Many-Body Physics & Biology Group

Dervis Can Vural, Assistant Professor. Physics Department, University of Notre Dame. Email:, Tel: 617-401-5659, Room: 384g








We are a theoretical group interested in the many body behavior of physical and biological systems in which disorder and strong interactions play an important role.

We use a combination of analytical and computational methods. Systems of interest include disordered materials, complex networks, population genetics and evolution, inverse problems, reliability theory, swarms and active matter, and various foundational questions in quantum many-body theory and statistical mechanics.

Our research is driven by three fundamental questions: (I) Statistical Mechanics: How do macroscopic observables emerge from microscopic constituents? (II) Control: How should a many-body system be driven so that it responds in a particular way (III) Inversion: Can microscopic laws be determined from incomplete macroscopic observations? To what extent do the constituents of a system determine macroscopic behavior uniquely?

Aging, Failure and Death

agingStandard theories of aging typically focus on microscopic mechanisms such as oxidative damage or telomere shortening. However we age and die not because we run out of cells, but because small scale failures on the cellular level manifest at larger scales, leading to a macroscopic catastrophe.

We view aging as an emergent universal property of systems that consist of a large number of interdependent components (read more). The video below shows how a large number of interdependent nodes fail.

We organized a nice workshop surrounding these topics.

Failure can also be viewed as a microscope that reveals the structure of a complex system. The failure times of components informs how they are interconnected. Read more.

Recently, our experimental collaborators verified the interaction based picture of aging in synthetic tissues. Read more.

Disordered Materials

Materials such as glasses, polymers, disordered crystals, quasi-crystals and proteins lack long range order, and are thus referred as disordered solids. The standard model describing disordered solids at low temperatures postulate that these materials are composed of two level systems, i.e. atoms, or groups of atoms with two available discrete configurations.

All disordered solids universally exhibit certain properties which cannot be explained by the two level systems model.
Here is a critique of the said model, and here is a more general model, from which the universality is derived.

Evolution of Cooperation

We study how flow patterns influence the evolution of social cooperation. We have discovered that flow shear enables and promotes social behavior in microbes. Specifically, shear tears apart social communities clusters, and thereby limiting the spread of cheating strains. The videos above shows how flow patterns can shape social evolution. In a vortex, cooperative microbes can sustain only within an annular region. In a flowing pipe, they can only sustain near the boundaries, where the shear is larger than a critical amount. Everywhere else cheaters take over, causing the groups to weaken and die. Read more.

Rewinding Randomness

Any state of knowledge, regardless of how exact, will invariably and universally deteriorate into an entropy maximizing probability distribution. This loss of information can be quantified by the entropy generation rate. We address the converse question of how well one can reconstruct a past state given exact information concerning the present.


Theoretically relevant information such as original causes or underlying mechanisms are seldom directly observable. For example, one can easily measure the amount of proteins expressed in a cell but not necessarily know what interactions lead to the particular expression pattern. One can observe a certain species decline in numbers, but not exactly know which ecological relationship, or what triggering event causes the decline. When a virus spreads seemingly spontaneously, or when an invasive species expands its range, it is very difficult to know where it exactly came from, or its evolutionary trajectory that lead to its final success.

We develop algorithms and formulae to infer causal origins from raw empirical data, such as identifying the original trigerring event given an observed final state. Read more.

Quantizing Temperature

Consider a material whose particles are all entangled through interactions. Now split this material into two pieces and measure the energy of one particle in one of the halves. If the particle is found to be in a higher than average energy, it will thermalize with its neighbors and one half of the material will be slightly warmer. Due to the entanglement, the other half will cool down, just enough to conserve energy. To avoid superluminal communication between thermometers monitoring such a difference, we propose that temperature must be a quantum operator, and then the problem reduces to the good old EPR experiment. But if temperature is a quantum variable then this means there must be discrete temperature levels, temperature uncertainty relations, and superpositions of temperature. Read more.

Evolution of Interdependence

Evolution of Complexity1Evolution of Complexity 2

Start with a large number of unrelated species, put them together and wait for many generations. Eventually the interactions between them evolve into a tangled web of exchanges - so much, that no species can survive in isolation. Above are connectivity matrices and phylogenetic trees of two such communities subject to different selective pressures. Higher pressures induce community formation. Read more.

Model Awareness

A model need not be a passive descriptor of its subject. If the subject is affected by the model building process, the model must be updated in real time. In some cases, the very act model building can cause the otherwise correct model to fail, or cause an otherwise incorrect model to succeed. In other cases, attempting to predict the future can be detrimental to the predictor. Read more.

We have studied the dynamics of a predictive swarm aiming to maximize the uptake of some resource. A predictive agent tries to estimate what others will do, and update its behavior accordingly. All other predictive agents are doing the same, trying to predict the trajectory of agents that are trying to predict them! The video above shows a few iterations of the thoughts of a predictor, while it says “they know that I know that they know that I know...". On the left, the resources are initialized randomly, whereas on the right resources are concentrated in two spots, one large and one small. Read more.