus provides quantitative predictions to disentangle the role of different types of cues in shaping branched structures across scales.

No abstract

In the living cell, we encounter a large variety of motile processes such as organelle transport and cytoskeleton remodeling. These processes are driven by motor proteins that generate force by transducing chemical free energy into mechanical work. In many cases, the molecular motors work in teams to collectively generate larger forces. Recent optical trapping experiments on small teams of cytoskeletal motors indicated that the collectively generated force increases with the size of the motor team but that this increase depends on the motor type and on whether the motors are studied in vitro or in vivo. Here, we use the theory of stochastic processes to describe the motion of N motors in a stationary optical trap and to compute the Ndependence of the collectively generated forces. We consider six distinct motor types, two kinesins, two dyneins, and two myosins. We show that the force increases always linearly with N but with a prefactor that depends on the performance of the single motor. Surprisingly, this prefactor increases for weaker motors with a lower stall force. This counter-intuitive behavior reflects the increased probability with which stronger motors detach from the filament during strain generation. Our theoretical results are in quantitative agreement with experimental data on small teams of kinesin-1 motors.

Many active cellular processes such as long-distance cargo transport, spindle organization, as well as flagellar and ciliary beating are driven by molecular motors. These motor proteins act collectively and typically work in small teams. One particularly interesting example is two teams of antagonistic motors that pull a common cargo into opposite directions, thereby generating mutual interaction forces. Important issues regarding such multiple motor systems are whether or not motors from the same team share their load equally, and how the collectively generated forces depend on the single motor properties. Here we address these questions by introducing a stochastic model for cargo transport by an arbitrary number of elastically coupled molecular motors. We determine the state space of this motor system and show that this space has a rather complex and nested structure, consisting of multiple activity states and a large number of elastic substates, even for the relatively small system of two identical motors working against one antagonistic motor. We focus on this latter case because it represents the simplest tug-of-war that involves force sharing between motors from the same team. We show that the most likely motor configuration is characterized by equal force sharing between identical motors and that the most likely separation of these motors corresponds to a single motor step. These likelihoods apply to different types of motors and to different elastic force potentials acting between the motors. Furthermore, these features are observed both in the steady state and during the initial build-up of elastic strains. The latter build-up is non-monotonic and exhibits a maximum at intermediate times, a striking consequence of mutual unbinding of the elastically coupled motors. Mutual strain-induced unbinding also reduces the magnitude of the collectively generated forces. Our computational approach is quite general and can be extended to other motor systems such as motor teams working against an optical trap or mixed teams of motors with different single motor properties.