Abstract
It is clearly in the tradition of biologists to conceptualize the dynamical evolution of biological systems in terms of state-transitions of biological objects. This paper is mainly concerned with (but obviously not limited too) the immunological branch of biology and shows how the adoption of UML (Unified Modeling Language) state-transition diagrams can ease the modeling, the understanding, the coding, the manipulation or the documentation of population-based immune software model generally defined as a set of ordinary differential equations (ODE), describing the evolution in time of populations of various biological objects. Moreover, that same UML adoption naturally entails a far from negligible representational economy since one graphical item of the diagram might have to be repeated in various places of the mathematical model. First, the main graphical elements of the UML state-transition diagram and how they can be mapped onto a corresponding ODE mathematical model are presented. Then, two already published immune models of thymocyte behavior and time evolution in the thymus, the first one originally conceived as an ODE population-based model whereas the second one as an agent-based one, are refactored and expressed in a state-transition form so as to make them much easier to understand and their respective code easier to access, to modify and run. As an illustrative proof, for any immunologist, it should be possible to understand faithfully enough what the two software models are supposed to reproduce and how they execute with no need to plunge into the Java or Fortran lines.
Highlights
A cell turns out to be infected by the presence of a given virus, with a probability p - an inactive gene becomes active with a probability p - a T-cell differentiates from type double negative (DN) to double positive cells (DP) with probability p1 and from DP to SP with probability p2
The main reason for this last section is to advocate a possible understanding and manipulation of them both with no need to get into the final code
This paper advocates that with some minor enrichment of the UML state-transition diagrams to better align with scientific investigation and reporting, this standardized graphical language can serve as an effective medium of communication and development for both theorists and experimentalists
Summary
As a matter of fact, it is definitely a stream of biology that has a long tradition of software and mathematical modeling and could be more receptive to the proposals of this paper. Among examples of state transition are: during its early stay in the thymus, a pro T-cell is subject to a succession of differentiation steps, such as DN (Double Negative) DP (Double Positive) to export mature T-cells (Fig.1a,b) - a viral encounter that turns an healthy target cell into an infected one - a T lymphocyte that, by encountering this same infected cell, switches from a naive state to an effector one to a memory one
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