Welcome to the Gerland group -

Physics of Complex Biosystems


In physics, interactions between particles follow laws. In biology, interactions between biomolecules serve a function. These very different points of view are beginning to merge as research over the past years has demonstrated how, in some exemplary cases, the laws of physics constrain the implementation of biological function.

We investigate several such cases. For instance, we study how the spatial arrangement and coordination of enzymes determines the efficiency of a multi-step reaction. These spatial arrangements can be natural (as in biomolecular complexes) or engineered with the modern methods of bio-nanotechnology. In both cases, fundamental functional tradeoffs emerge, which must be characterized to understand the optimization of such systems.

Methods from theoretical physics help to describe the functioning of these complex biomolecular systems on a quantitative level, while the biological function leads to new questions, with many parallels in the engineering disciplines. Seen from this perspective, a bacterium is a microscopic bioreactor programmed by evolution to rebuild itself from a variable set of resources and in fluctuating environments. How is this bioreactor programmed? Which strategies enable the control of a diverse set of physico-chemical processes in a way as to robustly produce a highly complex product? Quantitative analysis and modeling facilitates insight into the underlying design principles.


Recent Research Highlights

Trade-offs and design principles in the spatial organization of catalytic particles

Catalytic particles are spatially organized in a number of biological systems across different length scales, from enzyme complexes to metabolically coupled cells. Despite operating on different scales, these systems all feature localized reactions involving partially hindered diffusive transport, which is determined by the collective arrangement of the catalysts. Yet it remains largely unexplored how different arrangements affect the interplay between the reaction and transport dynamics, which ultimately determines the flux through the reaction pathway. Here we show that two fundamental trade-offs arise, the first between efficient inter-catalyst transport and the depletion of substrate, and the second between steric confinement of intermediate products and the accessibility of catalysts to substrate. We use a model reaction pathway to characterize the general design principles for the arrangement of catalysts that emerge from the interplay of these trade-offs. We find that the question of optimal catalyst arrangements generalizes the well-known Thomson problem of electrostatics.

Self-Assembly of Informational Polymers by Templated Ligation

A key aim in origins of life research is to understand how self-replicating evolving systems can spontaneously emerge in non-equilibrium environments. Informational polymers such as RNA play pivotal roles in these scenarios, as information carriers and, according to the “RNA world” hypothesis, also as the first functional molecules catalyzing reactions. However, it is not clear how RNA strands that are long enough to act as functional molecules came into being.
In order to answer this question, the structure that is already induced by the growth of short informational polymers must be understood. Short informational polymers can grow via a process called templated ligation: Two strands bind (hybridize) next to each other on a third strand, the so-called template, which enables the joining (ligation) of the two strands into one.
We studied how this growth process shapes the length distribution of informational polymers. Using stochastic simulations, we find that the process generally leads to a minimum and a maximum in the length distribution. The maximum constitutes a source of informational polymers of a specific length that can be tuned by changing physical parameters. As such, these strands can serve as the building blocks for a structure-forming process on longer length scales. We theoretically characterize the underlying dynamics that shape the length distribution, and confirm the non-monotonous behavior in a proof-of-principle experiment with DNA strands.

Programmable pattern formation in cellular systems with local signaling

Complex systems, ranging from developing embryos to systems of locally communicating agents, display an apparent capability of “programmable” pattern formation: They reproducibly form target patterns, but those targets can be readily changed. A distinguishing feature of such systems is that their subunits are capable of information processing. Here, we explore schemes for programmable pattern formation within a theoretical framework, in which subunits process local signals to update their discrete state following logical rules. We study systems with different update rules, topologies, and control schemes, assessing their capability of programmable pattern formation and their susceptibility to errors. Only a fraction permits local organizers to dictate any target pattern, by transcribing temporal patterns into spatial patterns, reminiscent of the principle underlying vertebrate somitogenesis. An alternative scheme employing variable rules cannot reach all patterns but is insensitive to the timing of organizer inputs. Our results establish a basis for designing synthetic systems and models of programmable pattern formation closer to real systems.