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DBA Report Summary

Project ID: 648032
Funded under: H2020-EU.1.1.

Periodic Reporting for period 1 - DBA (Distributed Biological Algorithms)

Reporting period: 2015-05-01 to 2016-10-31

Summary of the context and overall objectives of the project

Biologists are is seeking for rigorous tools to understand complex, interactive, and dynamic biological ensembles. The past decade has been very fruitful in terms of applying techniques from physics to study biological systems. This project suggests that an holistic approach to biology can also be based on algorithmic considerations. Our general perspective is to view biological ensembles as composed of probabilistic agents that aim to collectively solve certain tasks, where the tasks' specifications depend on the environment and on the current state of the ensemble. Our approach is, first, to translate a biological process of interest into a formal algorithmic model and then mathematically analyze the model while aiming to gain fundamental understandings regarding the behavior of the motivating biological process. Ultimately, these understandings would be combined with actual experiments in biology for the purpose of obtaining biological insights. We concentrate our experimental studies on several selected biological systems, including ant colonies, cells of the immune system, and recently also bat groups (see below).

Analyzing biological processes, we focus our attention on the following objectives: (1) identify quantitative connections between parameters (e.g., communication, memory, and time complexities), (2) analyze biological processes and evaluate performances using algorithmic measures, and (3) identify the algorithmic challenges that the biological systems face. Overall, to illustrate the impact of our methodology, we aim at obtaining predictions using theoretical algorithmic considerations and verify them empirically by designing suitable experiments. The project contains three related tasks. Task A considers search problems which are relevant to a wide range of biological groups, Task B considers central foraging problems, and Task C considers opinion formation problems, such as consensus and rumor spreading, relevant to almost all cooperative ensembles in nature.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

"Overall, the project has proceeded even better than expected. The action's implementation is highly positive and follows the expected time schedule.

One of the important advances is the establishment of new promising collaboration with a bright Zoologist from Tel-Aviv University - Dr. Yossi Yovel (supported by an ERC starting grant), who specializes in bat groups. Incorporating this collaboration, the project currently implements its objectives also with respect to bats (in addition to the previously mentioned systems). This fascinating biological system expands the boundaries of the proposal, taking into account competing individuals rather than considering only purely cooperating ones.

On the research level, significant advances have already been established with respect to all objectives. These advances are indicated by intermediate results related to all tasks A, B, and C. In particular, the project has already yielded several publications that were published in prestigious computer science venues, including the conferences STOC and SODA and the Distributed Computing journal, as well as in impactful biology journals (see below for more details). In addition to these publications, we have also established several important results that are currently being written as well as conducting several ongoing research projects that progress well. These relate to both purely theoretical computational studies as well as to experiments on bat behavior, and movement trajectories of immune cells and ants.

Below I provide references to several selected publications, and briefly discuss their contribution:

1. E. Fonio, Y. Heyman, L. Boczkowski, A. Gelblum, A. Kosowski, A. Korman, and O. Feinerman. "A locally-blazed ant trail achieves efficient collective navigation despite limited information". eLife, November 2016. (A. Korman and O. Feinerman are joint corresponding authors.)

This paper concerns both Tasks A and B, and provides advances with respect to Objectives 2 and 3. It was published in the important biology journal eLife 2016 (impact factor above 8). The paper provides insights that are valuable both for biologists (discovering a new kind of ant trail) and for computer science researchers (initiating a new model for computation under noisy information).

2. P. Fraigniaud, A. Korman, and Y. Rodeh. "Parallel Exhaustive Search without Coordination". STOC 2016.

This paper is related partially to Task B and partially to Task A. It provides advances with respect to Objective 1, as it allows to establish new lower bounds concerning central search foraging, such as those performed by ants around their nest.

3. L. Boczkowski, A. Korman, and E. Natale. "Minimizing Message Size in Stochastic Communication Patterns: Fast Self-Stabilizing Protocols with 3 bits". SODA 2017.

This paper concerns Task C and indicates advances with respect to Objective 3. It studies the communication bandwidth limitations under which populations of agents can disseminate information in fault-tolerant stochastic environments.

4. O. Feinerman, B. Haeupler, and A. Korman. "Breathe before Speaking: Efficient Information Dissemination despite Noisy, Limited and Anonymous Communication". Accepted to Distributed Computing journal.

This paper concerns Task C and indicates advances concerning Objective 3. It studies the impact of noise in communication on the ability to disseminate information in stochastic meeting patterns.

5. O.Feinerman and A. Korman. "The ANTS Problem". Accepted to Distributed Computing journal.

This paper concerns Task B and indicates advances with respect to Objective 1. It establishes the basic framework for relating information parameters and time efficiency parameters with respect to central search."

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

In general, our studies at the forefront of distributed computing and algorithm theory introduce new methodologies for studying collective animal behavior. In particular, our paper in eLife has outlined important new concepts in the context of decision making under unreliable conditions, which are inherent to biological systems. These were obtained by empirically verifying hypotheses that were established using algorithmic considerations. This study further allowed us to discover a completely new kind of ant trail, which is expected to attract significant interest from the community of collective animal behavior. In addition, this paper establishes new connections between the probability theory of Random Walks in Random Environments, the computer science theory of routing, and the biology field of collective behavior. Finally, we note that this paper received media coverage and articles centered around it appeared in “Le Monde” and “Haaretz” daily newspapers.

Our papers on collaborative search (published in STOC and Distributed Computing) initiated the problems of non-coordinating search and collective central search foraging from an algorithmic perspective. The models aim at establishing connections between efficiency, memory and communication in the context of probabilistic search, a computation problem which is, in general, considered extremely hard. These new models have already attracted much attention from the distributed computing community.

Our other paper in Distributed Computing initiated the computational study of noise in communication in the context of stochastically interacting agents, a new subject at the interface of computer science, physics and biology, that is also expected to attract signification attention from the community.
Record Number: 198227 / Last updated on: 2017-05-18
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