From microbial biofilm communities to multicellular organisms, 3D macroscopic structures develop through poorly understood interplay between cellular processes and mechanical forces. Investigating wrinkled biofilms of Bacillus subtilis, we discovered a pattern of localized cell death that spatially focuses mechanical forces, and thereby initiates wrinkle formation. Deletion of genes implicated in biofilm development, together with mathematical modeling, revealed that ECM production underlies the localization of cell death. Simultaneously with cell death, we quantitatively measured mechanical stiffness and movement in WT and mutant biofilms. Results suggest that localized cell death provides an outlet for lateral compressive forces, thereby promoting vertical mechanical buckling, which subsequently leads to wrinkle formation. Guided by these findings, we were able to generate artificial wrinkle patterns within biofilms. Formation of 3D structures facilitated by cell death may underlie self-organization in other developmental systems, and could enable engineering of macroscopic structures from cell populations.pattern formation | self-assembly | systems dynamics S elf-organization in space and time is a fundamental developmental process, defined by the autonomous formation of 3D macroscopic structures by replicating cell populations (1-3). Such 3D pattern formation underlies the development of all multicellular organisms and cellular communities, and appears to be governed by two principal processes. First, genetic programs control cellular processes, such as growth, death, and differentiation. Second, 3D structure formation involves macroscopic movement of cell populations that are determined by mechanical properties and physical forces (4). Recent studies have investigated each of these processes separately in different biological systems (5-8). However, insight into the direct interplay between cellular and mechanical processes that drives development requires simultaneous measurement of both processes, and thus constitutes a major challenge.Compared with multicellular organisms, microbial biofilms are simpler systems for investigating the interaction between cellular and mechanical aspects of 3D self-organization during development. Interestingly, these microbial communities still exhibit diverse cellular behaviors and complex spatial organization (9-14). For example, biofilms can develop from a single cell and give rise to complex 3D wrinkle structures that are visible to the naked eye, comprising billions of cells (9, 10, 15) (Fig. 1A). Aside from replication, bacterial cells can also exhibit other behaviors, such as genetically controlled cell death (9,16,17) and excretion of ECM components (9,13,(18)(19)(20)(21). In fact, one of the defining features of any biofilm is that cells are embedded within an ECM composed of diverse molecules, such as polysaccharides and amyloid fibers (19-21). The ECM is required for wrinkle formation and appears to provide the biofilm with resilience against environmental extrem...

Gene regulatory circuits with different architectures (patterns of regulatory interactions) can generate similar dynamics. This raises the question of why a particular circuit architecture is selected to implement a given cellular process. To investigate this problem, we compared the Bacillus subtilis circuit that regulates differentiation into the competence state to an engineered circuit with an alternative architecture (SynEx) in silico and in vivo. Time-lapse microscopy measurements showed that SynEx cells generated competence dynamics similar to native cells and reconstituted the physiology of differentiation. However, architectural differences between the circuits altered the dynamic distribution of stochastic fluctuations (noise) during circuit operation. This distinction in noise causes functional differences between the circuits by selectively controlling the timing of competence episodes and response of the system to various DNA concentrations. These results reveal a tradeoff between temporal precision and physiological response range that is controlled by distinct noise characteristics of alternative circuit architectures.

Supplementary data are available at Bioinformatics online.