Computing and biology have been converging over the past two decades. At first, biological research approached computing under the push of technological needs, leading to the development of bioinformatics. More recently, the intriguing relationship between the philosophical aspects of computing and the digital roots of biological information processing has fostered a peer-to-peer crosstalk of the two sciences, and computer science is joining mathematics, chemistry and physics as a foundational pillar of systems biology. This shift in the role of computing is propelling our approach. We are engaged in something new, which aims at devising proper abstractions of living systems in order to capture their intrinsic concurrency, causality and probabilistic nature into algorithmic descriptions that can be executed, analyzed and simulated by computers. We call our approach algorithmic systems biology.
As per Nobel Laureate Sydney Brenner,
 

Biology needs a theory able to highlight causality and abstract data into knowledge to elucidate the architecture of biological complexity.

We believe algorithmic systems biology is able to take this challenge. Algorithmic approaches require modelers/biologists to think about the mechanisms governing the behavior of the system under question and favor computational thinking. Algorithms can help in coherently extracting general biological principles that underlie the enormous amount of data produced by high-throughput technologies. In a paper published recently in Nature, Nobel Laureate Paul Nurse advocates that a better understanding of living organisms requires
 

both the development of the appropriate languages to describe information processing in biological systems and the generation of more effective methods to translate biochemical descriptions into the functioning of the logic circuits that underpin biological phenomena.

Algorithmic descriptions of biological systems need a syntax to be described and a semantics to associate them with their intended meaning so that an executor can precisely perform the steps needed to implement the algorithms with no ambiguity. Both theoretical and practical results developed in the realm of programming languages are available to support the analysis of models and their translation into executable forms for numerical evaluation and simulation.
By relying on core computer science technologies like algorithms, programming languages and compilers, the main challenges we are addressing with algorithmic systems biology include:
• the relationships between genotype and phenotype, that is, between low-level local interactions and emergent high-level global behavior
• how to cope with partial knowledge of the systems under investigation
• efficient management of multi-level and multi-scale systems in time, space and size
• showing causal relations between interactions
To tackle these formidable challenges, modeling formalisms which are candidates to propel algorithmic systems biology should complement and interoperate with mathematical modeling, address parallelism and complexity, express causality and be algorithmic, quantitative, interaction-driven, composable, scalable and modular. In algorithmic systems biology, biological problems define the requirements of conceptual tools and provide the case studies for validating and benchmarking proposed solutions.