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Distributed Biological Algorithms

Periodic Reporting for period 4 - DBA (Distributed Biological Algorithms)

Reporting period: 2019-11-01 to 2021-04-30

Biologists are is seeking for rigorous tools to understand complex biological ensembles. This project suggests that a holistic approach to biology could benefit from algorithmic investigations. Our general perspective is to view biological ensembles as composed of probabilistic agents that aim to collectively solve certain computational tasks. Our approach is, first, to translate the biological process 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. We concentrate our experimental studies on several selected biological systems, including ant colonies and bat groups.

Analyzing biological processes, we focus our attention on the following objectives: (1) identify quantitative connections between computational resources, (2) analyze biological processes and evaluate performances using algorithmic measures, (3) identify the algorithmic challenges that the biological systems face, and (4) explain empirical observations based on rigorous algorithmic analysis. Overall, to illustrate the impact of our methodology, we aim to obtain predictions based on theoretical algorithmic considerations and then verify them empirically by designing suitable experiments. The project contains three related tasks. Task A considers searching and navigation by biological organisms, Task B considers algorithmic aspects in foraging ants and bats, and Task C considers opinion formation problems, such as consensus and rumor spreading.
Overall, the project has proceeded even better than expected. Indeed, significant achievements were accomplished in all tasks, yielding publications in prestigious venues in both computer science and biology, including:

[1] "Intermittent inverse-square Lévy walks are optimal for finding targets of all sizes", Science Advances 2021. This paper theoretically studies the computational benefits of utilizing a Levy flight pattern while searching for targets.

[2] "A locally-blazed ant trail achieves efficient collective navigation despite limited information", eLife 2016. The paper combines extensive experiments in P. longicornis ants and theoretical investigations. It demonstrated the role played by noise in the navigation process of ants. The paper has received media coverage, with articles centered around it appearing in "Le Monde" and "Haaretz" daily newspapers.

[3] "Navigating in trees with permanently noisy advice", TALG 2021. The paper investigates the model in [2], focusing on tree structures, and exhibiting several phase transition phenomena.

[4] “Ant collective cognition allows for efficient navigation through disordered environments.” eLife, 2020. The paper studies collaborative transport by P. longicornis ants, combining extensive experiments with theoretical investigations. It shows that in highly unreliable environments, these ants use their numbers to collectively extend their sensing range, and thus shorten their traversal times.

[5] "Breathe before speaking: efficient information dissemination despite noisy, limited and anonymous communication", Distributed Computing, 2017. The paper introduces the computational study of communication noise in the context of stochastically interacting agents.

[6] "Limits on reliable information flows through stochastic populations", PLoS Comp. Biology 14(6), 2018. (Extended abstract in ITCS 2018.) This paper indicates that when there is no structure, the communication is random, and when all facets of the communication are noisy, basic distributed computations cannot be completed efficiently. We further demonstrated this fundamental lower bound through an experiment in Cataglyphis niger ants.

[7] "Reinforcement learning enables resource-partitioning in foraging bats", Current Biology, 2020. The paper provides insights into the mechanisms allowing lesser longnosed bats to exploit replenishing resources while being in competition. The paper combines extensive experiments in bats with theoretical investigations. An article centered around this paper appeared in the daily Israeli newspaper Ynet.

[8] "Multi-round cooperative search games with multiple players. JCSS 2020. (Extended abstract in ICALP 2019). This paper investigates a simplified version of the setting in [7], from a mechanism design perspective.

[9] "Parallel Bayesian Search with No Coordination", J. ACM 2019. (Extended abstract in STOC 2016.) This paper studies distributed search by multiple agents. It highlights the significance of non-coordinating algorithms for being both highly efficient and robust to failures.

[10] "The ANTS Problem", Distributed Computing 2017. This paper establishes a basic framework for relating information parameters and time efficiency parameters with respect to central search foraging.
In general, our studies at the forefront of distributed computing and algorithm theory introduced new methodologies for studying collective animal behavior. Indeed, combining proof-based algorithmic investigations and biological experiments, as done in this project, is highly uncommon. We demonstrated this methodology in four of our papers:

- In [2] we discovered a new kind of ant trail. This discovery is expected to attract significant interest from the community of collective animal behavior. We used algorithmic analysis to understand the computational challenges faced by ants and to show how the new kind of ant trail can be used to overcome these challenges.

- In [4] we studied navigation by ants in complex labyrinths. We observed empirically that the locality in the ants' algorithm is non-trivial, and used algorithm analysis to explain the benefits of such non-locality.

- In [6] we established lower bounds encapsulating a vast class of computational biological systems. We complemented our theoretical finding by providing empirical support concerning the recruitment process employed by desert ants. To the best of our knowledge, this paper is the first-ever in which lower bounds from computer science have been demonstrated by experiments on animal behavior!

- In [7] we gathered and analyzes unprecedently large amounts of data regarding the foraging decisions of bats in the field. We then used algorithmic analysis to show that bats use a simple reinforcement learning strategy to explore and exploit nectar from multiple cacti.

Paper [1] proves that the inverse-square Lévy walk optimally finds targets of all sizes. Due to numerous empirical observations of Lévy movement patterns across taxa, the efficiency of Lévy walks as a foraging strategy has received significant attention from theoreticians during the last two decades. Our strong result in [1] may help to resolve the ongoing debate in the physics and biology disciplines regarding the usefulness of Lévy walks in animal foraging. Moreover, it is expected to influence future experiments on animals performing Lévy walks.

The project also introduced new models that are expected to interest theoreticians in computer science. Papers [9,10] initiated the problems of non-coordinating search. In particular, the lower bounds established in [10] consist of the first non-trivial lower bounds on the memory of probabilistic searchers. Papers [5,6] initiated the computational study of noise in communication in the context of stochastically interacting agents. Paper [3] introduced a new model of search with noisy advice.