49 research outputs found
Universal motifs and the diversity of autocatalytic systems
Autocatalysis is essential for the origin of life and chemical evolution. However, the lack of a unified framework so far prevents a systematic study of autocatalysis. Here, we derive, from basic principles, general stoichiometric conditions for catalysis and autocatalysis in chemical reaction networks. This allows for a classification of minimal autocatalytic motifs called cores. While all known autocatalytic systems indeed contain minimal motifs, the classification also reveals hitherto unidentified motifs.We further examine conditions for kinetic viability of such networks, which depends on the autocatalytic motifs they contain and is notably increased by internal catalytic cycles. Finally, we show how this framework extends the range of conceivable autocatalytic systems, by applying our stoichiometric and kinetic analysis to autocatalysis emerging from coupled compartments. The unified approach to autocatalysis presented in this work lays a foundation toward the building of a systems-level theory of chemical evolution
Information-theoretic analysis of the directional influence between cellular processes
Inferring the directionality of interactions between cellular processes is a
major challenge in systems biology. Time-lagged correlations allow to
discriminate between alternative models, but they still rely on assumed
underlying interactions. Here, we use the transfer entropy (TE), an
information-theoretic quantity that quantifies the directional influence
between fluctuating variables in a model-free way. We present a theoretical
approach to compute the transfer entropy, even when the noise has an extrinsic
component or in the presence of feedback. We re-analyze the experimental data
from Kiviet et al. (2014) where fluctuations in gene expression of metabolic
enzymes and growth rate have been measured in single cells of E. coli. We
confirm the formerly detected modes between growth and gene expression, while
prescribing more stringent conditions on the structure of noise sources. We
furthermore point out practical requirements in terms of length of time series
and sampling time which must be satisfied in order to infer optimally transfer
entropy from times series of fluctuations.Comment: 24 pages, 7 figure
Generation and filtering of gene expression noise by the bacterial cell cycle
Supplementary methods. (DOCX 1071 kb
Selection dynamics in transient compartmentalization
Transient compartments have been recently shown to be able to maintain
functional replicators in the context of prebiotic studies. Motivated by this
experiment, we show that a broad class of selection dynamics is able to achieve
this goal. We identify two key parameters, the relative amplification of
non-active replicators (parasites) and the size of compartments. Since the
basic ingredients of our model are the competition between a host and its
parasite, and the diversity generated by small size compartments, our results
are relevant to various phage-bacteria or virus-host ecology problems.Comment: 11 pages, 10 figure
Robustness of compositional heredity to the growth and division dynamics of prebiotic compartments
An important transition after the origin of life was the first emergence of a
Darwinian population, self-reproducing entities exhibiting differential
reproduction, phenotypic variation, and inheritance of phenotypic traits. The
simplest system we can imagine to have these properties would consist of a
compartmentalized autocatalytic reaction system that exhibits two growth states
with different chemical compositions. Identifying the chemical composition as
the phenotype, this accounts for two of the properties. However, it is not
clear what are the necessary conditions for such a chemical system to exhibit
inheritance of the compositional states upon growth and division of the
compartment. We show that for a general class of autocatalytic chemical systems
subject to serial dilution, the inheritance of compositional information only
occurs when the time interval between dilutions is below a critical threshold
that depends on the efficiency of the catalytic reactions. Further, we show
that these thresholds provide rigorous bounds on the properties required for
the inheritance of the chemical compositional state for general growth and
division cycles. Our result suggests that a serial dilution experiment, which
is much easier to set up in a laboratory, can be used to test whether a given
autocatalytic chemical system can exhibit heredity. Lastly, we apply our
results to a realistic autocatalytic system based on the Azoarcus ribozyme and
suggest a protocol to experimentally test whether this system can exhibit
heredity.Comment: 30 pages, 22 figure
Towards Parsimonious Generative Modeling of RNA Families
Generative probabilistic models emerge as a new paradigm in data-driven,
evolution-informed design of biomolecular sequences. This paper introduces a
novel approach, called Edge Activation Direct Coupling Analysis (eaDCA),
tailored to the characteristics of RNA sequences, with a strong emphasis on
simplicity, efficiency, and interpretability. eaDCA explicitly constructs
sparse coevolutionary models for RNA families, achieving performance levels
comparable to more complex methods while utilizing a significantly lower number
of parameters. Our approach demonstrates efficiency in generating artificial
RNA sequences that closely resemble their natural counterparts in both
statistical analyses and SHAPE-MaP experiments, and in predicting the effect of
mutations. Notably, eaDCA provides a unique feature: estimating the number of
potential functional sequences within a given RNA family. For example, in the
case of cyclic di-AMP riboswitches (RF00379), our analysis suggests the
existence of approximately functional nucleotide sequences.
While huge compared to the known natural sequences, this
number represents only a tiny fraction of the vast pool of nearly
possible nucleotide sequences of the same length (136
nucleotides). These results underscore the promise of sparse and interpretable
generative models, such as eaDCA, in enhancing our understanding of the
expansive RNA sequence space.Comment: 33 pages (including SI
Predicting evolution using regulatory architecture
The limits of evolution have long fascinated biologists. However, the causes of evolutionary constraint have remained elusive due to a poor mechanistic understanding of studied phenotypes. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology models with population and single-cell quantification and with new genetic tools, and they have been applied to a range of complex cellular functions and engineered networks. In this article, we review these developments, which are revealing the mechanistic causes of epistasis at different levels of biological organization¤mdash¤in molecular recognition, within a single regulatory network, and between different networks¤mdash¤providing first indications of predictable features of evolutionary constraint
Individuality and universality in the growth-division laws of single E. coli cells
The mean size of exponentially dividing E. coli cells cultured in different
nutrient conditions is known to depend on the mean growth rate only. However,
the joint fluctuations relating cell size, doubling time and individual growth
rate are only starting to be characterized. Recent studies in bacteria (i)
revealed the near constancy of the size extension in a single cell cycle (adder
mechanism), and (ii) reported a universal trend where the spread in both size
and doubling times is a linear function of the population means of these
variables. Here, we combine experiments and theory and use scaling concepts to
elucidate the constraints posed by the second observation on the division
control mechanism and on the joint fluctuations of sizes and doubling times. We
found that scaling relations based on the means both collapse size and
doubling-time distributions across different conditions, and explain how the
shape of their joint fluctuations deviates from the means. Our data on these
joint fluctuations highlight the importance of cell individuality: single cells
do not follow the dependence observed for the means between size and either
growth rate or inverse doubling time. Our calculations show that these results
emerge from a broad class of division control mechanisms (including the adder
mechanism as a particular case) requiring a certain scaling form of the
so-called "division hazard rate function", which defines the probability rate
of dividing as a function of measurable parameters. This gives a rationale for
the universal body-size distributions observed in microbial ecosystems across
many microbial species, presumably dividing with multiple mechanisms.
Additionally, our experiments show a crossover between fast and slow growth in
the relation between individual-cell growth rate and division time, which can
be understood in terms of different regimes of genome replication control.Comment: 39 pages, 7 main figures, 17 supplementary figure