Modifying clonal selection theory with a probabilistic cell

Abstract

Problem-solving strategies in immunology currently utilize a series of ad hoc, qualitative variations on a foundation of Burnet's formulation of clonal selection theory. These modifications, including versions of two-signal theory, describe how signals regulate lymphocytes to make important decisions governing self-tolerance and changes to their effector and memory states. These theories are useful but are proving inadequate to explain the observable genesis and control of heterogeneity in cell types, the nonlinear passage of cell fate trajectories and how the input from multiple environmental signals can be integrated at different times and strengths. Here, I argue for a paradigm change to place immune theory on a firmer philosophical and quantitative foundation to resolve these difficulties. This change rejects the notion of identical cell subsets and substitutes the concept of a cell as comprised of autonomous functional mechanical components subject to stochastic variations in construction and operation. The theory aims to explain immunity in terms of cell population dynamics, dictated by the operation of cell machinery, such as randomizing elements, division counters, and fate timers. The effect of communicating signals alone and in combination within this system is determined with a cellular calculus. A series of models developed with these principles can resolve logical cell fate and signaling paradoxes and offer a reinterpretation for how self-non-self discrimination and immune response class are controlled.

Speculations on the evolution of humoral adaptive immunity

“…It is the simultaneous actions of these interleaved cell modules that underpin why the vertebrate immune system is extremely complex. 31,32 Nevertheless, despite the additional layers of regulation, the core adoption of the 3-TM that utilizes regulable timers based on cyclins, Myc and death molecules or their homologs appears common to all.…”

Modes of Communication between T Cells and Relevance for Immune Responses

“…The priming of T cells (reviewed here [2][3][4][5]) results in a population of individually highly heterogenous cells, which nevertheless respond as a collective, in a reproducible and integrated manner [6]. Considerable work has focused on understanding the various intrinsic and extrinsic cues driving this stochastic diversification process [3,4,[7][8][9][10], but what often gets neglected is the fact that T cells never act on their own. Understanding T cell behaviour as highly inter-dependent and governed by population-level interactions may offer valuable clues.…”

Introduction to modeling viral infections and immunity

“…These include the gamut from stochastic models and equations 28 to agentbased models, 19 including partial differential equations, 27 and probabilistic models. 31 Altogether, this volume represents a unique opportunity for researchers in a variety of allied fields to learn about the state-of-…”

“…In fact, simple observation or description of experimental results are very difficult to interpret, and adequate modeling and understanding of the assumptions involved are crucial for reaching any conclusions from the data. The final review by Hodgkin proposes a unifying concept for cell fate decisions, such as division, death or differentiation, based on independent functional components within the cell subject to stochastic variation, and influenced by cellular calculus integrating signals reaching the cell. In support of this concept, the author reviews a sizable body of work of very quantitative experiments measuring cell division, cell integration of multiple signals and mathematical models that provide unifying insights into these multiple processes.…”

“…Thus, one can see that although simple ordinary differential equations are the tool of choice in most cases, many other mathematical techniques are important in modeling pathogens and immune response. These include the gamut from stochastic models and equations to agent‐based models, including partial differential equations, and probabilistic models …”